435 results on '"Yongbo, Li"'
Search Results
202. A fusion CNN driven by images and vibration signals for fault diagnosis of gearbox
- Author
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Qiting Zhou, Gang Mao, and Yongbo Li
- Subjects
History ,Computer Science Applications ,Education - Abstract
Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.
- Published
- 2022
203. Application of oscillatory time frequency manifold for extraction of rolling element bearing fault signature
- Author
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Lei Li, Khandaker Noman, Yongbo Li, Hao Fu, and Zichen Deng
- Subjects
History ,Computer Science Applications ,Education - Abstract
To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.
- Published
- 2022
204. Tensor CSAMT and AMT Studies of the Xiarihamu Ni-Cu Sulfide Deposit in Qinghai, China
- Author
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Gang Wang, Zhenyu Zhang, Wei Zhu, Yongbo Li, Dayong Wang, Da Lei, and Xiangyun Hu
- Subjects
010504 meteorology & atmospheric sciences ,Mineralogy ,Low frequency ,010502 geochemistry & geophysics ,01 natural sciences ,Overburden ,Depth sounding ,Geophysics ,Electrical resistivity and conductivity ,Magnetotellurics ,Frequency domain ,Electrical impedance ,Geology ,0105 earth and related environmental sciences ,Audio frequency - Abstract
The frequency domain electromagnetic sounding method is one of the important means of exploring mineral resources. This paper combined tensor controlled source audio frequency magnetotelluric (CSAMT) and audio frequency magnetotelluric(AMT) methods to investigate Xiarihamu copper-nickel ore in Qinghai Province. Spliced tensor CSAMT high frequency (200 Hz-8533 Hz) and AMT low frequency (1 Hz-200 Hz) apparent resistivity and impedance phase data, as well as spliced data, were used for a 2D inversion and to generate a 2D geoelectric model. Coupled with the known geological, geophysical, and drilling data, the inversion results were analyzed. The results show that the combined use of tensor CSAMT and AMT can overcome the inherent defects of these methods, such as limited exploration depth and the "dead band" effect. This method produced good results in terms of exploring the underground electrical structure, finding the low resistivity body, and determining the thickness of the overburden, as well as the occurrence and the buried depth of the ore body. These results provide an effective method and exploration strategy to find similar potential deposits in the surrounding areas. (C) 2018 Elsevier B.V. All rights reserved.
- Published
- 2018
205. A method based on refined composite multi-scale symbolic dynamic entropy and ISVM-BT for rotating machinery fault diagnosis
- Author
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Xihui Liang, Yongbo Li, Yu Wei, and Xianzhi Wang
- Subjects
0209 industrial biotechnology ,Binary tree ,Computer science ,Cognitive Neuroscience ,020208 electrical & electronic engineering ,Composite number ,02 engineering and technology ,Computer Science Applications ,Support vector machine ,Sample entropy ,020901 industrial engineering & automation ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Entropy (information theory) ,Permutation entropy ,Laplace operator ,Algorithm ,Eigenvalues and eigenvectors - Abstract
Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series, which has merits of high computational efficiency and robustness to noise comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE). However, the variance of the MSDE values increases as the length of a time series becomes shorter using multiscale analysis. To address this shortcoming, a new method, namely refined composite multi-scale symbolic dynamic entropy (RCMSDE), is proposed to extract the fault information of rotating machinery. Then, Laplacian score (LS) is utilized to reduce the dimension of eigenvectors. In the end, the selected features are taken as the input of the improved support vector machine based on binary tree (ISVM-BT) for fault type identification. The effectiveness of the proposed method is validated using both the simulation and two experimental tests. Results demonstrate that the proposed method generates highest classification accuracy in comparison with existing methods such as MSDE, refined composite multiscale sample entropy (RCMSE) and refined composite multiscale permutation entropy (RCMPE).
- Published
- 2018
206. Hierarchical Diversity Entropy for the Early Fault Diagnosis of Rolling Bearing
- Author
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Xianzhi Wang, Shubin Si, and Yongbo Li
- Subjects
Bearing (mechanical) ,Computer science ,Applied Mathematics ,Mechanical Engineering ,Aerospace Engineering ,Ocean Engineering ,Fault (power engineering) ,law.invention ,Control and Systems Engineering ,law ,Electrical and Electronic Engineering ,Entropy (energy dispersal) ,Algorithm ,Diversity (business) - Abstract
Intelligent fault diagnosis provides great convenience for the prognostic and health management of the rotating machinery. Recently, the entropy-based feature extraction method has aroused researchers’ attentions due to its independence with prior knowledge, unnecessary of preprocessing, and easy to perform. The multiscale diversity entropy has been proven to be a promising feature extraction method for the intelligent fault diagnosis. Compared to the existing entropy methods, the multiscale diversity entropy has advantages of high consistency, strong robustness and high calculation efficiency. However, the multiscale diversity entropy encounters the challenge to extract features for early fault diagnosis due to the weak fault symptoms and strong noise. This can be attributed to the multiscale diversity entropy only concerns the fault information embedded in the low frequency, which ignores the information hidden in the high frequency. To address this defect, the hierarchical diversity entropy (HDE) is proposed, which can synchronously extract fault information hidden in both high and low frequency. Based on HDE and random forest, a novel intelligent fault diagnosis frame has been proposed. The effectiveness of the proposed method has been evaluated through simulated and experimental bearing signals. The results show that the proposed HDE has the best feature extraction ability compare to multiscale sample entropy, multiscale permutation entropy, multiscale fuzzy entropy, and multiscale diversity entropy.
- Published
- 2021
207. A bibliometric analysis of global research on spinal cord injury: 1999-2019
- Author
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Han Wu, Yi-Han Zhong, Hao Feng, Baixing Wei, and Yongbo Li
- Subjects
Paraplegia ,Bibliometric analysis ,Rehabilitation ,business.industry ,medicine.medical_treatment ,MEDLINE ,Library science ,General Medicine ,medicine.disease ,Functional recovery ,United States ,Rats ,Neurology ,Bibliometrics ,medicine ,Animals ,Humans ,Neurology (clinical) ,Publication data ,Citation ,business ,Spinal cord injury ,Spinal Cord Injuries - Abstract
Bibliometric review. The spatial structure of the global spinal cord injury (SCI) research field has not been summarized or analyzed. The objective of this study was to understand the current status and global trends of SCI research, and provide scholars knowledge to integrate into their plans for future research. Not applicable. The Web of Science database was searched for articles related to SCI published between 1999 and 2019. Metrics based on publication data, including publication counts, H indices, countries, institutions, authors, and journals were extracted. Co-citation analysis, collaboration analysis, and co-occurrence analysis of keywords were conducted using CiteSpace. The search identified a total of 41,012 articles related to SCI. Overall, the number of publications increased annually. The United States was the top ranked country by publication count, H index, and citation count. Harvard University and the University of Toronto made the most contributions. M.G. Fehlings was the top ranked author. Spinal Cord published the largest number of articles, and was the most frequently cited journal. The top 5 ranked keywords that appeared most frequently were spinal cord injury, functional recovery, adult rat rehabilitation, and paraplegia. Twelve major clusters of keywords and 15 clusters of co-cited references were generated. This study comprehensively analyzed and summarized the trends in SCI research during the past 20 years. Findings should provide scholars information on the countries, institutions, authors, and journals that are active in the field of SCI research, and a knowledge base for future projects.
- Published
- 2021
208. Analyzing the Critical Success Factors for Industrial Symbiosis—A Chinese Perspective
- Author
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Mark Christhian Barrueta Pinto and Yongbo Li
- Subjects
Globalization ,Industrialisation ,Scope (project management) ,Industrial symbiosis ,Critical success factor ,Revenue ,Context (language use) ,Business ,China ,Industrial organization - Abstract
Due to globalization and industrialization, several industrial developments have been evident, which increases the global revenue significantly in recent years. Meanwhile, such increasing industrial activities prints many negative impacts on the environment through resources scavenging, an increase in waste, pollution and so on. Several strategies have been introduced to reduce these negative characteristics of industrial operations without comprising in production quantities. Industrial symbiosis is one such strategy that assists the industries in the industrial parks to engage in eco-efficient operations. Despite its key advantages, very limited studies exist in the developing context, especially with China industries. Though there are several attempts made in industrial symbiosis for its effective implementation, but no previous study details the success factors of implementation of industrial symbiosis even with the Chinese context. Addressing this gap, this study identified the common success factors for industrial symbiosis and analyzed the effective factors within the Chinese industrial context. The analytical hierarchy process has been adapted for this analysis, in which the China industrial eco park design managers are considered as case decision-makers. The results revealed that among considered 12 factors, technological factors are the key success factor for the effective implementation of industrial symbiosis in the Chinese context. With the findings, this study is having both scientific and societal contributions toward the implementation of industrial symbiosis in the Chinese context. Further, the future scope for research in addition to limitations has been presented.
- Published
- 2021
209. Analyzing the Influence of Leagile Manufacturing Tools in Peru Context
- Author
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Yongbo Li and Mark Christhian Barrueta Pinto
- Subjects
Process management ,law ,Perspective (graphical) ,CLARITY ,Context (language use) ,Business ,Manufacturing systems ,law.invention - Abstract
Recent manufacturing sectors facing several challenges worldwide due to various disruptions and economic instabilities. Hence, it becomes necessary for the industries to think about new strategies that could sustain in these tough environments. Such a strategy has been discussed in this paper, Leagile manufacturing within Peru context. Despite having several benefits with the implementation of Leagile manufacturing system, since there are many tangible and intangible barriers hinders its effective implementation in Peru industries. These barriers mainly rise due to less clarity among the tools and lack of ease to implementation within the concern of an industrial perspective. This study considers addressing this gap by evaluating the most influential tools for the effective implementation of Leagile manufacturing. From the combined support of both primary and secondary data, this study considered 12 Leagile manufacturing tools, which further evaluated based on their influence in implementation on Peru context with Decision Making and Trial Evaluation Laboratory (DEMATEL).
- Published
- 2021
210. Gear Tooth Fault Detection Based on Designed Convolutional Neural Networks
- Author
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Shubin Si, Xiaoqiang Du, and Yongbo Li
- Subjects
Computer science ,Health condition ,social sciences ,Single tooth ,Fault (power engineering) ,behavioral disciplines and activities ,Convolutional neural network ,Signal ,Fault detection and isolation ,Vibration ,Gear tooth ,Control theory ,population characteristics ,human activities ,health care economics and organizations - Abstract
Gearbox is one of the most important parts of the rotating machinery, so health monitoring of the gearbox is essential. The accurate positioning of tooth failure of gear is an important function of the fault diagnosis system. This paper proposes a detection strategy based on designed convolutional neural networks to detect and locate gear tooth failure. The detection strategy aims to compare the characteristic gap between the normal gear and the faulty gear in the same period extracted by the convolutional neural network, and assign weights to the faulty gear vibration signal to obtain the weight sequence of the faulty vibration signal, so as to obtain the faulty tooth weight. Finally, the health condition of the gear can be evaluated by comparing the weight between all teeth of the gear. The proposed detection strategy is tested through simulation vibration signal and experiment vibration signal. The result shows that the proposed method can successfully identify gear failure and effectively detect single tooth failure on gear.
- Published
- 2021
211. Reinforcing increase of ��TC in MgB2 smart meta-superconductors by adjusting the concentration of inhomogeneous phases
- Author
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Hongyan Zou, Honggang Chen, Xiaopeng Zhao, Li Tang, Guangyu Han, and Yongbo Li
- Subjects
Work (thermodynamics) ,Technology ,Materials science ,SMSCs ,MgB2 ,Analytical chemistry ,EL inhomogeneous phase ,FOS: Physical sciences ,ΔTc ,Electroluminescence ,inject energy ,01 natural sciences ,Article ,Superconductivity (cond-mat.supr-con) ,0103 physical sciences ,General Materials Science ,010306 general physics ,010302 applied physics ,Superconductivity ,Microscopy ,QC120-168.85 ,Condensed Matter - Superconductivity ,Doping ,QH201-278.5 ,Engineering (General). Civil engineering (General) ,TK1-9971 ,Descriptive and experimental mechanics ,Superconducting transition temperature ,Particle ,Particle size ,Electrical engineering. Electronics. Nuclear engineering ,TA1-2040 ,Intensity (heat transfer) - Abstract
Incorporating with inhomogeneous phases with high electroluminescence (EL) intensity to prepare smart meta-superconductors (SMSCs) is an effective method for increasing the superconducting transition temperature (Tc) and has been confirmed in both MgB2 and Bi(Pb)SrCaCuO systems. However, the increase of ΔTc (ΔTc = Tc ‒ Tcpure) has been quite small because of the low optimal concentrations of inhomogeneous phases. In this work, three kinds of MgB2 raw materials, namely, aMgB2, bMgB2, and cMgB2, were prepared with particle sizes decreasing in order. Inhomogeneous phases, Y2O3:Eu3+ and Y2O3:Eu3+/Ag, were also prepared and doped into MgB2 to study the influence of doping concentration on the ΔTc of MgB2 with different particle sizes. Results show that reducing the MgB2 particle size increases the optimal doping concentration of inhomogeneous phases, thereby increasing ΔTc. The optimal doping concentrations for aMgB2, bMgB2, and cMgB2 are 0.5%, 0.8%, and 1.2%, respectively. The corresponding ΔTc values are 0.4, 0.9, and 1.2 K, respectively. This work open a new approach to reinforcing increase of ΔTc in MgB2 SMSCs.
- Published
- 2021
- Full Text
- View/download PDF
212. Risk Prediction and Optimization of Electricity Retail Market Based on Risk Factors of Electricity Retail Companies
- Author
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He Chuan, Yahai Zhang, Yongbo Li, and Hanhan Qian
- Subjects
Competition (economics) ,Electric power system ,Deregulation ,Linear programming ,business.industry ,Capital (economics) ,Scale (social sciences) ,Retail market ,Business ,Electricity ,health care economics and organizations ,Industrial organization - Abstract
With deregulation of electrical market in the China, the electricity retail companies (ERCs) and the electricity retail market (ERM) experiences fast development in the recent years. Different from the generation and transmission market, the retail market is more open to the participators, and has more competition and higher risk. Considering the risk factors e.g. the business scale, the capital, the employee, and the complaints, an optimization model to reduce the risk of the ERCs and ERM with higher predicted risk is newly proposed in this paper, where the risk factors are normalized by the threshold value and the time before quantifying the risk. The risk optimization problem with constraints to adjustable risk factors is nonlinear and solved by the interior point method. The numerical results validate the optimization effect on the risk of the ERCs and the ERM.
- Published
- 2020
213. Robust 3D Hand Detection from a Single RGB-D Image in Unconstrained Environments
- Author
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Yi Liu, Yunkai Jiang, Jun Zhou, Wendi Cai, Yongbo Li, and Chi Xu
- Subjects
Computer science ,3D hand detection ,02 engineering and technology ,RGB-D sensor ,lcsh:Chemical technology ,Biochemistry ,Article ,Analytical Chemistry ,Imaging, Three-Dimensional ,Robustness (computer science) ,human–computer interaction ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,unseen lighting condition ,Computer vision ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,adaptive RGB-D fusion ,Lighting ,business.industry ,Detector ,020206 networking & telecommunications ,Hand ,Atomic and Molecular Physics, and Optics ,RGB color model ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
Three-dimensional hand detection from a single RGB-D image is an important technology which supports many useful applications. Practically, it is challenging to robustly detect human hands in unconstrained environments because the RGB-D channels can be affected by many uncontrollable factors, such as light changes. To tackle this problem, we propose a 3D hand detection approach which improves the robustness and accuracy by adaptively fusing the complementary features extracted from the RGB-D channels. Using the fused RGB-D feature, the 2D bounding boxes of hands are detected first, and then the 3D locations along the z-axis are estimated through a cascaded network. Furthermore, we represent a challenging RGB-D hand detection dataset collected in unconstrained environments. Different from previous works which primarily rely on either the RGB or D channel, we adaptively fuse the RGB-D channels for hand detection. Specifically, evaluation results show that the D-channel is crucial for hand detection in unconstrained environments. Our RGB-D fusion-based approach significantly improves the hand detection accuracy from 69.1 to 74.1 comparing to one of the most state-of-the-art RGB-based hand detectors. The existing RGB- or D-based methods are unstable in unseen lighting conditions: in dark conditions, the accuracy of the RGB-based method significantly drops to 48.9, and in back-light conditions, the accuracy of the D-based method dramatically drops to 28.3. Compared with these methods, our RGB-D fusion based approach is much more robust without accuracy degrading, and our detection results are 62.5 and 65.9, respectively, in these two extreme lighting conditions for accuracy.
- Published
- 2020
214. A Novel Bearing Fault Diagnosis Method Based on Multi-scale Transfer Sample Entropy
- Author
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Shun Wang, Xianzhi Wang, Yu Ren, Yongbo Li, and Shubin Si
- Subjects
Sample entropy ,Support vector machine ,Statistical classification ,Bearing (mechanical) ,law ,Computer science ,Feature extraction ,Entropy (energy dispersal) ,Transfer of learning ,Fault (power engineering) ,Algorithm ,law.invention - Abstract
To solve the defects that the generalization ability of the traditional data-driven fault diagnosis model reduces or even fails in mechanical system diagnosis, a multi-scale transfer sample entropy is proposed based on the idea of transfer learning. First, the multi-scale transfer sample entropy method is proposed to extract fault features based on multi-scale sample entropy and feature transfer learning. Second, the parameters of the multiscale transfer sample entropy method are optimized to further improve the fault identification accuracy. Finally, the experiment results of rolling bearing show that the proposed multi-scale transfer sample entropy can effectively improve the generalization ability of the data-driven model and realize the accurate identification of different fault locations of the rolling bearing under a small number of samples.
- Published
- 2020
215. Refined time-shift multiscale diversity entropy: a novel feature extraction algorithm for fault diagnosis of planetary gearbox
- Author
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Shun Wang and Yongbo Li
- Subjects
History ,Computer Science Applications ,Education - Abstract
Planetary gearboxes play a critical role in aerospace and heavy industry fields, such as wind turbines, heavy vehicles and construction machines. Intelligent fault diagnosis is significant for safe operation and fault prevention of planetary gearboxes. Recently, multiscale diversity entropy and related entropy methods are proposed to extract features of time series and applied for the fault diagnosis. However, there are still some limitations in fault feature representation and stability for multiscale diversity entropy. To solve the problem, in this paper, a novel planetary gearboxes fault diagnosis method via refined time-shift multiscale diversity entropy (RTSMDE) is proposed. First, a novel entropy algorithm called RTSMDE is proposed to measure the complexity of time series and extract fault features of the vibration signals, which is robust and efficient in performance. Then, the obtained features are utilized to fulfil automatically the fault pattern identifications using support vector machine. To confirm the superiority of the RTSMDE-based fault diagnosis method, simulated signals and experimental studies are constructed and three used widely methods are employed to present a comprehensive comparison. The results indicate that RTSMDE performs best and obtains the highest accuracy.
- Published
- 2022
216. A novel energy demodulation method using B-spline approximation for bearing fault detection
- Author
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Yuanbo Xu, Yongbo Li, Youming Wang, Junsuo Qu, Yu Wei, and Zhaoxing Li
- Subjects
Applied Mathematics ,Electrical and Electronic Engineering ,Condensed Matter Physics ,Instrumentation - Published
- 2022
217. Restrictions on managerial outside job opportunities and corporate tax policy: Evidence from a natural experiment
- Author
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Zhihong Chen, Yongbo Li, and Tai-Yuan Chen
- Subjects
Natural experiment ,Sociology and Political Science ,business.industry ,media_common.quotation_subject ,Enterprise value ,Doctrine ,Accounting ,Monetary economics ,Tax avoidance ,Tax haven ,Dismissal ,Economics ,Path analysis (statistics) ,business ,health care economics and organizations ,Corporate tax ,media_common - Abstract
We examine the effect of restricting executives’ outside job opportunities on corporate tax avoidance and tax risk, using a natural experiment of the staggered adoption and rejection of the inevitable disclosure doctrine (IDD). Based on a difference-in-differences analysis, we find strong evidence that the IDD decreases effective tax rates. We also find that the IDD reduces tax risk though the evidence is weaker. The effect is generally more pronounced when the risk of dismissal due to poor performance is higher. Additional analyses show that the IDD increases the use of tax haven operations. Finally, a path analysis shows that the increase in tax avoidance contributes about 6% to 7% of the total effect of the IDD on firm value. Overall, the results suggest that career concerns motivate executives to reduce their firms’ tax burden using low risk strategies.
- Published
- 2022
218. Multiscale symbolic fuzzy entropy: An entropy denoising method for weak feature extraction of rotating machinery
- Author
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Yang Yang, Shun Wang, Yongbo Li, and Zichen Deng
- Subjects
0209 industrial biotechnology ,Computer science ,Mechanical Engineering ,Computation ,Noise reduction ,Feature extraction ,Process (computing) ,Aerospace Engineering ,02 engineering and technology ,Fault (power engineering) ,01 natural sciences ,Signal ,Computer Science Applications ,020901 industrial engineering & automation ,Fuzzy entropy ,Control and Systems Engineering ,0103 physical sciences ,Signal Processing ,Entropy (energy dispersal) ,010301 acoustics ,Algorithm ,Civil and Structural Engineering - Abstract
The entropy-based method has been demonstrated to be an effective approach to extract the fault features by estimating the complexity of signals, but how to remove the strong background noises in analyzing early weak impulsive signal remains unexplored. To solve this problem, this paper proposes symbolic fuzzy entropy (SFE) based on symbolic dynamic filtering and fuzzy entropy to eliminate the noises and improve the calculation efficiency. The main idea of SFE is to use symbolic dynamic filtering to remove the noise-related fluctuations while significantly simplifying the circulation calculation, thereby, generating better performance in resisting the background noises and high computation efficiency. The superiority of SFE is verified via two simulated signals and other three entropy methods. For comprehensive feature description, we further extend SFE into multiscale analysis by incorporating with the coarse gaining process, called MSFE. Experimental results demonstrate that the proposed MSFE method has the best performance in extracting weak fault characteristics compared with three existing MSE, MFE, and MPE methods.
- Published
- 2022
219. Risk Assessment and Classification to Electricity Retail Market
- Author
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Zhemin Lin, Yongbo Li, Hanhan Qian, and Xinyu Yu
- Subjects
Artificial neural network ,Warning system ,business.industry ,Computer science ,Econometrics ,Entropy (information theory) ,Incidence matrix ,Electricity ,Cluster analysis ,business ,Risk assessment ,Operational risk - Abstract
With fast development of the retail market of China in recent years, the risk of the market needs to be quantified. This paper provides a detailed framework to assess and classify the risk of the market. The neural network is used to predict the risk. Time-dependent weight is used to describe cumulative effect. With the S-shaped curve, the risk factors are classified by safety, competition and elimination. The entropy method is used to set weights of the risk factors. The K-means clustering is used to classify the companies or periods. The risk factors, the operational risk, and the risk indicators form a hierarchical framework. The state aggregation based on serial tracking and incidence matrix finds transition rate matrix for early warning and coordination. The numerical results of a market with 9 companies are provided to validate the effectiveness of the proposed model.
- Published
- 2020
220. A Novel Nonlinear Analysis Tool: Multi-scale Symbolic Sample Entropy and Its Application in Condition Monitoring of Rotary Machinery
- Author
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Shun Wang and Yongbo Li
- Subjects
Sample entropy ,Vibration ,Nonlinear system ,Computer science ,Computation ,Feature extraction ,Condition monitoring ,Entropy (information theory) ,Experimental data ,Algorithm - Abstract
Sample entropy (SE) has been employed for fault diagnosis of rotary machinery (FDRM). However, SE has low computation efficiency for long time series. To solve this problem, symbolic sample entropy (SSE), a novel measure of time series regularity, is proposed to estimate the complexity. However, SSE fails to account for the multiple scale information inherent in measured vibration signals. Therefore, we combine the concept of multi-scale analysis with SSE, called multi-scale SSE (MSSE). To evaluate the effectiveness of the proposed MSSE method, we apply several simulated signals to verify the merits of SSE in impulsion detection and calculation efficiency. Furthermore, we utilize one experimental data to validate its effectiveness in recognizing several fault types of rotary machinery. Experimental results indicate that MSSE has an advantage in extracting fault features compared with multi-scale entropy (MSE), multi-scale fuzzy entropy (MFE), and multi-scale permutation entropy (MPE) methods.
- Published
- 2020
221. Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
- Author
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Xiaohui Yang, Shun Wang, Bing Han, Yongbo Li, and Qingqi Zhu
- Subjects
Computer science ,rotating machinery ,Feature extraction ,02 engineering and technology ,Fault (power engineering) ,01 natural sciences ,Signal ,lcsh:Technology ,lcsh:Chemistry ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Lempel-Ziv complexity ,General Materials Science ,010301 acoustics ,Instrumentation ,lcsh:QH301-705.5 ,Fluid Flow and Transfer Processes ,business.industry ,lcsh:T ,Process Chemistry and Technology ,feature extraction ,020208 electrical & electronic engineering ,Health condition ,General Engineering ,Pattern recognition ,fault diagnosis ,lcsh:QC1-999 ,Computer Science Applications ,Vibration ,Character (mathematics) ,Safe operation ,lcsh:Biology (General) ,lcsh:QD1-999 ,lcsh:TA1-2040 ,Artificial intelligence ,business ,lcsh:Engineering (General). Civil engineering (General) ,lcsh:Physics - Abstract
The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.
- Published
- 2020
222. Global Research on Osteosarcoma, 1999–2019: A Bibliometric and Visual Analysis
- Author
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Yongbo Li, Baixing Wei, Han Wu, and Yuanpei Cheng
- Subjects
Oncology ,medicine.medical_specialty ,business.industry ,Internal medicine ,medicine ,Osteosarcoma ,medicine.disease ,business - Abstract
Background Osteosarcoma is a primary malignant bone tumor that occurs in children and adolescents. Increasing numbers of scholars have studied its development and treatment. To fully understand the current status of osteosarcoma research and global trends therein, we performed a bibliometric and visual analysis of osteosarcoma studies published between 1999 and 2019. Methods We searched the Web of Science database for publications on osteosarcoma. The basic characteristics of this sample of publications, such as H indices, annual outputs, languages of publication, and authors, journals, institutions, and countries of origin, were determined. Co-citation, collaboration, and keyword co-occurrence were analyzed using CiteSpace software. Results The sample comprised 16,934 articles. The number of publications increased annually. H indices and total numbers of citations were far higher for articles from the United States than for those from other countries. Among institutions, the largest proportion of articles originated from Shanghai Jiaotong University. R. Gorlick was the author with the highest H index and total number of citations. Oncology Letters published the largest number of articles and Cancer Research was the most frequently cited journal. The five most frequently appearing keywords were “osteosarcoma,” “cancer,” “expression,” “apoptosis,” and “metastasis.” The analysis generated 10 major clusters of keywords and 23 clusters of co-cited references. Conclusions The findings of this study have guiding significance for researchers seeking cooperating institutions and partners for osteosarcoma research, popular journals and important literature in the field, an understanding of the knowledge base for this research, and up-to-date identification of research hotspots and trends. Keywords: osteosarcoma, bibliometric, citespace, visual analysis, co-citation
- Published
- 2020
223. Short-term outcomes of laparoscopic intersphincteric resection with intraoperative radiotherapy using low-energy X-rays for primary locally advanced low rectal cancer: a single center experience
- Author
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Min Wang, Tiejun Wang, Yongbo Li, Shuang Wang, An Shang, Donglin Li, Wangsheng Xue, Jianzheng Yang, and Zeyun Zhao
- Subjects
Male ,Anal Canal ,Single Center ,Low-energy X-rays ,0302 clinical medicine ,Antineoplastic Combined Chemotherapy Protocols ,Medicine ,Digestive System Surgical Procedures ,Aged, 80 and over ,Radiotherapy Dosage ,Middle Aged ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Combined Modality Therapy ,Chemotherapy regimen ,Lap ISR ,Treatment Outcome ,medicine.anatomical_structure ,Oncology ,030220 oncology & carcinogenesis ,Low rectal cancer ,Female ,030211 gastroenterology & hepatology ,medicine.drug ,medicine.medical_specialty ,IORT ,lcsh:Surgery ,Anastomosis ,lcsh:RC254-282 ,Internal anal sphincter ,Capecitabine ,T1–3N+M0 ,03 medical and health sciences ,Humans ,Aged ,Retrospective Studies ,Intraoperative Care ,Rectal Neoplasms ,business.industry ,Research ,Cancer ,lcsh:RD1-811 ,T3N0M0 ,medicine.disease ,Anus ,Surgery ,Stenosis ,Laparoscopy ,Radiotherapy, Adjuvant ,business ,Follow-Up Studies - Abstract
Background Even with the augmentative application of anal-preservation surgery in low rectal cancer, the role and indications of laparoscopic intersphincteric resection (Lap ISR) are still under debate, especially for T3 or node-positive (T3N0M0, T1–3N+M0) cancer, mainly due to the oncological safety and functional outcomes. INTRABEAM (Carl Zeiss, Germany) intraoperative radiotherapy (IORT) using low-energy X-rays features in accurate irradiation, less exposure, and reduced complications. Taking advantages of Lap ISR and INTRABEAM IORT, this innovative approach aims to increase the probability of the anal preservation with acceptable postoperative outcomes. Materials and methods From December 2015 to August 2019, we retrospectively analyzed the short-term outcomes of 12 patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1–3N+M0) primary locally advanced low rectal cancer. They all had received Lap ISR and INTRABEAM IORT with a dose of 16–18 Gy applied by an applicator through the anus (natural orifice). Then, with no pre- or postoperative radiotherapy given, the patients were suggested to receive 6–8 cycles of the XELOX chemotherapy regimen (oxaliplatin, 130 mg/m2 and capecitabine, 1000 mg/m2). Results All patients achieved R0 resection. The median radiation time was 27 min and 15 s, and the mean radiative dose was 17.3 Gy (range 16–18 Gy). The median follow-up time was 18.5 months (range 3–45 months). Two patients experienced local recurrence. Two male patients experienced anastomotic stenosis. Furthermore, one of them experienced perianal abscess and the other one experienced pulmonary metastasis after refusing to receive chemotherapy. One female patient with internal anal sphincter invasion experienced distant metastases to the liver and gluteus maximus muscle 35 months after IORT. No acute radiation injuries or symptoms were observed. Although they experienced a reduction in anal function, every patient was satisfied with the postoperative outcomes. Conclusions For patients evaluated preoperatively with T3 or node-positive (T3N0M0, T1–3N+M0) primary locally advanced low rectal cancer, Lap ISR with INTRABEAM IORT may be a safe and feasible approach for anal preservation without compromising oncological outcomes.
- Published
- 2020
224. Two essays on the legal effect in shaping corporate policy
- Author
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Yongbo Li
- Published
- 2020
225. Accurate Hand Detection from Single-Color Images by Reconstructing Hand Appearances
- Author
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Wendi Cai, Wei Longsheng, Yongbo Li, Chi Xu, and Jun Zhou
- Subjects
hands detection ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Color ,02 engineering and technology ,hand appearance reconstruction ,lcsh:Chemical technology ,01 natural sciences ,Biochemistry ,Convolutional neural network ,Article ,Analytical Chemistry ,Task (project management) ,human–computer interaction ,convolutional neural networks ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Computer vision ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Layer (object-oriented design) ,Instrumentation ,Pose ,Color image ,business.industry ,010401 analytical chemistry ,generative adversarial network ,Models, Theoretical ,Hand ,Atomic and Molecular Physics, and Optics ,0104 chemical sciences ,Databases as Topic ,Gesture recognition ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Artificial intelligence ,business - Abstract
Hand detection is a crucial pre-processing procedure for many human hand related computer vision tasks, such as hand pose estimation, hand gesture recognition, human activity analysis, and so on. However, reliably detecting multiple hands from cluttering scenes remains to be a challenging task because of complex appearance diversities of dexterous human hands (e.g., different hand shapes, skin colors, illuminations, orientations, and scales, etc.) in color images. To tackle this problem, an accurate hand detection method is proposed to reliably detect multiple hands from a single color image using a hybrid detection/reconstruction convolutional neural networks (CNN) framework, in which regions of hands are detected and appearances of hands are reconstructed in parallel by sharing features extracted from a region proposal layer, and the proposed model is trained in an end-to-end manner. Furthermore, it is observed that the generative adversarial network (GAN) could further boost the detection performance by generating more realistic hand appearances. The experimental results show that the proposed approach outperforms the state-of-the-art on public challenging hand detection benchmarks.
- Published
- 2019
226. Do Political Connections Weaken Tax Enforcement Effectiveness?
- Author
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Lillian F. Mills, Yongbo Li, Fang Zhang, and Kenny Z. Lin
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Economics and Econometrics ,Double taxation ,050208 finance ,Public economics ,Direct tax ,05 social sciences ,050201 accounting ,Tax reform ,Tax avoidance ,Value-added tax ,Ad valorem tax ,Accounting ,0502 economics and business ,Economics ,State income tax ,Finance ,Indirect tax - Abstract
This paper examines whether ties to politicians by corporate boards of directors weaken the effectiveness of tax authorities in constraining tax avoidance in China. We use a unique data set to measure geographic time-variant tax enforcement, including the probability of income tax audit, the expertise of tax officers, and the consequences of underreporting tax liabilities. Based on a sample of 11,121 firm-years from 2003 to 2013, we find that the deterrent effect of the probability that a firm's taxable income understatement will be detected and lead to heavy penalties is significantly undermined if the board is politically connected. To enhance our analysis, we use opportunities for income shifting, the most likely mechanism through which Chinese firms avoid taxes on an ongoing basis, to illustrate how connected boards exert power to unwind the constraining effect of tax enforcement. Overall, our results suggest that a board's ties to politicians can be a significant challenge to the effective enforcement of tax compliance in a politically controlled economy. This article is protected by copyright. All rights reserved.
- Published
- 2018
227. Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry
- Author
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Amir-Reza Abtahi, Yongbo Li, and Mahya Seyedan
- Subjects
021103 operations research ,Supply chain management ,Computer science ,business.industry ,Supply chain ,0211 other engineering and technologies ,Automotive industry ,General Decision Sciences ,02 engineering and technology ,Management Science and Operations Research ,Fuzzy logic ,Industrial engineering ,Software ,Data envelopment analysis ,Performance measurement ,business ,Envelopment - Abstract
Supply chain performance evaluation problems are evaluated using data envelopment analysis. This paper proposes a fuzzy network epsilon-based data envelopment analysis for supply chain performance evaluation. In the common data envelopment analysis models which are used for evaluation of decision-maker units efficiency, there are several inputs and outputs. One of the bugs of such models is that the intermediate products and linking activities are overlooked. Considering these intermediate activities and products, the current study evaluates the performance of decision-maker units in an automotive supply chain. There are ten decision-maker units in the supply chain in which there are three suppliers, two manufacturers, two distributors, and four customers. Moreover, the overall efficiency of input-oriented (input-based) model and input-oriented divisional efficiency are calculated. In order to improve the efficiencies, the projections onto the frontiers are obtained by using the outputs of the solved model and Lingo software. In order to show the applicability of the proposed model, it is applied on automotive industry, as a case study, to evaluate supply chain performance. Then, the overall efficiencies of DMUs and each sections (divisions) of DMUs were calculated separately. Therefore, every organization can apply this evaluation method for improving the performance of alternative factors.
- Published
- 2018
228. Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine
- Author
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Xianzhi Wang, Binbin Liu, Yuantao Yang, Yongbo Li, and Xihui Liang
- Subjects
0209 industrial biotechnology ,Binary tree ,Acoustics and Ultrasonics ,Computer science ,Mechanical Engineering ,Feature extraction ,02 engineering and technology ,Condensed Matter Physics ,Cost reduction ,Vibration ,Sample entropy ,Support vector machine ,020901 industrial engineering & automation ,Mechanics of Materials ,0202 electrical engineering, electronic engineering, information engineering ,Entropy (information theory) ,020201 artificial intelligence & image processing ,Permutation entropy ,Algorithm - Abstract
Early fault diagnosis of rolling bearings is crucial to operating and maintenance cost reduction of the equipment with bearings. This paper aims to propose a novel early fault feature extraction method based on the proposed hierarchical symbol dynamic entropy (HSDE) and the binary tree support vector machine (BT-SVM). Multiscale symbolic dynamic entropy (MSDE) has been recently proposed to characterize the dynamical behavior of time series. MSDE has several merits comparing with multiscale sample entropy (MSE) and multiscale permutation entropy (MPE), such as high computational efficiency and robustness to noise. However, MSDE only utilizes the fault information in the low frequency components and consequently the fault information hidden in the high frequency components is discarded. To address this shortcoming, a new method, namely HSDE, is proposed to extract the fault information in the high frequency components. Then, the BT-SVM is utilized to automatically complete the fault type identification. The effectiveness of the proposed method is validated using simulated and experimental vibration signals. Meanwhile, a comparison is conducted between MPE, hierarchical permutation entropy (HPE), MSE, hierarchical sample entropy (HSE), MSDE and HSDE. Results show that the proposed method performs best to recognize the early fault types of rolling bearings.
- Published
- 2018
229. Seismic Performance of Precast Bridge Columns with Socket and Pocket Connections Based on Quasi-Static Cyclic Tests: Experimental and Numerical Study
- Author
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Hongya Qu, Zhiqiang Wang, Hongyi Wei, Tiantian Li, and Yongbo Li
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Materials science ,business.industry ,0211 other engineering and technologies ,020101 civil engineering ,02 engineering and technology ,Building and Construction ,Structural engineering ,Column (database) ,Bridge (interpersonal) ,Finite element method ,0201 civil engineering ,Precast concrete ,021105 building & construction ,Cyclic loading ,business ,Quasistatic process ,Civil and Structural Engineering - Abstract
In this study, four 1/3-scale bridge column specimens were investigated both experimentally and numerically. The specimens consisted of two cast-in-place (CIP) reference columns and two pre...
- Published
- 2019
230. Investigation of Last Resort Service in Overseas Retail Electricity Market and Suggestion to Domestic Provincial Retail Market
- Author
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Jing Li, Kai Wang, Yongbo Li, Hanhan Qian, Jiejie Huang, and Zhemin Lin
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ComputingMilieux_GENERAL ,Service (business) ,Finance ,business.industry ,Retail market ,Electricity pricing ,Electricity market ,Power grid ,business ,China ,Electricity retailing - Abstract
The retail electricity market is becoming prosperous in China in the recent years. However, due to many uncertain factors, the retail providers may go bankrupt and have to withdraw from the retail market, thus the customers will suffer from unexpected supply interruption and economic loss, which will disturb the healthy operation of the electricity market. The purpose of the last resort service is to take the responsibility of the bankrupted providers, and maintain the power supply to the customers. This service is often provided by retail electric providers in foreign countries while undertaken by the power grid companies in China. In this paper, the experiences of the last resort service in several foreign countries are investigated. The qualification, pricing, customer allocation, and operating process about the last resort service are investigated. The benefit and risk of the last resort service are analyzed to determine the sharing of the responsibilities, interests, and punishments among the participants of the retail market. Based on the investigation, some practical suggestions to the last resort service are given to the provincial retail market in China.
- Published
- 2019
231. Laparoscopic intersphincteric resection with intraoperative radiotherapy using low-energy X-rays for locally advanced ultra-low rectal cancer
- Author
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Zhang Mingwei, Zou Yongbo, Zeyun Zhao, Song Xiaowei, Li Tao, Shuang Wang, Tiejun Wang, Yongbo Li, Jianzheng Yang, Chunyu Wang, Wang Xinyu, Wangsheng Xue, and Min Wang
- Subjects
medicine.medical_specialty ,Colorectal cancer ,medicine.medical_treatment ,IORT ,lcsh:Surgery ,Anal Canal ,Anastomosis ,X-Ray Therapy ,lcsh:RC254-282 ,030218 nuclear medicine & medical imaging ,Capecitabine ,Low-energy X-rays ,03 medical and health sciences ,Ileostomy ,0302 clinical medicine ,Technical Innovations ,medicine ,Rectal Adenocarcinoma ,Humans ,Anus preservation ,Retrospective Studies ,business.industry ,Rectal Neoplasms ,lcsh:RD1-811 ,Middle Aged ,medicine.disease ,Anus ,Prognosis ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Chemotherapy regimen ,Radiation therapy ,Lap ISR ,medicine.anatomical_structure ,Treatment Outcome ,Oncology ,030220 oncology & carcinogenesis ,Quality of Life ,Surgery ,Female ,Laparoscopy ,Radiology ,Intrabeam X-rays radiotherapy system ,Neoplasm Recurrence, Local ,business ,medicine.drug - Abstract
Background In order to overcome the shortcomings of laparoscopic intersphincteric resection (Lap ISR), an alternative method of delivering intraoperative radiotherapy by Intrabeam X-rays radiotherapy system (XRS) is proposed in this paper. Intrabeam XRS is a device that uses low-energy X-rays source generated by a mobile controller unit, which is featured in accurate irradiation, reduced complications, and less exposure. The purpose of this study is to discuss the feasibility of Lap ISR with intra-operative radiotherapy using low-energy X-rays for locally advanced ultra-low rectal cancer in Asian woman. This novel proposed method will greatly increase the anus preserving probability and improved the quality of life. Methods A 53-year-old woman diagnosed with rectal adenocarcinoma had a strong desire to preserve the anal function and presented at the Jilin University Second Hospital, Jilin, China. The tumor’s size was 4 cm × 3 cm. It was located 2 cm from the anus merge and invaded the levator ani muscle. Preoperative clinical staging was T4N1M0 and could be reached R0 resection. After the consent form was signed by the patient, Lap ISR combined with the applicator put through the anus (natural orifice) to the tumor bed was performed and prophylactic ileostomy synchronized the anastomosis. Patient only received 1-cycle chemotherapy regimen of oxaliplatin with capecitabine postoperatively due to personal reasons. Pre- or postoperative radiotherapy was not given. Results After clinical follow-up, until now, there is not any sign of local recurrence. Anus function and short-term complications are acceptable. The short-term effect is satisfying and we look forward to further assess the long-term effect. Conclusion Laparoscopic intersphincteric resection with IORT using low-energy X-rays for the patients with late-stage ultra-low rectal cancer could provide an opportunity of preserving the anus function, and it is feasible for the selected patients. Trial registration Retrospectively registered; Trial registration: NCT03393234; Registered time: 05 January 2017.
- Published
- 2018
232. Statistical scheme for fault detection using Arduino and MPU 6050
- Author
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Saman Abbas, Arfa Hassan, Li'na Wang, Yongbo Li, Peng Zhao, Zhenbao Liu, and Liu Xin
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Signal processing ,law ,Inertial measurement unit ,Computer science ,Arduino ,Real-time computing ,ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS ,Gyroscope ,Accelerometer ,Fault (power engineering) ,Signal ,Fault detection and isolation ,law.invention - Abstract
Unmanned Aerial Vehicles (UAVs) are widely used for various civilian, security and military applications. UAVs are equipped with a number of sensors such as accelerometers and gyroscopes, the measured data reliability of these sensors is critical and data obtained from these sensors must be accurate. Sometimes, sensors are prone to damage because of various electric/communication problems. This paper proposes a statistical scheme for catering real-time experimental results of fault detection for inertial measurement unit sensors (accelerometer and gyroscope) by using statistical analysis measures. The objective is to detect sensor bias fault, total sensor failure and drift or additive type fault in gyroscope and accelerometer measurements by processing their data via statistical measures. The scheme of processing the sensor data aims to suggest best statistical measures for fault detection in a multiple-sensor setting, after graphical signal analysis (comparison of the graphs of signal overture of all sensors) has been completed. This scheme can be helpful in diagnosis and subsequent isolation of sensors with fault in high-reliability applications where accuracy of data is critical.
- Published
- 2019
233. A Reinforcement Learning Approach for Online Service Tree Placement in Edge Computing
- Author
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Tian Lan, Nakjung Choi, Yimeng Wang, and Yongbo Li
- Subjects
Service (systems architecture) ,Computer science ,Distributed computing ,020206 networking & telecommunications ,02 engineering and technology ,Network congestion ,Tree (data structure) ,020210 optoelectronics & photonics ,Tree structure ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,Reinforcement learning ,Heuristics ,Edge computing - Abstract
We consider the problem of optimally mapping an edge computing service that is modeled as a tree with multiple processing sub-tasks and data flows onto the underlying physical network. As new computing and data analytics applications require more complicated data processing structures, and different types of data (e.g., images, videos, and numbers) sensed at geographically distributed locations must be collected and processed to obtain a complex and comprehensive result, highly intelligent algorithms are needed to solve this challenging problem. In this paper, we propose a learning-based hierarchical service tree placement strategy that aims to optimize the net utility, defined as achieved utility minus network congestion. The key idea is to decouple a service tree into appropriate sub-trees each containing a single computing sub-task as well as associated data flows and to recursively leverage Q-learning to place each sub-tree while maintaining the dependencies of sub-tasks in the service tree structure. It enables a scalable solution for large networks with unknown arrival statistics and complex service structures. Numerical results show that our solution can significantly outperform baseline heuristics in online service tree placement.
- Published
- 2019
234. Fault Diagnosis of Gearbox based on Convolutional Neural Network and Infrared Thermal Imagining
- Author
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Xiaoqiang Du, Yongbo Li, and Shubin Si
- Subjects
Imagination ,021103 operations research ,Computer science ,business.industry ,media_common.quotation_subject ,0211 other engineering and technologies ,Experimental data ,Image processing ,Pattern recognition ,02 engineering and technology ,01 natural sciences ,Convolutional neural network ,Catastrophic failure ,0103 physical sciences ,Softmax function ,Thermography ,Artificial intelligence ,business ,010301 acoustics ,Classifier (UML) ,media_common - Abstract
Diagnosis of gearbox is crucial to prevent catastrophic failure and reduce financial losses. In this study, we introduce a novel fault diagnosis technique using the infrared thermography (IRT). The IRT-based techniques have merits of non-contact measurement and high-scalability. Since the convolutional neural network (CNN) is proven to be powerful in image processing, a fault diagnosis strategy is designed by combining the IRT and CNN. Then, the pattern identification is achieved by using softmax regression (SR) classifier. One experimental data is used to validate the effectiveness of the proposed method. Results demonstrate that this diagnosis strategy can recognize gearbox with various oil-level faults. Furthermore, some important distinguishable areas of IRT images are marked for further focused research field.
- Published
- 2019
235. A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy
- Author
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Xihui Liang, Yuantao Yang, Minqiang Xu, Guoyan Li, and Yongbo Li
- Subjects
Scheme (programming language) ,0209 industrial biotechnology ,Scale (ratio) ,Computer science ,Aerospace Engineering ,02 engineering and technology ,Fault (power engineering) ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,Civil and Structural Engineering ,computer.programming_language ,Binary tree ,business.industry ,Mechanical Engineering ,020208 electrical & electronic engineering ,Pattern recognition ,Filter (signal processing) ,Computer Science Applications ,Vibration ,Support vector machine ,Control and Systems Engineering ,Signal Processing ,Artificial intelligence ,business ,Laplace operator ,computer - Abstract
The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.
- Published
- 2018
236. Determination of Five Plant Growth Regulator Containing Carboxyl in Bean Sprouts Based on Chemical Derivatization by GC-MS
- Author
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Keting Zou, Li Tian, Guofu Qin, and Yongbo Li
- Subjects
Detection limit ,Chromatography ,010401 analytical chemistry ,food and beverages ,010502 geochemistry & geophysics ,01 natural sciences ,Applied Microbiology and Biotechnology ,0104 chemical sciences ,Analytical Chemistry ,Acetic acid ,chemistry.chemical_compound ,chemistry ,Reagent ,Methanol ,Gas chromatography–mass spectrometry ,Safety, Risk, Reliability and Quality ,Derivatization ,Safety Research ,Quantitative analysis (chemistry) ,Boron trifluoride ,0105 earth and related environmental sciences ,Food Science - Abstract
An efficient and sensitive analytical method for the simultaneous determination of five plant growth regulators (PGRs) has been successfully developed, including 3-Indoleacetic acid (IAA), 2-Naphthylacetic acid (2-NAA), Beta-indolebutyric acid (IBA), 2-(2,4-dichlorophenoxy), acetic acid (2.4-D), and 4-Chlorophenoxyacetic acid (4-CPA) in bean sprouts. Using 10% boron trifluoride of methanol as the derived reagent and the reaction was derivatized at 70 °C for 30 min, the derivatives of five PGRs have been completely separated within 19 min. It has a linear relationship in the concentration range of 0.01–0.20 mg/kg and the correlation coefficients (r2) in excess of 0.997. The average recoveries were ranged from 72.7–98.6% at three spiking different concentration levels of 0.05, 0.10, and 0.15 mg/kg, respectively. The relative standard deviations (RSDs) were ranged from 1.2 to 9.0%. The limits of detection and the limits of quantification were ranged from 0.00024 to 0.00172 mg/kg and 0.0008–0.0052 mg/kg, respectively. On the basis of these, the measurement has been successfully applied to the analysis of the exogenous phytohormones in the bean sprouts. Except for 4-CPA and IAA, the rest of the analytes were undetected in samples. In sum, the current method can be used for accurate determination and confirmation of PGRs in many other crops, such as bean sprouts.
- Published
- 2018
237. Design of a multi echelon product recovery embeded reverse logistics network for multi products and multi periods
- Author
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Neha Agarwal, Kiran Garg, P. C. Jha, Jyoti Dhingra Darbari, Devika Kannan, and Yongbo Li
- Subjects
021103 operations research ,Operations research ,Computer science ,Environmental compliance ,Supply chain ,05 social sciences ,Product recovery ,0211 other engineering and technologies ,General Decision Sciences ,Time horizon ,02 engineering and technology ,Reverse logistics ,Management Science and Operations Research ,Fuzzy logic ,Profit (economics) ,0502 economics and business ,050203 business & management - Abstract
Product recovery, accompanied by cradle to cradle policies from the contemporary supply chain, becomes an essential element in meeting environmental compliance and waste management policies. Incorporation of reverse logistics into the traditional supply chains becomes a complementary factor for efficient product recovery. To begin with product recovery, consumers are encouraged to return their end-of-use/end-of-life products, and the steps of collecting and planning the movement of returned products are crucial decisions. The efficient planning of a cost effective recovery process in reverse logistics requires dealing with the uncertainty underlying in the quantity and quality of the returned products. In this paper, we propose establishing an initial collection point within a permissible radius of the customer zones to overcome some of the issues of uncertainty. The uncertainty in the quantity and quality of the returned products are modelled using fuzzy triangular numbers. To capture the real world conditions of the proposed problem, our model aims at maximizing the profit incurred in the recovery process in an uncertain environment. The model was solved with the help of fuzzy mathematical programming. The model is validated by a company case belonging to the manufacturing of electronic products. To increase the applicability of the product recovery process in the industry, we propose a recovery process for the planning horizon consisting of multi periods and multi products. The outcomes of the proposed model indicate that for the successful realisation of such network, customers need to be legally enforced to return their end of used products in the channels established for value recovery.
- Published
- 2018
238. Inhomogeneous Phase Effect of Smart Meta-Superconducting $$\hbox {MgB}_{2}$$ MgB 2
- Author
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Honggang Chen, Xiaopeng Zhao, Yongbo Li, Weichang Qi, and Guowei Chen
- Subjects
Superconductivity ,Materials science ,Condensed matter physics ,Dopant ,Doping ,Energy-dispersive X-ray spectroscopy ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,Phase (matter) ,0103 physical sciences ,Superconducting critical temperature ,General Materials Science ,010306 general physics ,0210 nano-technology ,Intensity (heat transfer) - Abstract
The inhomogeneous phase of a smart meta-superconductor has a great effect on its superconductivity. In this paper, the effect of concentration, dimensions, electroluminescence (EL) intensity, and distribution of the inhomogeneous phase on the superconducting critical temperature $$(T_{C})$$ has been systematically investigated. An ex situ solid sintering was utilized to prepare smart meta-superconducting $$\hbox {MgB}_{2}$$ doped with six kinds of electroluminescent materials, such as $$\hbox {YVO}_{4}{:}\hbox {Eu}^{3+}$$ and $$\hbox {Y}_{2}\hbox {O}_{3}{:}\hbox {Eu}^{3+}$$ flakes. Elemental mappings through energy dispersive spectroscopy (EDS) show that the inhomogeneous phase is comparatively uniformly dispersed around the $$\hbox {MgB}_{2}$$ particles; thus V, Y, and Eu were accumulated at a small area. The measurement results show that the optimum doping concentration of the meta-superconducting $$\hbox {MgB}_{2}$$ is 2.0 wt%. The offset temperature ( $$T_{C}^{{ off}}$$ ) of the sample doped with 2.0 wt% dopant A is 1.6 K higher than that of pure $$\hbox {MgB}_{2}$$ . The improvement in $$T_{C}^{{ off}}$$ is likely related to the sizes, thickness, and EL intensity of the inhomogeneous phase of $$\hbox {MgB}_{2}$$ smart meta-superconductor. This experiment provides a novel approach to enhance $$T_{C}$$ .
- Published
- 2018
239. Learning Parametric Sparse Models for Heavy Noisy Removal From Images
- Author
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Yongbo Li, Zhonglong Zheng, Weisheng Dong, Xin Li, Xuemei Xie, and Guangming Shi
- Subjects
General Computer Science ,Computer science ,Noise reduction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,image retrieval ,Image (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,Image retrieval ,Image restoration ,0105 earth and related environmental sciences ,Parametric statistics ,sparse prior ,Noise measurement ,business.industry ,General Engineering ,Pattern recognition ,Noise ,Computer Science::Computer Vision and Pattern Recognition ,Parametric model ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,parametric model learning ,business ,Heavy noise removal ,lcsh:TK1-9971 - Abstract
Despite rapid advances in the field of image denoising, heavy noise removal has remained an under-explored area. When the strength of noise becomes comparable to or even more dominating than that of signal, restoration of important structures from such heavily-contaminated images becomes more challenging. Existing model-based and learning-based image denoising techniques often cannot delivery satisfactory results, which call for new insights and algorithmic tools. In this paper, we propose to remove heavy noise with the help of similar images retrieved from datasets, which leads to a hybrid approach combining sparsity-based and learning-based methods. Specifically, the parametric sparse prior of underlying clean image is learned from the retrieved reference images and the input noisy image. Leveraging prior from the reference images, the estimated parametric model contains much more accurate information of image details. When compared against existing conventional methods, the proposed hybrid approach is more capable of restoring fine-detailed structures in the presence of heavy noise. Experimental results show that the proposed method dramatically outperforms current state-of-the-art image denoising methods both subjectively and objectively.
- Published
- 2018
240. Fault Diagnosis of Rolling Bearing Under Speed Fluctuation Condition Based on Vold-Kalman Filter and RCMFE
- Author
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Ke Feng, Zhenbao Liu, Yu Wei, Xianzhi Wang, and Yongbo Li
- Subjects
0209 industrial biotechnology ,General Computer Science ,Computer science ,Feature extraction ,02 engineering and technology ,law.invention ,fault pattern identification ,020901 industrial engineering & automation ,law ,0202 electrical engineering, electronic engineering, information engineering ,Entropy (information theory) ,General Materials Science ,Bearing (mechanical) ,Vold-Kalman filtering (VKF) ,020208 electrical & electronic engineering ,Rolling bearing ,General Engineering ,Rotational speed ,Kalman filter ,Vibration ,refined composite multi-scale fuzzy entropy (RCMFE) ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,variable speed conditions ,lcsh:TK1-9971 ,Algorithm ,Laplace operator - Abstract
Rolling bearings' operation under variable speed conditions exhibits complex time-varying modulations and spectral structures, resulting in difficulty in the fault diagnosis. In order to effectively remove the influence of the rotational speed and extract the fault characteristics, this paper develops a fault diagnosis scheme based on the Vold-Kalman filter (VKF), refined composite multi-scale fuzzy entropy (RCMFE), and Laplacian score (LS). In the proposed method, the VKF is first adopted to remove the fault-unrelated components and give a clear representation of the fault symptoms. Second, the RCMFE is applied to extract fault features from the denoised vibration signal. Third, the LS approach is introduced to refine the fault features by sorting the scale factors. In the end, the selected features are fed into the logistic regression to automatically complete the fault pattern identifications. The proposed method is experimentally demonstrated to be able to recognize the localized defect on the inner race, outer race, and rolling element under variable speed conditions.
- Published
- 2018
241. Application of DEMATEL approach to identify the influential indicators towards sustainable supply chain adoption in the auto components manufacturing sector
- Author
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K. Mathiyazhagan and Yongbo Li
- Subjects
Sustainable development ,Renewable Energy, Sustainability and the Environment ,business.industry ,020209 energy ,Strategy and Management ,Control (management) ,Developing country ,Globe ,02 engineering and technology ,010501 environmental sciences ,Environmental economics ,01 natural sciences ,Industrial and Manufacturing Engineering ,medicine.anatomical_structure ,Multinational corporation ,Manufacturing ,Sustainability ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Small and medium-sized enterprises ,Business ,Marketing ,0105 earth and related environmental sciences ,General Environmental Science - Abstract
The problem of industrial pollution has increased globally at presently. Countries across the globe are aware of the concept of sustainable implementation in industries from a local to the global level to reduce pollution. Recent research supported this by focusing on Small and Medium Enterprises (SMEs) rather than Multinational Corporations (MNC). Sustainability development is a key to ensure control of the hazards and pollution in traditional activities. In India, many SMEs are involved in manufacturing varied products and services for customers. The auto component manufacturing sector is playing as an important role in the Indian economy by creating numerous job opportunities. Based on this, researchers gave special attention to auto components SMEs in sustainable development (SD) because of pollution which harms the environment. Analysis of SD is an essential activity for industries to ensure environmental improvement. Analyzing the indicators for SD helps confirm the development of sustainability in industries. However, analyzing the indicators for SD is a challenge for managers and needs detailed analysis and significant effort. Indicator analysis is a vague concept for managers who lack alternatives to evaluate SD in their industry. The aim of this present paper is to develop a set of measurement indicators for SD from literature. This paper, with the help of discussions with experts and Decision-making Trial and Evaluation Laboratory (DEMATEL) methodology analyzes sustainable development indicators in the auto components sectors. Fifteen SD indicators were considered for this study from a relevant literature background. These SD indicators indicate the industries’ sustainable development. Based on their interest, a questionnaire for this study was circulated among 35 leading auto components manufacturing SMEs in North India. This study will identify the influential indicator for SD based on experts' judgments through a pairwise comparison matrix. The results from the study clearly states that Indian auto component manufacturing industries believe that among the fifteen listed indicators, carbon management indicator helps in measuring sustainable development. Due to this reason, embedded or embodied carbon (SI2) indicator plays an influential role. Finally, the study concludes with insights about the future path of sustainability performance in a developing country like India.
- Published
- 2018
242. Morphology-tailored synthesis and luminescent properties of Y2O3:Eu3+ phosphors
- Author
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Chaoshun Yang, Weichang Qi, Guowei Chen, Xiaopeng Zhao, and Yongbo Li
- Subjects
Materials science ,Morphology (linguistics) ,Sintering ,Nanotechnology ,Phosphor ,02 engineering and technology ,010402 general chemistry ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,medicine.disease_cause ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,0104 chemical sciences ,Electronic, Optical and Magnetic Materials ,Nanomaterials ,Chemical engineering ,medicine ,Electrical and Electronic Engineering ,0210 nano-technology ,Luminescence ,Ultraviolet ,Excitation ,Monoclinic crystal system - Abstract
The size and shape of functional micro/nanomaterials have crucial influence on their physical and chemical properties. The monoclinic Y4O(OH)9NO3 with nanosheets, submicrospheres, and microrods have been successfully prepared by a non-aqueous solvothermal method. The precursors Y4O(OH)9NO3 decompose into cubic Y2O3:Eu3+ phosphors after sintering at 800 °C for 2 h. The morphologies and sizes of Y2O3:Eu3+ particles can be easily controlled by changing the growth rates of various crystallographic facets during solvothermal process. With the increment of the rare-earth nitrates concentration, the morphologies transform from 2D nanosheets to sub-microspheres, and eventually to microrods. The bright red emission, owing to Eu3+ f-orbital transitions, can be easily observed under ultraviolet excitation. Furthermore, the dependence of Y2O3:Eu3+ luminescence performances on different morphologies are discussed in the present work.
- Published
- 2017
243. Early Fault Diagnosis of Rotating Machinery by Combining Differential Rational Spline-Based LMD and K–L Divergence
- Author
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Xihui Liang, Minqiang Xu, Yuantao Yang, Yongbo Li, and Wenhu Huang
- Subjects
0209 industrial biotechnology ,Engineering ,business.industry ,020208 electrical & electronic engineering ,Wavelet transform ,02 engineering and technology ,Frequency spectrum ,Vibration ,Spline (mathematics) ,020901 industrial engineering & automation ,Control theory ,Moving average ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Spline interpolation ,business ,Instrumentation ,Frequency modulation - Abstract
First, an improved local mean decomposition (LMD) method called differential rational spline-based LMD (DRS) is developed for signal decomposition. Differential and integral operations are introduced in LMD, which can weaken the mode mixing problem. Meanwhile, an optimized rational spline interpolation is proposed to calculate the envelope functions aiming to reduce the large errors caused by moving average in the traditional LMD. A series of product functions (PFs) is obtained after the application of the proposed DRS-LMD. Then, Kullback–Leibler (K–L) divergence is adopted to select main PF components that contain most fault information. The machine fault can be easily identified from the amplitude spectrum of the selected PF component. The effectiveness of the proposed DRS-LMD and K–L strategy is tested on simulated vibration signals and experimental vibration signals. Results show that the proposed method can increase the decomposition accuracy of the signals and can be used to detect early faults on the gears and rolling bearings.
- Published
- 2017
244. Improved non-contact variable-frequency AC impedance instrument for cement hydration and pore structure based on SVM calibration method
- Author
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Ruben Paul Borg, Chao Li, Tao Wu, Yongbo Li, Shengwen Tang, and Yifei Wang
- Subjects
Materials science ,Artificial neural network ,Applied Mathematics ,Acoustics ,020208 electrical & electronic engineering ,010401 analytical chemistry ,02 engineering and technology ,Condensed Matter Physics ,01 natural sciences ,0104 chemical sciences ,law.invention ,Support vector machine ,Parasitic capacitance ,law ,0202 electrical engineering, electronic engineering, information engineering ,Calibration ,Current sensor ,Electrical and Electronic Engineering ,Resistor ,Alternating current ,Instrumentation ,Electrical impedance - Abstract
The non-contact alternating current (AC) impedance method is widely used in the study of electrochemical properties of cement-based materials,the instrument of this paper provide a new way for the study of pore structure of cement-based materials. Due to the loss of magnetic materials and the parasitic capacitance of measuring circuit, the ratio difference and phase difference of current sensor inevitably exit. Traditionally the standard resistor calibration method is used to determine the ratio error and phase error under different frequencies based on polynomial fitting (FT). In this paper, a machine-learning support vector machine (SVM) model is used to predict the calibration data with a small test sample. For the accuracy assessment, SVM model has the best comprehensive performance among FT, artificial neural network (ANN). The actual impedance test of typical cement-based materials under different frequencies is given and the results verify the excellent performance of SVM.
- Published
- 2021
245. Chinese Patent Law : A Practical Guide
- Author
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Yongbo Li, Fanwen Kong, Yang Yang, Yongbo Li, Fanwen Kong, and Yang Yang
- Subjects
- Patent laws and legislation--China
- Abstract
Numerous non-Chinese entities seek patents in China, and allegations of infringement are not uncommon. In this invaluable guide to Chinese patent law and the rules applicable to infringement challenges, the authors—all members of a leading Chinese law firm with wide experience in advising foreign clients—bring their experience in real-world patent litigation, reinforced with an abundance of relevant case law, in order to introduce the Chinese patent regime in a systematic and practical manner. In their thorough analysis of the scope of protection of patent rights and invalidation procedures, supported throughout by detailed examination of patent infringement lawsuits, the authors clarify such essential elements of Chinese patent law practice as the following: assertion of a claim; choosing the basis of the right to sue; publicity of claims; principle of internal interpretation priority; correction of ambiguity in a claim; interpretation of claims defined by preparation methods; defining the effect of a sequence of steps of a process claim; meaning of “persons of ordinary skill in the art”; concept of “means plus function”; application of the All Elements Rule; determination of functional features; and doctrine of equivalents. Each doctrine and the underlying principle is thoroughly examined with reference to relevant court decisions. With this eminently practical and authoritative guide, corporate counsel and patent lawyers will proceed confidently through the Chinese patent application system, assess risks and opportunities, understand how precedents might affect their case, protect their rights in China once they have a patent, and handle infringement proceedings with full awareness of applicable principles and procedures.
- Published
- 2021
246. Design of the dewatering project of pit with pensile curtains in the floodplain area near Yangtze River
- Author
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Junhua, Gao, primary, Yisheng, Wang, additional, Yongbo, Li, additional, Shizheng, Cui, additional, Chong, Xu, additional, and Xiangdong, Song, additional
- Published
- 2019
- Full Text
- View/download PDF
247. Aeroengine Performance Degradation Evaluation Method Based on Hierarchical Bayes Integrated with DNN Fusion Decision
- Author
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Huawei Wang, Qiang Fu, and Yongbo Li
- Subjects
History ,Fusion ,business.industry ,Computer science ,Machine learning ,computer.software_genre ,Computer Science Applications ,Education ,Bayes' theorem ,Evaluation methods ,Artificial intelligence ,business ,computer ,Degradation (telecommunications) - Abstract
According to the performance degradation law of aeroengine in operation, we proposed a method of aeroengine performance degradation evaluation based on the fusion decision of Hierarchical Bayes method (HB) and deep neural network (DNN). Hierarchical Bayes method was used to build the aeroengine reliability model. Then Based on the aeroengine operation monitoring parameters and reliability model, we used deep learning method to extract the performance degradation law and to evaluate aeroengine performance level, realized the fusion decision of engine performance degradation assessment. 9467 monitoring samples, contaminated with sensor noise, collected from 50 aeroengines was used to evaluate the quality of the network, and the average accuracy is 92.01%. The results showed that this method showed good robustness and reduced the risk of the evaluation results error caused by noise.
- Published
- 2021
248. Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings
- Author
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Minqiang Xu, Xihui Liang, Wenhu Huang, and Yongbo Li
- Subjects
0209 industrial biotechnology ,Signal processing ,Engineering ,business.industry ,020208 electrical & electronic engineering ,Bandwidth (signal processing) ,Mode (statistics) ,02 engineering and technology ,Residual ,Hilbert–Huang transform ,Vibration ,020901 industrial engineering & automation ,Control and Systems Engineering ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,Demodulation ,Algorithm design ,Electrical and Electronic Engineering ,business - Abstract
This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.
- Published
- 2017
249. Design and analysis of a new down‐the‐hole electromagnetic hammer driven by tube linear motor
- Author
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Wangyong He, E Chen, Yongbo Li, Shengwen Tang, Tao Wu, and Yiru Tang
- Subjects
010302 applied physics ,Engineering ,business.industry ,020208 electrical & electronic engineering ,Mechanical engineering ,Drilling ,Thrust ,02 engineering and technology ,Structural engineering ,Drill pipe ,Linear motor ,01 natural sciences ,Finite element method ,Power (physics) ,law.invention ,law ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Hammer ,Electrical and Electronic Engineering ,Hydraulic machinery ,business - Abstract
Percussive-rotary drilling is an effective method for the hard rock drilling. As hydraulic and pneumatic hammers have many problems especially in the deep core drilling, a new electromagnetic hammer driven by a tube linear motor is introduced in this study. The study mainly covers the mechanical structure and electromagnetic design of the hammer, respectively. Due to the direct use of electric power, the electromagnetic hammer has the advantage of high efficiency while it must meet the minimal percussive energy requirement under the limitation of drill pipe diameter. In this study, the validity of electromagnetic design is verified through the finite-element analysis software Ansoft/Maxwell 2D and numerical calculation of electromagnetic force. The working temperature in deep down-hole is relatively high, the influence of high temperature in deep hole on the electromagnetic thrust of the linear motor is then analysed. A motion model of linear motor hammer is established to calculate and analyse the impact power and frequency under different conditions. The results show that the performance of linear motor hammer can reach or even exceed the one of same diameter hydraulic hammer, especially in the low-frequency range. The prototype test also confirms the validity of the design.
- Published
- 2017
250. A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection
- Author
-
Yuantao Yang, Wenhu Huang, Yongbo Li, Guoyan Li, and Minqiang Xu
- Subjects
0209 industrial biotechnology ,Engineering ,Computation ,Aerospace Engineering ,Feature selection ,02 engineering and technology ,Machine learning ,computer.software_genre ,Pattern identification ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,Entropy (information theory) ,Planetary gearbox ,Civil and Structural Engineering ,Downtime ,business.industry ,Mechanical Engineering ,020208 electrical & electronic engineering ,Health condition ,Pattern recognition ,Computer Science Applications ,Support vector machine ,Control and Systems Engineering ,Signal Processing ,Artificial intelligence ,business ,computer - Abstract
Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel fault diagnosis method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the fault features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault types of planetary gearboxes.
- Published
- 2017
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