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To solve the limitations existing in the standard whale optimization algorithm (WOA), a convergence factor based on the sine function is designed to balance the exploration and development abilities of the WOA, furthermore, an improved opposition-based learning strategy is proposed to accelerate the convergence rate of the algorithm, in addition, the mutation operation based on the population diversity is employed to reduce the probability that the algorithm gets caught in the local optimum, the simulation result shows that the novel WOA presents a superior convergence rate and accuracy to the basic WOA.

Bearings are an important part of large industrial production equipment, and their damage is often accompanied by significant economic losses and personal safety issues. Algorithms based on the traditional machine learning, the recognition rate is not high, and the data preprocessing has great influence on the result. Models based on deep learning, which is relatively simple, and the shallow features extracted include noise. Aiming at the above problems, this paper proposes an end-to-end deep one-dimensional convolution residual network (DOD-CRN) fault diagnosis model. Training on the original vibration signal, and the feature extractor is constructed according to the characteristics of the data, which included the extraction of shallow features and high-level features. Experiments show that DOD-CRN has 98.3% fault recognition rate under complex fault signals, which is higher than other algorithms and can effectively detect faults.

In order to filter out image noise better and make it have better clarity, continuity and anti-noise performance in image edge extraction. Firstly, this paper constructs a new threshold function, compared with the traditional soft and hard threshold function and some existing improved methods, the threshold function has better adjustability and it is also continuous and almost smooth everywhere. When dealing with the wavelet coefficients, the real information on them can be retained more, and the noise can be effectively filtered at the same time. The simulation experiment shows that the image processed by the new threshold function has a high PSNR and a small MSE, which can be closer to the original image. Finally, the improved threshold function de-noising algorithm and the dyadic wavelet transform modulus maximum edge detection algorithm are combined to apply to image edge detection. By combining the advantages of the two algorithms, so that we can get clearer and more continuous image edges, and the contour is more complete.

With the optimization of the military-civilian integration development strategy since the 18th National Congress, the military-listed companies have once again become the focus of attention from all walks of life. It is of great practical significance to explore the factors and mechanisms that affect their business performance. This paper takes the military enterprises listed in China as the research object, and focuses on the study of the background of the military-civilian integration era. Therefore, the annual data of the enterprises from 2015 to 2017 is selected as a sample to study the influence of ownership structure on business performance by constructing multiple linear regression models. The results show that the degree of shares concentration and the balance of shares are positively correlated with the business performance, while the proportion of state-owned shares and corporate institution shares are negatively correlated with the business performance. Finally, based on the research results, suggestions for improving the operating performance of state-owned military listed companies are given.

Rotating machinery are continuously operated tools for power generation and mechanical applications. The smooth operation of these tools is fundamental in businesses to accomplish their profitability. The status of such machines can be monitored by continuously assessing their working parameters that aid to identify abnormal behaviors. Upon detection of abnormal behaviors, such machines can be early scheduled for maintenance. Conditioned Based Maintenance (CBM) is one such approach that continuously monitors the machine and recommends taking action before equipment fails. This approach uses monitoring parameters such as temperature, pressure, and vibration signals to minimize unnecessary breakdown and catastrophic failure. Vibration parameters based machine fault identification approach is one of the widely used methods in CBM. Various machine malfunctions can be predicted and detected based on energy at specific frequencies of vibration signals. Common faults such as unbalanced and misalignment are often identified by examining the operating frequencies and their harmonics. Based on machine malfunctions, the dominant differences are expected at these frequencies. Barring few dominating peaks, other peaks are not exactly located at harmonic speed, so it can be misleading to use information from only harmonic speed. Rather than just examining operating frequencies and their harmonics, we looked at the local peaks and identified their frequencies. The acceleration amplitude at the operating speed and the local maximum acceleration amplitude are selected as a vibration feature for the fault classification. The proposed KNN classifier demonstrated its reliability with an accuracy of over 96% for the tested data set of 25 samples.

For civil and military surveillance applications, a remote through-wall radar offers more practical solution compared to the conventional wired methods. For example, it helps emergency and military personnel to quickly detect tiny movements of limbs, breathing, or heartrate in a complex environment such as a collapsed building. This paper describes a low cost through-wall radar for monitoring vital signs. Three test cases along with their results are presented. Testing highlights the challenges of isolating human movement through various materials along with effectiveness of the proposed system in identifying human movements.

In this paper a center of mass estimation technique that uses the Weighted Segmental method and a motion capture system is presented. Balance is an essential ability needed by all humans for everyday routine activities. Therefore, improving balance ability results in a better and more convenient way of living. There are various major factors and aspects influence on balance, it is important to know which aspects have the greatest effect. And that is the focus of proposed research and experimental testing. The process of estimating the center of mass in a human body depends on the center of pressure of the body, human balance is strongly related to both of them. The center of pressure is the projection of the center of mass onto the XY plane, which can be found using a force plate. In this work, we used a Wii Balance Board with a compatible output program to replace the force plate, which reduced the cost of the system by $5,000-10,000 dollars. Based on the tests, 90% of accuracy was achieved for the center of pressure measurement. The motion capture system is operated from a laptop or desktop computer. Three calibrated cameras detect passive markers attached on the test subject. Weighted Segmental equations are generated and the center of mass of each segment and whole body are calculated and plotted based on recorded 3-dimensional data by the motion capture system and the force plate. Coupled with a passive marker motion capture system, we are able to mathematically determine the center of mass.

The current tracking control strategy is the key technology to realize dynamic reactive power regulation and harmonic current compensation in D-STATCOM system. In order to improve the precision and dynamic response speed of the D-STATCOM, a direct current control algorithm based on proportional vector proportional integral (PVPI) was proposed. The principle, performance and parameters of the controller are introduced in detail, The effectiveness and reliability of control algorithm are verified by simulation and has been tested on a practical installation of 3.3kV, ±1.5Mvar, and has higher engineering value.

In view of the rapid development of the modern science and technology, image processing technology is also widely used in various fields. More and more digital images are tampered, which brings a lot of difficulties and troubles. This paper analyzes the shadows of objects which are produced by the point light source or the line light source, and makes a preliminary judgment by calculating the grey level. Based on Lambert illumination model and optical properties of consistency, this paper proposes a new technique to judge whether an image is tampered or not. At last, the experimental results are obtained by analyzing the shadows of a lot of images.

Current vibration based maintenance methods can be improved by using principle component analysis to identify fault patterns in rotating machinery. The intent of this paper is to study the effects of using principle component analysis in a vibration based fault detection process and to understand the capability of this method of maintenance. Because vibration-based maintenance practices are capable of identifying motor faults based on their respective vibration patterns, principle component analysis observed in frequency domain can be used to automate the fault detection process. To test this theory, an experiment was set up to compare health conditions of a motor and determine if their patterns could be grouped using principle component analysis. The result from this study demonstrated that the proposed method successfully identified healthy, unbalance and parallel misalignments of rotary rotor. Therefore, it is capable of detecting faults in early stages and reducing maintenance costs.

Tools for predicting the plant process performances and forming the basis for controlling the process operation are essential for any wastwater treatment plant (WWTP). In this paper, extreme learning machine (ELM) with the ability of black-box modeling is considered. To further improve the performance of ELM, three intelligent algorithms are considered. In the proposed hybrid evolutionary ELM, intelligent algorithms (BDE, SaDE and TDE) are applied to optimize the network hidden neuron parameters and Moore-Penrose (MP) inverse is used to analytically determine the output weights. The proposed algorithms BDE-ELM SaDE-ELM and TDE-ELM are applied in the prediction of effluent from a biological WWTP. The simulation results show that hybrid evolutionary ELMs all performs better than basic ELM. And TDE is a superior method to improve the performance of ELM comparing with BDE and SaDE. Further more, TDE-ELM performs the best in the prediction accuracy among the three proposed methods.

To enhance the listed firms' willingness to pay out cash dividend and to improve the level of ratio of cash dividend to net profit, the China Securities Regulatory Commission has enacted six files about cash dividend policy of listed firms from2001 to 2013, which are defined “Semi-mandatory Dividend Policy” by academia‥ The policy does not stipulate the specific ratio of cash dividend to net profit but relates the level of cash dividend policy to listed firms' refinancing qualification, which has triggered controversy during its implementation. The empirical analysis selects the firms listed on the Shanghai or Shenzhen stock exchange before 2012 as samples. The analyzing year interval varies from 2001 to 2014. For building panel data model, design the willingness of paying cash dividend and the ratio of cash dividend to net profit separately as the dependent variable and the policy variable divided by year as the main independent variables with listed firms' characteristics and the industry characteristics as controlling variables.

This paper presents a generalized approach for health monitoring of electric machines based on the measured vibration data. In this methodology the source of the vibration can be detected through data analysis in frequency domain measured by accelerometer sensors attached to the surface of the machine's stator. This method can separate and distinguish the source of vibration in the case of multiple and superimposed failures on a typical electric machine. Although, the analysis have been performed on a 200W, 4 poles, three phase induction machine, this easy to use technique can be expanded to other types of electric machines as well. The results are proving that the accelerometer data can be used to detect the source of failure even if the failures have occurred in the electrical part of the machine.

In industrial applications, the uptime of machines can be enhanced through equipment monitoring. This minimizes the risks of unpredicted failures and consequent plant outages. Since all failure modes can cause an increase in machine vibrations, monitoring this area is the predominant and most widely used method to determine equipment condition, and to predict failures. The objective of this study is to detect faults in rotating equipment with the use of vibration analysis. A motor condition monitoring experiment is set up, and the motor's operational speed is controlled by an AC motor drive. The vibration of the motor is measured and monitored. The measured vibration data is analyzed using spectrum analysis software and a MATLAB program. The overall vibration level is monitored, the vibration severity is compared with the standard severity table and is used to determine the condition of the motor. The specific natural frequency corresponds with which kind of fault or failure mode is identified. This information provide insight on the condition of the machine.

In order to deal with the rapid growth of mobile data traffic and reduce the infrastructure capital expenditure (CAPEX) and operating expense (OPEX), C-RAN is introduced into wireless access network architecture. C-RAN can implement the separation of baseband processing unit (BBU) and remote radio head (RRH). Baseband processing resources are shared by the centralizing deployment of BBU in data center, while RRH is only as a wireless access point that is responsible for sending and receiving signals. Software Defined Network (SDN) and Network Function Virtualization (NFV), the candidate technologies for future development of wireless access network, can promote the virtualization of wireless access network. In this paper, the dynamic allocation of carrier resource in BBU pool is studied, and a dynamic combination allocation algorithm based on Karnaugh map and enhance genetic algorithm (KMEG) for resource allocation of carrier resource is also presented. The simulation results show that this algorithm can deal with different mobile service request for underlying resource effectively, which can adjust system capacity dynamically on demand and achieve the dynamic share of heterogeneous underlying resources in BBU pool. In addition, this algorithm can improve the receiving rate of VNs compared with the static resource allocation algorithm, which can enhance the utilization rate of underlying resources and spectrum efficiency greatly.

Image in the process of collection and storage can produce noise. Wavelet threshold de-noising is a method to remove noise effectively. The threshold function is a key in wavelet threshold de-noising method. In view of the hard-threshold function discontinuity and soft-threshold function deviation problem, through the analysis of the existing wavelet threshold de-noising methods, we present the improvement de-noising method of threshold function with the parameters in adjustable. Through experiments, we compare our method with the existing methods of wavelet threshold de-noising. In terms of the evaluation criteria, we find that the improved method of threshold function can remove the noise effectively.

Mutual modes derived from Chromatographic fingerprints of high-quality traditional Chinese medicine (TCM) can provide standards for quality assessment. This paper demonstrates a new method to apply non-negative matrix factorization (NMF) in deriving mutual mode from high performance liquid chromatography (HPLC) fingerprints of TCM. The HPLC fingerprints of the same species of TCM are taken as data set in NMF analysis, from which feature bases containing global features of the original data can be extracted. Reconstruction is performed to utilizing the first feature base and the mutual mode of HPLC fingerprints is obtained by projecting to basis matrix. Satisfactory results have been achieved in the experiment of species identification for Exocarpium Citrus Grandis, which has two primary species, that is, Citius Grandis `Tomentosa' (CGT) and Citius Grandis (L.) Osbeck (CGO). Forty-seven representative batches of CGT collected from GAP in Huazhou (Guangdong Province, China), were served as the original data for mutual mode of CGT. Furthermore, six batches of CGT and six batches of CGO from different places were utilized to test the effectiveness of the mutual mode of CGT. The mutual mode of CGT derived by the proposed NMF-based method can cover the basic information on the whole, since the main representative peaks were contained. Compared with the existing approaches, the similarity results to the proposed mutual mode are more clearly distinguished among different species to some extent. The research indicates that the NMF-based method has a better capability of maintaining and summarizing the data information of original Chromatographic fingerprints and can provide an effective approach to establish the mutual model of HPLC fingerprints of TCM.

The influence of emitter layer thickness and interface defect states on the photovoltaic performance of bifacial HIT solar cell was simulated by the program of AMPS-1D developed by the Pennsylvania State University. The simulation results indicate that the conversion efficiency of the solar cell reduces with increasing the emission layer thickness. However, the influence of the emission layer thickness on each specific photovoltaic characteristic is different between the high and low doping concentration of emission layer. The results also show that the interface defect states have obvious effect on the open circuit voltage of bifacial HIT solar cell especially on the p type substrate.

Distribution of data stream is always changed in the real world. This problem is usually defined as concept drift [1]. The state-of-the-art decision tree classification method CVFDT[2] can solve the concept drift problem well, but the efficiency is debased because of its general method of handling instances in CVFDT without considering the types of concept drift. In this paper, an algorithm called Efficient CVFDT (E-CVFDT) is proposed to improve the efficiency of CVFDT. E-CVFDT introduces cache mechanism and treats the instances in three kinds of concept drift respectively, i.e. accidental concept drift, gradual concept drift, instantaneously concept drift. Besides, in E-CVFDT, the cached instances which have similar attributes will be sent in batches to calculate the information gain calculation rather than in sequence adopted by CVFDT. The experiments are carried out on the MOA platform. The results show that E-CVFDT algorithm achieves not only better efficiency but also higher accuracy than CVFDT algorithm.

The traditional Canny operator does not have the adaptive ability in the selection of the variance of the Gaussian filtering. Filtering requires human intervention, and the selection of the variance of Gaussian filtering affects the edge preserving and denoising effect. An improved edge detection algorithm is proposed in this paper. The Gaussian filtering is replaced with the morphological filtering. Experimental results show that the improved Canny operator can filter the salt & pepper noise effectively, improve the accuracy of edge detection, and achieve an ideal effect of edge detection. The experiment results show that the objective evaluation and visual effect are good.

Comparing the two improved methods, which are improved Sobel operator and improved wavelet transform using the multi-scale morphological filtering, subjective visual have achieved better results. However there are advantages and disadvantages in objective evaluations. So this paper makes the gained edges image fusion with the two improved methods using the wavelet transform fusion technology. The experimental results show that the fused image has increased significantly in information entropy and the average gradient compared to the improved Sobel operator, and it also has improved the peak signal to noise ratio and the distortion degree compared to the improved wavelet edge detection method. The fused image can concentrate the advantages of the two improved methods together and make complementary advantages. Eventually, the good de-noising effect and complete edge are achieved.

Practical knowledge was very important for professional development of teachers in vocational colleges. In vocational colleges, constitutes of teachers' practical knowledge included not only self-knowledge, knowledge of the subjects, students' knowledge, and knowledge of the scenario, but also vocational skills and teaching experience in the professional training of teaching scene. In accordance with the special law of vocational education and the principle of knowledge conversion, teachers' practical knowledge in vocational colleges was constructed by watching demonstration lessons, practicing in enterprise, writing educational narrative based on reflection and action research.