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Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

In paper production, a jumbo reel is cut into multiple intermediate rolls, and each intermediate roll is then sheeted as finished goods. This problem is called a cutting stock problem and is proven to be NP-hard. The objective is to minimize material waste or trim loss from all the cuttings. In the case that any intermediate roll is not entirely used for its associated order, the intermediate roll itself could turn to be a dead stock. We use the concept of universal sizes of intermediate rolls to eliminate the dead stock. A pre-defined number of universal sizes of intermediate rolls is to be used to serve all the orders. The problem is solved using Reinforcement Artificial Bee Colony algorithm with Integer Linear Programming subroutine. This proposed approach is then tested with a set of 1,055 orders and 127 different sizes of sheet papers from a paper manufacturer. The results reveal that our method outperforms other algorithms. Our method offers the total trim loss of 3.51%, compared to the trim loss reported by the industry of at least 5%. This approach not only reduces the number of partially cut rolls, but also decreases the number of the jumbo reels needed to serve all the orders. Therefore, both the inventory cost and material cost can be saved.

The explicitly numerical or well-defined correlations between the dielectrics responses (DRs) and dielectric essences of oil-impregnated paper (OIP) insulation have not been fully understood yet. As a result, it is rather difficult to quantitatively diagnose the critical insulation condition, like determining moisture content in insulation paper (MCP) of power equipment only using electrical-based techniques such as frequency domain spectroscopy (FDS). To obtain MCP value, from a new perspective of parametric study on the circuital DR equivalence of OIP insulation-extended Debye model (EDM), the present contribution introduces a novel approach by exploring the pattern when EDM parameters vary with MCP and temperatures T. Further, mathematical correlations are developed between sensitive R-C values and MCP-T values. On the above analysis, small-scale physical models of real-life transformer bushings were prepared as test samples. Their OIP condenser bodies were artificially absorbed different controlled quantities of moisture and conditioned at different temperatures to record corresponding FDS results. Then a hybrid genetic algorithm combined with the Levenberg-Marquardt algorithm was proposed to estimate the EDM parameters by fitting the measured FDS data. Sensitive parameters therein were identified using subspectrum decomposition and formulated with MCP-T values so that a reliable moisture content estimation can be achieved once the testing temperature and FDS recordings of an insulation body are known.

Trap plays an important role in the charge transportation of three-layer oil-paper and the investigation of trap parameters at different degradation degree is of great significance for further research of the partial discharge (PD) mechanism. This article considers the turn-to-turn structure of on-board traction transformer. A defect model of three-layer oil-paper insulation was designed for PD test. The PD degradation samples are selected from the inception of PD to the breakdown of PD to study the characteristics of surface potential decay on degradation samples by using isothermal surface potential decay method. Given that, the trap distribution at different degradation stages was calculated and its influence on PD characteristics was investigated. The result shows that AC fluctuation voltages caused by inter-harmonic has a great influence on PD distribution, surface charge distribution and trap distribution. With the increase of degradation, the decay rate of surface potential increases and the decay rate of AC fluctuation voltages is larger than that pure AC. And also, the number of shallow trap with AC fluctuation voltages is larger than that with pure AC. The rate of free charge increases with the number of shallow trap increasing. More free electrons involves in the process of PD, accelerating the aging rate of three-layer oil paper samples. Therefore, it can be concluded that surface charge and trap distribution are helpful to clarify PD mechanism.

The oil-paper insulation system is widely used for the insulation purposes of power transformers as a result of its ability to withstand high thermal and electrical stresses. The presence of a gas cavity between impregnated papers layer is a serious problem. When localized breakdown takes place within this cavity, partial discharge (PD) occurs and ages the rest of the insulation system. This aging phenomenon declines the expected life time of the insulation system and subsequently the life time of the power transformers. Dissolved gas analysis is a recommended technique that is used in order to assess the state of the insulation system inside the transformers. In this paper, PD is recorded in the case of gas cavity presence between the layers of the impregnated papers under the effect of AC and DC voltage stress. This record is extended from the instance of PD inception until the breakdown of the whole insulation system. Furthermore, the concentration of the gases that dissolve in the insulating oil due to the aging of the impregnated paper is measured eight times during the record of the PD. Finally, the degradation of the insulating papers is studied optically through the use of a microscope at different stages of the aging test. Depending on PD measurements, dissolved gas analysis (DGA) results, and the optical study of the paper aging, the evolution of the aging of oil-paper insulation system as a result of internal PD is declared when AC and DC voltage stresses are applied. It is obtained that the aging evolutionary process totally differs with the alteration of the applied voltage.

An electrical tree discharge model of multilayer insulation paper was prepared, and a voltage of 20 kV was used for electrical tree cultivation and initiation tests. Based on the microscopic image information and partial discharge (PD) information during the aging of the electrical tree, the development of electrical tree discharges between layers of paper comprising epoxy resin-impregnated paper insulation was studied. The results show that, by analysing PD information such as the discharge volume phase spectrum and the discharge repetition rate phase spectrum, the development of the electrical tree branch progresses in four stages: initial, growth, stagnation, and bursting. During the growth and stagnation stages, the main discharge paths of the electrical tree are mainly formed, and there are many filamentous fibres on the path edge. During the burst stage, more branches will be formed on the electrical tree, which will cause the greatest damage to the insulation. With the extension of the electrical tree path, the discharge magnitude and discharge times also increase. There are specific discharge patterns in different growth stages of the electrical tree, and the number of discharge events in each positive half-cycle is significantly greater than that in a negative half-cycle. Combining the microscopic image information and PD information during the aging of such an electrical tree, a theoretical basis for evaluating insulation state by PD monitoring is realised. The long-chain molecules of cellulose and epoxy resin break bonds and oxidise, which decompose into a large number of free radicals, monosaccharides, and other low-molecular-weight products.

The low frequency section of frequency domain spectroscopy (FDS) of oil-paper insu- lation system can effectively reflect the ageing state and moisture of the oil-immersed paper. Meanwhile, steady-state insulation resistance is the one of indexes for the condition of transformer. In actual measurement, these two both are time-consuming and poor accessibility, thereby restraining their field applications. Aim at this problem, this paper analyses the polarization process of the oil-paper insulation in transformer, and adopts a low frequency equivalent circuit model for the oil-paper insulation to characterize the polarization process at low frequency. The simulated annealing particle swarm optimization (SAPSO) algorithm is proposed to identify the parameters in equivalent circuit, which are affected by change of insulation state. Compared with the experimental results, the errors of the steady-state insulation resistance and the tan δ below 10-3 Hz, which are calculated by the identified parameters, are less than 2.5%. This method can avoid the influence from external environment and reduce the measurement time. The time and frequency domain parameters calculated can provide reference for the ageing assessment of oil-immersed paper.

Kevlar pulp treated with low-temperature plasma and nano cellulose fibrils (NCFs) have been introduced as reinforcements in the preparation for composite insulating paper with low relative permittivity, dielectric loss, good mechanical properties, and thermal stability. More polar groups of Kevlar fibers could be exposed after plasma treatment. Results showed that the relative permittivity of the composite paper containing 20% Kevlar pulp, 10% NCF, and 70% softwood pulp is decreased by 29.6% and the dielectric loss is decreased by 43.2% at 50 Hz, 25°, compared with the conventional paper formed by pure softwood pulp. The change was due to the introduction of Kevlar pulp with intrinsic low dielectric constant and loss, which decreases the whole polarizability of the paper. The mechanical properties of the composite paper became worse after introducing 20% Kevlar pulp. About 10% NCF can work as a mechanical reinforcement to compensate for the tensile and Yang's modulus loss. Scanning electron microscopy results showed that NCF could fill the void defects and improve the interface bonding of the fibers to enhance the paper mechanical properties. The composite paper also had better insulating and thermal properties than the conventional paper. It has been concluded that the Kevlar pulp/NCF/softwood pulp composite paper has the potential to replace the pure softwood paper for the application of high-voltage ac transformer insulation.

This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the self-made hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy.

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

When the inner insulation of oil-impregnated paper bushing breaks down in service, gases decomposed by oil and paper may cause an explosion. The explosion of bushing occurs from time to time because of the lack of an effective monitoring method. In order to provide an effective online monitoring method for the bushing to avoid explosion, this paper presents a method to monitor the PD defect in bushing by measuring the inner pressure of bushing. In this paper, the relationship of variation between characteristic gases content and inner pressure is studied, the effect of inner temperature on inner pressure is analyzed and the method for reducing this effect is also presented, and the monitoring method is proved to be useful by PD tests on a real bushing. The results show that the inner pressure increases obviously due to the generation of characteristic gases by internal discharge in oil-impregnated paper bushing, and the pressure increases with the deterioration of the discharge; although the inner pressure has a great influence on the inner pressure, the influence can be reduced or eliminated greatly by the method of common-mode rejection. According to the results, the internal discharge defect of the oil-impregnated paper bushing can be detected by monitoring the pressure, and the bushing explosion can be avoided.

Paper-based strain sensors have attracted attention as a cost-effective and environmentally friendly approach to wearable electronic devices. This paper proposes a paper-based strain sensor created solely using hand-painting, which is a simple, equipment-free process utilized in green electronics. Commercial printing paper is coated with polydimethylsiloxane diluted with heptane, which provides moisture resistance and mechanical robustness while increasing the hydrophobicity of the surface. The electrodes and conduction channels are fabricated using a pencil for graphite deposition and a brush for the deposition of tin-doped indium oxide nanoparticles, respectively. With a gauge factor of 41.98 in tensile strain tests and 21.36 in compressive strain tests, our strain sensor exhibits a good sensitivity that is similar to that of previously reported paper-based sensors. We also demonstrate that our proposed strain sensor can be used as a weak motion detection device for humans and robots.

The aging of the transformer oil-paper insulation distributes spatially, which results in changes in paper resistivity in different regions. This paper establishes an iterative inversion algorithm using the finite element method to calculate the oil-immersed paper resistivity in different regions of a transformer. This algorithm sets the transformer dielectric loss factor tan δ at low frequency, the dielectric parameters of the transformer oil as inputs and the oil-immersed paper resistivity as output. The resistivity obtained from inversion can be used as a reference to access the insulation state of the oil-paper insulation. This paper aims at achieving the nondestructive detection of the partial state of the oil-paper insulation.

Failures of bushing caused by corrosive sulfur has gradually appeared in recent years. Apart from copper and sulfur, certain amount of aluminum was also found both in oil and paper after disassembling. The corrosion mechanism of the aluminum foil and its effect on oil-paper insulation were unknown. In this study, series of sulfur corrosion experiments were taken to investigate the sulfur corrosion of aluminum foil. The results showed the aluminum foil could certainly be corroded in oil containing corrosive sulfur (DBDS). Thermal products of insulting paper triggered the corrosion by removing the alumina film on the surface. Then, the contact corrosion occurred between aluminum and DBDS. In addition, the mechanisms of corrosion products generation both in oil and on paper were studied. The corrosion products in oil was DBDS-Al(3,4) and that on paper was RCOOAln. It caused the decrease of electrical properties of oil-paper. Particularly, the dielectric loss of oil and paper were dramatically enhanced.

The accelerating rate of scientific publications makes it extremely difficult for researchers to find out the relevant papers and related works. Recommender systems that aim at solving the information overload problem have attracted lots of attention. However, existing paper recommendation works generally rely on the simple citation-ships between papers, which ignore the heterogeneity of the academic graphs. In this paper, we solve the personalized paper recommendation problem in the setting of heterogeneous information networks. A heterogeneous graph representation based recommendation method named HGRec is proposed. First, the author and paper profiles are constructed based on the extracted contents information. Second, we initialize the node vectors by employing the word-embedding technique. Third, we jointly update the node embeddings in the heterogeneous graph by proposing two meta-path based proximity measures. Finally, the paper recommendation is completed by calculating the similarity of the generated author and paper feature vectors. We present experiments on a real academic network, the DBLP network. The comparative results demonstrate the effectiveness of the proposed personalized recommendation approach compared to state-of-the-art methods.

© 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

Oil-paper insulation will inevitably endure impulse voltage with a superimposed dc voltage (superimposed voltage) because of introduced lightning or switching, which can induce charge accumulation and trigger flashover during the operation of a converter transformer. Temperature rise is also a nonnegligible factor that can significantly influence the performances of oil-paper insulation. This paper aims to reveal the effects of ambient temperature and superimposed voltage on surface charge and flashover behaviors in an oil-paper insulation system. Surface potential decay (SPD) experiments were performed under superimposed voltage with various polarity and amplitude combinations and at the temperature ranging from 20 °C to 80 °C. The results show that the initial surface potential and decay rate increase as the amplitude of the impulse voltage increases at 20 °C. The initial surface potential declines, and the process of SPD is accelerated at higher temperatures because of the increased concentration of shallow traps. Subsequently, flashover tests were conducted under superimposed voltage. It is found that flashover occurs after applying superimposed voltages even with a rather low dc voltage. In addition, the dc voltage component is primarily responsible for flashover. Weibull results also indicate that it is prone to trigger flashover at higher temperatures, because thermal excitation helps more carriers to escape from deep traps and participate in the process of flashover. Accordingly, the insulation characteristics of oil-paper system can be significantly influenced by superimposed voltage and temperature, which should draw great concern among the power grid.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters’ influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

Software fault prediction (SFP) is a research area that helps development and testing process deliver software of good quality. Software metrics are of various types and are used in SFP for measurements. Inheritance is a prominent feature, which measures the depth, breadth, and complexity of object-oriented software. A few studies exclusively addressed the efficacy of inheritance in SFP. This provokes the need to identify the potential ingredients associated with inheritance, which can be helpful in SFP. In this paper, our aim is to collecting, organizing, categorizing, and investigating published fault prediction studies. Findings include identification of 54 inheritance metrics, 78 public datasets with various combinations of 10 inheritance metrics, 60% use of method level & use of private datasets, an increased number of studies using machine learning approaches. This study will facilitate scholars to studying previous literature on software fault prediction having software metrics, with their methods, public data sets, performance evaluation of machine learning algorithms, and findings of experimental results in a comfortable, and efficient way, emphasizing the inherited aspect specifically.

Citation count is a widely-used indicator for calculating the academic impact of scientific papers, but it is limited because it assumes all citations are of similar value and weights each equally. By examining the influence changes in papers' citation distribution and the cited papers' unequal contributions to the citing ones, this study aims to distinguish citations and, on this basis, evaluate the academic impact of the papers. Three indices of time-weighted citation count, citation width and citation depth are proposed to distinguish citations and perform the evaluation task. The experimental results show that papers exhibit different influence intensity characteristics in different periods of citation life. Those papers got larger citations in recent years are more influential and more active to gain new citations. The papers show the different scope of influence in their citing environment, although they were originally published in the same journal and the same year. In addition, the different frequency of mentions and the different subject similarities with the citing works suggest that the papers have different importance and usefulness for subsequent research. These results suggest that these three indices do help to distinguish citations and reveal the different intensity and contribution of influences in citations. Finally, the three indices are integrated into the overall evaluation of the academic impact of the paper, and the weight of each index is calculated by the entropy weight method. Quite different overall impacts in the paper are shown due to their different performances in the three indices, even though they have the same total number of citations.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

The problem of sulfur corrosion has seriously affected the safe and stable operation of the power transformers. Currently, adding passivators into the transformer oil is the most effective method to suppress the problem. However, no explicit provisions have been made for the optimum concentration of the specific passivators. In this paper, the transformer oils with different corrosive sulfurs and different aging degrees were used to study the protective performance of three different passivators. By comparing the copper surface, the optimum concentrations for each passivators were acquired. Moreover, by testing the breakdown voltage, dielectric loss factor, volume resistivity, and moisture content of the insulating oil after adding passivators with the optimal concentration, the effect of three passivators on the oil quality was obtained. Results show that the three passivators have different protective performance on different types of corrosive sulfurs. Irgamet 39 has a better protective effect at lower concentrations than the other two. The protective effect of all three passivators on dodecyl mercaptan is poor, and a new type of passivator capable of protecting against dodecyl mercaptan needs to be developed. Moreover, the three passivators have different protection performance on the transformer oils with different aging degrees. The protective performance is poor especially for the oils with longer running years or passivators with lower concentrations. Among the three passivators, Irgamet 39 had the smallest impact on the oil quality, followed by T571 and then TTA. The above research can provide some references for guiding the passivator addition in actual projects and preventing the occurrence of sulfur corrosion failures.

With the wide application of vegetable insulating oil in power transformers, it is essential to investigate the influence of moisture on the frequency domain dielectric spectrum (FDS) analysis in order to propose an accurate moisture assessment method for vegetable oil-paper insulation system. In this regard, various vegetable oil-paper samples are prepared in a laboratory environment. FDS curves for all samples are obtained at different moisture contents and operating temperatures. A new method to estimate the moisture content in insulation paper is proposed based on the low-frequency characteristic quantity. Also, the effect of operating temperature and water migration between the oil and paper insulation on the FDS analysis is investigated. Based on this analysis, a compensation factor is introduced to eliminate the influence of temperature on paper moisture assessment. Furthermore, an improved normalization method for the temperature effect on the FDS of vegetable oil-paper insulation with different moisture contents is proposed. The proposed moisture assessment and temperature normalization methods are validated thorough their application on an operating power transformer.

The condition of a power transformer can be predicted based on the contents of the transformer oil. One of the most common parameters that are observed in the transformer oil is 2-Furaldehyde, a compound that is produced due to the aging of Kraft paper. Although the optical characterization of 2-Furaldehyde has been widely reported, the fundamental theories of the observation of the optical absorbance peaks have not been elaborated. This study investigates the optical characteristics of 2-Furaldehyde in the near infrared region. Ten samples with different concentrations of 2-Furaldehyde were prepared and verified using the conventional method. The samples were then characterized using optical spectroscopy method with 50 mm path length cuvettes. Three peaks were observed at 1610 nm – 1640 nm, 1100 nm – 1115 nm and 860 nm – 890 nm wavebands and were correlated to the overtones of the aromatic C-H stretch in 2-Furaldehyde. The appearance of these overtones corresponding to the Morse Oscillation Theory was discussed. This fundamental knowledge is significant in developing a portable optical device that enables the detection of 2-Furaldehyde on site.

Delivering confidential messages in wireless network is gaining continuous attraction as a fundamental building block for the Internet of things (IoT). This paper overviews the confidential message transmission techniques in broadcast channel (BC) where a transmitter serves multiple users by delivering confidential messages to their intended users. Each confidential message must be prevented from extracting its information by any unintended users. First, we consider a basic network consisting of two users. In the two-user BC, the secret dirty paper coding (S-DPC) achieves the secrecy capacity region. However, since the DPC requires a high computational complexity for characterizing the capacity region, two sub-optimal transmission techniques with low complexities are explained. Second, we extend our discussion to the network including more than two users. For $K$ -user BC with confidential messages, the optimal transmission scheme and the secrecy capacity region have not yet been derived. Instead of presenting the optimal scheme, we explain two sub-optimal transmission techniques and compare their features and performances. In conclusion, we discuss the open problems in the confidential message transmission for networks with more than two users.

Voice-over IP (VoIP) technology is a kind of digital transmission technology based on IP network. It is one of the important methods to use voice service in VoIP as steganographic carrier to ensure secure transmission. However, the traditional steganographic code has some problems, such as low embedding efficiency and weak concealment, which cannot meet the requirements of VoIP streaming media information hiding for the security of secret information. Therefore, a steganographic algorithm combining F5 and simplified wet paper code (SWPC) algorithm is proposed. The main idea is to embed secret messages in each row of the carrier matrix using the F5 algorithm, and then the SWPC algorithm is used to embed the columns according to the wet and dry characteristics of the wet paper code without affecting the results before row embedding. We use the VoIP streams encoded by the ITU-T G.729a codec as a carrier to verify the proposed scheme. The experimental results demonstrate that the proposed scheme can achieve relatively better IP speech data steganographic transparency and that it can outperform F5-WPC and SWPC approaches.

This paper investigated the influence of paper-wrapped on the deposition of sulfur corrosion on the insulation winding (paper-wrapped copper conductor) in oil-immersed insulation containing dibenzyl disulfide (DBDS). The distribution of sulfur corrosion deposit on a 500-kV electric reactor was investigated and analyzed, and the properties of oil were analyzed. Aging experiments were also conducted for oil-paper insulation with 500-mg/kg DBDS under IEC 62535 corrosion test condition. The corrosion degree of insulation winding was evaluated, and the properties of oil were also discussed. Results showed that the enwinding pattern of the insulating paper on the copper conductor affected the deposition distribution of copper sulfide to be an alternate appearance of a non-corroded crevice and a strip-shaped corrosion deposit on the copper conductor according to the results of field and laboratory. Furthermore, for the insulation winding, the increased number of paper layers on the copper conductor led to the aggravation of sulfur corrosion, prevented the diffusion of copper ions in the oil, and accelerated the formation of acids, which co-affected the properties of the oil. The enwinding of insulating paper on the copper conductor led to the appearance of a tiny crevice on the insulation winding, which is an important occurrence precondition of crevice corrosion. Finally, the mechanism underlying crevice corrosion for insulation winding was theorized as electrochemical corrosion leading to copper sulfide formation.

Furfural content in transformer oil is considered a credible method to evaluate the aging extent of insulation paper and is extensively applied to the aging assessment of insulation system in transformers. Clearly, the accuracy of furfural analysis is directly related to the stability of furfural in oil, which, nevertheless, may be affected by various factors. In this research, furfural was added to insulation oil directly to analyze its stability in oil firstly. Furfural contents in different conditions, such as different temperatures, copper, and oxygen atmosphere, are measured regularly. The results corroborate that the furfural content in the oil keeps constant when the oil temperature is less than 130°C without copper or oxygen; copper cannot make furfural in oil degrade without oxygen when the temperature is below 90°C. These results indicate that the furfural is stable at the common operating temperature of transformer oil (40°C ~ 70°C) as long as no oxygen is in oil. Oxygen is the main degradation cause of furfural in oil, and high temperature and copper accelerate this progress. The oxidation product of furfural in oil has been explored, which shows that the furoic acid is the main degradation product of furfural when the temperature is less than 90°C. Thus, the furoic acid content in oil can be measured as a reference to increase the accuracy of the furfural analysis.

Random unitary beamforming (RUB) achieves multiuser diversity gain over multiple-input multiple-output broadcast channels with partial channel state information (CSI). RUB can asymptotically achieve the same growth of rate $M\log \log K$ as the optimal dirty paper coding or sub-optimal zero-forcing beamforming, which need full CSI at the transmitter. In this paper, we propose energy efficient RUB systems that select the optimal number of streams and transmission power to maximize energy efficiency (EE). In contrast to other beamforming schemes that use full CSI, optimizing RUB in terms of EE is a difficult task due to the CSI constraints. To solve this problem, we first select the EE-optimal number of streams and transmission power by using the statistical characteristics of RUB, which have been studied in previous works. Next, based on partial CSI feedback from users, we propose the method whereby the transmitter of RUB can adaptively control the transmission power. Simulation results demonstrate that our proposed systems can improve the EE performance of RUB and that the analytical results derived in this paper are accurate.

Citations play an important role in ranking of authors, journals, institutions, and organizations. Sometimes, citing documents cite a reference many times in their full-text, which is further used in many application scenarios, such as: 1) finding relationship between cited and citing papers; 2) identifying influential cited paper from set of references in citing paper; 3) identification of suitable citation functions; and 4) study of in-text citations in different logical sections of papers to conclude different findings. The accurate identification of in-text citations remained an open area of research. Recently, the complexities involving automatic identification of in-text citations have been reported with an accuracy rate of 58%. This is due to many issues as highlighted by the state-of-the-art research. This paper investigates such issues in further details: 1) by taking benefits from the previous research; 2) by analyzing different referencing formats; and 3) by experimenting on a comprehensive data set. Based on the investigation, this paper proposes a taxonomy and workable system, which utilizes a set of heuristics build from detailed study. The proposed model is then applied on unseen diversified data set taken from the Journal of Universal Computer Science and CiteSeer. The proposed model was able to achieve an average F-score of 0.97 as compared with the baseline 0.58.

This paper reports the latest technological advances made by the Industrial Technology Research Institute (ITRI) in flexible displays, especially the flexible substrate, thin-film transistor (TFT) backplane, and active matrix organic light-emitting diode display. Using the leading cholesteric liquid crystal technology of ITRI, we develop a rewritable, environmentally friendly thermal printable e-paper. The epaper, devised to reduce traditional paper consumption, achieves a high resolution of 300 dpi with a memory function. In addition, we report on the ITRI's initial success in demonstrating a complete R2R process for multisensing touch panels on 100-μm thick flexible glass substrates provided by Corning.

A human-centric emotion estimation method based on correlation maximization with consideration of changes with time in visual attention and brain activity when viewing images is proposed in this paper. Owing to the recent developments of many kinds of biological sensors, many researchers have focused on multimodal emotion estimation using both eye gaze data and brain activity data for improving the quality of emotion estimation. In this paper, a novel method that focuses on the following two points is introduced. First, in order to reduce the burden on users, we obtain brain activity data from users only in the training phase by using a projection matrix calculated by canonical correlation analysis (CCA) between gaze-based visual features and brain activity-based features. Next, for considering the changes with time in both visual attention and brain activity, we obtain novel features based on CCA-based projection in each time unit. In order to include these two points, the proposed method analyzes a fourth-order gaze and image tensor for which modes are pixel location, color channel and the changes with time in visual attention. Moreover, in each time unit, the proposed method performs CCA between gaze-based visual features and brain activity-based features to realize human-centric emotion estimation with a high level of accuracy. Experimental results show that accurate human emotion estimation is achieved by using our new human-centric image representation.

Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance Rds,on is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using Rds,on, to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analytical tool to implement a long-term mission profile that requires reliability assessment.

In this paper, we propose a simple framework for closed-loop system identification: the stabilized output error method. Traditional closed-loop system identification methods rely on the linearity of the target system, and they require the identification of noise models or prior knowledge or identifiability of the feedback controllers to obtain unbiased estimates. But in many real-world applications, the noise dynamics and feedback controllers are complex and difficult to identify, and the nonlinearity may not be ignored. The proposed framework introduces a virtual controller that stabilizes the error between model prediction and the output of the target system. This enables us to apply the output error method, which gives unbiased estimates without depending on the noise model and is applicable to a wide range of models, including nonlinear systems, to closed-loop system identification problems. The paper describes the framework and gives the theoretical support and design guidelines for virtual controllers. Through numerical examples, we show the effectiveness of the proposed framework in various situations, which include identification of (a) linear gray box models, (b) systems in the presence of disturbances having realistic complexity, and (c) a nonlinear unstable system in a human-in-the-loop environment.

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.

In the process of power transmission and distribution, non-technical losses are usually caused by users’ abnormal power consumption behavior. It will not only affect the dispatch and operation of the distribution network, bring hidden dangers to the security of the power grid, but also damage the operating costs of power companies and disrupt the operation of the power market. Aiming at users’ abnormal electricity consumption behavior, this paper proposes a model based on particle swarm optimization and long-short term memory with the attention mechanism (PSO-Attention-LSTM). Firstly, according to the actual electricity theft behavior, six typical electricity theft modes are summarized, and 4 composite modes are obtained by combining them, so as to comprehensively test the detection performance of the model for various electricity theft behaviors. Secondly, a detection model based on PSO-Attention-LSTM is proposed, and the model is built using the TensorFlow framework. The model uses the attention mechanism to give different weights to the hidden state of LSTM, which reduces the loss of historical information, strengthens important information and suppresses useless information. Use PSO to solve the difficult problem of model parameter selection, and optimize the hyperparameters to improve the model performance. Finally, the data set of the University of Massachusetts was used for simulation and compared with convolutional neural network-long short term memory (CNN-LSTM), attention mechanism-based long short term memory (Attention-LSTM), LSTM, gated recurrent unit (GRU), support vector regression (SVR), random forest (RF) and linear regression (LR) to verify the effectiveness and accuracy of the method used in this article. In this paper, Matlab software is used to analyze and visualize the detection result data.

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

Parafoil systems are strongly nonlinear due to the flexibility of the canopy and suspension lines, and this characteristic cannot be simulated accurately by existing dynamic models. Hence, model-independent control methods are necessary for parafoil systems. This paper introduces a model-free adaptive control method based on the iterative feedback tuning (IFT-MFAC) method for parafoil systems that is a data-driven control method in which only input/output (I/O) data are needed during construction. In this paper, the MFAC construction process and stability analysis are explained, and then the IFT method is used to tune the two stepping factors of the MFAC method. A six-degree-of-freedom (DOF) dynamic parafoil system model is built to assess the performance of the method in a simulation with disturbances added to imitate real flights. A series of simulations and hardware-in-loop (HIL) tests are designed to verify the performance of the IFT-MFAC controller, and it is compared with the PID control method, the active disturbance rejection control (ADRC) method, and the MFAC method.

Distribution network connected distributed energy resources (DER) are able to provide various flexibility services for distribution system operators (DSOs) and transmission system operators (TSOs). These local and system-wide flexibility services offered by DER can support the frequency ( $f$ ) and voltage ( $U$ ) management of a future power system with large amounts of weather-dependent renewable generation and electric vehicles. Depending on the magnitude of frequency deviation, other active network management-based frequency control services for TSOs could also be provided by DSOs in coordination with adaptive control of DER. This paper proposes utilisation of demand response based on frequency-dependent HV/MV transformer on-load tap-changer (OLTC) operation in case of larger frequency deviations. The main principle underlying the proposed scheme lies in the voltage dependency of the distribution network connected loads. In this paper, it is also proposed to, simultaneously with frequency-dependent OLTC control, utilise reverse reactive power -voltage (QU) - and adaptive active power -voltage (PU) -droops with distribution network connected DER units during these larger frequency deviations, in order to enable better frequency support service for TSOs from DSO networks. The effectivity and potential of the proposed schemes are shown through PSCAD simulations. In addition, this paper also presents a holistic and collaborative view of potential future frequency control services which are provided by DSO network-connected resources for TSOs at different frequency deviation levels.

Deep convolutional neural networks (DCNNs) have attracted extensive attention in synthetic aperture radar (SAR) image target detection. However, there is usually a lack of labeled data in SAR images which results in difficulties to improve the detection performance with DCNN based methods. Transfer learning borrows generic knowledge from other tasks for the target task and can be an effective solution to the small sample problem, but the features’ generality and specificity across various models are not discussed in target detection. In addition, conventional DCNN based models have too many parameters related to the prior information of the targets which increase the difficulty of transfer learning between different tasks. In this paper, we mainly conduct research in three folds to address these mentioned issues. First, the generality and specificity of features in different network models are analyzed in the field of SAR target detection. Second, a transitive transfer framework is developed for detection under small sample condition. Third, an anchor free rotatable detector with flexible structure is designed for SAR images detection to suit the transfer detection framework proposed in this paper and a reasonable explanation is given for its upper hand with small samples. Experimental results demonstrate that: 1) transitive transfer framework is valid in SAR target detection; 2) the anchor free detector in this framework is superior to the anchor based one; 3) the proposed detector achieves better performance under small sample condition by comparison with state-of-the-art methods.

In this paper, a novel Takagi-Sugeno (T-S) fuzzy controller based on eigenstructure assignment (EA) and optimized by differential evolution algorithm (DE) is proposed, and the application of this control strategy in the hydro-turbine governing system (HTGS) is studied. Based on the non-linear model of HTGS, the corresponding state-space equations (SSE) are obtained by linearization through multiple equilibrium points. Combining with the principle of T-S fuzzy control, a T-S fuzzy model of HTGS integrating multiple SSE with generator power angle as a prerequisite is established. This paper adopts the EA method to design the fitness function in the optimization process and uses DE to complete the optimization operation to improve the performance of T-S fuzzy control in HTGS. Which makes each Linear-Quadratic-Regulator (LQR) controller gain in the fuzzy control reach the optimal under the constraint conditions. The simulation results show that compared with the standard fitness functions IAE (integral absolute error), ITAE (integral time absolute error) and ISE (integral square error), the fitness function designed using the EA method can expand the angle between the left and right eigenvectors of the T-S closed-loop system, and push the closed-loop pole from the imaginary axis to the left. That will make the adjustment time of the system shorter and the robustness against external interferences enhanced. The results also show that the proposed control strategy has better dynamic performance during the non-linear motion of HTGS and is superior to the existing control strategy.

The present article outlines the core technologies needed to realize the Hyperloop transportation system (HTS). Currently, the HTS vacuum tube train concept is viewed as the fastest way to cross the earth’s surface. However, the concept has not yet been demonstrated for subsonic or near-sonic speeds in large-scale implementations. Among the challenging technical areas are the tube’s depressurization, the capsule’s air resistance, and choked flows occurring around the capsule. There is also a need for effective levitation and propulsion solutions compatible with the velocities being proposed. Moreover, several conflicting objectives have been identified in the HTS design. It is highlighted that the tubes should be wide enough to lower the capsule’s drag forces but that a large-sized tube comes at the expense of higher operational energy costs and infrastructure investments. Another struggle is aiming at a low-cost passive track design and, and at the same time, a lightweight vehicle. One technical path is to turn the whole guideway into an electric propulsor, arriving at a ’lightweight capsule solution’ (LCS). Alternatively, the vehicle could be transformed into a ’modified airplane’ that stores massive amounts of onboard energy, yielding an energy-autonomous ’low infrastructure solution’ (LIS). In a case study, it is shown that even LIS technology is compatible with short-haul flights (1500km) but that it requires an energy reservoir of about 30% of the capsule’s overall mass. Results and discussions presented in this paper are supported by analytical predictions with parameters or input data either supported by referred simulations or experiments. In general, the paper aims to open up the discussion, provide a sufficient understanding of the Hyperloop field’s multidisciplinary aspects, and establish a foundation for further investigations. In the end, new research tasks have been defined, which have the potential to go beyond the state of current knowledge. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

The increasing number of megawatt-scale photovoltaic (PV) power plants and other large inverter-based power stations that are being added to the power system are leading to changes in the way the power grid is operated. In response to these changes, new grid code requirements establish that inverter-based power stations should not only remain connected to the grid during faulty conditions but, also provide dynamic support. This feature is referred in the literature to as momentary cessation operation. The few published studies about momentary cessation operation for PV power plants have not shed much light on the impact of these systems on the overall power system stability problem. As an attempt to address this issue, this paper proposes a control scheme for PV inverters that improves the transient stability of a synchronous generator connected to the grid. It is shown through the paper that the proposed control scheme makes the PV inverter’s dc link capacitors absorb some of the kinetic energy stored in the synchronous machine during momentary cessation. Besides that, the proposed solution is also able to improve voltage stability through the injection of reactive power. Experimental and simulation results are presented in order to demonstrate the effectiveness of the proposed control scheme.

With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of $\ell _{2,1} $ norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of non- uniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.

The working life evaluation of metal oxide varistor (MOV) is always expected by MOV manufacturers and users. In Oct. 2019, the International Electrotechnical Commission (IEC) SC37B established a project team PT 61643–333 led by the author of this paper to draft a technical report about MOV life assessment. As an early researching achievement of PT 61643-333, this paper provides an evaluation method for the impulse life of MOV with the median rank regression (MRR) method based on the Weibull distribution, which provides an effective means for evaluating the impulse life of MOV. Firstly, the theory of Weibull distribution and impulse life characteristics of MOV are introduced. Then the evaluation method and procedure of MOV impulse life is derived from the combination of the MRR method and the impulse characteristics of MOV. Finally, an application example of the proposed evaluation method is given with the test results from real MOV samples. By establishing three basic impulse life characteristics equations, the life evaluation of MOV under arbitrary impulse current can be achieved. In addition, the fitting validity of six groups of samples is checked to prove the effectiveness of the proposed parameter model. And further sensitivity analysis of the variables has been done to examine the effect of changes in the values of the parameters on the propose model.

Supply chain resilience is the key for cross-border e-commerce enterprises to continuously obtain competitive advantages. This paper studies the influencing factors of cross-border e-commerce supply chain resilience (CBSCR), so as to further enhance the competitiveness of global supply chain and ensure the safe operation of cross-border e-commerce supply chain. Based on the ternary theory of supply chain resilience, this paper firstly constructs the CBSCR influencing factor system, and then analyzes the comprehensive influence degree, causal relationship and logical hierarchy among the influencing factors by using the fuzzy DEMATEL-ISM method. The results show that: recovery speed of supply chain disruption, response time of supply chain disruption, coordination ability among cross border e-commerce partners, experience sharing among cross-border e-commerce partners, joint planning among cross-border e-commerce partners, establish a good enterprise reputation for fairness, risk management team among partners, and develop a collaborative culture among partners are the key influencing factors of CBSCR. On this basis, it is proposed that the adaptability of supply chain is a top priority, which can be started from strengthening risk management culture, cooperation among partners and construction of supply chain agility.

This paper presents a lightweight Multi-task Semantic Attention Network (MTSAN) to collectively deal with object detection as well as semantic segmentation aiding real-time applications of the Advanced Driver Assistance Systems (ADAS). This paper proposes a Semantic Attention Module (SAM) that introduces the semantic contextual clues from a segmentation subnet to guide a detection subnet. The SAM significantly boosts up the detection performance and computational cost by considerably decreasing the false alarm rate and it is completely independent of any other parameters. The experimental results show the effectiveness of each component of the network and demonstrate that the proposed MTSAN yields a better balance between accuracy and speed. Following the post-processing methods, the proposed module is tested and proved for its accuracy in the Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS). In addition, the proposed lightweight network is deployable on low-power embedded devices to meet the requirements of the real-time applications yielding 10FPS @ 512 X 256 on NVIDIA Jetson Xavier and 15FPS @ 512 X 256 on Texas Instrument’s TDA2x.

This paper is concerned with the optimal state estimation problem for linear discrete-time systems with both multiplicative and cross-correlated noises. The measurement outputs for state estimation are collected from multiple sensors whose sampling rates are different that provide asynchronous data. In addition, the noises that affect the measurement information are correlated among different sensors and also coupled with the process noises as well. The aim of the addressed problem is to propose an optimal state estimation algorithm such that the estimation error is minimized in the mean-square sense with the existence of asynchronous data, possible sensor faults and correlated noises. In order to mitigate the impact of measurement missing, this paper utilizes neural networks to compensate the state estimation when measurement packets are dropping. Then, a fault detection mechanism that utilizes normalized innovation test is adopted to ensure that the abnormal data would be detected and removed. By resorting to the projection theorem and mathematical induction approach, sufficient conditions are derived for the existence of the desired optimal state estimator where the optimized estimation gains are formulated and can be computed iteratively at each time step. The proposed theoretical results are demonstrated via an illustrative numerical example.