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There is a relative lack of research on the development of creepage discharge in aged oil-paper insulation subjected to AC-DC combined voltage in converter transformers. In this study, based on the physical and chemical properties of oil-paper, we propose a sphere-plate electrode model to study the creepage discharge parameters and characteristic spectra of aged oil-paper subjected to AC-DC combined voltage. The results show that the creepage discharge inception voltage of aged oil-paper is related to the AC voltage component of the AC-DC combined voltage. However, when the DC voltage component is 60 kV, the flashover phenomenon was found to occur in one of the severely aged samples before the AC voltage was applied. Despite being subjected to the same DC voltage, the flashover phenomenon did not occur in three other samples aged to different degrees. Large-amplitude discharges were found to appear earlier in the severely aged samples. At the same creepage discharge stage, as the aging time of the oil-paper increased, the number of large-amplitude discharges was found to rapidly increase, and the time interval between adjacent discharges was found to gradually decrease. At each stage of the creepage discharge, the small-amplitude discharge has a “hump” shaped distribution. The larger the DC voltage component, the shorter the endurance time of aged oil-paper, and the more severe the damage to the oil-paper. The DC voltage component was found to have an accelerating effect on the development of creepage discharge in aged oil-paper insulation.

This paper presents a broadband, simple, re-useable and low-cost approach for noninvasive sweat monitoring using a passive microwave circuit and a cellulose filter paper as a superstrate. The proposed sensor is composed of filter paper with the ability to absorb the sample liquid under test (LUT) placed on top of a coplanar waveguide (CPW) transmission line. Various samples of sodium chloride (NaCl) solutions with concentrations in the range of 0.01-2 mol/L and models of artificial sweat are used to test the proposed sensor. The difference in transmission coefficient (S21) between dry and wet states is used to determine the concentrations of tested solutions in the band of 1-6 GHz. The sensor detects concentrations as low as 0.01 mol/L (0.58 g/L) and quantities as low as 137 μL with a maximum sensitivity of 46.7 dB/g/L. The proposed sensor presents a simple approach to sample and characterize liquids with enhanced sensitivity and consistent performance using microwave signals.

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.

Insulated tubular busbar is a new type of current-carrying equipment with excellent performance. However, the research on this equipment is insufficient because of the short application and the lack of technical digestion, which has resulted in many accidents. In this paper, the full-scale model of epoxy-impregnated-paper insulated tubular busbar with bubble defects was designed and produced, its electrical breakdown process was investigated by withstand voltage test. In the experiment, the failure mode of breakdown caused by gas expansion was found for the first time. The research showed that the electrical-mechanical breakdown process could be divided into two stages: gas increase stage and breakdown threshold stage. The research also obtained the characteristics and the change criterions of the two stages. The gas in the bubble defects generated from epoxy decomposition which caused by partial discharge. When the gas pressure exceeds the critical fracture toughness of the bubble defects wall, the unstable crack extension occurs, therefore, improving the ability about resistance to partial discharge of epoxy resin or improving the fracture toughness of epoxy resin by modifying would enhance the breakdown performance of insulated tubular busbar. The result also has reference significance for other epoxy-impregnated-paper insulated electrical equipment.

This paper presents the design and analysis of a paper-based chipless RFID sensor for wetness content measurement in plant leaves. The sensor is designed using a passive microwave resonator to measure the ambient physical parameter. The resonator is designed with a rectangular loop inductively coupled with an interdigital capacitor. The experimental analysis of the designed sensor is illustrated here to prove its efficacy. The resonator provides adequate backscattered signal for wireless measurement in the microwave frequency band. When interrogated with a couple of UWB antennas and a VNA, it yields accurate leaf wetness response of the plant. The commercial competence of this sensing device is validated through its experimental analysis on three different types of leaf samples. This sensor is suitable for plant health monitoring in any small-scale gardening applications such as nurseries and greenhouses.

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.

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%.

Oil-immersed insulation paper (OIP) bushing is one of the most important equipment in power transmission and distribution systems. Moisture is one of the most serious factors that affect the insulation status of OIP bushing. In this paper, the OIP bushing was assumed to be in a stable and simplicity condition, and then the transient moisture distribution of capacitor core and its effects on the electric field were studied. First, by dissecting a 110 kV OIP bushing, the specific sizes for modelling were obtained. Afterwards, a moisture diffusion model for OIP bushing was established based on COMSOL Multiphysics software, and it was also verified by moisture diffusion experiment. Then, the moisture distribution for different conditions were obtained and analyzed. Finally, a model for the electric field of OIP bushing was established, and then the effects of moisture on the electric field were studied.

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.

Transformer insulation paper is a key indicator for transformer remaining operational life. Paper decomposition is evaluated using the degree of polymerization (DP) which calls for samples of insulation paper from operating transformers. Since collecting such paper samples is extremely difficult, other indicators have been used to indirectly reveal the DP value of insulation paper. This includes dissolved gases in transformer oil such as carbon oxides and hydrocarbon gases, furan compounds, methanol, ethanol, and moisture along with some oil characteristics such as interfacial tension. However, for the same oil sample, these individual parameters lead to different DP values. This is attributed to the lack of accuracy of the established mathematical and artificial intelligence models correlating DP with each of the above mentioned individual parameters. This paper presents a self-learning method to estimate the DP value of transformer insulation paper based on multiple transformer oil aging parameters. The proposed method comprises data processing, fuzzy c-means and linear regression. Results reveal that estimating the DP value based on multiple aging parameters is more accurate than estimating it using one single parameter as per the current practice. The proposed method not only helps to understand the correlation between multiple oil aging parameters and the DP value of paper insulation, but also promotes the establishment of more accurate life assessment models for power transformers based on these oil aging parameters.

© 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.

Methanol is regarded as an available indicator to analyze the aging condition of cellulose insulation. However, the diffusion mechanism of methanol molecules in the paper-oil insulation system is still unclear, since it cannot be explored by using only experiments. Given this issue, the molecular dynamics simulations were performed to investigate the diffusion mechanism of methanol molecules. In the current work, the mean square displacement of methanol during the diffusion process was calculated and analyzed, as well as its trajectory tracking. Then, the research on interaction energies and hydrogen bonds in paper/oil system were conducted. Finally, the effect of temperature on the diffusion of methanol molecules was also studied. Findings revealed that the diffusion of methanol in cellulose insulation is faster than that in insulating oil, besides, the presence of hydrogen bonds affects the diffusion rate of methanol molecules in cellulose insulation and insulating oil. Thus, the present findings could support the investigation of the diffusion mechanisms of methanol molecules, by which the aging condition evaluation of cellulose insulation could be also promoted.

Accurate modeling and simulation of electric field transients in HVDC cables is an important support to optimize insulation system design and to evaluate the influence of voltage transients and steady-state conditions on accelerated ageing mechanisms and insulation reliability. Traditionally, field models considering time-independent permittivity and conductivity are used, but this approach neglects polarization mechanisms and charge trapping-detrapping phenomena. This article includes polarization dynamics in the field model and shows that its impact on transient electric field simulations in HVDC paper-insulated cables can be significant. A method is presented to infer the model parameters from experimental polarization and depolarization current measurements.

Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.

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.

In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.

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.

This paper presents a thorough review of transmitarray devices particularly aiming antenna beamsteering, gathering some of the most relevant solutions published by the scientific community in the field. First, the background for realizing 1-D and 2-D antenna beamsteering with a transmitarray is introduced. Subsequently, several examples of unit-cells for transmitarray implementation and complete transmitarray designs presented in the literature are outlined. Each solution is analyzed in detail, identifying the nature of its layout, e.g., based on microstrip patches, frequency selective surfaces (FSS), or metamaterials (MMs), and the method employed to enable electronic reconfigurability, e.g., p-i-n diodes, varactor diodes, or microelectromechanical systems (MEMS). In addition, some models with the capability of controlling the wavefront polarization modes are also included herein since these are the base of hybrid transmitarrays, i.e., transmitarray with both electronic beamsteering and polarization control. Finally, all the models are compared against each other in order to highlight their benefits and limitations, summarizing their main characteristics, such as the frequency of operation and bandwidth, insertion loss, physical dimensions, and maximum beamsteering range, when available.

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.

Recently, it has been shown that channel feedback helps to enhance the physical layer security (PLS) of communication systems. The current use of feedback focuses on generating secret keys protecting the transmitted messages, and this feedback scheme is proved to be optimal for some physically degraded channel models. In this paper, we revisit the model of the state-dependent wiretap channel with the noncausal state at the transmitter, which plays an important role in the PLS of the wireless communication systems. We propose a new feedback scheme for this model and show that the proposed new scheme performs better than the already existing secret key-based feedback scheme. The results of this paper are further explained via a Gaussian example.

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.

In general, considering standard production, as well as coconut oil production, in oil consumption industries is an important factor. Oil color is an important element, as it is an important factor for consumers or buyers in selecting coconut oil. In the process of producing coconut oil, the cold-pressed method has been chosen to maintain the essential quality of coconut oil. The quality of the coconut oil is inspected from the production process by means of light passing through the coconut oil. Then, the production staff compares the turbidity of coconut oil with the master sample. The turbidity of coconut oil in every production must be compared with a master sample to maintain standards control. According to previous studies, there are many methods for determining coconut oil turbidity. One method that has been utilized is determining turbidity from light passing through the medium in which the transmitted light can be absorbed through the turbidity of the variable medium. This process is applied together with image processing to determine the coconut oil turbidity. In this research, we propose a method for measuring coconut oil turbidity by the Moving Average Median of Hue (MAMoH), which is better in detecting the coconut oil turbidity than the Median of Gray Scale (MoGS) method, Median of Hue (MoH) method, and Random Position Median of Hue (RPMoH) method. In terms of the percentage accuracy of the efficiency test; the MAMoH method has 99 percent accuracy, while the MoGS method is not applicable, the MoH method has 88.04 percent accuracy, and the RPMoH method has 85.91 percent accuracy. Thus, the MAMoH method is considered an appropriate method for measuring coconut oil turbidity.

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.

Abandoned mineral management has become an important global issue, and the collection, recycling, and disposal of mineral processing tailings (MPT) is critical in both mining engineering and environmental protection. In this study, flocculation sedimentation experiments and slurry rheological tests were carried out with unclassified tailings, and the flocculation sedimentation behaviour of the tailings under different conditions was investigated. The experimental results showed the followings: (1) Under close sedimentation effect conditions, different flocculants showed different effects on the fluidity of the slurry underflow, which was used as an important basis for optimization of the flocculation effect. (2) A tailings solid mass concentration of 40% and a flocculant dosage of 25 g/t were obtained as preferable sedimentation conditions. (3) Based on a microparticle agglomeration characteristics tests at the initial stage of flocculation, a flocculation concentration kinetic model was established that conformed to the fractal characteristics of the particles, which improved the applicability and accuracy of the concentration kinetic equation and the characteristic variables. In this study, an objective method for determining the parameters of full tailings flocculation sedimentation was realized. The results of an industrial trial operation also verified the reliability of the preferred parameters, which provided an effective basis for refined control of an efficient tailings recycling process.

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.

This paper tackles the problem of detecting incongruities between headlines and body text, where a news headline is irrelevant or even in opposition to the information in its body. Our model, called the graph-based hierarchical dual encoder (GHDE), utilizes a graph neural network to efficiently learn the content similarity between news headlines and long body paragraphs. This paper also releases a million-item-scale dataset of incongruity labels that can be used for training. The experimental results show that the proposed graph-based neural network model outperforms previous state-of-the-art models by a substantial margin (5.3%) on the area under the receiver operating characteristic (AUROC) curve. Real-world experiments on recent news articles confirm that the trained model successfully detects headline incongruities. We discuss the implications of these findings for combating infodemics and news fatigue.

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.

The aim of this paper is to design, analyze and optimize the “Multi-Stack Slotless Axial Flux Switching Permanent Magnet Machine”. In the design process, mathematical models are implemented, and the Finite Element Method (FEM) is performed to analyze the machine performances. The paper aims to minimize the occurrence of cogging torque and torque ripple in the multi-stack slotless stator AFPM machine. As a consequence, it reduces the vibrations in the machine and increases its life span. Multi-stack slotless stator AFPM machine with a right-angled trapezoid-shaped PM is proposed and comparison is done with conventional shape AFPM machine. In order to examine the performance of multi-stack slotless stator AFPM machine Finite Element Analysis (FEA) is used. To further enhance the characteristics of the designed machine with the proposed right-angled PM shape, optimization is done by considering inner and outer pole pitch as the design variables. In optimization process, krigging method assigned with Latin Hyper-cube Sampling and a genetic algorithm (GA) is performed due to suitability with non-linear data. Then, finite element analysis by JMAG-Designer is performed to verify the results. It is determined that optimized model has achieved 65% reduction in torque ripples as compared with the conventional design. Hence, this work attempts to optimize the performance of the AFPM machine.

This paper reveals the fundamental reason for the asymmetrical issue in magnetic-geared machines (MGMs) based on flux modulation theory. The analysis indicates that the magnetic circuits of three-phase windings in MGMs are inherently asymmetrical. This asymmetrical issue is even more severe in conventional consequent-pole MGMs (CP-MGMs) because of the more distorted magnetic field. Hence, to address the asymmetrical issue, a novel structure featuring enhanced harmonic elimination capability is proposed in this paper. Consequently, torque density can also be improved. In particular, the proposed CP-MGM employs modular structure and axially embedded permanent magnets (PMs) to achieve symmetrical back electromotive force (EMF) waveforms and improved torque capability, respectively. To further improve the electromagnetic performance of the proposed CP-MGM, the PM arc ratio and flux modulator width ratio are analytically designed, which provides a general design guideline for CP-MGMs. To illustrate the merits of the proposed CP-MGM, a few other MGMs are included for a fair comparison based on finite element analysis (FEA). Results show the proposed CP-MGM can achieve more symmetrical back EMF waveforms and lower torque ripple, as well as lower PM consumption and higher torque density, as compared with its MGM counterparts. Published version Schaeffler Group Research Grant M4062765.040.601001

An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid. In order to accomplish this shuffling in such a way that the player is able to reach the end of the game, some novel algorithms in graph theory must be utilized, where the game world and its item locations are represented as a graph and each edge on the graph has some rule for which items are required to traverse it. In this paper, we define these algorithms formally and evaluate them with different metrics that can guide a developer’s decision about which algorithm works best for their game.

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 simple rules that govern the interactions between the different components of a complex system often lead to interesting behaviours that are unexpected. The experiments described in this paper involved creating a variation of a traditional Braitenberg Vehicle, by placing sensors on ten different possible locations on a simulated vehicle, and incorporating an obstacle avoidance behaviour using a subsumption-like architecture, which resulted in different unusual and unexpected behaviours being produced. The vehicle was allowed to explore a simulated environment which contained a single bright light in the centre with walls on the border. By using a novel combination of the Prediction by Partial Matching compression algorithm and ${k}$ -means clustering, interesting emergent behaviours were effectively discovered within a search space of over 10,000 simulations produced from a simple interaction of light and proximity sensors on a vehicle and a single light source. The clustering algorithm discovered five distinct behaviours: circling and spiralling behaviours; interesting behaviours creating intricate rose petal-like structures; behaviours that create simple rose petal-like structures; behaviours with large movements and low complexity; and behaviours with less movement. The novel algorithm demonstrated in this paper has useful potential in the science of complex systems and modelling to help expedite the systematic exploration of a substantial search space of simulations in order to discover interesting behaviours.

Handwritten mathematical expressions are an essential part of many domains, including education, engineering, and science. The pervasive availability of computationally powerful touch-screen devices, similar to the recent emergence of deep neural networks as high-quality sequence recognition models, result in the widespread adoption of online recognition of handwritten mathematical expressions. Also, a deeper study and improvement of such technologies is necessary to address the current challenges posed by the extensive usage of distance learning, and remote work due to the world pandemic. This paper delineates the state-of-the-art recognition methods along with the user's experience in pen-centric applications for operating with handwritten mathematical expressions. Recognition methods have been categorized into classes, with a description of their merits and limitations. Particular attention is paid to end-to-end approaches based on encoder-decoder architecture and multi-modal input. Evaluation protocols and open benchmark datasets are considered as well as the comparison of the recognition performance, based on open competition results. The use of handwritten math recognition is illustrated by examples of applications for various fields and platforms. A distinctive part of the survey is that we also considered how UI design relies on the use of different recognition approaches, which is aimed at helping potential researchers improve the performance of the introduced approaches toward the best responses in practical applications. Finally, this paper presents the prospective survey of future research directions in handwritten mathematical expression recognition and their applications.

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.