bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simple modelling of S‐type NbOx locally active memristor
Authors: Liang Yan , Wang Guangyi , Xia Chenyang , Lu Zhenzhou 
Published: May 2021

AbstractNanoscale S‐type NbOx locally active memristors (LAMs) open up new opportunities in the brain‐inspired neuromorphic computing. Simple yet accurate models for these memristors can provide benefits for designing related circuits and systems. Considering that the DC voltage–current plot of the NbOx LAM under current sweeping is composed of three regions, that is, high resistance region, negative differential resistance region, and low resistance region, a three‐segment piecewise‐linear method is applied to fit these three regions. Based on this developed relation of the voltage and current at DC, a simple model for the NbOx LAM is proposed. The parameters of the proposed model can be easily identified in terms of the quasi‐static and dynamic electrical characteristics. A series of numerical simulations corroborate that the proposed model can accurately emulate the quasi‐static voltage–current characteristics and oscillating behaviours of the NbOx LAM.
Improved adaptive parameters estimation considering transient‐state estimation error in sensorless control system of IPMSM
Authors: Kawamura Naoki , Zanma Tadanao , Nomura Yuta , Koiwa Kenta , Liu Kang‐Zhi , Hasegawa Masaru 
Published: May 2021

AbstractPosition sensorless vector control of interior permanent magnet synchronous motors (IPMSMs) has been used for downsizing, cost reduction, and high reliability. In the position sensorless vector control, the q‐axis inductance decreases as the q‐axis current increases due to the load variation, which results in deterioration of the position estimation performance without considering the q‐axis inductance. Here, the authors propose an innovative adaptive estimation based on the dynamic certainty equivalence principle for high‐performance position sensorless control for IPMSMs. The effectiveness of the proposed adaptive scheme is verified through experiments.
Transfer learning based dynamic security assessment
Authors: Lam Hoi Andy , Dong Zhao Yang 
Published: May 2021

AbstractWith the increasing deployment of wide‐area monitoring systems (WAMS) and phasor measurement units (PMUs), along with artificial neural network (ANN) and high‐performance distributed computation technique for smart grid and smart metering environment, online dynamic security assessment (DSA) plays a key role for early unstable event detection on power system security. It is especially important at a post‐fault operation that the timing by DSA to detect an unstable event is critical to emergency remedial control action. However, excessive update training is one of the constraints for ANN to be effectively performed at pre‐fault and post‐fault operations on online DSA. This paper describes how transfer learning is successfully employed to shorten the training time for online DSA. It also helps to improve the validation accuracy if the training dataset of scratch ANN model is insufficient. Besides, a new approach of using the densely connected convolutional network with kernel principal component analysis (KPCA) is proposed to eliminate the traditional step of dimensionality reduction.
Down‐sampling template curve to accelerate LDDMM‐curve with application to shape analysis of the corpus callosum
Authors: Huang Weikai , Tang Xiaoying 
Published: May 2021

AbstractLarge deformation diffeomorphic metric mapping for curve (LDDMM‐curve) has been widely used in deformation based statistical shape analysis of the mid‐sagittal corpus callosum. A main limitation of LDDMM‐curve is that it is time‐consuming and computationally complex. In this study, down‐sampling strategies for accelerating LDDMM‐curve are investigated and tested on two large datasets, one on Alzheimer's disease (155 Alzheimer's disease, 325 mild cognitive impairment and 185 healthy controls) and the other on first‐episode schizophrenia (92 first‐episode schizophrenia and 106 healthy controls). For both datasets a variety of down‐sampling factors are tested in terms of registration accuracy, registration speed, and most importantly disease‐related patterns. Experimental results revealed that down‐sampling template curve by a factor of 2 can significantly reduce the running time of LDDMM‐curve without sacrificing the registration accuracy. Also, the disease‐induced patterns, or more specifically the group comparison results, were almost identical before and after down‐sampling. It is also shown that there was no need to down‐sample the target population curves but only the single template curve of the study of interest. Comprehensive analyses were conducted.
Surface discharge imaging in presence of deposited space charges in non‐uniform DC electric field
Authors: Florkowski Marek , Krześniak Dariusz , Kuniewski Maciej , Zydroń Paweł 
Published: May 2021

AbstractAn investigation is made of the influence of an accumulated charge sprinkled on the surface of an epoxy resin plate in corona mode on partial discharge inception and its forms as analyzed by optical imaging at DC voltage. The effects of the polarities of both the deposited hetero‐ and homocharges were investigated. The occurrence of first streamer discharges was detected electrically and observed by optical imaging. The various forms of streamers were acquired by optical imaging and associated with the polarity and intensity of the accumulated surface charges. Surface charge sprinkling on the epoxy specimen surface was quantitatively evaluated by surface charge mapping. In addition, the effect of the subsequent discharges occurring in a heterocharge configuration above the inception voltage was observed. In terms of their directions and lengths, the kinetics of the streamer filaments revealed a strong dependence on both the polarity and the intensity of an accumulated charge. Compared with the reference charge‐free surface, the strongest drop in dielectric strength in the interelectrode space with a sprinkled charge was obtained for the heterocharge configuration. The presented results provide a contribution to the assessment of streamer behaviour in the presence of accumulated surface charges, including its polarity and intensity.
Automatic detection of safety helmet wearing based on head region location
Authors: Gu Yuwan , Wang Yusheng , Shi Lin , Li Ning , Zhuang Lihua , Xu Shoukun 
Published: May 2021

AbstractIn order to solve the problem of difficult and low precision in the detection of safety helmet wearing in the complex pose of construction worker, a detection method of safety helmet wearing based on pose estimation is proposed. In the pose estimation model of OpenPose, the residual network optimized feature extraction is introduced to obtain the skeletal point information of the construction worker, and then the pose of the construction worker is estimated based on the skeletal point information, three‐point localization method is proposed for the front and back pose, and skin colour detection method is proposed for the side pose, and then to determine the head region. The YOLO v4 is used to detect the safety helmet region, and then the construction worker's safety helmet wearing is judged according to whether the head region intersects the safety helmet region or not. Experimental results show that the detection accuracy of the method is higher than other methods, and the adaptability to the environment is stronger.
Multi‐view intrinsic low‐rank representation for robust face recognition and clustering
Authors: Wang Zhi‐yang , Abhadiomhen Stanley Ebhohimhen , Liu Zhi‐feng , Shen Xiang‐jun , Gao Wen‐yun , Li Shu‐ying 
Published: May 2021

AbstractIn the last years, subspace‐based multi‐view
face recognition has attracted increasing attention and many related methods have been proposed. However, the most existing methods ignore the specific local structure of different views. This drawback can cause these methods' discriminating ability to degrade when many noisy samples exist in data. To tackle this problem, a multi‐view low‐rank representation method is proposed, which exploits both intrinsic relationships and specific local structures of different views simultaneously. It is achieved by hierarchical Bayesian methods that constrain the low‐rank representation of each view so that it matches a linear combination of an intrinsic representation matrix and a specific representation matrix to obtain common and specific characteristics of different views. The intrinsic representation matrix holds the consensus information between views, and the specific representation matrices indicate the diversity among views. Furthermore, the model injects a clustering structure into the low‐rank representation. This approach allows for adaptive adjustment of the clustering structure while pursuing the optimization of the low‐rank representation. Hence, the model can well capture both the relationship between data and the clustering structure explicitly. Extensive experiments on several datasets demonstrated the effectiveness of the proposed method compared to similar state‐of‐the‐art methods in classification and clustering.
Automatic image segmentation based on label propagation
Authors: Belizario Ivar Vargas , Linares Oscar Cuadros , Neto João do Espirito Santo Batista 
Published: May 2021

AbstractThis article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre‐segmented with super‐pixels, followed by feature extraction using colour information of each super‐pixels. The resulting graph consists of vertices which represent super‐pixels, whereas the edge weights are a measure of similarity between super‐pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state‐of‐the‐art methods, produce better results in terms of segmentation quality and processing time.
SeqFace: Learning discriminative features by using face sequences
Authors: Hu Wei , Huang Yangyu , Zhang Fan , Li Ruirui , Li Hengchao 
Published: May 2021

AbstractDeep convolutional neural networks (CNNs) have greatly improved the Face Recognition (FR) performance in recent years. Almost all CNNs in FR are trained on the carefully labeled datasets containing plenty of identities. However, such high‐quality datasets are very expensive to collect, which restricts many researchers to achieve state‐of‐the‐art performance. In this paper, a framework, called SeqFace, for learning discriminative face features is proposed. Besides a traditional identity training dataset, the designed SeqFace can train CNNs by using an additional dataset which includes a large number of face sequences collected from videos. Moreover, the label smoothing regularization (LSR) and a new proposed discriminative sequence agent (DSA) loss are employed to enhance the discrimination power of deep face features via making full use of the sequence data. Only with a single ResNet model, the method achieves very competitive performance on several face recognition benchmarks, including LFW, YTF, CFP, AgeDB, and MegaFace. The code and model are publicly available at the website https://github.com/huangyangyu/SeqFace.
Second‐kind Fredholm integral‐equation analysis of scattering by layered dielectric gratings
Authors: Tsitsas Nikolaos L. 
Published: May 2021

AbstractScattering of the E‐polarised plane electromagnetic wave from two different but of the same‐period all‐dielectric rectangular bar gratings on substrates is analysed using a volume‐integral‐equation (VIE) method. Here, VIE is a Fredholm integral equation of the second kind with unknown function being the electric field in the gratings regions. Discretisation of the VIE is performed by applying an entire‐domain Galerkin technique. The developed algorithm possesses guaranteed convergence, provides accuracy, and is easily controlled by the order of discretisation. Numerical results for frequency‐selective reflection, manipulation of light to propagate in prescribed directions, and the ultrahigh‐Q lattice‐modes resonances are presented and discussed.
A non‐isolated high step‐up DC–DC converter using magnetic coupling and voltage multiplier circuit
Authors: Pourjafar Saeed , Shayeghi Hossien , Hashemzadeh Seyed Majid , Sedaghati Farzad , Maalandish Mohammad 
Published: May 2021

AbstractThis paper introduces a new DC–DC structure with a high voltage gain. The proposed structure can achieve higher voltage gain by using magnetic coupling and voltage multiplier circuit (VMC). The VMC is also used as a clamp circuit to suppress the voltage spike through the switch which leads to increase the overall system efficiency. Moreover, the peak voltage through the semiconductors of the proposed converter is low. The existence of soft‐switching such as zero‐current switching (ZCS) in OFF‐state and zero‐voltage switching (ZVS) in ON‐state of diodes is another merit of the proposed converter. The recommended structure uses one power MOSFET with low on‐state resistance (RDS‐ON) that leads to decrease the conduction loss. For demonstrating the proficiency of the proposed structure, technical analysis, mathematical survey, and comparison results with other similar previous structures are carried out. Finally, laboratory results with 200 W output power and 50 kHz switching frequency are provided, which prove the usefulness of the suggested converter. The proposed converter's efficiency in 200 W output power is 96.98 %. Also, the voltage gain, maximum voltage stress across the power switch and didoes for n (turn‐ratio) = 2 and D (duty‐cycle) = 0.6 are equal to 10, 0.25, and 0.75, respectively.
Design and optimization of deflection type dual‐stator switched reluctance wind power generator
Authors: Li Zheng , Wang Xin , Yu Xuze , Wang Xueting , Sun Hexu 
Published: May 2021

AbstractIn this paper, a deflection type dual‐stator switched reluctance wind power generator is proposed. This kind of generator can effectively improve the power generation efficiency and response speed of the generator with development potential. The generator consists of an external generator and an internal generator, which can be coordinated to improve the efficiency of the generator. In this paper, with the goal of reducing the loss of the generator core, Taguchi algorithm is used to screen out the parameter variables that has a great impact on the loss of the generator core. Based on the method of central composite design (CCD), the experimental sample data set is constructed and the rationality of the model is evaluated by analysis of variance and the optimal parameters of the generator are determined. The test results of the prototype verify the correctness of design, core loss calculation and optimization scheme.
Current stress and switching loss evaluation of a unified expandable power converter used for grid‐integration of renewable energy sources
Authors: Bizhani Hamed , Muyeen S. M. , Tatari Fatemeh R. , Techato Kuaanan , Ben‐Brahim Lazhar , Blaabjerg Frede 
Published: May 2021

AbstractDue to the intermittent nature of the renewable energy systems (RESs), more specifically, solar panels and wind turbines, their sole use does not lead to a smooth and reliable power. To overcome this issue, the concurrent grid‐integration of RESs to form a microgrid is reported. In the DC‐bus microgrid, the produced power by RES is initially given to the shared DC‐bus through an individual source‐side converter and then transmitted to the utility via a common grid‐side converter. By increasing the number of RESs, the number of required power converters, and therefore, the investment cost also increase. Using the cost‐effective multi‐input low‐switch converters is a promising alternative to alleviate this significant need for individual converters. Recently, a nine‐switch‐based unified expandable power converter (UEPC) has been presented for concurrent integration of AC and DC sources with a tangible fewer switch count. This unified structure has been utilized in two configurations named AC‐AC‐AC and AC‐AC‐DC. In this paper, both configurations are evaluated and compared in terms of current stress and switching loss. Considering the current stress analysis, the best port for interfacing with the grid to lower the total current rating of power switching devices is also determined. The high‐performance capability of both configurations is finally verified using MATLAB/Simulink.
Study on harmonic interaction between paralleled STATCOMs with cascaded H‐bridge topology in wind farm clusters
Authors: Chen Jikai , Hu Yang , Wang Yongquan , Li Jiang , Wang Xiaozhe , Jiang Qirong , Zheng Xuemei 
Published: May 2021

AbstractTo ensure reasonable reactive power distribution and voltage standard, a large number of STATCOMs are integrated to wind farms and afflux stations. However, the issue of device tripping caused by the interaction between STATCOMs is emerging. This paper sets out to bring a clear understanding of the fault mechanism of single STATCOM tripping caused by the harmonic circulation between STATCOMs in a clustered wind‐farm afflux station in North‐China grid. The equivalent circuit of harmonic circulation between two paralleled STATCOMs based on cascaded H‐bridge topology is established first. Thereafter, the intrinsic relationship between high‐order harmonic circulation and inter‐converter harmonic voltage difference as well as the phase deviation of SPWM carriers are analysed. A generalized model is also built to estimate the higher‐order harmonics. The impact of non‐characteristic harmonics on DC voltage of each H‐bridge cell is investigated by calculating the difference of active power flowing through DC capacitors. It is pointed out that the harmonic circulation is one of the main reasons resulting in the DC overvoltage protection action of STATCOM. Finally, the correctness of theoretical analysis is verified by real‐time simulation and hardware experiments.
A bi‐level model for co‐expansion planning of generation and energy storage system (ESS) with contract pricing
Authors: Rafiei Seyed Arash , Rafiei Seyed‐Reza , Goldani Saeedreza , Falaghi Hamid 
Published: May 2021

AbstractIn co‐expansion planning of generation and storage systems, the investor aims to determine the time, location, and capacity of the generation energy storage units. The main challenge is to install these systems in a way that the investor's profit is maximized. In addition, market‐clearing must take place in order to optimize social welfare. This paper studies a co‐expansion planning problem as a bi‐level model that involves the expansion of generation and energy storage units. Generating units include wind and gas turbines and the energy storage system (ESS) is compressed air. This paper addresses the upper‐level problem of increasing investor profits and the lower‐level problem of increasing social welfare. Simulation results show that an investor can achieve the highest possible profit by simultaneously investing in the wind and gas turbine as well as the storage systems using strategic behaviour (price offer/bid close to market price) and participating in the spot market and guaranteed purchase contract. In addition, improving the load growth rate and guaranteed purchase contracts increase the investment in the generation‐storage units. The simulation results of this study are compared with other studies for validation purposes.
A novel power management strategy based on combination of 3D droop control and EKF in DC microgrids
Authors: Sabzpoosh Saravi Vahid , Sakhaei Hossein , Kalantar Mohsen , Anvari‐Moghaddam Amjad 
Published: May 2021

AbstractVoltage regulation and power management are necessary to maintain balance of supply and demand in DC microgrids. In such systems, power sharing is normally done through parallel operation of distributed energy resources equipped with droop controllers. However, low power sharing accuracy and not allowing the microgrid to maximise the available power from the renewable sources are two main problems associated with conventional droop control methods. In addition, the 2D droop method is a parameter‐dependent method for extracting maximum available energy from renewable sources. In this paper, a novel power management strategy based on the optimal 3D droop coefficients is developed for a DC microgrid. Optimal state estimation is attained using a combination of extended Kalman filter and adaptive recursive least square method. Reference currents of renewable energy sources (wind and solar) and battery energy storage system are estimated using the proposed prediction based model. The proposed strategy not only increases the power sharing accuracy but also remains the bus voltage around a nominal value. The performance of the proposed method is evaluated for the considered DC microgrid in two different scenarios. Results show the high effectiveness and robustness of the proposed method. It has been concluded that precise estimation of the sources reference currents and 3D droop coefficients are critical for optimal power management and bus voltage regulation in DC microgrids.
Rotors for wave energy conversion—Practice and possibilities
Authors: Ermakov Andrei , Ringwood John V. 
Published: May 2021

AbstractThis review studies some of the existing ideas which create a fundamentally new direction for wave energy converter (WEC) development, obtaining energy from the elliptical motion of particles in water waves using a rotor. The review focuses on three main aspects of rotor‐based WECs: experimental study of the developed prototypes, derivation and development of the mathematical models, and control effectors and philosophies for the proposed devices. The range of developed small scale prototypes and their experimental studies are presented. The shortcomings of the current mathematical and hydrodynamic models are identified, while an overview of the proposed and new possible control effectors and strategies is conducted. This allows us to see the state of the development of the different concepts and problems to be solved in bringing wave energy rotors to operational reality.
Active jamming recognition based on bilinear EfficientNet and attention mechanism
Authors: Xiao Yihan , Zhou Jingyi , Yu Yongzhi , Guo Limin 
Published: May 2021

AbstractAs electromagnetic environments are increasingly complex, there are more kinds of radar jamming signals. Active jamming recognition has problems of the low recognition accuracy and the high computational complexity, especially under a low jamming‐to‐noise ratio (JNR). Herein, a deep learning network based on bilinear EfficientNet and attention mechanism is proposed to recognise and classify eight kinds of jamming signals automatically. Firstly, the one‐dimensional interference signal is transformed into a two‐dimensional time–frequency image by the time–frequency analysis. Based on the transfer learning of EfficientNet‐B3, the effective features of the time–frequency image are automatically extracted by the two‐way network with attention mechanism, and the active interference classification is realised. The experimental results show that the method's overall recognition rate for eight kinds of interference signals is more than 97.5% when the JNR is −8 dB and is close to 100% at −2 dB.