bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on efficiency optimization control strategy of WPT system based on optimal load matching
Authors: Xueying Qiu , Kangheng Qiao , Lei Wang , Yan Liang , Hailang Xie 
Published: April 2021

Abstract
Aiming at the problem that the transmission efficiency of SS-compensated MCR-WPT (Magnetic resonance wireless power transmission) is affected by load changes, this paper uses mutual inductance theory to analyze the equivalent circuit model, obtains the relationship between load and transmission efficiency, and proposes a controllable rectifier bridge for load matching, the front-end cascaded buck converter makes the output voltage regulation control scheme. Finally, modeling and simulation are used to verify the correctness of the load matching theory and control strategy.
A Focused Microwave Power Transmission Scheme Based on Phase Adjustable Microwave Source
Authors: Peng Wenxin , Li Songnong , Zhang Huaiqing , Chen Jianyu , Xiao Hui 
Published: April 2021

Abstract
In this paper, an electric filed focus scheme based on phase adjustable microwave source is used to improve the power transmission efficiency. A microwave power transmission system is consisted of the microwave source, a transmitting antenna and a receiving antenna. Measurement results proved that when compared with the same phases condition, the output DC power of the receiving antenna is increased from 23.5W to 31.5W and the relative increment of power is 34% under the focal phase condition. In addition, the system-level MW-DC power transmission efficiency is increased by 2.7%.
Application of laser displacement measurement system in thermal test of aircraft
Authors: Wanqing An , Guanghui Li , Xiaoning Liu , Tao Wang , Yi Li , Qingyu Wang 
Published: April 2021

Abstract
At present, there is no research on nanosecond displacement measurement technology for spacecraft thermal test in China. Firstly, the principle of laser displacement sensor measurement is introduced. A system of hardware and software design and implementation was designed. Finally, a set of test system is built to verify the application of laser displacement meter in the spacecraft vacuum thermal test displacement measurement system. The experimental results and data analysis show that the laser displacement meter can meet the nanosecond displacement measurement requirements of spacecraft in vacuum and low temperature environment.
The Optimal EV Charging Strategy in Smart Grid and Insight into Practical Solutions
Authors: Wang Jianmin , Wu Jiang 
Published: April 2021

Abstract
In recent years, the massive increase in electric vehicles (EV) and renewable energy (RER) has caused serious problems in the power system network. During peak hours of electricity consumption, the soaring demand for electricity from variable renewable energy sources severely undermines the stability and efficiency of the power supply. This article first reviews the infrastructure and charging strategies of electric vehicle batteries. After modeling and analyzing the charging activities, the results show that the charging behavior can transform the charging demand from the peak time period to the off-peak time period, and the online MSS optimization of electric vehicle charging is extracted through the Jacobian matrix. Finally, it provides a practical overview of the existing and future solutions to manage the large-scale integration of EVs into the network.
Blind Quality Index for Tone-mapped HDR Images and Multi-Exposure Fused Images
Authors: Fang Jingli , Jiang Mingxing 
Published: April 2021

Abstract
Tone mapping operators (TMOs) and multi-exposure fusion algorithms (MEFs) are employed to visualize the informative contents of high dynamic range (HDR) images on the standard dynamic range (SDR) devices, but also lead to visual quality degradation. Thus, it is urgent to evaluate the perceptual quality of tone-mapped images (TMIs) and multi-exposure fusion images (MEFIs). This paper proposes a blind quality index for TMIs/MEFIs by capturing exposure, colorfulness and structure characteristic features. The connection between feature space and associated subjective ratings is established via a regression model. The proposed method is tested on the public database and compared to the state-of-the-art blind image quality assessment (IQA) methods. The experimental results show that the proposed method achieves superior performance.
An unsupervised learning method for named entity relation extraction of space knowledge graph
Authors: Wei Zhanji , Huang Lingyong , Wan Gang , Mu Yao , Yin Yunxia 
Published: April 2021

Abstract
Knowledge graph is a kind of typical artificial intelligence technology, which can improve cognitive ability of machines. Space is a booming domain for human beings. In this paper, space knowledge graph is proposed in order to improve intelligence of space information system. A major obstacle of space knowledge graph construction is to identify and extract core named entities and relations in space domain. An unsupervised learning method to extract core named entities and relations is proposed in this paper. Firstly, confidence level index used to measure importance of entities and conditional confidence level index used to measure credibility of relations between entities are defined and investigated. Then, an entity and relation extraction procedure based on maximum likelihood estimation and apriori algorithm is designed. Finally, a case study is carried out and influence of super parameter setting on the extraction result is discussed. The method proposed in this paper can be used to construct space knowledge graph at a lower cost compared to supervised method, and can be extended to other domain knowledge graph construction.
Kent-PSO optimized ELM fault diagnosis model in analog circuits
Authors: Liu Zongpeng , Lin Zhiwei , Wang Chengji 
Published: April 2021

Abstract
Fault information in analog circuits is complex and diverse, so as to improve the accuracy of fault diagnosis, a Kent mapping and Particle Swarm Optimization (PSO) combined optimization Extreme Learning Machine (ELM) model is proposed. Firstly, the original data set of the circuit is normalized to obtain the fault data set. Secondly, Kent mapping is used to initialize the position of particles in the particle swarm, which makes the initial particle swarm more evenly distributed in the search space and enhances the global optimization ability. Third, aiming at the problem of the input weight and hidden layer bias generated randomly by the ELM are easy to lead to poor generalization ability, the Kent-PSO algorithm is used to optimize the input weight and hidden layer bias of ELM to obtain better and more stable ELM network parameters and improve the fault diagnosis ability. The diagnosis example of Sallen-Key bandpass filter shows that the proposed method has better fault diagnosis performance than PSO-ELM model.
Study of a method to reduce the harmonics of the output voltage of medium frequency power supply
Authors: Zhang Yinfeng , Shao Ying , Liu Xinzhi , Sun Pan , Lei Kang 
Published: April 2021

Abstract
Since the electrical equipment with nonlinear load began to be used, the harmonic suppression technology has become the focus of research. In this paper, a robust multi resonance servo control method is proposed and applied to the medium frequency power supply. The method can not only perform the closed-loop system stability and asymptotic tracking, but also make the power supply output possess the advantage of rapid dynamic response, and low-order strong harmonic suppression. First, this paper presents a mathematical model, analyzes the time-domain robust control - linear matrix inequality and regional D stability and turns the analysis of the problem of robust control problem to a one of linear matrix inequality (LMI) convex optimization problem through the augmentation of system matrix dimension. Then, the YALMIP toolbox is used to solve for the status feedback gain matrix. Finally the whole system is simulated and tested. The results show that the designed controller makes the dynamic recovery time of the closed-loop system less than 5ms, and the output voltage THD is reduced to less than 4.5% with the nonlinear load.
Method of Enhancing the Accuracy of Indoor Positioning (RSSI to UL-AOA)
Authors: Xu Ganqi 
Published: April 2021

Abstract
Nowadays, with the rapid development of urbanization worldwide, the indoor area has become complex. Even with floor maps and guidance signs, people waste time on self-location in an unfamiliar building for the reason that the mature location services like GPS are limited as outdoor positioning series, and the method of indoor positioning is relatively missing or rough. Therefore, the better indoor positioning systems are in a highly command. The indoor positioning system has the goals for realizing the location marked and tracking of people and objects in indoor scenes. This paper focused on the main positioning method in use, which is distance-based positioning algorithm. Then, the advantages and disadvantages of the mainstream algorithms RSSI and AOA were discussed. At last, by comparing the current research, the author put forward an optimized indoor positioning algorithm based on AOA, which may become the mainstream in the future.
Research on fault line selection method for distribution network in small current grounding system
Authors: Liu Guanhua , Bai Jieyu , Li Lusheng 
Published: April 2021

Abstract
When a single-phase-to-ground fault occurs in the distribution network, there is a wealth of information in the transient process of the fault line, from which the fault line selection criterion can be extracted. In this paper, based on the analysis of the related principle of wavelet transform and the concept of entropy, the comprehensive wavelet entropy is defined, and the principle and process of fault line selection are put forward by analyzing the variation coefficients of a group of comprehensive wavelet entropy. Finally, the simulation software is used to verify the fault line selection method under different fault conditions to prove its applicability and feasibility.
SA RCNN for Occluded Damper Detection in the Inspection of Power Transmission Line
Authors: Zhou Feng , Wang Guoyou , Zheng Yi 
Published: April 2021

Abstract
Dampers can prevent wires from breaking due to vibration, so it plays an important role in ensuring the stable operation of transmission lines. But the accuracy of popular object detection algorithm is still very low, because the occlusion phenomenon of dampers is serious due to the variable shooting angle. In this paper, we introduce our Supervised Attention RCNN (SA RCNN) to improve the detection effect of occluded dampers. SA RCNN mainly consists of two components: Box Supervised Attention Module (BSAM) and One Proposal Multiple Predictions (OPMP). In order to solve the problem of false detection caused by similarity of features, BSAM enhances features by using ground truths as supervised information to guide the generation of attention. In order to solve the problem of missed detection due to the NMS strategy, OPMP predicts a set of instances for one proposal rather than a single one. Experiments show that SA RCNN obtains 4.2% AP improvements on the dataset of occluded dampers compared to FPN.
Research on the Construction Method of 3D Dense Map Based on Visual Slam
Authors: Sun Tianfang , Shi Xiangbin 
Published: April 2021

Abstract
Aiming at the problem that visual SLAM (Simultaneous Localization and Mapping) cannot satisfy both high-precision positioning and rapid mapping in large scenes, this paper proposes a map construction method. First, when calculating the pose, the method of combining EPNP and graph optimization is used to optimize the pose estimation to improve the accuracy of the pose. Then, the paper uses Lie algebra to define the vertices and edges of the pose graph, which improves the convergence speed of the model. Finally, when constructing a three-dimensional map, a divide-and-conquer map method is proposed, which uses multiple sub-maps that contain each other for point cloud registration and down-sampling filtering, so as to improve the accuracy of map construction. Experimental results show that the algorithm in this paper has good performance. In the TUM test set, the speed can reach 0. 139s/frame, which is about 0.2s higher than the original method, and the accuracy error RMSE value is reduced to 0.055m. The feasibility of the algorithm is proved, and it can meet the accuracy and speed requirements of 3D dense map construction in large scenes.
Multi label image retrieval based on depth hash
Authors: Qin Zihui 
Published: April 2021

Abstract
In recent years, hash is a popular method of image retrieval. Convolutional neural network is used to generate image features and hash codes, so as to achieve fast and effective image retrieval. In order to solve the problem of poor image retrieval effect caused by complex background of Multi-target Image, this paper proposes a hash generation method based on the combination of target region recommendation network and convolutional neural network. The RPN network is selected as the target region recommendation algorithm. After the image passes through the RPN network, multiple target regions and the four-dimensional coordinates of each region will be generated. According to the four-dimensional coordinates, the main target region will be filtered and extracted. Then, the feature of the largest target area is extracted by the convolutional neural network (GoogleNet), and the corresponding hash code is generated, which is finally retrieved in the database. Experiments are carried out on voc2012 data set and self collected data set to verify the algorithm. When the number of test images is 1000, the experimental results show that the total correct rate of the retrieval results of this method is 95.5%, which is about 5 percentage points higher than the existing methods.
Multi-Granularity Matching Network for Multi-Paragraph Machine Reading Comprehension
Authors: Chen Xu 
Published: April 2021

Abstract
Multi-paragraph aims to allow machines to read multiple paragraphs and infer the answer to the given question. Usually, the model needs to use the selector to narrow the range of candidate paragraphs, and then use the reader to find the answer. For the selector, the previous work generally matches the question and the paragraph on the word level and the paragraph level. In this paper, we study the encoding and matching algorithm at the chunk level, which is supposed to make our model more accurate. Combining it with gated self matching mechanism, we design a multi-granularity matching network as the selector. The experimental results show that our model achieves competitive performance on Quasar-T.
A Skewness Fitting Model for Noise Level Estimation and the Applications in Image Denoising
Authors: Zhou Bin , Zhong Biying , Feng Jun 
Published: April 2021

Abstract
In this paper, a novel noise level estimation technique is presented based on skewness fitting. The difference between exact skewness and observed value on each channel contributes to an objective function. The skewness concentration is helpful to guarantee the reasonability of the model. The optimal solution means an estimation of the noise variance and it is easy to be applied in some parametric denoising model. The accuracy and efficiency of proposed algorithm were verified by the numerical experiments.
Research on mail classification algorithm based on improved convolutional neural network
Authors: dan Song , Kui Lu , Xufan Dai 
Published: April 2021

Abstract
Aiming at the problems of high dimension and sparse data in traditional text classification methods, this paper proposes an e-mail classification method based on i-cnn model by combining convolutional neural network (CNN) and inception V1 model. In the convolution and pooling operation, 1 * 1 convolution kernel is added to reduce the thickness of eigenvectors, reduce the parameters and improve the computational performance. Through data validation, i-cnn model for e-mail classification results as high as 92.18%, in the comparative experiment, i-cnn model compared with several machine learning classification models, achieved the highest classification accuracy, in the comparison with or without the emergence structure model, i-cnn model accuracy is higher than CNN model. It shows that the model has a good classification effect, and the integration of inception V1 model can improve the accuracy of text classification.
An Improved Algorithm of D-S Evidence Fusion
Authors: Lei Hongxuan , Sheng Li 
Published: April 2021

Abstract
In the theory of evidence fusion, it is a very important task to deal with the conflicts between evidences correctly. Based on the credibility degree of evidence proposed in [10], in order to increase the credibility degree of each group of evidence, the concept of falsity is introduced, which is another concept to represent the conflict between two groups of evidence, this treatment makes up for the deficiency of normalized evidence weight coefficient when one group of evidence credibility degree is zero. Because we consider not only the credibility degree of evidence, but also the falsity of evidence, the weight coefficient of unreliable information is effectively reduced. The numerical simulation results show that the algorithm has the advantages of simple idea, fast convergence speed and strong focusing.
Sparse representation algorithm for fusion error
Authors: Li Zijun 
Published: April 2021

Abstract
In recent years, face recognition technology has been widely used in security, finance, media data retrieval and other fields due to its convenience, concealment, and stability, and has become a popular research field in various scientific research institutions. Among them, face recognition under complex conditions under different lighting, different expressions, and partial occlusion has become a research hotspot. Sparse representation algorithm, as the mainstream algorithm to solve the problem of face recognition under complex conditions, although it improves the recognition accuracy compared with other algorithms, there is still room for improvement. Aiming at the partial occlusion problem in face recognition, this paper conducted many experiments under different training samples through the sparse representation algorithm of fusion error. The experimental results show that the algorithm improves the recognition rate and provides a new solution for the problem of face recognition under partial occlusion.
Lightweight design of SM4 algorithm and realization of threshold scheme
Authors: Chen B W , Xia X , Liang Q M , Zhong W D 
Published: April 2021

Abstract
Aiming at the lightweight design of SM4 cryptographic algorithm, a lightweight cryptographic algorithm scheme is proposed and implemented in hardware. Based on the work of Li et al., a 4-bit S-box is selected. The original 8-bit look-up table S-box in the SM4 algorithm is replaced with a 4-bit cyclic shift S-box, and the same circuit structure is used to save resources in hardware implementation. A threshold scheme based on the principle of secret sharing is proposed to improve the overall security of the scheme against side-channel attacks. The input information is divided into three groups, and all operations are converted from GF(24) to GF(22) through a multiplier and a squarer, which further reduces the circuit consumption and improves the security of the algorithm. In the realization of the threshold scheme, virtual values are intro-duced to ensure that the scheme meets the uniformity, and the decomposition method is used in the inverter to re-duce the number of operations and the proportion of space. After theoretical deduction, the threshold scheme as a whole has the ability to resist first-order and second-order DPA attacks. The overall hardware implementation of the program uses a total of 2803 logic elements, which proves that it achieves the goal of lightweight while ensuring safety.
Design and Application of Home Fire Alarm System
Authors: Xie WenLan , Haoming Huang 
Published: April 2021

Abstract
With the application and development of Internet of things technology, the demand for home intelligent devices is higher and higher. Home fire safety is particularly important. In view of the current home fire safety problems, this paper proposes a home fire alarm system based on the combination of microcontroller and sensor. The system takes microcontroller and sensor as the main components, and uses the combination of combustible gas detection module, temperature detection module and flame detection module to form a fire alarm system. The functions of gas leakage early warning exhaust, fire sound and light alarm exhaust and high temperature detection alarm are realized. Combustible gas detection, temperature detection and flame detection all collect data through sensors, and then compare with the preset alarm value to judge whether the fire occurs. The design has the advantages of accurate detection, high degree of intelligence, convenient installation, strong scalability and so on. It gives a new solution to the fire safety of the home.
Coordination of Preventive Dispatch and Black-Startunder Extreme Operational Conditionsusing Robust Optimization
Authors: Kai Saijiang , Zhang Feng , Huang Ping 
Published: April 2021

Abstract
Extreme operational conditions, e.g., wind storms and ice storms, have been posing great challenges for power grid operation. The extreme conditions are likely to cause widespread blackout, therefore a black-startprocedure is needed to restore the non-blackstart generating untis and transmission assets. To falicilate the balck-start procedure, the coordination between post-event preventive dispatch and post-event black-start dispatch is established, considering the uncertainty of forced transmission line outage scenarios. A two-stage robust optimization model is built to coordinate the preventive dispatch and black-start procedure. An iterative decompostion algorithm is applied to solve the proposed model. A synthesized Chinese 372-bus system is used to illustrate the proposed method. It is shown that, in the worst-case blackout scenario, the coordinated black-start procedure can accelerate the entire process by 15% and reduce the black-start capacity requirements.
A Dialogue State Tracking Model with Slot Embedding
Authors: Qiu Boyu , Sun Yingfei 
Published: April 2021

Abstract
Dialogue State Tracking (DST) is the core component of the task-oriented dialogue systems. Although recent neural DST models have made great progress, they often ignore the phenomenon that the current dialogue state is closely related to the earlier dialogue states. In this paper, we try to introduce the slot embedding into the transformer to focus on those special tokens, which ever appear in earlier dialogue states. On the basis, we leverage the copy mechanism to predict the state over the dialogue utterances. Our model also imitates the architecture of reading comprehension model to make full use of the current utterances. The experimental results verify the benefits of the slot embedding, and our model achieves significant improvements than baselines on MultiWOZ 2.0 and MultiWOZ 2.1 datasets.
Inactivation of lead and zinc in sediment using lanthanum modified zeolite
Authors: Wang Zhe , Li Wen , Ji Pengfei , Liu Yuling , Miao Xiaobiao , Li Jiake 
Published: April 2021

Abstract
Access accumulation of zinc and lead in sediment would be harmful to human beings. In the present study, Lanthanum Modified Zeolite (LMZ) was developed with fly ash and its binding capabilities toward zinc and lead were investigated. The Langmuir adsorption maximum of LMZ for Zn2+ and Pb2+ were 22.78 and 105.26 mg/g, higher or at least comparable to adsorbents developed in recent studies. Dosing LMZ to polluted sediment could transfer zinc and lead from environmental available forms to refractory form, and higher dosage would enhance the immobilization performance.