bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation of defocusing effect based on two-step ABCD algorithm while a modal decomposition
Authors: Zhang Yuying , Wang Tingfeng , Liu Lisheng , Li Yuanyang , Guo Jin 
Published: September 2020

Abstract
Depicting the multimode laser beam by modal decomposition can potentially assess light field variations in the fiber, during propagation. The practical engineering conditions in the lab however could not realize ideal levels, hence further research on factors influencing this method, such as defocus, is especially necessitated. The grid spacing in observation plane by Fast Fourier Transform is fixed and unchangeable within diffraction imaging, hence possibly yielding erroneous data during obtaining light field intensities. Our research resolves these issues via a Two-step ABCD algorithm, applied in the modal decomposition to characterize various guided modes at the output of multimode fibers. A direct benefit is that the image plane size can be altered, further refining laser facula clarity. Furthermore, the quantitative expressions that analyze defocus factors impacting modal decomposition are acquired. The conclusions thereby prove the modal decomposition algorithm can keep effectiveness in the range of −0.25% to 0.25% of relative defocus for low order eigenmodes, having no suitable limited band for high order eigenmodes, with reference value in engineering applications.
Effect of null aether field on weak deflection angle of black holes
Authors: Övgün A. , Sakallı İ. , Saavedra J. 
Published: September 2020

Abstract
We study light rays in the static and spherically symmetric gravitational field of the null aether theory (NAT). To this end, we employ the Gauss-Bonnet theorem to compute the deflection angle formed by a NAT black hole in the weak limit approximation. Using the optical metrics of the NAT black hole, we first obtain the Gaussian curvature and then calculate the leading terms of the deflection angle. Our calculations indicate how gravitational lensing is affected by the NAT field. We also illustrate that the bending of light stems from global and topological effects.
Critical fault diagnosis method of small components of power transmission based on optimizational YOLOv3 algorithm
Authors: Chen Chen Jia 
Published: September 2020

Abstract
In order to solve the problem that the image recognition algorithm in UAV (Unmanned Aerial Vehicle) inspection of power transmission has low recognition rate for small, indiscoverable and high-risk component failures, this paper proposes a critical fault diagnosis method based on the optimizational YOLOv3 algorithm. We add SPP net under the basic framework of the original YOLOv3 and extract multi-scale depth features in the same convolutional layer which can transform all the input images into a uniform size and solve the problem caused by different input image sizes and formats. An optimizational scale prediction framework of YOLOv3 is proposed, which expands the original three-scale prediction frameworks to four scales and carries out multi-scale integration. This improvement is used to strengthen the feature extraction of small and indiscoverable fault. The optimizational algorithm is verified by using the dataset of electrical components of transmission system in the actual scene which have small and indiscoverable faults. The results show that the optimizational YOLOv3 improves the recognition rate of the selected categories by 3.98% on average, and increases the recognition rate of the small size and indiscoverable fault by 11.95% on average.
The Influence of IGBT Aging on the EMI of Traction Converter
Authors: Feng Q P , Lin Y W , Jin K L , Zhang J Q , Li X Q , Wang Q F 
Published: September 2020

Abstract
The Insulated Gate Transistors (IGBT) is a kind of semiconductor that is prone to aging in power electronic equipment. After aging, the IGBT will not only degrade during its operating performance, but will also reduce the reliability of the electromagnetic compatibility (EMC) in its working circuits. This article summarizes and analyzes the aging mechanism of the IGBT and the impacts of the changed characteristic parameters raised by the aging on the characteristics of IGBT while working. The aging will lead to changes in the high-frequent parasitic parameters, which play an essential role in affecting the IGBT with high-frequency and high-power on the electromagnetic interference (EMI) of its working circuits. To further illustrate these issues mentioned above, a typical “AC-DC-AC” traction converter circuit is taken as an example, and adopt a simulation model of conducted interference of traction converter systems by using Saber sketch software to study the influences of the impacts of changes from aging parasitic parameters on EMI of traction converter circuits.
Calculation model of mutual coupling between two ferrite-cored square coils in wireless power application for electric vehicles
Authors: Zhang Yameng , Zhang Xi , Zhu Chong , Cao Dongpu 
Published: September 2020

Abstract
Wireless power transfer (WPT) devices have been widely accepted by more and more industrial producers and consumers due to their safe, convenient, and high-efficient features. Square coils with ferrite cores are usually utilized as magnetic coupling devices by WPT systems for electric vehicles (EVs). However, the research on coupling coils often depends on three-dimensional finite element analysis (3-D FEA) tools such as ANSYS Maxwell or HFSS, which are troublesome and time-consuming. This paper explores a calculation model of mutual coupling between two square coils with ferrite cores based on magnetic mirror method. With this model, accurate results and fast operation speed can be obtained, and 3-D FEA tool is used to compare and verify the performance of this work.
Study on Primary Harmonic Eliminator for Ferromagnetic Resonance in the Neutral Non-effective Grounding System
Authors: Zhao Cong , Li Suya , Li Songyuan , Li Nan , Guo Bowen , Yan Wei 
Published: September 2020

Abstract
The ferromagnetic resonance overvoltage and overcurrent can often appear in the neutral non-effective grounding system, which is caused by the electromagnetic voltage transformer saturation under a certain condition. At present, two different types of primary harmonic eliminators are mainly used to solve the ferromagnetic resonance, which are voltage-sensitive resistor type and thermal resistor type. In this paper, the simulation model based on ATP-EMTP program is established and the results of different harmonic eliminators are compared. The results prove that the primary harmonic eliminator of thermal resistor type can more effectively suppress the ferromagnetic resonance.
Research of virtual impedance layering and distributing control strategy of hybrid renewable energy microgrid
Authors: Yu Qingguang , Jiang Zhicheng , Zhao Mengchu , Liu Yuming , Guo Min 
Published: September 2020

Abstract
Micro-grid technology will combine hybrid renewable energy and power generation technology, clean and environmental protection, improve the utilization of renewable energy; the use of distributed generation units installed in the local characteristics of the governor from the transmission loss and cost. This paper introduced the virtual impedance into the traditional droop control, make the output impedance and line impedance of the inverter are mainly inductive, so the droop control algorithm is implemented. This paper presented a new method based on the virtual impedance to adjust the frequency and voltage of the micro-grid bus two times, to realize the two adjustment of the frequency and voltage of the micro grid. In addition, for the micro-grid power network interaction, based on PQ closed loop control method is proposed to response to the dispatching command. The simulation results show high performance of the designed control strategy.
An improved Chinese text multi-label classification method based on CNN
Authors: Xin Yuanxia , Zhang Zhi 
Published: September 2020

Abstract
Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm.
Research on the Intelligent Assessment Algorithm of Bone Age Based on Attention Mechanism
Authors: Zhao X X , Li D Y , Li J , Kang J , Yang L 
Published: September 2020

Abstract
Bone age is a reliable index to reflect the maturity of physical development, which is of great significance to evaluate the growth and development of children and adolescents, diagnosis and treatment of diseases. Traditional bone age assessment based on artificial has many problems, such as its time-consuming and subjective result, which may lead to great fluctuation of assessment results. Based on the X-ray image of the hand bones, this study proposes an intelligent prediction model of bone age in Deep Learning based on attention mechanism, combined with the traditional methods of bone age interpretation in Deep Learning. In the pre-processing stage, U-Net is used to remove the background of X-ray image of hand bones, and the dense connection network of attention mechanism is used to extract image features, and the mean absolute error function is introduced to improve the accuracy of this model. In the data set of RSNA competition, the mean absolute error of the method proposed in this study is 0.38 ± 0.10 years old, and obtained the best results reported at present.
Optimization and High-performance Hardware Implementation of Multipath Interference Compensation Algorithm
Authors: Liu Chuan , Lang Shinan , Cai Yiheng , Wu Qiang 
Published: September 2020

Abstract
Our previous sparse regularization method for multipath interference (MPI) compensation in pulsed Time-of-Flight (ToF) depth camera works well on desktop computer. However, pulsed ToF depth cameras are mainly applied in fields such as autopilot vehicles and robots. In these situations, exporting depth images to a computer and running MPI compensation in MATLAB every time are not realistic. Therefore, according to the hardware structure of the multipath interference real-time compensation platform, we use QR decomposition to optimize the sensing matrix in our algorithm for better hardware implementation, while maintaining the accuracy of our algorithm. For the optimized algorithm, we designed an efficient parallel data processing hardware structure that matches the features of our algorithm to optimize the calculation efficiency. Then, we implemented it on the realtime compensation platform. The experimental results show that this algorithm architecture runs stably under the clock frequency and it runs faster than desktop computer.
A Wavelet Analysis-Based Big Data Spectral Clustering Algorithm for Electric Internet of Things
Authors: Zhang Hao , Liu Xin , Liu Donglan , Yu Hao 
Published: September 2020

Abstract
As traditional spectral clustering algorithm is not ideal in big data scenarios, this paper proposes a wavelet analysis-based big data spectral clustering algorithm for Electric Internet of Things. Firstly, using wavelet analysis to measure the dissimilarity of the network communication data, and then the similarity relationship between EIoT data is established to obtain the similarity matrix. Finally the spectral clustering algorithm is used to cluster the data based on similarity matrix obtained above. The experimental results show that the accuracy and purity of the proposed spectral clustering algorithm based on wavelet analysis are respectively 36.2 percent and 19.6 percent higher than traditional algorithms, indicating that the proposed spectral clustering algorithm based on wavelet analysis is effective for EIoT data clustering.
Usability Evaluation for User Interface Design of Application for Recommender System to Enhance the Potential of Community-Based Tourism in Phatthalung, Thailand
Authors: Jomsri Pijitra 
Published: September 2020

Abstract
User experience and user interface design are the important processes in software design and development, especially in the design of mobile applications, which must support the growing needs of users. Moreover, the community tourist attractions in Phatthalung Province are still lack of important information for community enterprises and tourist attractions that offers information matched the needs of users. This research, therefore, aimed to create a user interface design on mobile devices by using user experience, and to study the format of the Application for Intelligent Recommender System to enhance the Potential of Community-Based Tourism in Phatthalung so as to support the work of designers. This research compared the 3 models of UI and evaluated the heuristic of user interface to assure that the proposed format conforms to the basic operations of the user interface. The results showed that most users liked visual presentations from Model 1 and the presentation of tourist attractions with maps suggesting trips to tourist attractions including having media related to the community which helped to attract tourists.
Improved HOG Feature Vehicle Recognition Algorithm Based On Sliding Window
Authors: Ji Yang , Zhong Jun 
Published: September 2020

Abstract
With the continuous development in recent years, machine learning has been extensively used in many application scenarios, especially in vehicle recognition. At present, the histogram of oriented gradients (HOG) algorithm has been widely used in vehicle recognition and facial recognition. This paper studies the HOG algorithm, introduces the vehicle recognition using the HOG algorithm, and introduces a detection method based on sliding window. In this research, an improved HOG algorithm based on sliding window is proposed for vehicle recognition and compared with the traditional HOG algorithm. In randomness test, the traditional recognition method often causes its HOG features to be trapped in a large number of invalid features due to the uncertainty of the vehicle distribution. This makes it difficult to be identified by the classifier, resulting in erroneous images, with an accuracy of only 83.33%. The optimized sliding window algorithm eliminates the influence of overlapping windows, and the number of accurately-recognized images is largely increased, with an accuracy of 91.02%.
A Stereoscopic Warehouse Stocktaking Method Based on Machine Vision
Authors: Peisheng Tao , Ronggui Dao , Yubin Zhang 
Published: September 2020

Abstract
In order to solve the problem of high security risk, low efficiency and low accuracy of the manual stocktaking during stocktaking of stereoscopic warehouse, the image processing method of python+OpenCV visual library software was adopted. Firstly, the sampling camera is controlled by the stacker PLC to traverse all cargo positions and obtain physical images of the goods. Then, OpenCV platform is used to provide operators for image preprocessing, image segmentation and one-dimensional barcode recognition, so as to identify the barcode information and determine the material type. Finally, the inventory report is generated by comparing with WMS system. The research shows that the OpenCV-based method can quickly and accurately identify the one-dimensional barcode, and realize the automatic unmanned intelligent inventory, effectively reduce the workload of staff, improve the production efficiency, avoid the potential risk of manual inventory, and achieve the expected goal.
A New Image Fusion Method Based on PCANet
Authors: Zhu Ying , Li Min , Chen Feng , Qi Xiaoxiang , Qiu Xiaohua 
Published: September 2020

Abstract
In recent years, a simpler principal component analysis network (PCA Net) has been proposed. The network needs fewer parameters to be adjusted, which can solve the problem of long training time. However, because of the infrared and visible images obtained under different environments, the gray distribution of the images is different. If only one base layer fusion strategy is used, the generality of the algorithm may be reduced. To solve this problem, this paper investigates two different base layer fusion strategies. First, the visible dataset is used to train PCANet; second, based on the alternating guided filter to single-scale decomposition of the image to obtain the base layer and the detail layer,then Use the convolution kernel of the trained first layer network to extract the depth features of the source image, process it by kernel-norm to get the activity level map, and then construct the detail layer fusion weights; The base layer uses the two methods of maximum normalization of gray difference and weighted average to obtain the preliminary fusion map of the two base layers. By analyzing the methods in this chapter and other methods in simulation experiments. The QNCIE method in this chapter is 1.25% higher than the other methods. The QFMI is 150% higher than other methods. QP, QY and QCB are higher than other methods.
Depthwise and Spatial Factorized Network: a Light-weight Network for Real-time Semantic Segmentation
Authors: Liu Xiaoyu , Yan Guangyu , Chen Yuanyuan 
Published: September 2020

Abstract
Real-time semantic segmentation requires high quality output in relatively short computing time. The existing deep neural network model usually has complex hierarchical structure and a large number of parameters. For the application of real-time computing, the network has higher requirements for computing performance. To address the speed and accuracy trade-off, a novel structure, named Depthwise and Spatial Factorized Network (DSFNet) is proposed in this paper. In DSFNet, two important strategies are introduced. Firstly, Depthwise and Spatial Factorized Convolution is used to reduce the number of parameters in encoder network remarkably. After that, residual attention mechanism is utilized in decoder with a fast downsampling strategy to obtain sufficient receptive field. These two strategies make the process of upsampling more precisely and achieve better performance. Comprehensive experiments on Cityscapes dataset verify the effectiveness of the proposed DSFNet. The DSFNet achieves 63.08 mIoU at 58 FPS with residual attention mechanism.
Compression and Rendering of Space Environment Volume Data Based on Improved HVQ Algorithm
Authors: Bao L L , Cai Y X , Cui Y M , Liu S Q , Shi L Q 
Published: September 2020

AbstractWe propose an improved HVQ approach by combining two detail levels to compress space environment volumes, based on the variation characteristics including smooth variation and significantly positive correlation between variations in the same direction at two different scales. First, the space environment data is divided into 43blocks. Then blocks are decomposed into a two-level hierarchical representation and each block is represented by a mean value and a detail vector. Finally, the detail vectors are encoded by a vector quantizer. During the vector quantization process, PCA-split is applied to compute an initial codebook, and then LBG-algorithm is conducted for codebook refinement and quantization. We take advantage of the codebook-retraining method to speed up the quantization of time series with temporal coherence. Furthermore, we employ the progressive rendering based on GPU for realtime interactive visualization. The results of our experiments prove that the algorithm proposed in this paper can significantly improve the compression rate and decoding speed without sacrificing fidelity in space environment domain.
Preservation of historical and cultural traditions of the Polish population in the Arctic of the second half of the XIX century: illustrated with the example of Arkhangelsk province
Authors: Samylovskaya E , A Kudryavtseva R-E 
Published: September 2020

Abstract
The paper is devoted to the problem of preserving the national identity of Poles in exile in the Russian Arctic (Arkhangelsk province) during the second half of the XIX century. The research is based on modern Russian and Polish historiography, as well as on published memoirs of Poles in exile. Analysis of sources has shown that the basis for preserving the national identity of Poles in exile was their Catholic faith and national traditions.
Personal determinants of trust in Arctic ships’ crews
Authors: Tatianina LG , Vakhnina EG 
Published: September 2020

Abstract
The article discusses some theoretical aspects of the psychology of trust in groups and organizations and contains a review of several studies devoted to the influence of personal attributes on trust in communication. It discusses the results of an empirical study concerning a topical issue that has been little studied, namely the personal determinants of trust among members of ships’ crews operating in the Arctic. A comparison of crew members’ personality profiles showed that those demonstrating the same level of trust have particular attributes that make it possible to speak of a distinctive psychological type. The results of the study showed that among the characteristics which are regularly manifested by members of Arctic ships’ crews are emotional stability, self-control over behavior and emotions, low anxiety levels, courage, the strength of character, as well as a propensity for sociability, trustfulness, and conformity. Whether a person trusts other crew members depends on such personal attributes as trustfulness, sociability, emotional stability, self-confidence, and a propensity for compliance with group norms. Such values as belevolence and universalism prevail in the value structure demonstrated by crew members, which is reflected in goodwill, empathy, understanding, positive interaction in the group, and a propensity for fostering the well-being of the group. Crew members with a high level of trust are characterized by group values; they believe in the importance of traditions and their observance. Crew members with a low level of trust usually have individual values (a need for strong emotions and new impressions), with group values being less important to them. The results of the study show that there is a strong correlation between trustful interaction in a crew and the personal characteristics of crew members.
Subcritical water extraction of Diosgenin from Dioscorea nipponica Makino and its antioxidant activity
Authors: Zhao Wen-Ying , Hao Hao-Qi , Xiao Wei , Zhu Qing-Shu 
Published: September 2020

Abstract
Subcritical water extraction (SWE) was used as an alternative and environmentally friendly method for extraction of diosgenin from Dioscorea nipponica Makino (DNM). The effects of extraction temperature (75–175 °C) and time (15–45 min) on the diosgenin yield and antioxidant activity of extracts from DNM were analyzed. The profiles of extraction compounds at different temperatures were determined by HPLC. Results showed that diosgenin extraction was significantly (P < 0.05) affected by the extraction time and temperature and their interaction. The antioxidant activity was also affected by treatment time and temperature. The high antioxidant capacity was correlated with the high diosgenin content. Subcritical water may be a potential medium for diosgenin production.