bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sensitivity Optimization of Plain Silver Surface Plasmon Resonance Imaging Sensor
Authors: Wang Zhiyou , Wang Maojin 
Published: December 2021

Abstract
Plain silver surface plasmon resonance imaging (SPRi) sensor has been studied extensively due to its high sensitivity and desirable stability in liquid environments. To further enhance sensitivity performance of the sensor, angular sensitivity, angular slope and depth-width ratio (DWR) of SPR curve, and imaging sensitivity are evaluated at different thickness combinations of the gold and silver films respectively. In this work, the angular slope of SPR curve is found to be the critical factor to the optimized imaging sensitivity of plain silver SPRi sensor. In the comparative study, the above parameters of the plain silver SPR sensor, single gold film and bimetallic SPRi sensors are compared. Plain silver SPRi sensor is proved to be of the highest imaging sensitivity, which is 4.08 and 1.18 times imaging sensitivity of the single gold film and bimetallic SPRi sensors separately.
In-Situ Measurement of the Density Ratio of the K-Rb-21Ne Comagnetometer Based on Electron Spin Relaxation Rate
Authors: Zhao Tian , Wei Kai , Zhai Yueyang , Tang Chencheng 
Published: December 2021

Abstract
Accurate measurement of alkali metal density ratio in hybrid vapor cells is essential for high sensitivity of comagnetometers. We introduce and investigate a new method of measuring the density ratio based on electron spin-relaxation rate. The Bloch equations including the parameter of density ratio are studied, and the density measurement results show that measurement uncertainty is less than 12%. The advantage of this method is measuring the in-situ density ratio, which is more precise and useful for optimizing sensitivity of comagnetometers by adjusting the density ratio in real time.
DA-M2Det: An Iris Classification Network for UBM Images
Authors: Wang Luquan , Lao Junxing , Yang Lingfeng , Zeng Yaguang , Chen Yong 
Published: December 2021

Abstract
Primary angle closure glaucoma (PACG) is primarily diagnosed by ophthalmologists through morphological analysis of the iris in ultrasonic biomicrocopy(UBM). In recent years, Deep convolutional neural networks (CNNs) show potential for quick category definition in eye disease. According to the characteristics of iris in UBM images, we proposed a network (DenseNet and Attention gate) DA-M2Det to automatic classification iris morphology. Firstly, in the framework of M2Det network, We used the backbone of DenseNet to replace the VGG backbone of M2Det, better extraction of basic feature layers. Secondly, three scales of attention gate (AG) was added to the Thinned U-shape Module (TUM), enable the network to pay more attention to the iris region. Finally, we use the retraining method to further improve the accuracy of iris classification. The classification results of VGG-16, M2Det, ResNet-50 and DA-M2Det networks are compared experimentally. The results show that, in three different iris shapes (including arch, flat and depression), DA-M2Det achieves an average classification accuracy of 85%, which is higher than that of the other three networks. Experimental results show that DA-M2Det can accurately classify irises into three categories, assisting ophthalmologists to quickly diagnose the cause of glaucoma and accurately perform clinical treatment thereby.
A Real-Time Peak Extraction Algorithm for Dynamic Displacement Measurement Based on Spectral Confocal Microscopy
Authors: Liu Lijia , Ma Hua , Shi Zhendong , Bai Jinxi , Zhang lin 
Published: December 2021

AbstractSpectral confocal is widely used in various fields such as topography detection, roughness measurement, thickness measurement, for its advantages ability in displacement measurement. Developed from confocal microscopy, the spectral confocal microscopy greatly improves the efficiency of displacement measurement because there is no need for longitudinal scanning, In other words, we need to obtain the wavelength information corresponding to the peak point of the spectral signal, which is called peak abscissa but not the peak value. Therefore, Accurate and efficient peak abscissa calculation algorithm occupies an important position in displacement monitoring based on spectral confocal. However, the existing methods are too complex to apply to real-time dynamic online detection, for they usually focus on determining the peak value by means of curve fitting and peak extraction. In this paper, we proposed an efficient and accurate peak abscissa calculation method by shifting, difference, linear fitting, zero point and peak abscissa calculation (SDLZ). Compared with the Gaussian fitting method, the results demonstrate that the SDLZ can greatly improve efficiency, can be applied to field programmable gate array microcontrollers, with high measurement accuracy, providing key solutions for equipment needs.
A New Method for Auto-Inspecting Both Opposite Surfaces of Tec Components Based on Equal-Optical-Path Imaging System
Authors: Liao Tingdi , Huang Qilu , Yan Shaobin , Chen Wenzhi , Duan Yafan , Huang Yantang 
Published: December 2021

Abstract
An new optical method for simultaneously inspecting opposite surfaces defects of thermoelectric cooler (TEC) components that meets condition of equal optical paths for both surfaces imaging has been proposed. The optical apparatus for surfaces defects inspection has been designed and established with “confocal” imaging system consisting of two trapzoid reflection prisms and one optical images combiner. Experimental investigations on defects inspection with the “confocal” imaging system have been carried out. The results showed that the proposed optical method can be used to simultaneously inspect the defects of opposite surfaces of TEC components without need to employ a tele-centric imaging lens with large depth of focus. It was concluded that the optical inspection method can meet the technical requirements for inspecting opposite surfaces (both side surfaces, or both top and bottom surfaces) defects of TEC components and has advantages of equal good imaging quality, increased inspection accuracy and throughput, simplified system configuration and improved system reliability etc.
A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM
Authors: Zhang Xin , Ja Qingmo , Ruan SaiSai , Hu Qin 
Published: December 2021

Abstract
As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.
Optical Design of a Shortwave Infrared Dual Camera CASSI Based on Improved Offner-Wynne Imaging Spectrometer
Authors: Song Chong , Huo Lipeng , Huang Yong , Yan Yangdong , Wang Gang , Cui Dongsen , Niu Yanting , Liu Bijun 
Published: December 2021

Abstract
Based on the optical system characteristics of coded aperture snapshot spectral imager (CASSI), an optimized optical system of shortwave infrared dual camera CASSI was designed based on improved Offner-Wynne imaging spectrometer. The operating wavelength of the optical system ranges from 900nm to 1700nm, and the focal length is 1200mm. It consists of two parts: the two dimensional imaging system and the multispectral CASSI imaging system. The key technical parameters of the two parts are the same and there is no visual axis difference. Therefore, the optimized optical system can effectively improve real-time performance, optical transmittance and compactness of the dual camera shortwave infrared CASSI, which is conducive to the application in optical measurement scenes in the shooting range.
Propagation Property of a Second-order Noncanonical Optical Vortex Beam in a Strongly Focusing System
Authors: Zhang Han , Xiao Weiwei , Zhang Tianhu , Zhaoxinying  , Pang Xiaoyan 
Published: December 2021

Abstract
The noncanonical optical vortex is an optical vortex with a nonconstant phase gradient around its center, i.e. the phase is not a linear function of the azimuthal angle. The expression of the strongly focused field of a (conventional) second order noncanonical vortex beam has been derived analytically and the field distribution is discussed. It has been found that the intensity distribution at the focal plane can exbibit rich patterns, which is more complicated than that of the first-order. The transverse focal shift phenomenon in current case also exits and is changed with the semi-aperture angle α in a different way. It also can be seen that the intensity maxima can be one to four on the focal plane. Our research may provide a new method for controlling of the structured optical field, and will give theoretical supports for the study of higher-order noncanonical optical vortices.
The Influence of High Temperature Weather on Human Body Temperature Measurement by Infrared Thermal Imaging Thermometer
Authors: Mei Guangdong , Peng Senlin , Zeng Zhiwei , Liao Tingdi , Huang Yantang 
Published: December 2021

Abstract
Infrared thermography thermometer is a non-contact temperature measuring equipment, which is widely used in the stage of large-scale epidemic of the covid-19 pandemic. It is used for rapid screening of human body temperature in crowded places at the entrance and exit of airports, docks, shopping malls, stations and schools. But when the outdoor temperature approaches or exceeds the body temperature in summer, can this method of measuring body surface temperature by infrared thermal imager be used as a standard for screening fever? Under the condition of high temperature in summer, the field experiment of measuring body temperature by infrared thermal imager is carried out, the experimental results are analyzed. We recommend the use of relative temperature difference for screening patients with fever.
QoS-Aware Power Allocation for Multi-UAV Aided Networks
Authors: Quan Xiaoya 
Published: December 2021

Abstract
UAV base stations (UAVBS’s) have been proposed as a revolution for the new architecture of 5G networks. The UAVBS’s can be deployed as access points to provide wireless services to users in emergency scenarios. However, it is challenging to solve the highly coupled problem for UAVBS deployment and power allocation. In the meanwhile, the hybrid analog and digital beamforming is leverage to reduce the hardware cost for beamforming in 5G networks. In this work, we first use k-means algorithm to solve the 3D placement of UAVBS’s by exploiting the optimal coverage altitude. Next, power allocation problem is resolved using the difference-of-two-convex functions (D.C.) programming algorithm. Furthermore, the quality of service (QoS) for each user is guaranteed by adjusting the transmitted power. Finally, extensive experiments are conducted to demonstrate the feasibility of the proposed algorithm.
Sum Rate Analysis for Massive MIMO Downlink With MRC Beamforming and User Selection
Authors: Wu Yiyang , Chang Chang , Xie Fei , Ju Dacheng , Pan Yilun 
Published: December 2021

Abstract
Average allocation of data rate to each user is inefficient since the resource a base station can allocate is limited. Thus, user selection and user scheduling need to be applied into multi-user massive multiple-input multiple-output (MIMO) downlink system. In this paper, we mainly focus on the methods of user selection. First, we establish a downlink system model including transmission model and channel model. Then, two user-rate based user selection algorithms via the signal-to-interference-plus-noise-ratio (SINR) are proposed, where the SINR is generated by MRC beamforming. Finally, simulation results are provided to compare the performance of two proposed algorithms and their fairness towards selected users. In the simulation results, location-based selection algorithm and random selection algorithm are jointly compared. The second proposed algorithm possesses the highest total sum-rate and is the optimal algorithms among the four algorithms.
Which one is better: Assessment and Locomotion imitation strategies in Changing Environments
Authors: Chen Cunying , Zhang Hua 
Published: December 2021

Abstract
Imitation is ubiquitous, yet what self-regulation orientations’ role played in imitation strategies is poorly understood, which is particularly challenging in dynamic and uncertain environments. According to regulatory mode theory, we model two imitation strategies: assessment and locomotion. Assessment pays more attention on comparation among different alternatives, they repeatedly measure, evaluate, and compare desired means and try to find out the ‘best’ one. Contrariwise locomotion refers to ‘keep moving’, once choosing one alternative, they change some choices and learn from the resulting performance feedback. Using a computational model, we explore the performance implications of dynamic environments for these two imitation strategies. Consequently, when environment is stable, assessment is more effective in maintaining the lead, whereas locomotion prevails as environmental changes become more frequent and substantial. We contribute to the literatures on strategy, imitation, and NK studies.
Optimization of Inspection Plan for Transformer Verification Line Under Uninterrupted State
Authors: Guo Shunsheng , Yin Bitao , Sun Xiang , Peng Zhao , Tu Xiaobin 
Published: December 2021

Abstract
At present, transformer verification line of metering centre adopts fixed cycle inspection method manually. This method requires downtime for detection, which costs a lot of time and cost. Moreover, the inspection cycle is determined based on experience and lacks rigorous basis. To solve this problem, a hybrid delivery of inspection devices is proposed to realize non-stop detection and reduce the cost of inspection time. Considering impact of cost and false detection risk on inspection cycle, a multi-objective optimization model of inspection cycle based on inspection and false detection cost is proposed. Based on NSGA-II algorithm, perturbation population is introduced to enhance the global search ability, which aims to minimize the cost of inspection and false detection. Taking the verification line’s inspection plan of the metering centre as an example. It is solved by ENSGA-II algorithm, and feasibility of hybrid delivery mode is verified, which reduced downtime by 14.58%. A more reasonable inspection cycle is obtained, inspection cost is reduced by 29.57%, and false detection cost is reduced by 6.34%. It provides a reference for the formulation of inspection plan in the actual production process.
Research on Robustness of Related Images for Power Equipment Inspection
Authors: Zhou Chunlei , Chen Xiangzhou , Liu Wenli , Dong Tianyu , Yun Huang 
Published: December 2021

Abstract
With the increase in the number of traction substations year by year, manual inspections are gradually being replaced by unattended inspections. Target detection algorithms based on deep learning are more widely used in intelligent inspections of power equipment. However, in practical applications, it is found that due to the small target to be detected, the accuracy of the deep learning model will decrease when the shooting angle is inclined and the light conditions are poor. This is because the algorithm’s robustness is low, and the detection ability of the model will be seriously affected when the angle or illumination difference with the sample is large. Based on this, the feature fusion part of the YOLOv3 algorithm and the selection of the loss function and the size of the anchor frame are improved, and the improved ASFF fusion method is used to classify various images in the power equipment. Actual measurement and repeated experiments show that the proposed method can be effectively applied to image recognition of various power equipment, optimize robustness, and greatly improve the image recognition efficiency of power equipment.
An Intelligent Fault Diagnosis Method for CNN-SVM Circuit Breaker Based on Quantum Particle Swarm Optimization
Authors: Huang Zhongting , Wang Longying , Ge Qiyun , Chen Yongyi , Zhang Dan 
Published: December 2021

Abstract
In order to make use of fewer fault data samples to diagnose the main fault types of circuit breakers accurately in real time, an intelligent fault diagnosis method for circuit breakers based on convolutional neural network (CNN) and quantum particle swarm optimization (QPSO) is proposed. Firstly, the key features of the circuit breaker operational signal are extracted through the CNN model, and the extracted feature vectors are input into the support vector machine (SVM) for fault diagnosis. In order to improve the diagnostic performance, this paper uses QPSO algorithm to optimize the parameters of the classifier, it effectively solves the local optimal problem. The experimental results show that the method presented in this paper has achieved good results in fault diagnosis of circuit breakers, and the accuracy of diagnosis is up to 100%, which highlights the superiority of this method.
Design and research of temperature measurement system for intermediate joint of 10kV power cable
Authors: Mao Mengting , Liu Zhuan , Xu Rui , Wang Longwei 
Published: December 2021

Abstract
Cable intermediate joint is the weak link of power cable. Its operation determines the quality of power transmission and distribution in power system. The temperature rise of the intermediate joint is the direct manifestation of a variety of faults. Therefore, monitoring the temperature of the intermediate joint of power cable and finding and eliminating cable faults in advance is of great significance to ensure the safe and reliable operation of power system. Based on the analysis of the temperature characteristics of the cable intermediate joint, the design scheme of the temperature measurement system of the power cable intermediate joint is given, and its hardware system and software system are designed. Finally, the temperature measuring device for the intermediate joint of power cable is obtained, tested on the spot, and the power consumption of the data is analyzed. The debugging results show that the system has good characteristics and meets the design requirements.
A new method for solving the linear recurrence relation with nonhomogeneous constant coefficients in some cases
Authors: Yang Ben-Chao , Han Xue-Feng 
Published: December 2021

Abstract
Recursive relation mainly describes the unique law satisfied by a sequence, so it plays an important role in almost all branches of mathematics. It is also one of the main algorithms commonly used in computer programming. This paper first introduces the concept of recursive relation and two common basic forms, then starts with the solution of linear recursive relation with non-homogeneous constant coefficients, gives a new solution idea, and gives a general proof. Finally, through an example, the general method and the new method given in this paper are compared and verified.
MRDRL-ROS: a Multi Robot Deep Reinforcement Learning Platform based on Robot Operating System
Authors: Cai Zhongxuan , Liang Zhen , Ren Jing 
Published: December 2021

Abstract
Deep reinforcement learning (DRL) has greatly improved the intelligence of AI in recent years and the community has proposed several common software to facilitate the development of DRL. However, in robotics the utility of common DRL software is limited and the development is time-consuming due to the complexity of various robot software. In this paper, we propose a software engineering approach leveraging modularity to facilitate robot DRL development. The platform decouples learning environment into task, simulator and hierarchical robot modules, which in turn enables diverse environment generation using existing modules as building blocks, regardless of the underlying robot software details. Experimental results show that our platform provides composable environment building, introduces high module reuse and efficiently facilitates robot DRL.
Evaluation method of fault indicator status detection based on hierarchical clustering
Authors: Chen Ling 
Published: December 2021

Abstract
In order to solve the evaluation problem of fault indicator arrival detection and operation detection, an evaluation method of fault indicator status detection based on hierarchical clustering is proposed. Firstly, the evaluation model for status detection of fault indicator in distribution network is established. Then, through the fault indicator state detection single result table and the fault indicator operation state detection result table, the single equipment evaluation of fault indicator status detection is described in detail. Secondly, through the fault indicator state detection manufacture result table and the fault indicator state detection manufacture evaluation value, the manufacturer equipment evaluation of fault indicator status detection is described in detail. Finally, through the fault indicator state detection itemized result table and fault indicator state detection itemized evaluation value, the itemized equipment evaluation of fault indicator status detection is described in detail. The case analysis shows that the method is effective and feasible, can meet the actual needs of the site, and has positive significance.
A fault diagnosis method based on the Support Vector Machine in rod pumping systems
Authors: Li Juanni , Shao Jun 
Published: December 2021

Abstract
Monitoring the working status of the sucker rod pump is an important part in petroleum engineering. With the development of artificial intelligence technology, more methods have been applied to the fault diagnosis of rod pumping systems. An evolutional fault diagnosis method based on Support Vector Machine (SVM) in sucker rod pumping systems is proposed. Fourier descriptors and Light Field compression algorithm are used in this method to extract the graphic features of the indicator diagram. SVM is used to build fault classification model. This method is verified experimentally through data of indicator diagrams and the results show that it has a shorter training time and higher accuracy.
Study on a calculation model of infrared radiation characteristics of rocket engine plume
Authors: Wu Nan , Mu Chengpo , He Yang , Liu Huan , Liu Taiye 
Published: December 2021

Abstract
In order to study the infrared radiation (IR) characteristics of rocket engine plume in the mid infrared band, a calculation model for IR transfer of rocket engine plume was built. The flow field data are calculated by software FLUENT. Based on HITRAN database, the IR characteristic parameters are calculated after spectral line correction. The Line of Sight (LoS) is used to solve the radiation characteristics in the plume flow field, and the IR characteristics distribution of the plume in the mid infrared band is obtained, which agree well with the results from open literature. The method has the advantages of simple model, less parameters and fast calculation speed in this paper.