bluuebunny/crossref_metadata_2025_split huggingface dataset:
Human speech emotion recognition via feature selection and analyzing
Authors: Lun Xiangmin , Wang Fang , Yu Zhenglin 
Published: January 2021

Abstract
Speech emotion recognition is one of the important research topics in the field of multimedia processing and human-machine interface. To obtain the most influential features of the speech data for emotion recognition, in this paper, 64 statistical features of the speech signal including short-term energy, pitch, frame, format, and spectrum energy were extracted with speech emotion database. Mean Impact Value (MIV) and the improved Correlation-based Feature Selection (CFS) were employed to select the most influential feature set. BP neural network (BPNN) was used to identify the accuracy. The proposed MIV-CFS method selected the features related to speech emotion, with less recognition error, the recognition accuracy all higher than 88%, and the highest recognition accuracy is 91.61%.
Automatic Insulator Detection for Power Line Using Aerial Images Powered by Convolutional Neural Networks
Authors: Tan Jicheng 
Published: January 2021

Abstract
The inspection and fault diagnosis of power transmission line via aerial images is becoming widely used in smart grids and electrical power systems. However, the accuracy of detecting objects with complicated backgrounds still remains an important challenge in the defect detection system. In this article, a deep-learning based method is presented to realize the automation of insulator fault detection. In the proposed approach, the insulator localization network based on Single Shot Multibox Detector (SSD), a one-stage detecting backbone, is utilized to extract multi-level features and make predictions in order to determine the insulator situation in the aerial images. Additionally, Densely Connected Convolutional Networks (DenseNet) is newly introduced to strengthen the insulator detecting system’s classification ability. To address the data scarcity problem, a two-stage data augmenting strategy is also applied. The first stage is mainly based on the image combination, while the second stage includes random affine transformation, Gaussian blur, brightness and contrast transformation, and salt-and-pepper noise. The insulator detection network and the classification network precisions of the proposed method have reached 0.95 and 0.98 using the text dataset, respectively. The results have shown that the proposed method can ensure robustness and accuracy during insulator detection.
Application of MIV-NARX to Identify Road Roughness
Authors: He Yijie , Zhang Guofang , Song Jingfen 
Published: January 2021

Abstract
Aiming at the deficiencies in the existing research on road roughness recognition based on neural networks, the road roughness and 16 vehicle response data are simulated based on the filtered white noise model and the smoothness seven-degree-of-freedom model, NARX neural network is built to identify road roughness. The coefficient of determination and the root mean square error are introduced as the evaluation indicators of the model, the MIV method is used to evaluate and screen each input response. Research shows that MIV method improves the performance of NARX neural networks, MIV-NARX can effectively identify road roughness.
Digest-based Security Protocol for IoT devices
Authors: Wang Huinan , Zhang Jianmin 
Published: January 2021

Abstract
Short output universal hash functions are widely used in manual authentication protocols. A digest function is one new universal hashing algorithm. The aim of this algorithm is to reduce the CPU processing time, and have the best proven bounds. However, typical manual authentication protocols using digest are based on asymmetric key algorithms, which is not suitable in many Internet of things scenarios. This paper thus proposes a series of digest protocols only use symmetric key algorithms.
The Scattering Characteristics of Vortex Radio-Frequency Waves for Wireless Communication
Authors: Hui Yan , Wang Wennai 
Published: January 2021

Abstract
As a solution to the shortage of transmission capacity, the vortex radio frequency waves have been applied to wireless transmission technology. The potential of vortex radio frequency waves applying to the fields of wireless communication, radar detection and radar imaging will be invaluable. The scattering characteristics of the vortex waves are the basis for its application in radar detection imaging and identification. This paper focuses on the theoretical derivation, including the main effects on the scattering characteristics, and the future applications and prospects of scattering characteristics. The state of art in research area is reviewed, followed by a brief discussion on future directions.
Home-style smart medicine box for the elderly
Authors: Miao Fengyi , Zhang Yu , Wang Xincheng , Xiong Xinhong 
Published: January 2021

Abstract
With the intensification of the aging of our country’s population, the health care of the elderly has become a social problem that must be solved in my country. Elderly people often suffer from chronic diseases and need long-term medication. However, it is difficult for the elderly to take medicines on time and in doses due to deterioration of their physical functions. Based on the above background, the project team researched and designed a smart medicine box that can intelligently remind and automatically dispense medicine. The device is mainly divided into four functional modules: medicine dispensing module, medicine taking module, medicine dispensing module, and control module. It realizes the five basic functions of medicine addition, temporary storage and sealing, medicine absorption, automatic medicine dispensing, and timing reminder and Additional features such as data sharing.
Analysis on the Maintainability of Smart Meter Software for Full Lifetime
Authors: Qiao Shuya , Qian Xiaoyao , Zhou Ming , Wang Yang 
Published: January 2021

Abstract
With the deepening of Internet concept and electricity marketization reform, more requirements are put forward for metering equipment, and the scale and complexity of software also increase. The new generation of intelligent electricity meters meet the IR46 standard and adopt the multi-core modular design scheme, which also puts forward higher requirements for the maintainability of software. This paper starts from the key control points of the whole life cycle of intelligent electricity meters and combines the characteristics of software testing, so as to analyze and evaluate the maintainability of the software. Firstly, the factors that influence the maintainability of watt-hour meter products are analyzed, and the software key data is considered from the perspective of software development and design. Then, the index system of the maintainability of the intelligent software is established, and the key points of the maintainability test and the weight of each index are analyzed in combination with the testing technology of the intelligent meter software and the analytic hierarchy process.
Feature extraction of target echo based on wavelet energy spectrum
Authors: Zhou Bo 
Published: January 2021

Abstract
The characteristics of target echo are related to many complicated factors, such as transmission waveform, the structure, attitude and motion state of the target and multi-channels transmission of ocean channel. The characteristics of target echo in different time-frequency spaces are discussed. The echo is recognized by the combination of different characteristic spaces such as energy, frequency band, entropy, kurtosis and variance of the wavelet energy spectrum. Firstly, the wavelet energy spectrum in time domain by using the continuous wavelet transformation, then the energy spectrum entropy describing quantitatively signals varying with time was calculated along the time axis, and their time-wavelet energy spectrum were different. The data processing shows that segmented spectrum center, spectral bandwidth can classify different types of underwater echoes to some extent.
Wi-Fi Devices Individual Identification based on Envelope Front Characteristic
Authors: Lv Dongze , Chen Junlin , Che Yanting 
Published: January 2021

Abstract
There are some characteristics are used for traditional Wi-Fi devices individual identification methods, such as: amplitude and phase characteristics during the period that Wi-Fi devices are turned-on or turned-off, MAC address, and IP address. In fact, on one hand, MAC address and IP address are easily to be changed. Traditional methods can’t identify individual Wi-Fi devices if MAC address or IP address changed. On the other hand, the amplitude and phase characteristics of Wi-Fi devices can only be identified during the short period of turned-on or turned-off. The traditional methods have application limitations. Therefore, the Wi-Fi devices individual identification based on envelope front characteristic is proposed in this paper. Firstly, envelopes of Wi-Fi signals are extracted. Secondly, front characteristics of Wi-Fi envelope are extracted. Then envelope front characteristics are used to compare and match with models saved in the library to realize Wi-Fi devices individual identification. By using the method proposed in this paper, Wi-Fi devices individual identification can be realized whether MAC address or IP address changed. Scope of identification application can be effectively expanded into the whole time when Wi-Fi devices transmit signals by using this method. Since envelope front characteristics have the advantages of non-changing and non-copying, accuracy of Wi-Fi individual identification is also improved by using this method.
Internet of Things Access Control System Based on Hyperledger
Authors: Yang Zheming , Shao Di , Qu Liangyong , Zhang Mei 
Published: January 2021

Abstract
The current access control of IoT devices adopts the traditional method of centralized control, which leads to the shortcomings of single point of failure and low efficiency of the entire system. Due to the increasing amount of data generated by the Internet of Things network and the more complex data structure, the traditional access control system has exposed the problem of insufficient security when communicating. Based on the characteristics of blockchain decentralization, non-tampering, and programmable Hyperledger, an attribute-based access control mechanism combining blockchain with the Internet of Things is proposed to realize dynamic changes to the access control strategy and ensure the access control of the Internet of Things Safety and reliability.
Failure Section Identification in Smart Distribution Network Based on IFIAPN Considering the Auto-reclosing and DG
Authors: Cheng Xuezhen , Xu Guangyuan , Li Jiming 
Published: January 2021

Abstract
This paper proposes a fault location method for smart distribution networks based on the intuitionistic fuzzy inhibitor arc Petri net (IFIAPN) technique that employs discrete evidence such as the status of protective devices and fault indicators (FIs) to estimate the faulted section. To eliminate the possible false fault section estimations and pinpoint the actual faulted location, an IFIAPN algorithm is improved. It amends the certainty and uncertainty of the information according to the action logic of the protection system and the direction of fault propagation. The authors further utilize the directional fault indicator to cope with the impact on the bidirectional power flows caused by distributed generation, and introduce the inhibitor arc to represent the action characteristics of auto-reclosing to optimize the diagnostic model. The proposed method can precisely determine the fault location in distribution systems with many laterals and sub-laterals in the presence of distributed generators (DGs). Finally, feasibility, fault tolerance, and effectiveness of the proposed method are verified by the IEEE 33 node feeder distribution system.
Design of Intelligent Counting Equipment System for Uniform Materials
Authors: Xiao Suhua , Wang Zhiyong , Qiao Mingjuan , Mo Ling , Wang Wei 
Published: January 2021

Abstract
In fields such as manufacturing and agriculture, manual methods are generally used to count materials such as hardware parts and seeds, which had problems such as slow speed, low efficiency, and low accuracy, and a lot of pressure was put on people. Based on machine vision and motion control, a uniform material counting equipment system was designed. The equipment uses a two-stage conveyor belt for material transportation, and vibration frequency control and speed control of materials of different specifications were achieved. Because of application of precision motion control technology, the camera collects the image of the material and identifies the material to be inspected, and automatically counts the quantity of the material by calculating the area of the material. The test was implemented with screws, hole gaskets, soy, jujubes and other materials, and the results show that: materials with a size of 2mm-20mm can be detected; the detection accuracy rate is 100%.the system has realized high-speed, high-efficiency, high-accuracy automatic counting, and has good application value.
Detection of Hidden Disease of Concrete Bridge Based on Infrared Thermal Imaging
Authors: Jin Hui , Zou Lan-Lin 
Published: January 2021

Abstract
Any object with temperature above absolute zero can radiate infrared energy outward [1]. According to this characteristic, the infrared thermal imaging method is applied to diseases of concrete bridges such as internal voids. The relationship between the hidden disease of concrete bridge and infrared thermal image is analyzed theoretically. The concrete model with internal cavity defects is established and the finite element simulation analysis is carried out to verify the feasibility of infrared thermal imaging method to detect the internal defects of bridge concrete. The final finite element simulation results are in agreement with the theoretical analysis, indicating that the method of detecting the internal defects of bridge concrete by infrared thermal imaging is feasible.
Fault Diagnosis Method of Gearbox Based on Sound Pressure Signal Analysis
Authors: Guo Lei , Wang Yongjian , Ju Jinxing , Liu Zhengyong 
Published: January 2021

Abstract
The gearbox structure is complex, so it is difficult to determine the root cause of the problem after abnormal sound appears. The method of sound pressure signal analysis is used to analyze the abnormal sound of gearbox. The spectrum analysis method is used as the data processing method. The sound pressure of a kind of abnormal sound of gearbox is collected. By analyzing its frequency characteristics and structural characteristics, it is judged that the gear is abnormal. The accuracy of the inference based on the result of sound pressure analysis is verified by the shape and position detection of the gear. The results show that through the frequency analysis of the sound pressure signal, the root cause of the gearbox problem can be accurately determined.
A Method of Finding Shortest Paths for Bumblebees
Authors: Yu Guyang , Shi Huajun 
Published: January 2021

Abstract
In October 2010, a new British study said that bumblebees fluttering from flower to flower showed the ability to easily solve the “traveler salesman problem.” There is no good explanation for how the bumblebee solved the problem. In this paper, a simple and efficient algorithm is proposed to explain how bumblebee solves the traveling salesman problem.
Application of Monocular Direct Vision Odometry in Augmented Reality
Authors: Zhang Zuoming , Wang Zixuan , Wang Hanwen , Wang Xin 
Published: January 2021

Abstract
In recent years, the unlabeled augmented reality system has been gradually applied to various mobile devices, among which stable, accurate, and fast registration is the key to realizing this function. For this technique, this paper introduces camera exposure parameters and puts the data association and pose estimation into a unified nonlinear optimization problem. Moreover, the direct monocular vision odometer is transplanted into the augmented reality system through the position adjustment module. We compare it with the traditional visual odometry method that matches the feature points. The results show that this improved method can be used to track more quickly and build a more visual semi-dense point cloud map, which can be used to support the registration and tracking of virtual objects in augmented reality.
Vehicle detection based on point cloud intensity and distance clustering
Authors: Wang Zhao , Wang Xing , Fang Bin , Yu Kun , Ma Jie 
Published: January 2021

Abstract
In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.
Method for constructing multi-dimensional feature map of malicious code
Authors: Ma Haocong , Zhang Ji , Zhou Junhua , Zhai Xiang , Xue Junjie , Ji Hang 
Published: January 2021

Abstract
Malicious code is characterized by a large number of types, rapid increase in number, continuous update of transmission routes, and continuous enhancement of back analysis and back detection methods. Therefore, how to effectively detect and analyze malicious code has been a problem of great concern. This paper studies the features of binary file and disassembly file of malicious code, introduces the concept of information gain, and proposes a method to construct the multi-dimensional characteristic graph of malicious code. Finally, the convolutional neural network is used to classify the multi-dimensional feature graph of malicious code, which provides a new idea for the feature extraction of malicious code.
Breast Regions Segmentation Based on U-net++ from DCE-MRI Image Sequences
Authors: Sui Dong , Huang Zixuan , Song Xinwei , Zhang Yue , Wang Yantao , Zhang Lei 
Published: January 2021

Abstract
Background analysis of breast cancer can depict the progress and states of the tumour, which is based on the whole breast segmentation from MRI images. The focus of this paper is to construct a pipeline for breast region segmentation for the possibility of breast cancer automatic diagnosis by using MRI image serials. Studies of breast region segmentation based on traditional and deep learning methods have undergone several years, but most of them have not achieved a satisfactory consequence for the following background analysis. In this paper, we proposed a novel pipeline for whole breast region segmentation method based on U-net++, that can achieve a better result compared with the traditional U-net model which is the most common used medical image analysis model and achieve a better IoU than CNN models. We have evaluated the U-net++ model with tradition U-net, our experiments demonstrate that the U-net++ with deep supervision achieves a higher IoU over U-net model.
An Integrative Review of Image Captioning Research
Authors: Wang Chaoyang , Zhou Ziwei , Xu Liang 
Published: January 2021

Abstract
In the field of computer vision, image captioning is a new frontier research. The basic task of image captioning is to generate a descriptive natural language for the input image. This paper investigates and analyzes the related research of image captioning. Firstly, the task and application scenarios of image captioning are introduced. Secondly, the image captioning algorithm based on template and the image captioning algorithm based on encoder-decoder structure are analyzed, and the advantages and limitations of each method are discussed. Then, the benchmark dataset and evaluation for image captioning are introduced. Finally, the future development of image captioning is prospected.
Research on “Six in One” Campus Security System Based on Multi-node Data Feedback
Authors: Liu Hongbin 
Published: January 2021

Abstract
In terms of the current incompatibility of various campus monitoring networks and the difficulty of timely sharing as well as the unified storage and management of heterogeneous data in heterogeneous networks, a security monitoring system for multi-node heterogeneous data is designed in the paper, in which the data communication format, sensor encapsulation protocol, gateway design and implementation, database design and monitoring platform construction are studied, realizing the collection and forwarding of heterogeneous network data. Moreover, the data exchange format uses a lightweight format to facilitate the transmission and storage of data, and the sensor protocol is encapsulated to solve the heterogeneous problem caused by the difference in data conversion formulas and meanings between different sensor networks. Furthermore, simulation experiments have proved that the system can efficiently and reliably collect, store and process heterogeneous data in a heterogeneous network in real time, and the built monitoring platform can centrally display and manage campus monitoring information in real time.
Finite control set model predictive control of quasi-z-source inverter photovoltaic grid-connected
Authors: Sha Mai Chao , Zhu Rui Jin , Yang Xu Guang , Han Xu 
Published: January 2021

Abstract
The PI control of the traditional quasi-Z inverter(qZSI) has difficulty in parameter correction and the shortcomings of hysteresis, while the traditional model predictive control (MPC) has a slower response and less precision for current control. In this paper, the current is transformed to the d-q coordinate system, and a suitable output vector is selected according to the limited switch state, which can track the reference current and perform finite set model predictive control (FCS-MPC). Compared with the traditional predictive control method, the current distortion rate is reduced, so that the photovoltaic inverter has a better output result.
Development and Application of Several New Technologies in Safety Locking Device of the Substation Screen Cabinet
Authors: Yue Mengkui , Liu Zhongfeng , Jiang Jinzhou , Qu Hang 
Published: January 2021

Abstract
Anti-misoperation locking device is a locking device for control operation circuit and primary equipment. At present, three faults of relay protection often occurs in the secondary cabinet of substation. There will be a lot of safety threats if the secondary cabinet is lack of locking device. Therefore, in this study effective improvement strategies will be put forward in the aspect of technology and further analyze the technology of the secondary cabinet safety measures locking device and then replace the traditional setting mode of the secondary safety measures so that to technically prevent the violation of regulations in secondary maintenance and effectively prevent the occurrence of three fault events of relay protection. This technology can be applied to the secondary work site of power industry which is significant to ensure the safe operation of power grid. This study illustrates the application effect of the technology of the safety measures locking device for secondary panel cabinet in substation with specific cases which could provide help for the the relevant staff.