bluuebunny/crossref_metadata_2025_split huggingface dataset:
Model-based Intelligent Recognition for Aluminum Plate Seam Defects
Authors: Liu Hongming 
Published: November 2021

Abstract
In order to study the application of nonlinear ultrasonic in the quantitative identification of defective aluminum plate, different depth cracks are machined on the aluminum alloy plate with a thickness of 10 mm by wire cutting to simulate the defects in the plate. The normal and defective aluminum plates are selected to establish the experimental model, and the continuous wavelet transform (CWT) is used to extract the characteristic parameters of the aluminum plate nonlinear ultrasonic signal. The dimensions of the data are reduced by principal component analysis (PCA), and the principal component with the top three contribution rate are selected as the characteristic value. Finally, the support vector machine (SVM) algorithm is used to analyze the aluminum alloy plate state and classify the defect signal. The experimental results show that the feasibility of nonlinear ultrasonic signal recognition of aluminum plate defects is verified by combining principal component analysis and support vector machine model.
Fruit Processing Equipment based on Model Processing
Authors: Kang Di , Fan Youhua , Chen Zejun , Ma Fang , Peng Shaofeng , Wang Yong 
Published: November 2021

Abstract
The technology and equipment of this study can improve the technical level of the equipment for peeling and cleaning of Camellia oleifera fruit. Based on the biological characteristics of Camellia oleifera fruit, the size distribution model of Camellia oleifera fruit was established, and the equipment structure and key parameters were determined based on the working principle and design method of the shelling and cleaning mechanism. Based on the analysis of the movement track of the shell stripping and cleaning executive parts, the test scheme is established, and it is optimized that when the crankshaft speed is 240-260r/min and the track speed is 0.4-0.6m/s, the treatment capacity can reach the level of 2000kg/h, and the purification rate is more than 99%. When the vibration frequency of the vibrating motor is set to 50Hz and the horizontal inclination of the separation belt is 50 ° ∼ 55 °, the cleaning effect of the equipment is the best.
Distortion of AISI 1020 steel substrate in the process of laser cladding of E-300 powder material
Authors: Lyukhter A B , Frolov K A , Kochuev D A , Zavitkov A V , Gusev D S 
Published: November 2021

Abstract
The results of experimental studies to determine the effect of power during laser powder cladding on temperature deformations of the substrate at a constant cladding rate and the mass flow rate of the powder are presented. Steel 1020 was used as the substrate material, from which samples of sizes 90x90 mm and a thickness of 8 mm were made. Laser powder cladding was performed by using a wear-resistant powder E-300 on a robotic complex with an ytterbium fiber laser and a coaxial powder feed. Single tracks were applied to the sample by laser powder cladding using various parameters of technological modes. The amount of deformation of the substrate was estimated taking into account the depth of the mixing layer. The cladding mode is selected, which provides minimal temperature deformations, with maximum process performance.
Expansion of technological capabilities of the electron beam welding installation
Authors: Starkov I N , Rozhkov K A , Olshanskaya T V , Trushnikov D N , Zubko I A 
Published: November 2021

Abstract
The direction of electron beam technologies is promising and is rapidly developing. Quite recently, the electron beam was a tool for welding, and nowadays, electron-beam additive technologies and beam hardening technologies have become widespread. At the moment, there is no electron beam system that unites all these technologies. Expensive equipment has been developed to implement each technology. The article deals with expanding the technological capabilities of the 15E1000 electron-beam welding installation in order to implement new methods and techniques for processing metals with an electron beam.
Optimization of CSO algorithm based on adaptive inertia weight coefficient
Authors: Suo Dongnan 
Published: November 2021

AbstractThe traditional CSO algorithm is easy to fall into local extremum in optimization. In this paper, a CSO algorithm based on weight coefficient is proposed. In the CSO algorithm, the inertia weight coefficient is introduced into the hen position formula, and the learning factor influenced by the rooster is added to the chick position formula. Finally, using the idea of heredity, individuals with excellent fitness value are selected for crossover and mutation with a certain probability. Through the simulation comparison of five typical test functions, the simulation results show that the improved CSO algorithm can avoid local optimization, strengthen the global extreme value search ability, and improve the convergence speed and accuracy range of the algorithm.
Knowledge dialogue generation with multi-head attention mechanism
Authors: Yang Fengrui , Wu Xiaosi , Zhao Luwei 
Published: November 2021

Abstract
An increasing number of research efforts are focusing on knowledge dialogue generation. Less attention is focused on increasing knowledge diversity in generated responses. A model of knowledge selection guided by a multi-head attention mechanism is proposed. First, the current input discourse and knowledge content are input into the Bi-GRU module to obtain the coding vector, and then obtain multiple aspects of semantics from the user input discourse coding vector based on the multi-head attention mechanism, so as to select different knowledge. A punishment item method is proposed to force different attention to focus on different aspects, and finally, use the user input and selected knowledge for the decoding stage. Experiments with manual and automated evaluations have proven that the model is superior to the baseline model compared to previous work.
Improved visual inertial odometry based on deep learning
Authors: Wang Jiabin , Gao Faqin 
Published: November 2021

Abstract
The traditional visual inertial odometry according to the manually designed rules extracts key points. However, the manually designed extraction rules are easy to be affected and have poor robustness in the scene of illumination and perspective change, resulting in the decline of positioning accuracy. Deep learning methods show strong robustness in key point extraction. In order to improve the positioning accuracy of visual inertial odometer in the scene of illumination and perspective change, deep learning is introduced into the visual inertial odometer system for key point detection. The encoder part of MagicPoint network is improved by depthwise separable convolution, and then the network is trained by self-supervised method; A visual inertial odometer system based on deep learning is compose by using the trained network to replace the traditional key points detection algorithm on the basis of VINS. The key point detection network is tested on HPatches dataset, and the odometer positioning effect is evaluated on EUROC dataset. The results show that the improved visual inertial odometer based on deep learning can reduce the positioning error by more than 5% without affecting the real-time performance.
Structured Knowledge Base Q&A System Based on TorchServe Deployment
Authors: Ye Xia , Liu Ruiheng , Yue Zengying 
Published: November 2021

Abstract
Structured tabular data are widely used in various information systems, especially with the development of big data technology, making it more difficult to query on these complex data. SQL facilitates the query on structured tabular, however, the mastery of SQL has a certain threshold for most non-expert users. Therefore, in order to facilitate ordinary users to quickly obtain the required information in complex structured data, we design and implement a Q&A system for structured knowledge. First, we make a detailed distinction between Q&A scenarios for structured data and design different approaches, respectively. Then, we introduce deep learning models in system algorithm layer to enhance the generalization ability. Finally, the TorchServe framework is used to optimize system deployment and improve system performance using batch inference. The experimental results show that the prototype system has certain generalization ability and also has some advantages in performance compared with traditional methods.
Application Analysis on PSO Algorithm in the Discrete Optimization Problems
Authors: Chen Qinglong , Peng Yong , Zhang Miao , Yin Quanjun 
Published: November 2021

Abstract
Particle Swarm Optimization (PSO) is kind of algorithm that can be used to solve optimization problems. In practice, many optimization problems are discrete but PSO algorithm was initially designed to meet the requirements of continuous problems. A lot of researches had made efforts to handle this case and varieties of discrete PSO algorithms were proposed. However, these algorithms just focus on the specific problem, and the performance of it significantly degrades when extending the algorithm to other problems. For now, there is no reasonable unified principle or method for analyzing the application of PSO algorithm in discrete optimization problem, which limits the development of discrete PSO algorithm. To address the challenge, we first give an investigation of PSO algorithm from the perspective of spatial search, then, try to give a novel analysis of the key feature changes when PSO algorithm is applied to discrete optimization, and propose a classification method to summary existing discrete PSO algorithms.
Target Tracking Using Kalman Filter Based Algorithms
Authors: Ling Jiankun 
Published: November 2021

Abstract
Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
Multi-operator feature enhancement methods for industrial defect detection
Authors: Zhou Bo , Fan Yi Chao , Liu YuXin , Yin XuDong 
Published: November 2021

Abstract
Deep learning based object detection algorithms have been gradually applied to industrial defect detection, but the resulted accuracy does not fully meet the needs of industrial inspection. In order to enhance image features, this paper proposes a series of image preprocessing schemes based on edge detection operators, using a single-operator preprocessing scheme, a multi-operator serial preprocessing scheme and a multi-operator parallel preprocessing scheme for image preprocessing of data to enhance the edge features of images. The validation experiment of the SSD based object detection algorithm is performed on dataset used for industrial inspection, to verify the effectiveness of the processing schemes above. The result shows that the multi-operator based image preprocessing method is effective in improving the accuracy of surface defect detection in the field of industrial defect detection.
Automatic Term Recognition Method for Military Domain
Authors: Song Ani , Jia Xiaoxia , Jiang Wei 
Published: November 2021

Abstract
With the development of military intelligence, higher requirements are put forward for automatic term recognition in military field. In view of the characteristics of flexible and diverse naming of military requirement documents without annotated corpus, the method of this paper uses the existing military domain core database, and matches the data set and core database by Aho-Corasic algorithm and word segmentation technology, so that the terms to be recognized in the data set can be divided into three types. The possible rules of word formation of military terms are summarized and phrases that conform to the rules of word formation are found in the documents as the term candidate set. The core library and TF-IDF method are used to calculate the value of the candidate terms, and the candidate terms whose value is greater than the threshold are selected iteratively as the real terms. The experimental results show that the F1 value of this method reaches 0.719, which is better than the traditional C-value method. Therefore, the method proposed in this paper can achieve better automatic term recognition effect for military requirement documents without annotation.
Research on Wafer Surface Defect Pattern Detection Method Based on Incremental Learning
Authors: Yu Naigong , Li Xin , Xu Qiao , Jiang Kai 
Published: November 2021

Abstract
Wafer manufacturing is an important step in quality control and analysis in the semiconductor industry. The defect pattern classification algorithm of wafer maps has received extensive attention from academia and industry. At present, most methods for detecting wafer surface defect patterns focus on static data model classification and analysis. However, in the production process, static data models cannot satisfy the dynamic analysis of wafer defect patterns in the form of streaming data. In this regard, this paper proposes a wafer surface defect pattern detection method based on incremental learning. Our experiment uses Resnet as the backbone network, and the data set uses the WM811K wafer data set. Experiments have proved that our method can achieve better classification accuracy in the field of wafer defect detection, which provides the possibility for continuous learning of wafer defects in the future.
Design of Single-lamp Monitoring System for Airfield Lighting Based on Broadband Power Line Carrier Communication
Authors: Yang Xiaolong , Yang Hao , Li Tuo , Ma Xiaodan , Chen Fei 
Published: November 2021

Abstract
Considering the problems of low communication rate and large communication delay of the traditional single-lamp monitoring system for airfield lighting based on narrow-band power line carrier communication technology, a solution of single-lamp monitoring system for airfield lighting based on broadband power carrier communication technology is proposed. Firstly, this paper has made a comparison of advantages and disadvantages between narrow-band power line carrier communication technology and broadband power line carrier communication technology, and briefed the problems faced by traditional single-lamp monitoring system based on narrow-band power line carrier communication. Taking technical characteristics into consideration, broadband power line carrier communication technology is selected for the development of a new single-lamp monitoring system for airfield lighting. Secondly, this paper articulated the design and development process of new airfield lighting single-lamp monitoring system based on broadband power line carrier communication technology, from the formulation of the overall architecture scheme of the system to the realization of local convergent equipment, and focused on the analysis of hardware and software development process of single-lamp monitoring module. Finally, this paper set up a test environment, and verified the performance index by experiments. The test results show that the system meets the design index requirements, can improve the performance of the single-lamp monitoring system for airfield lighting, effectively solves the problems faced by the traditional system, and can further meet the needs of the airport for airfield lighting monitoring and control.
Event Monitoring of Transformer Discharge Sounds based on Voiceprint
Authors: Cai Rui , Wang Qian , Hou Yucheng , Liu Haorui 
Published: November 2021

Abstract
This paper investigates the operation inspection and anomaly diagnosis of transformers in substations, and carries out an application study of artificial intelligence-based sound recognition technology in transformer discharge diagnosis to improve the timeliness and diagnostic capability of intelligent monitoring of substation equipment operation. In this study, a sound parameterization technology in the field of sound recognition is used to implement automatic discharge sound detections. The sound samples are pre-processed and then Mel-frequency cepstrum coefficients (MFCCs) are extracted as features, which are used to train Gaussian mixture models (GMMs). Finally, the trained GMMs are used to detect discharge sounds in the place of transformers in substations. The test results demonstrate that the audio anomaly detection based on MFCCs and GMMs can be used to effectively recognize anomalous discharge in the high scenario of transformers.
Research on manipulator control based on deep reinforcement learning
Authors: Chi Haoyu 
Published: November 2021

Abstract
With the gradual improvement of the influence of intelligent robots in production and life, it has greatly facilitated people's production and life. Therefore, people's requirements for intelligent robots are also increasing, and are developing towards more humanization and intelligence. However, at present, there are still many imperfections in the field of intelligent robot technology in China. In order to solve the problems in work, we must further strengthen the research on artificial intelligence theory and robot technology. Only in this way can we realize the all-round development of intelligent robot system. So this paper will discuss the deep reinforcement learning in the theory of artificial intelligence, and explain its basic theory, research status, existing problems and future development direction. Moreover, under the background of the overall improvement of the current industrial development level, this paper will also talk about the manipulator widely used in the industrial field and the research status of manipulator control based on deep reinforcement learning, hoping to provide effective help for the development of related fields.
Monitoring and Early Warning of Transmission Line External Breakage Based on Satellite-Ground Coordination
Authors: Yang Zhi , Deng Yuanjing , Li Mengxuan , Liu Yi , Zhao Binbin , Chen Zhiyong , Liu Shuai , Kong Liang , Zhao Bin , Wang Jian 
Published: November 2021

Abstract
This article first proposes a high-precision spatio-temporal registration method between satellite remote sensing images and ground sensors. Then, using satellite remote sensing images, an intelligent identification model for typical external damage hidden dangers of transmission lines based on satellite remote sensing is established to realize intelligent identification of transmission line construction work areas and mining affected areas. Aiming at the results of intelligent identification of construction work areas and mining-affected areas, the proposed YOLOv4-based external damage identification algorithm for transmission lines is used to detect external damage hidden dangers. Through the method in this paper, it is possible to realize a regular general survey of hidden dangers of external damage (construction work area, mining affected area) with full coverage of transmission channels, and carry out targeted 24-hour monitoring on the ground. The test results show that the satellite-ground coordinated transmission line external damage monitoring and early warning in this paper. The method timely and accurately realizes the monitoring and early warning of the external breakage of the transmission line.
Research on LDPC in an OFDM System
Authors: Shi Chong-Yue , Li Hui , zhou You-Ling , Wang Ping , Li Qian , Xue Liu-Xun , Xu Jie 
Published: November 2021

Abstract
In this paper, LDPC codes and OFDM technologies are studied, and combines LDPC and OFDM to simulate and verify the performance. The encoding of LDPC and logarithmic domain BP decoding algorithm and the principle of OFDM are introduced. Through MATLAB, the simulation of OFDM-LDPC system in different channel, different code length, different iteration times, LDPC and OFDM-LDPC in the same conditions are compared. Through simulation analysis, we can know that the performance of OFDM-LDPC system will improve with the increase of the code length. When the SNR is small, the number of iterations has little effect on the performance of OFDM-LDPC system. When the SNR is large, the performance of the system will be improved with the increase of the number of iterations. The performance of OFDM-LDPC codes is better than that of LDPC codes. The performance of OFDM-LDPC code is better than LDPC code in multipath Rayleigh fading channel.
Development of the Electroencephalograph-based Brain Computer Interface System
Authors: Gao Xiang , Fnu Gesangzeren , Wan Xianshu 
Published: November 2021

AbstractA practical BCI-based application design contains a variety of design stages are needed to be considered. The design challenges are majorly present in 3 major stages: brain signal acquisition, signal processing unit, and signal classification. Combinations of different approaches have to be employed to achieve the functional and accurate performance of the overall design. Those design choices, algorithms, and methodologies that are meant to solve design challenges presented in the previously mentioned three stages have become a hot subject of a number of studies. This paper aims at providing a thorough overview of existing methodologies for BCI-based application design, comparing their principles and performance and recommending suitable design choices that would yield an objective result for the application.
Features of growth and development of Abies alba Mill. seedlings in the conditions of Voronezh (Russia)
Authors: Degtyareva S , Dorofeeva V , Shipilova V 
Published: November 2021

Abstract
Phenological observations are one of the methods for studying the reaction of introduced species to new conditions and subsequent introduction into regional phytocenoses. This research wants to assess growth and development of Abies alba Mill., currentlyexotic in the Central Black Earth region of Russia. Seeds, obtained from individuals growing in the natural range (the Carpathians) in 1987 were sown in the university nursery. Then five-year-old seedlings were planted in the University arboretum. At the age of 22 the arboretum habitat was inferior to natural analogues. Phenological observations of bud opening and formation of annual growth coincide with the beginning of the growing season in natural plantings. High rates of seed germination (80%), high annual growth rates of seedlings (and transplanted plant) determined final success of silver fir in forest-steppe conditions. Precipitation has a dominant influence on growth and development, as shown by the correlation analysis between climate and growth, despite the fact that the species is drought-resistant in natural habitats. The study emphasizes the factors that control growth and development of silver fir. The use of this method of sowing is ecologically sound, which can further improve the success of species adaptation to the individual conditions of regional phytocenoses.