bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Optimized Convolutional Neural Network Based On Attention Pooling for Text Classification
Authors: Yao Chengjie , Cai Mengmeng 
Published: July 2021

Abstract
Convolutional neural network (CNN) is widely used in Natural Language Processing (NLP) and has achieved relatively good results. Since the convolutional layer of CNN can be initialized by encoding important semantic features, CNN can extract important semantic features in the convolutional layer. The pooling layer of CNN uses the feature of max-pooling to filter the features, but it does not consider the key information in the sentence and the context semantic information in the text. Therefore, a novel neural network algorithm combining with the attention mechanism and the pooling layer is proposed in this paper to make the model pay attention to the keywords in the sentence and automatically maintain the most meaningful text message, thereby improving the performance of text classification. Our paper uses important information features to initialize the convolution filter so that the model can notice the important semantic features. The resulting feature is enhanced by combining the attention mechanism in the pooling layer, so that the model can maintain the important information features of the text. Experiments show that the proposed model achieves excellent results for multiple text classification tasks, including sentiment classification and topic classification.
The Influence of Signal Incident Angle on Generalized Sidelobe Cancellation System
Authors: Xiao Shijing , He Fangmin , Li Bin , Meng Jin 
Published: July 2021

Abstract
Through the simulation modeling of the generalized sidelobe cancellation system, the influence of the interference signal and expected signal at different incident angles on the system is analyzed. The results show that the cancellation ratio is affected by the expected signal, the interference signal and the original beam pattern, and the expected signal loss factor is affected by the angle of incidence of the expected signal. At the same time, the influence of the angle deviation of the interference signal on the system is simulated, and the result shows that the angle deviation of the cancellation ratio is very sensitive to the angle deviation from the beginning to the later.
Novel DOA Estimation of Coherent Signal based on Virtual Array
Authors: Jiang Heng , Li Lichun , Pu Mingang , Zhang Jinfeng 
Published: July 2021

Abstract
Conventional direction-of-arrival (DOA) estimations perform poorly in coherent signals due to the rank loss of spatial covariance matrix, and most of the existing methods solve the problem at the expense of Degree of Freedom (DOF) or accuracy. In this paper, a novel DOA estimation technique for coherent signal is presented based on virtual array. The method comprises three steps: Firstly, the covariance matrix is generated according to the output signals of the array. Then, the first row and first column are selected from the matrix according to the data model of each element of the covariance matrix, which can be regarded as a uniform and continuous single snapshot signal of virtual array. Finally, the forward and backward spatial smoothing is used to achieve decorrelation of the single snapshot signal of the virtual array element. The proposed method has no loss of DOF and maintains high accuracy for low signal-to-noise ratio (SNR) conditions. The feasibility of the proposed method is proved by theoretical derivation, and its effectiveness is also verified by numerical results.
Research on Similarity Measurement Algorithm of High-dimensional Data
Authors: Zhang Jianhua , Meng Xiaojun , Huangfu Huidong , Zhou Jianglong , Chen Haiyan , Shen Jiachen , Zhang Peng , Tao Wei 
Published: July 2021

Abstract
The similarity measurement of high-dimensional data is an important research content in data mining and other fields. Aiming at the shortcomings of the traditional similarity measurement algorithms, a new measurement algorithm is proposed. The results of comparison with traditional algorithms show that new algorithm can quantitatively and intuitively reflect the similarity of two sets of high-dimensional data, and has strong adaptability and superiority.
Cloud-based Approach for Prevention Maintenance Management Platform of Printing Press equipment
Authors: Cao Shaozhong , Yang Yanhong , Wei Xuhang , Jian Tengfei , Rehan Abdul , Zhang Panlin , Xiang Xuan 
Published: July 2021

Abstract
It is easy to understand that presentation maintenance can prolong the lifecycle of the equipment. Due to the lack of an efficient maintenance management platform for Printing Press equipment, we develop a cloud-based platform to record routine inspections, to notify management information, and to remind replacement with a monitoring analysis model. In that case, the producer of the printing press could master the data of prevention maintenance and provide better after-sales service. The platform consists of equipment data sensing analysis workflow, routine inspection, and equipment warning/failure detection. This enables effectively extending the service life of the equipment, reducing the maintenance costs of equipment, and providing on-demand access to computing resources. In the future, new artificial intelligence models could be added to provide smarter manufacturing solutions.
User Behavior Audit System of Real-time Web Log by Spark
Authors: Li Guonong , Hu Wenyu , Chen Jinbo , Ying Kanghui 
Published: July 2021

Abstract
With the rapid development of information technology, single sign-on authentication systems have been adopted by organizations to protect their information management systems and other service platforms. With the single sign-on method, users just log in the authentication systems once to access all the information in systems, which are more convenience, but accompanied by user permissions vulnerabilities and information security risks. Users’ browsing behaviors reflect the users’ usage habits, purposes, and work contents. Risks of information leakage can be reduced by auditing the users’ browsing behaviors. After analyzing the web logs of several service systems, we designed a user behavior audit system based on the Spark computing engine, which takes the real-time web log file as the data set. A possible preprocessing method for real-time web log data stream is proposed to extract users’ web session data. we used the Spark computing engine and the corresponding analysis to mine users’ behavior patterns. The online real-time detection and offline detection of user behavior are realized, and users are divided into three levels: normal, abnormal, and risky. The system administrators or auditors can view the calculation results of each stage online, manage the parameter configuration of the algorithm online, and adjust the user’s access right in real-time.
Radar signals modulation recognition based on bispectrum feature processing
Authors: Mi Xinping , Chen Xihong , Liu Qiang , Hu Denghua 
Published: July 2021

Abstract
Modulation recognition of radar signals is an important part of modern electronic intelligence reconnaissance and electronic support systems. In this paper, to solve the problem of low recognition accuracy and low noise resistance of radar signals under low signal-to-noise ratio(SNR), a recognition method based on variational mode decomposition(VMD) and bispectrum feature extraction is proposed. Based on the feature that bispectrum can suppress Gaussian noise, the feasibility of signals modulation recognition under low SNR is analyzed and the noise item is introduced. Due to the interference of noise item, the noise suppression effect of bispectrum is worse under 0dB. An improved VMD algorithm based on artificial bee colony(ABC) algorithm optimization and envelope entropy evaluation is proposed to preprocess the signal to improve the SNR. Finally, we designed a convolution neural network(CNN) classifier to recognize signals of different modulation types. The simulation results show that this method has better noise resistance than traditional methods, and can effectively identify different types of signals under low SNR.
Finger language recognition algorithm based on ATTAlexnet
Authors: Xie Qina , Kuerban Alifu , Abula Minawaer , Han Dingjie 
Published: July 2021

Abstract
The mainstream algorithm model structure based on deep neural networks is relatively simple, and it is easy to lose effective information in the pooling layer, the recognition accuracy is not high, and the recognition speed is slow. To solve this problem, a fusion convolutional attention mechanism and AlexNet’s Finger language recognition algorithm (ATTAlexNet). By introducing the convolutional attention mechanism in the AlexNet network, it can effectively perform feature learning, realize feature screening, enhance the characterization ability of the network, and introduce the AdderNet to replace the multiplication operation of the convolution layer in the AlexNet network, enhance the robustness of the network, and improve the network calculation speed. The experimental results show that ATTAlexNet is superior to other comparison algorithms, and under the same experimental conditions, the recognition rate of ATTAlexNet is increased by 2.0%, which proves that the ATTAlexNet algorithm can effectively realize finger language recognition, has fast calculation speed, and good robustness.
Multi-objective logistics park functional block layout based on genetic algorithm
Authors: Zhang Siqi , Li Guanyang , Yu Denghui 
Published: July 2021

Abstract
At present, the construction of logistics parks has become an indispensable part of the modern logistics system. Scientific and reasonable layout planning of the logistics parks not only effectively saves logistics costs, but also promotes the efficient operation of the entire logistics. This article uses a combination of qualitative analysis and quantitative analysis to analyze the layout structure of the functional areas, build a multi-objective mathematical model with the largest degree of adjacency and the smallest transportation cost, and set the functional blocks in horizontal and vertical modes to make the layout more rational, Using genetic algorithm to solve the model with Matlab software, and verify the rationality and effectiveness of the model through examples, providing a new idea for the layout of modern logistics parks.
Blind sidewalk segmentation based on the lightweight semantic segmentation network
Authors: Liu Xingjian , Zhao Xinge , Wang Sizhan 
Published: July 2021

Abstract
A lightweight semantic segmentation network is proposed to solve the problem of real-time segmentation of blind sidewalk. On the basis of U-Net’s encode-decoding structure, the inverse residual block composed of deep separable convolution is used to replace the ordinary convolution for feature extraction, and the number of convolution layer channels is compressed. Experiments show that the blind segmentation method used can effectively overcome the disadvantages of traditional methods that are affected by the environment easily. Compared with U-Net, the parameter amount is reduced by 25.2 times, and the reasoning speed is increased by 3.6 times, while the loss of precision is just less than 2%, which basically meet the requirement of real-time inference.
Wooden pallet image segmentation based on Otsu and marker watershed
Authors: Jia Fengyuan , Tao Zhaosheng , Wang Fusong 
Published: July 2021

Abstract
Pallet detection is the key step of cargo handling for warehouse robots. In the visual detection, pallet needs to be segmented from the image background. In order to increase the recognition rate and reduce the influence of the surrounding environment and the pattern of the pallet, Otsu algorithm and marker watershed algorithm are combined to realize the image segmentation of the pallet contour. Based on the difference of colour information between the pallet and the background, Otsu algorithm is used to segment the pallet for the first time, and marker watershed is used to complete the target recognition. The experimental results show that the method can effectively solve the problem of over segmentation of the pallet object, and improve the recognition rate, which has some reference value for the design of the visual detection system of the warehouse robot wooden pallet.
Application of two-dimensional truncated singular value decomposition in image restoration
Authors: Zhou Hongyu , Kou Ting , Song Xu 
Published: July 2021

Abstract
Large solution size and ill-posed problem often exists in image restoration. In order to overcome these problems, the application of two-dimensional truncated singular value decomposition(2-D TSVD) in large-scale image restoration is studied in this paper. The 2-D TSVD overcomes the inherent ill-posed problem and also solves the problem caused by limited storage in image restoration, which plays a regularization role. Experimental results show that this method is very effective in image restoration.
Research on key technologies of overlay multiple access based on deep learning
Authors: Huang Ronglan 
Published: July 2021

Abstract
NOMA (Non-Orthogonal Multiple Access), as one of the candidate technologies of 5G, can improve the spectrum efficiency and system capacity, and has attracted wide attention. The essence of NOMA is multi-user overlay transmission in power domain, and multiple users will schedule on the same sub-band. The fundamental principle and key technologies of NOMA technology, such as power multiplexing superposition coding technology and SIC (Successful Interference Cancellation) technology, are described. The BER performance of NOMA communication system based on power domain in downlink is studied, and the appropriate power allocation ratio range is obtained. From the overall point of view of the system, users are grouped with the goal of maximizing the channel gain difference of all matched users, and then the problem of maximizing the weighted sum rate is transformed into the problem of minimizing the weighted sum mean square error by using the deep learning algorithm. By updating each optimization parameter iteratively in turn, it converges to the local optimum of the objective function. This method can improve the weighting and rate performance of the system with fewer iterations.
A survey of ship target feature extraction based on video surveillance
Authors: Bi Zhenbo , Zhang Shiyou , Wu Yuanhong , Wang Guangwei , Qi Changshong 
Published: July 2021

Abstract
Ship target feature extraction plays an important role in ship recognition and tracking based on video surveillance. However, the current feature extraction of ship targets based on video surveillance does not have a complete summary, so this article reviews these methods. This article summarizes the main ship target feature extraction methods used in ship target recognition and tracking in different application environments, including traditional artificial ship feature extraction and automated learning ship feature extraction. It focuses on the core ideas, advantages and disadvantages of the representative methods in each type of problem, and discusses the current problems in the feature extraction of ship targets and the future development of feature extraction technology.
A bilateral matching method of decision-making for customized buses
Authors: Zhang Yuntao , Liu Ruiting , Yang Yijing , Wen Haoyu 
Published: July 2021

Abstract
As the cornerstone for urbanization, public transportation needs to raise attraction. Hence, the key motivation of this research is about balancing the needs of customized-buses and commuters. Regarding satisfaction and fairness of matching schemes, this paper explores the characteristics of customized-bus services and combines actual needs to establish a multi-objective bilateral matching optimization model including maximum matching correlation degree, minimum correlation deviation and maximum matching logarithm. To show the indicators information, the methodologies introduces the grey correlation analysis method adopting four data types. The satisfaction level is obtained by solving the maximum matching scheme that matches the appropriate customized-bus for commuters. The proposed model meets the requirements of operators and commuters, may further improve the attraction of public transportation and its service quality, which can be the key contributions of this paper.
High-speed optical sampling system combined with cavity-less pulse source
Authors: Shao Bo , Zhou Yue , Xu Kun 
Published: July 2021

Abstract
High-speed optical sampling can overcome the limitation faced by electronic systems, such as bandwidth and precision. In this work, a 4-fold optical sampling time-parallelization is combined with a cavity-less 10GHz pulse source. The 4-fold sampling time-parallelization is based on a 1x4 optical switching matrix. Three dual-outputs Mach-Zehnder modulators (D-MZM) in cascaded configuration are included. Instead of a 10GHz repetition mode-locked laser (MLL), a cavity-less 10GHz pulse source is designed and used to provide the 10GHz modulated pulse as used in the switching matrix, which consists of a Mach-Zehnder amplitude modulator (MZM), a phase modulator (PM) and a highly dispersive optical fiber. We demonstrate a 4-fold sample time-parallelization with a high repetition rate up to 10GHz and 2.5GHz per channel, also with a signal-to-noise ratio (SNR) of 30dB, and combined with a 10GHz cavity-less pulse source with a pulsewidth of 9ps, which can be used into the advancements in radar and telecommunication systems.
Smart lighting system based on Bluetooth
Authors: Feng Chenwei , Wang Xianling , Li Zhuo , Lin Weiming , Ji Huazhi , Shen Shaomin 
Published: July 2021

Abstract
A smart lighting system uses the concept of green, low-carbon, energy-saving and environmental protection to change people’s lifestyle and bring about high-quality life. This paper designs a smart lighting control system based on Bluetooth and Android. When the system is in automatic mode, the pyroelectric infrared sensor can be used to detect whether anyone is passing by to control the LED light on or off. The LED light can be also controlled by the Android app through Bluetooth communication when the system is in manual mode. The testing results show that the system is energy-saving, efficient and easy-to-use, and it can be widely used in various lighting scenes.
Wireless sensor network node sleep scheduling algorithm
Authors: Zhang Huanan 
Published: July 2021

Abstract
Wireless sensor network has been widely used in many fields, but in the application of the node battery can not be replaced, how to effectively save energy, prolong the lifetime of the network, is a very important and challenging work. In this paper, an algorithm is proposed to effectively prolong the network’s life time by dividing the intermediate nodes into two types, which are only responsible for collecting data and only responsible for sending data, and making the redundant nodes go to sleep directly after the completion of network layout, and then scheduling the dormant nodes reasonably. Through the performance analysis, the algorithm can achieve a better effect of prolonging the network lifetime when there are a large number of redundant nodes in the network.
Research on key technologies for industrial field communication quality analysis
Authors: Wang Zhen , Han Dantao , Gong Yanjie , Zhao Yanling 
Published: July 2021

Abstract
With the widespread application of intelligent manufacturing and industrial internet technologies, higher requirements for the reliability, stability and robustness of industrial communication networks have been put forward. To achieve these requirements, the detecting and evaluating diagnostic technology for industrial field communication is essential. This paper analyzes the key technologies and difficulties of detecting and evaluating diagnostic technology from the four aspects of access, collection, communication and evaluation & diagnosis and proposes some feasible solutions to provide a new perspective for further in-depth research.
A fast on-chip debugging design for RISC-V processor
Authors: Gao Shan , Xiao Wan’ang , Yang Zhenghong , Wu Dehua , Gao Wanlin 
Published: July 2021

Abstract
In order to improve the efficiency of on-chip debugging, a fast on-chip debugging design is proposed, which adopts JTAG interface and is applied in RISC-V processor. In this paper, we extend some debugging instructions, effectively reducing data entry by operating the debugging bus directly, and realize the breakpoint, pause, single step, et al., providing conveniences for the development and debugging of the software system.
Performance and characterization of MnOx-NiO catalysts for removing formaldehyde at room temperature
Authors: Ding Hua , Meng Mianwu , Lin Zhihao , Duan Chaomin , Zhang Qi , He Minyan 
Published: July 2021

Abstract
The effects of the hydrothermal co-precipitation factors of hydrothermal temperature, hydrothermal time, calcination temperature and calcination time on the crystal structure, microstructure, lattice defect and formaldehyde removal properties at indoor temperature of MnOx-NiO composite oxide catalysts were investigated by using the X-ray diffactiono (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS) and the catalytic activity testing apparatus. The results showed that the formaldehyde removal rate of the catalysts would increase first and then decrease with increasing in the hydrothermal temperature, or hydrothermal time, calcination temperature, calcination time. The maximum formaldehyde removal rate at indoor temperature is 95.57% with the best technology of the catalysts is as the following: 0.0125 mol potassium permanganate, 0.005 mol nickel nitrate, 5 ml sodium oxalate solution, 0.5 ml ammonia solution, hydrothermal 5 h at 130 °C and then calcination 5 h at 200 °C, the loose spherical accumulation specimen was composed of amorphous and crystal, and existed abundant Mn4+ species and lattice oxygen on the catalyst surface.
State-of-the-art of magnetic memory testing technique
Authors: Liu Shujun , Yong Qiwei , He Dean , Zuo Yonggang , Zhang Zhen , Zeng Guodong 
Published: July 2021

Abstract
Magnetic memory testing is a new nondestructive testing technique. It has the incomparable advantages of traditional nondestructive testing technique, and can find the stress concentration of ferromagnetic materials. In this paper, the development of magnetic memory testing is analysed, and the problems encountered in the development of magnetic memory is studied. The research on magnetic memory mainly focuses on the following three aspects, study on the mechanism of mechanical magnetic coupling, analysis of external influence factors of signal and extraction of damage evaluation parameters. Finally, in the paper, the development direction of magnetic memory testing in the future is summarized. That is, to strengthen the research on the micro mechanism of magnetic memory and to establish the correct mechanical magnetic coupling model.
Current status and thinking of gene editing technology
Authors: Yu Junfeng , Zhao Yanqing , Feng Haizhou , Yu Jiazeng , Li Tie 
Published: July 2021

Abstract
Gene editing refers to operations such as deleting, replacing, and inserting target genes to obtain new functions or phenotypes, and even create new species. As an important research field with the rapid development of life sciences, the development and application of gene editing technology has made the genetic modification of organisms into an unprecedented depth and breadth. Through the analysis of patent documents related to gene editing, the article reveals the development status of gene editing technology in terms of global output trends, patent application status of major institutions, life cycle, technical composition, and geographical distribution. It also puts forward countermeasures and suggestions in basic research, intellectual property protection, international exchanges and cooperation, and policy supervision to provide references for researchers and decision makers in related fields.