bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on CAN Bus Anomaly Detection Based on LSTM AndResNet
Authors: Song Jiaqi , Li Fei , Li Ruxiang , Li Yalin , Zhou Qiyang , Zhang Jiayan 
Published: February 2021

Abstract
With the rapid development of intelligent network vehicles, the safety of the vehicle network is taken seriously increasingly, the CAN as the most widely used car on-board network, its security problem has become one of the most important problemsin intelligent made car development, the communication of CAN bus network characteristic and the existing safety problems and analyzes the common means of attack, on the basis of this puts forward the anomaly detection model based on LSTM And ResNet, through the experimental results show that this method CAN effectively detect deception attack.
Research on Generating Adversarial Examples in Applications
Authors: Zhao Yang , Wang Juan , Liu Yingjiang 
Published: February 2021

Abstract
The deep neural network is a highly expressive model, which plays an extremely critical role in modern artificial intelligence applications. Adversarial samples can change the prediction results of neural networks by imposing imperceptible perturbations on the original images, which brings new challenges to deep learning. In this paper we summarize the methods of generating adversarial samples in recent years, intuitively feel the development of adversarial samples from the time of publication, and briefly classify them from the perspective of algorithm principles. At the same time, for practical applications, the advantages and disadvantages of the algorithm are analyzed and summarized, and the conditions for the algorithm to be suitable for application are proposed: no need to know the specific structure of the algorithm, high quality of the adversarial samples and convenient migration. The analysis points out that among the typical methods, MI-FGSM, ONE-PIXEL, and methods using GAN are more practical and worthy of further study.
Pneumonia Recognition Based on Convolutional Neural Network Feature Map Fusion
Authors: Liu Xiaozhong , Wang Zaixing , Zheng Lijun , Gao Jinhui 
Published: February 2021

Abstract
Pneumonia recognition has important research significance in computer-aided diagnosis, and there is a problem of low accuracy for pneumonia recognition. In this paper, an improved network is based on the convolutional neural network AlexNet, the AlexNet_Branch network. The AlexNet_Branch network adds a parallel branch convolutional neural network to AlexNet, and it connects AlexNet and the branch convolutional neural network at the fully connected layer. During training, the same image is simultaneously obtained by AlexNet and the branch convolutional neural network to obtain different feature maps, and then the feature maps are merged together at the fully connected layer to improve the accuracy of recognition. Through design experiments, different AlexNet_Branch networks composed of different layers of branch convolutional neural networks were built, and the network was trained and tested on the chest X-ray image set respectively. The results show that the addition of a branch convolutional neural network greatly improves the accuracy of pneumonia recognition, and the AlexNet_Branch network test accuracy consisting of a 16-layer branch convolutional neural network is 98.01%.
SQL Injection Attack Detection and Prevention Techniques Using Deep Learning
Authors: Chen Ding , Yan Qiseng , Wu Chunwang , Zhao Jun 
Published: February 2021

Abstract
Web application brings us convenience but also has some potential security problems. SQL injection attacks topped the list of Top 10 Network Security Problems released by OWASP, and the detection technology of SQL injection attacks has been one of the hotspots of network security research. In this paper, we propose a SQL injection detection method that does not rely on background rule base by using a natural language processing model and deep learning framework on the basis of comprehensive domestic and international research. The method can improve the accuracy and reduce the false alarm rate while allowing the machine to automatically learn the language model features of SQL injection attacks, greatly reducing human intervention and providing some defense against 0day attacks that never occur.
Intelligent Recognition of Transmission Line Inspection Image Based on Deep Learning
Authors: Qi Yue , Mu Shile , Wang Jun , Wang Liangliang 
Published: February 2021

Abstract
Under the background of high-speed development of the power grid, the traditional manual detection method is low efficiency and high cost, which are not able to meet the current requirements. With the development of UAV technology, the method of using UAV to inspect transmission lines has a positive effect on improving detection efficiency and quality. In recent years, deep learning has developed rapidly, which provides a new solution for the analysis and processing of UAV aerial inspection images. This paper focuses on the deep learning algorithm to study the insulator location and fault identification in aerial photographs, which plays an important role in realizing the automation and intelligence of UAV detection.
Design and Application of Anti-interference Control System for the Concentration of Tobacco Paper Reinforcing Agent
Authors: Tai Guangfeng , Jiang Changhui 
Published: February 2021

Abstract
Aimed at the improvement of the preparation accuracy of the tobacco paper reinforcing agent, the concentration control of the tobacco paper reinforcing agent was considered. The concentration of reinforcing agents is affected by many factors, such as influent flow, powder feeding speed, external interference, and so on. The compounding process has typical nonlinear, multivariable, strong coupling characteristics, and the traditional control method has a poor effect. In order to improve the quality of tobacco paper pulp, the linear auto disturbance rejection controller model of enhancer was designed to achieve the purpose of active disturbance resistance and improve the control effect of enhancer concentration. Through three groups of simulation experiments, simulation of ADRC to concentration tracking, concentration tracking under disturbance, and comparison with traditional PID control, the linear ADRC is capable of estimating and compensating well. The simulation results show that the linear ADRC has a good anti-interference performance. At the same time, the PLC control system and human-computer interaction software are designed to realize the monitoring of each process parameter in the preparation process of the reinforcing agent, which provides a practical approach for the concentration control in the preparation process of reinforcing agent.
Research and Application of Spacecraft Product Inspection System Based on Smart Glasses
Authors: Bian Yuchuan , Huang Lei , Gao Jing , Hu Siyu , Li Xintong 
Published: February 2021

Abstract
cenarios, studies the construction of the digital test system based on intelligent glasses, based on the standard operating procedure operation guide, inspection data online and conformity intelligent decision, heterogeneous data packet generation and analysis of key technology, realized the application of spacecraft product inspection process, improves the working efficiency and quality.
Research on the Architecture of Convolutional Neural Network Accelerator
Authors: Liu Ying , Ding Haitao , Ma Jiali 
Published: February 2021

Abstract
Convolutional neural networks are widely used in human production and life, but due to their large amount of calculation and complex calculation mode, their calculation speed is slow, so it is necessary to design a dedicated hardware accelerator. This paper firstly analyzes the algorithm of the convolutional neural network and decomposes the algorithm into multiple basic operations. For the convolution operation with the largest amount of calculation and complex operation mode, a near calculation storage array is designed according to its operational characteristics. Furthermore, a convolutional neural network accelerator architecture is proposed to realize the fast operation of a convolutional neural network.
Vehicle Object Detection Based on Improved RetinaNet
Authors: Zhang Luyang , Wang Haitao , Wang Xinyao , Chen Shuai , Wang Huaibin , Zheng Kai , wang Hailong 
Published: February 2021

Abstract
Aiming at the low efficiency of vehicle object detection in real scenes, this paper proposes an improved RetinaNet. An octave convolution structure and a weight pyramid structure are introduced respectively to improve the detection performance of RetinaNet for vehicles. Specifically, we use octave convolution instead of the traditional convolution layer to improve the feature map’s representation of detailed information. In addition, in order to improve the quality of feature fusion, a weighted feature pyramid network (WFPN) structure is proposed to limit the propagation of gradients between different levels. The experimental results on the DETRAC data set show that the method has good detection results for vehicle targets of different scales in different scenarios, and can better meet the needs of practical applications.
A Survey on Feature Extraction Methods of Heuristic Malware Detection
Authors: Li Nuannuan , Zhang Zheng , Che Xin , Guo Zhimin , Cai Junfei 
Published: February 2021

Abstract
In the age of the Internet, while the network is sending a lot of information to people, hackers also transmit a lot of malicious code through the network. Hackers use these malicious codes to steal sensitive information from infected people and damage machines and devices to achieve their evil goals. Therefore, it is very important to accurately detect malware to protect users from loss. There are now three major methods of detecting malware, which are signature-based, behavior-based, and heuristic-based methods. However, with the rapid increase in the types and number of malware, signature -based method can’t detect unknown malware and behavior-based cannot guarantee the False Positive Ratio. So these detection methods can no longer meet the needs. Therefore, some researchers proposed some heuristic-based detection methods. In this article we overview the methods used to extract features and the features extracted in heuristic detection and discuss the advantages and disadvantages of the features
Interpretable Saliency Map for Deep Reinforcement Learning
Authors: Zheng Hong , Dai Yinglong , Yu Fumin , Hu Yuezhen 
Published: February 2021

Abstract
Deep reinforcement learning (deep RL) achieved big successes with the advantage of deep learning techniques, while it also introduces the disadvantage of the model interpretability. Bad interpretability is a great obstacle for deep RL to be applied in real situations or human-machine interaction situations. Borrowed from the deep learning field, the techniques of saliency maps recently become popular to improve the interpretability of deep RL. However, the saliency maps still cannot provide specific and clear enough model interpretations for the behavior of deep RL agents. In this paper, we propose to use hierarchical conceptual embedding techniques to introduce prior-knowledge in the deep neural network (DNN) based models of deep RL agents and then generate the saliency maps for all the embedded factors. As a result, we can track and discover the important factors that influence the decisions of deep RL agents.
Making AI available for everyone at anywhere: A Survey about Edge Intelligence
Authors: Zhang Yusen , Li Bao , Tan Yusong 
Published: February 2021

Abstract
With the rapid development of the 5thgeneration of wireless network communication technology, a high-speed data rate, broader bandwidth, and low-latency wireless network has already come to the truth. Meanwhile, Edge Computing which put data processing at the edge of the network has already been well developed, and it has changed the computing mode tremendously. And also, Artificial Intelligence (AI) has made a great breakthrough from Computer Vision, Unmanned Vehicle to Natural Language Processing. Nevertheless, with the increasing model size and model depth, the training data set and communication delay has become the bottleneck of AI popularization. As zillions of bytes of data are being generated at the network edge, and also lots of AI applications are being utilized by enormous customers lied at the edge, the combination of Edge Computing and AI is imperative. This paper has done a deep investigation into the new field about the confluence of Edge Computing and AI, aiming of discussing the concept, architecture, and research ideas on Edge Intelligence. To the end, this paper provides the road-map for future researching work.
Weighted User Goal Sampling for Dialog Policy Learning
Authors: Yin Kai , Wang Zhenyu 
Published: February 2021

Abstract
A deep reinforcement learning algorithm has been widely used for learning dialog policy in a task-oriented dialog system. Dialog agent collects training data and improves its policy by interacting with users. Interact with real users is time-consuming and not realistic. Consequently, we usually build a user simulator instead of a real user. At the beginning of each dialogue, the simulator will sample a user goal extract from training data. Then the simulator will communicate with the dialog agent to accomplish this user goal. The existing user simulator usually samples this user goal randomly. This will cause the dialog agent to waste a lot of time to learn what it already learned. To solve this problem, we propose two user goal weighting methods which give relatively large weight to the user goal the current dialog agent can’t accomplish. This lets the dialog agent pay more attention to those user goals. Experiment result in a movie-ticket booking task shows that the proposed weighted user goal sampling method can effectively accelerate the policy learning progress compares to the random sampling method.
Rare Earth Magnetic Materials Surface Defect Recognition via Multi-Scale Residual Neural Network
Authors: Zhang Bin , Li Zhixi , Hao Yan 
Published: February 2021

Abstract
The work aims to solve the problems of low detection accuracy, and poor reliability of magnetic material surface defects. A lightweight multi-scale feature ResNet (LMSF-ResNet) method was proposed in this paper. The method reduces the calculation and parameters of the neural network by using group convolutions and channel split operator and merging features from different branches of distinct scales by using the multi-scale model’s structure. The algorithm employs the strategy of using the channel attention model to improve accuracy at the same time. This paper takes the defect images of rare earth magnetic materials as an example to verify its effectiveness. The results show that the method has higher detection accuracy and stronger stability, and can be widely used to accurately detect surface defects of magnetic materials.
Intelligent Detection of Urban Road Underground Targets by Using Ground Penetrating Radar based on Deep Learning
Authors: Gao Yao , Pei Lili , Wang Sa , Li Wei 
Published: February 2021

Abstract
Ground penetrating radar (GPR) is widely used in the field of intelligent road detection because of its non-destructive detection method, which is based on an electromagnetic wave reflection mechanism. However, this method requires large-scale data processing and also relies on manual judgment, which is time-consuming and laborious. To solve this problem, this paper analyzes and studies the GPR images of underground pipelines and uneven settlement of urban roads by means of road surface measurement and laboratory tests. The radar image data set is constructed by collecting radar images and denoising and marking them. Then, Deep Feature Selection Net is adopted to improve the fast region-based convolutional neural network (Faster R-CNN) to enhance the network’s ability to extract features from radar images. Finally, by comparing with the improved faster R-CNN model, it is found that the automatic identification rate of underground pipelines and uneven settlement in urban roads increases, reaching more than 80%.
Flood Forecasting Method of Small and Medium-sized Watershed Based on Convolutional Neural Network
Authors: Wang Jimin , Cao Ying , Li Jiahuan , Ji Changzheng 
Published: February 2021

Abstract
A data-driven flood forecasting model based on a convolution neural network is proposed for the small and medium-sized watershed. Pearson correlation coefficient analysis was used to determine the time length of the input data. The historical rainfall and discharge were used to create the two-dimensional input data matrix. NAS was used to determine the structure of the model. The experiment results in Tunxi watershed in Anhui Province show that the accuracy is high under the 1˜3h forecast period, with the forecast period increased, accuracy would decrease.
An Efficient Methodology for License Plate Localization and Recognition with Low Quality Images
Authors: Xiao Sainan , Yang Wangdong , Cao Buwen , Zhou Honglie , He Chenjun 
Published: February 2021

Abstract
It is challenging to find an effective license plate detection and recognition method due to the different conditions during the image acquisition phase. This paper aims to develop a new accurate and efficient method based on color difference and SVM recognition model that yields better performance for vehicle images under low quality. The proposed method is tested with 200 images which involve many difficult conditions, such as low resolution, night-lighting, dirt, complicated background, and distortion problems. The experimental results demonstrate very satisfactory performance for license plate detection in terms of speed and accuracy and are better than the existing methods like edge detection or HSV color conversion method. The overall probability of localization is close to 100%, with a false recognition rate of 2%.
Semantic Matching for Short Texts: A Cross Attention Mechanism
Authors: Zheng Senhong , Chen Fan , Wang Xuejie 
Published: February 2021

Abstract
Semantic matching is a hot topic in the field of NLP. It is widely used in information matching, machine translation, intelligent Q&A, and answering system. With the rapid development of artificial intelligence, people pay more attention to friendly human-computer interaction and how to obtain accurate and effective information. Aiming at the problem of the diversity and structure of Chinese, this paper introduces the cross attention mechanism. Texts provide attention to each other, then to information retrieval. Experiments on aetc-nlp dataset show that the method is effective.
Research on Evaluation System and Threshold Value of Water Resources Carrying Capacity Based on Principal Component Analysis
Authors: Liu Lihuan , Liu Jingshuo 
Published: February 2021

Abstract
The traditional water resources carrying capacity evaluation system obtained by the fuzzy comprehensive evaluation method has strong subjectivity in factor selection and threshold calculation, and the factor analysis is relatively one-sided. Therefore, water resources carrying capacity evaluation system based on principal component analysis is established and its threshold is studied. First, the main influencing factors of water resources carrying capacity, including water resources endowment, society, ecology, and other resources are analyzed. Then, the water resources carrying capacity evaluation system is established by selecting the relevant indicators in the evaluation system, comprehensively considering the selection principles, and selecting the index with the highest score as the main component. Finally, different methods are used to calculate the threshold value of the evaluation index. So far, the establishment of the evaluation system of water resources carrying capacity based on principal component analysis and the calculation of the threshold value is completed. The results of the case analysis show that the established water resources carrying capacity evaluation system are more scientific and effective than the traditional system.
Signal Sorting Algorithm of Hybrid Frequency Hopping Network Station Based on Neural Network
Authors: Wang Zhongyong , Zhang Beibei , Zhu Zhengyu , Gong Kexian 
Published: February 2021

Abstract
In a non-cooperative frequency hopping communication system, the frequency hopping network station sorting of the received hybrid signals plays an important role and becomes an active research area in recent years. In order to solve the problem that the currently widely used clustering algorithm can not achieve satisfactory accuracy. In this paper, we propose a signal sorting method for hybrid frequency hopping network stations by applying the neural network to classify the frequency hopping description words of signals. Additionally, the conjugate gradient algorithm is utilized in the neural network training process to improve the convergence speed. Simulation results demonstrate that when compared with the clustering algorithm, the proposed algorithm converges with fewer iterations and delivers better sorting accuracy, especially in a low signal to noise ratio environment.
Combined Model for Short-term Wind Power Prediction Based on Deep Neural Network and Long Short-Term Memory
Authors: Xiong Bangru , Meng Xinyu , Wang Ruihan , Wang Xin , Wang Zhengxia 
Published: February 2021

Abstract
Wind power generation is affected by weather and historical wind power, which presents the characteristics of instability and high volatility. Most wind power prediction models ignore physics information. In this paper, a novel combined predicting model that simultaneously considers physics information and historical information is presented to address the drawbacks of existing models. First, the physical characteristics of wind speed, wind direction, and temperature are obtained by Deep Neural Network(DNN), and time-series characteristics from historical wind power are extracted by Long Short-Term Memory(LSTM). Then, the physical features and the time-series features are fully connected for feature fusion to obtain the final time-series physical features. Finally, the short-term wind power prediction is performed according to the obtained merged features. Experimental results demonstrate that the DNN-LSTM model proposed in this paper achieves high accuracy and stability, and provides technical support for wind power system dispatch.
Research on Face Recognition Algorithm Based on HOG Feature
Authors: Li Yalan , Lu Ruhua , Huang Rui , Zhang Wenfen 
Published: February 2021

Abstract
As a common biometric recognition technology, face recognition is also an important research direction in the field of computer. Although compared with the initial research, the current research has made great progress, but there are still many difficulties in practical application. In this paper, by extracting HOG features, after introducing the detailed steps of PCA and LDA subspace feature extraction methods, dimensionality reduction feature extraction method combing PCA with LDA is applied to extract face features. This method first uses PCA to reduce the dimension of face features, and then uses LDA for linear discriminant analysis. Finally, the feature extraction methods based on PCA and LDA are tested and compared in FERET standard face database and CAS-PEAL database of Chinese Academy of Sciences