bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Hardness Detection Method of Crisped Grass Carp Based on Visible - Near Infrared Hyperspectral Technology
Authors: Wang Qing Xiu , Su Li Heng , Zou Juan , Chen Ning Xia , Wu Ting , Yang Ling 
Published: February 2021

Abstract
With the fish of crisped grass carp as the research object, this paper analyzes the hardness-hyperspectral data of crisped grass carp with a texture analyzer and a visible - near infrared hyperspectral imaging system (400-1100nm), and then proposes a method to quickly detect the hardness of crisped grass carp. In this study, the hardness of 15 fish crisped grass carp was analyzed with a texture analyzer, and it was found that there was a difference in hardness between different fish with a significance P<0.05.Four spectral bands affecting the hardness of crisped grass carp were found by using the random forest algorithm, which indicated that there was a connection between hardness and hyperspectrum. Four pretreatment methods were used, among which SNV pretreatment method had the best effect, R2was 0.80. PLS, SVM and BP-NN prediction models were established respectively, and R2and RMSEP of the models were used as evaluation indexes to compare the advantages and disadvantages. The results are as follows: R2is 0.8033, 0.8564, 0.8814 and RMSEP are 3.6417, 2.6891 and 2.6128 respectively, among which BP-NN prediction model R2is 0.8814 and RMSEP is 2.6128, which has the most significant effect. Therefore, BP-NN prediction model is more suitable for nondestructive testing of crisped grass carp hardness.
EIT-4LDNN: A Novel Neural Network For Electrical Impedance Tomography
Authors: Zhang Xinyu , Chen Xiaoyan , Wang Zichen , Zhang Miao 
Published: February 2021

Abstract
EIT (Electrical Impedance Tomography) is a non-invasive dynamic image detection technology that can well reflect the distribution of objects in a uniform field. But the inverse problem of EIT is a non-linear, highly ill-conditioned, and ill-posed problem. Images reconstructed by traditional iterative algorithms and non-iterative algorithms have too many uneliminated artifacts and low spatial resolution. This paper proposes a deep neural network containing a multi-layer fully connected network so that predicting the conductivity distribution of different samples through the excellent nonlinear fitting ability of the neural network. Compared with the CG(Conjugate Gradient) algorithm and TR ( Tikhonov Regularization) algorithm, the quality of images is improved and the noise is reduced, but the generalization ability and prediction accuracy of the network need to be further improved.
Implementation of A New Speech Negative Emotion Recognition System
Authors: Wang Yuqiong , Zhao Zehui , Huang Zhiwei 
Published: February 2021

Abstract
A new emotion recognition system based on speech is constructed to improve the ability of recognizing negative emotions. Multi-dimensional acoustic characteristics were tested and among them, short-term energy and Mel-frequency cepstral coefficients (MFCC) were selected to be used as parameters for recognition. The system consists two modes: single recognition and group recognition. Single recognition adopts BP neural network model based on MFCC, while group recognition adds support vector machine model based on short-term energy on the basis of single recognition which the group recognition rate of 20 speech can reach 97%. With the increase of the number of speech in each group, the recognition accuracy of negative emotion tends to 100%.
Multimodal Gesture Recognition Based on Attention Slow-Fast Fusion Networks
Authors: Zhang Xunlei , Tie Yun , Qi Lin 
Published: February 2021

Abstract
Gestures serve as the best alternative to traditional human-computer interaction (HCI), but there is still a great challenge to apply gestures to practical operations. Faced with the problem of generally low recognition accuracy in dynamic gesture recognition, we propose a fusion network with a slow-fast structure based on an attention mechanism to improve the recognition accuracy of dynamic gestures. The slow pathway acquires the temporal information of the input dynamic gesture, the fast pathway acquires the semantic information of the gesture in the input video, and suppresses the influence of non-gesture regions on the gesture features as much as possible through the attention mechanism, and finally performs the fusion operation according to the strategy of score fusion to obtain the recognition accuracy of the input dynamic gesture. We validate our proposed method on the ChaLearn large-scale gesture challenge gesture dataset IsoGD, and the experimental results are obtained to verify the effectiveness of our proposed structure by comparing it with the previous experimental results.
Swarm Security Intelligent Dynamic Networking Algorithm Based on Multiple Constraints
Authors: He Sixi , Yang Xiaohua , Wan Ya Ping , Liu Zhenghai , Zeng Tiejun 
Published: February 2021

Abstract
To reduce the security risks and guide management pressure of “mountain-type” scenic areas with steep and changeable topography, this paper combines the idea of swarm security intelligence and proposes a swarm security intelligent dynamic networking algorithm based on multiple constraints to ensure the swarm safety of the tour group and relieve the management pressure of tour guide through the dynamic group and the self-discovery after leaving the group. Based on multiple constraints such as signal strength, power consumption and fault probability etc, the algorithm optimizes the network topology in real-time, which can effectively reduce the high false-positive rate o f leaving the group due to node failure. Secondly, the algorithm constructs the Spf constrained fitness function and introduces the ant colony optimization algorithm to avoid the situation that the optimal routing path falls into the local optimal solution. Finally, the algorithm converts the adjacency matrix composed of routing table and node connection signals into areachable matrix to judge whether the member has left the group and reduce the false-negative rate of group loss in the case of multi-node connection. Experimental results show that the algorithm has good stability and robustness in mobile wireless adhoc networks
Automatic Classification of Japanese Question Intention Based on Deep Learning
Authors: Jin Lan , Su Yancong , Li Jianxun 
Published: February 2021

Abstract
In order to improve the effect of deep learning, this paper puts forward the method of deep learning of Japanese. Before classification, we need to preprocess the word segmentation and delete the termination word in the question, and then use the feature vector to represent it. By learning to store the information in the network, we use heuristic rules to classify the question intention, extract the feature vectors representing different types of questions, and then make statistical analysis on the corpus of actual marked questions, establish a classification system, and bind features based on word packets, Realization intention classification. The experimental results show that the classification accuracy and influence parameters are relatively higher after deep learning, and the automatic classification method of Japanese question intention should be unique.
Research on Identification Algorithm Based on ECG Signal and Improved Convolutional Neural Network
Authors: Wan Dechao , Zhang Luqiao , Bai Yunxiang , Xie Yu 
Published: February 2021

Abstract
In the research of ECG signal identity recognition, most of them adopt the method of feature extraction and recognition model separation, extract the time domain features, transform domain features of the original signal, or combine the features with the cross domain. Then the model is used to complete the recognition and classification. In this paper, an advanced improved convolutional neural network model is proposed, which integrates feature extraction and classification to complete identity recognition. ECG data selected from the ecg-id database and MIT-BIH arrhythmia database are directly sent to the model for automatic sign extraction after hierarchical denoising with wavelet tools and then identified. This method achieves the highest recognition rate of 98.49% on ecg-id database and 99.35% on ECG data of MIT-BIH arrhythmia database. The high reliability of the algorithm and the universality of wireless sensors in mobile devices make this research has high commercial value.
Research on Making Orthophotos of Mountainous Areas Based on WorldView-2 Images
Authors: Xing Zhu Rong , Feng You Gui , Xie Ji Ying , Wang Lei , Xu Yan Ming , Wei Yequn 
Published: February 2021

Abstract
Combining the RPC rational polynomial coefficient model, using WorldView-2 remote sensing images, combined with field surveyed control points, survey area digital elevation model DEM and other data to produce the mountain orthophoto map, combined with the detection points to analyze the accuracy of the results, solve the problem Topographic maps are difficult to measure and are subject to flight restrictions.
Classification of Chinese Hotel Reviews Based on GCN
Authors: Lin Zhiyu , Zhang Jingxuan , Zhang Haoxi 
Published: February 2021

Abstract
Text classification is a fundamental part of natural language processing and can help with many downstream tasks, such as emotion analysis, question and answer systems, and recommendation systems. The graph convolution neural network has the natural superiority in the non - Euclidean space data. For Chinese text data, there is a lot of correlation between the data, using the graph convolutional neural network for text classification can achieve good results. In our experiment, we use a simple one-hot encoding of the word vector to process our words and use of the graph convolutional neural network can achieve 94.24% accuracy in our data set.
Research on Phishing Websitedetection Based on R-SVM algorithm
Authors: Hu Shuting , Li Jiawei , Liu Mingmeng , Pan Bin 
Published: February 2021

Abstract
The Internet has brought us convenience, at the same timeimportant personal information has been exposed. Once the information is leaked, it may cause huge economic losses to individuals. Therefore, it is urgent to realize network security. The traditional method of detecting phishing websites is aimed at big data. Due to the relatively large amount of data, it indirectly causes low efficiency and often fails to solve user needs in a short time. In the detection, the operation is actually performed on the small data stream. If the small data is fundamentally classified, the efficiency will be greatly improved. In summary, based on the realization that we can identify phishing websites, we mainly solve the problems of speed and accuracy. Therefore, this paper uses the R-SVM algorithm to study the data to reduce the time spent on detecting samples and improve efficiency.
Research of Network Behavior Pattern Recognition Based on Power Monitoring System
Authors: Wang Yang Yingfu , Shi Junxia , Li Huixun , Chen Yongwei , Shen Jianqiang , Kan Min , Liang Ye 
Published: February 2021

Abstract
Based on the network security protection of the power monitoring system, this paper proposes a pattern recognition model for the operation behavior of the operation and maintenance personnel of power monitoring system. Through the technical research of the alarm pattern recognition of the network security management platform, and carries out the operation type data of the network security management platform Clustering analysis uses historical data to intelligently train the clustering model. Through training, the risk level classification of the operation behavior is obtained, and then real-time data is introduced into the model for detection, which can judge the degree of risk of the operation event in real time. With the improvement of the K-means clustering algorithm and the application of big data analysis, it further improves the he intelligence level of the platform and provides technical support for the intelligent protection of network.
Quality Prediction of Discrete Manufacturing Process Based on CGAN&Catboost Hybrid Model
Authors: Xu Peng , Ren Zhichen , Shen Yili , Yu Wendi , He Lianghua 
Published: February 2021

Abstract
Accurate prediction of product quality can be used to control the online production process of workpieces, an appropriate prediction model can effectively improve the yield of industrial production. In order to obtain missing data, the classic method is to regress them one by one. However, the traditional regression prediction method destroys the correlation of the data and cannot use the quality inspection index efficiently. This paper presents an improved hybrid model. The essence lies in a novel easily pluggable CGAN module that refines the previous step of regression data. compared with the regression method, the CGAN module can learn the joint d istribution of data. The experiment of real data result shows that our method has high accuracy compared with the existing ones
A Thematic Portfolio and Recommended Study on the Usefulness of Online Medical Reviews Based on QCA Methods
Authors: Wang Yonghong , Huang Lei 
Published: February 2021

Abstract
The online comment for doctors plays an important role in assisting patients to master the real medical situation and choose medical treatment, which greatly reduces the adverse effects of online medical market information asymmetry. but because ordinary patients lack professional medical knowledge and can not accurately and efficiently write comments that are useful to similar patients, we identify useful topics from online medical reviews and make combination recommendations based on patients with different diseases. guide patients to write comments from the most useful dimensions to alleviate the problem of information overload, thus maximizing the limited human resource utility. This study aimed at online medical platforms such as good doctors and micro-doctors collected online doctor reviews for major diseases and used LDA topic mining and fsQCA fuzzy set qualitative comparative analysis to analyze key topics that affect the usefulness of reviews and optimal topic combinations under different disease types.
Research on Automated Detection of Sensitive Information Based on BERT
Authors: Ding Meng , Wang Xing , Wu Changming , Wang Kaixuan , Yang Xue 
Published: February 2021

Abstract
With the booming of the Internet, Web public opinion plays an increasingly important role in the stability of the network community. Therefore, the sensitive information hidden on the Internet is likely to lead to unpredictable social impact. This paper focuses on the detection of Chinese sensitive information. First, we build a corpus to train the detection model. Secondly, we apply the Bert method to the detection problem. Then, many popular NLP methods are applied to this problem to show the progress of Bert in a sensitive information detection task. Finally, we got a sensitive information detection model based on BERT with a high F1 score of 97.31.
Research on Short Text Classification Based on TextCNN
Authors: Zhang Tianyu , You Fucheng 
Published: February 2021

Abstract
The TextCNN model is widely used in text classification tasks. It has become a comparative advantage model due to its small number of parameters, low calculation, and fast training speed. However, training a convolutional neural network requires a large amount of sample data. In many cases, there are not enough data sets as training samples. Therefore, this paper proposes a Chinese short text classification model based on TextCNN, which uses back translation to achieve data augment and compensates for the lack of training data. The experimental data shows that our proposed model has achieved good results.
Multi-view Bearing Fault Diagnosis Method Based on Deep Learning
Authors: Wang Ruihan , Meng Xinyu , Xiong Bangru , Wang Zhengxia 
Published: February 2021

Abstract
In recent years, the use of Time-frequency analysis to generate Time-frequency diagrams of vibration signals, and then the use of deep learning methods to classify them has become one of the mainstream methods for bearing fault diagnosis. However, a single Time-frequency analysis method usually cannot extract a complete vibration signal, so the accuracy of the model will be affected to a certain extent. Therefore, this paper proposes a multi-view bearing fault diagnosis method based on deep learning. The same segment of vibration signal is generated by different Time-frequency analysis methods, and then the pre-trained network is used to train the model. The Flatten layer was replaced by the Global Max Pooling (GMP) layer before the layer. The experimental results show that compared with the traditional feature fusion method, the method in this paper can not only achieve better accuracy, but also has stronger generalization.
Smart-YOLO: A Light-Weight Real-time Object Detection Network
Authors: Zhang Dongyang , Chen Xiaoyan , Ren Yumeng , Xu Nenghua , Zheng Shuangwu 
Published: February 2021

Abstract
In the computer vision, YOLO has an important position in the field of object detection, but due to its speed limitation, it is not suitable for scenes that require extremely strict real-time performance, such as smart cameras or some mobile devices. However, inverted bottleneck layers, which are built upon depth-wise separable convolution, have been the predominant building blocks in state-of-the art object detection models on mobile devices. In this work, we propose a new lightweight algorithm Smart-YOLO based on the YOLO framework which uses inverted bottleneck blocks and deep-wise separable convolution. We also put forward a new loss function to make up for the loss of accuracy caused by the replacement of the backbone network. The results show that compared with YOLOv3, the accuracy of our model is reduced by about 21%, but achieved up to 4.5× speedup, and the model size is only about 1/8 of the original. This shows that our network is smaller, faster, and more suitable for scenarios that require higher speed and efficient
Research on Inertial Space Intercept Game based on Deep Reinforcement Learning
Authors: Wei Wenshu , Wang Bin , Hou Linjian , Long Han 
Published: February 2021

Abstract
Aiming at the problem of the intercept game in the inertial space, this paper builds a model of basic actions of the two sides of the intercept game in the inertial space, explored the applicability of Deep Reinforcement Learning in inertial space game problem-solving. Based on Proximal Policy Optimization (PPO), this inertial space game problem is solved and the optimal solution is obtained by the reward designing with cumulative miss distance and minimum distance respectively, and finally, the effectiveness of the algorithm based on PPO is verified through the game simulation and results from comparison
Pathfinder-Based Simulation and Optimisation of Personnel Evacuation Modelling of a Shopping Mall
Authors: Han Fang , Liu Lijun , Zhang Yuanyuan 
Published: February 2021

Abstract
This study concerns a simulation and optimisation of the personnel evacuation modelling of a shopping mall in Weiyang District, Xi’an. First, investigations were conducted to determine the human flow rate tand occupant distribution during the rush hours to builda queuing theory model. Then, a simulation was implemented with an evacuation simulation software Pathfinder, followed by an analysis to the evacuation status and bottlenecks. On the basis of the evacuation status, proposals were put forward on how to overcome the bottlenecks to achieve the optimal matching among the personnel, emergency staircases and aisles. Finally, the improved scheme was simulated. Compared with time taken by the evavuation before improvement, the time taken was greatly shortened after improvement, proving the feasibility of the scheme. Through the simulation experimentaiton, it was found that ecacuation modelling can greatly facilitate reseaches on the evacuation problems in multistoryed shopping malls.
Research of Media Technology Development with Programmed Thinking in 5G Era
Authors: Li Jianxun , Su Yancong 
Published: February 2021

Abstract
In a society with highly developed information and communication capabilities in the 5G era, the traditional way of thinking has been unable to meet the digitalization requirements of efficient and feasible. Therefore, we propose a digitalization method to solve practical problems, which is called programmed thinking. In this way, we can imagine each problem as a combination of multiple functions, so that these functions can be determined according to the overall rationality of the requirements of the data content exchange, as well as the overall scheme design. Because this method is implemented in the form of modularization through the overall requirement analysis, even if there is an exception, it is also no need to redesign the entire requirement or function, only need to replace and modify the abnormal part of the method function, so as to achieve the effect of rapid response, switch at any time and simple upgrade.
Underdetermined Mixed Matrix Estimation Based on DPCKFCM Algorithm
Authors: Cao Wenrui , Xu Yan , Nan Zhefeng , Wei Yiming 
Published: February 2021

Abstract
Aiming at the problem of fuzzy C-means (FCM) in the estimation of underdetermined mixing matrix, that the estimation accuracy is not high and the robustness is poor, a density peak clustering (DPC) based on density peak clustering (DPC) is proposed. Improved Kernel-based Fuzzy C-means (KFCM). The kernel function is introduced into the FCM algorithm to construct the KFCM algorithm based on the Gaussian kernel function, which can effectively overcome the influence of noise points and isolated points on the clustering results and improve the estimation accuracy of the mixed matrix; the traditional DPC algorithm is improved and merged with the KFCM algorithm, and thresholds are set for the local density and high-density distance to achieve the initial clustering center of the KFCM algorithm and the automatic determination of the number of cluster centers improves the robustness of the algorithm. Experimental results show that the algorithm has greatly improved the estimation accuracy and robustness of the underdetermined mixed matrix.
Electronic Certificate Sharing Scheme with Searchable Attribute-Based Encryption on Blockchain
Authors: Li Xingjian , Tan Minsheng 
Published: February 2021

Abstract
With the promotion of “Internet +” and smart cities, as an important data voucher, electronic certificate brings great convenience to people’s life. However, the phenomenon of “data island” based on the electronic certificate database of each city hinders the sharing of an electronic certificate. In addition, problems such as centralization of data storage, poor security, and tamper-proof modification are common in electronic certificate libraries. To solve these problems, we present a blockchain-based electronic certificate sharing scheme, which uses the InterPlanetary File System (IPFS) and Ethereum smart contract to achieve secure storage and data sharing of electronic certificate by taking advantage of the non-tampering and decentralization of blockchain. At the same time, we achieve fine-grained access control by using attribute-based encryption of ciphertext policy and assigning attribute keys to users without affecting retrieval efficiency. Finally, through the simulation and performance analysis of the Ethereum test network, the analysis results show that our scheme is effective and feasible.