bluuebunny/crossref_metadata_2025_split huggingface dataset:
An improved deep reinforcement learning approach for the dynamic job shop scheduling problem with random job arrivals
Authors: Luo Bin , Wang Sibao , Yang Bo , Yi Lili 
Published: April 2021

Abstract
Deep reinforcement learning (DRL) method is a powerful way to solve the dynamic job shop scheduling problems (DJSSP). However, these DRL approaches are dispatching rules-based, meaning they are problem-specific, dependent on experience, and code effort. We propose a double loop deep Q-network (DLDQN) method with exploration loop and exploitation loop to solve DJSSP under random job arrivals, aiming to minimize the makespans of DJSSP. Simultaneously, by integrating into a single agent scheduling system, the proposed method could avoid complicated dispatching rules, enhancing the proposed method’s versatility. The experiment results have confirmed the superiority of our method compared to other algorithms.
A Multi-angle Improved Small Sample Clustering Algorithm
Authors: Bin Xiao , Chang Liu 
Published: April 2021

Abstract
The random selection of initial clustering centers, outliers, and the differences between attributes will affect the clustering effect of k-means. This article first uses the elbow method to determine the number of categories and then uses the square difference radius method to select the cluster seed center to optimize the cluster center’s reselection. Finally, the entropy method is used to calculate the difference between attributes. The results show that when the number of categories remains the same and abnormal data is added, the improved clustering algorithm from multiple perspectives is more accurate and stable for small sample data with small dimensions and large differences between categories.
A Multi-objective optimized node deployment algorithm for Wireless Sensor Networks Based on the Improved ABC
Authors: Liu Pingshan , Fang Junli , Huang Hongjun 
Published: April 2021

Abstract
In the Internet of Things(IoT) for smart fire applications, sensors are deployed at different places and collect multiple types of data during their work time. Since the coverage of a sensor is limited, how to deploy sensors is important to reduce the number of sensors and improve the quality of wireless sensor networks(WSNs) service. The service quality and network lifetime of wireless sensor networks also are dramatically varied across different coverage and node deployment strategies. There are two crucial problems in WSNs, i.e. node deployment with lowest cost, and coverage optimization with maximum coverage rate, need to be solved. In this research, we focus on the problem of coverage optimization and node deployment. Based on the binary coverage probability model, we develop the joint binary coverage probability model and Improved Artificial Bee Colony(IABC) algorithm. The result of simulation shows that our proposed deployment algorithm have a greater coverage ratio in the case of fewer iterations when the volume of deployed sensors is the same compared with the standard ABC algorithm.
A collaborative filtering recommendation algorithm based on user clustering with preference types
Authors: Yang Yan , Yao Huaxiong , Li Rong , Wang Sai 
Published: April 2021

Abstract
The traditional collaborative filtering algorithm is extensively applied in the field of personalized recommendation, but it still faces the challenges of data sparsity and scalability problems which result in low quality and efficiency of recommendation. To address the problems, a hybrid collaborative filtering recommendation algorithm is proposed based on user preference type clustering. First, by analyzing the relationship between users and item categories, we construct the user item category preference matrix. On this basis, user clustering is carried out and users with similar preference types are clustered into the same user groups. Then, to search for the nearest neighbors of target user, similarity of users is calculated in the cluster it belongs to by considering both the user ratings and their preferred item categories. Finally, an improved Slope One algorithm is proposed and applied to the nearest neighbor set to predict item ratings and then generate recommendations. The experiments indicate the proposed hybrid collaborative filtering algorithm can improve recommendation performance in the case of a great many users.
Research on chaotic flying sparrow search algorithm
Authors: Chen Xiaoxiao , Huang Xueyu , Zhu Donglin , Qiu Yaxian 
Published: April 2021

Abstract
The sparrow search algorithm has attracted much attention due to its excellent characteristics, but it still has shortcomings such as falling into the local optimum and relying on the initial population stage. In order to improve these shortcomings, the chaotic flying sparrow search algorithm is proposed. In the initialization, the chaotic mapping based on random variables is introduced to make the population distribution more uniform and speed up the optimization efficiency of the population. In the discoverer stage, the dynamic adaptive search strategy and levy flight mechanism are used to increase the search range and flexibility, and the random walk strategy is introduced to make the follower’s search more detailed and avoid premature phenomenon. The effectiveness of the improved algorithm is verified by six standard test functions, and the introduction of a variety of strategies greatly enhances the optimization ability of the algorithm.
HCHODetector: Formaldehyde concentration detection based on deep learning
Authors: Cao Zhihao , Shao Mingfeng , Shi Aiju , Qu Hongchun 
Published: April 2021

Abstract
Currently, deep learning technology is developing rapidly. Deep learning is mainly used in the fields of vision and hearing for human beings, but less in the field of olfactory. Formaldehyde is a common gas harmful to human health. However, the traditional methods of Formaldehyde concentration detection are inefficient in some cases. As for this problem, this paper proposes a novel formaldehyde detector namely HCHODetector. Specifically, this detector is based on deep learning and HSV colour space augmentation. Moreover, we propose a novel Mask-guided module and a novel pre-training network to enhance the colour discrimination ability of HCHODetector. As a consequence, the experimental results show that the detection error is within 0.08 mg/m3 in the actual environment, which provides a new idea for Formaldehyde concentration detection.
Power patent classification method based on deep neural network
Authors: Min Hua 
Published: April 2021

Abstract
Aiming at the problems in power patent classification, a power patent classification method based on deep neural network is proposed. Firstly, the power patent is analyzed for preprocessing. Then, the key words in the patent abstract are extracted to construct a power vocabulary dictionary. Then, the power patent classification model is built according to the deep neural network. Finally, the power patent classification model based on deep neural network is used to classify the power patents, and the accuracy is close to 98%. It can be seen that the deep neural network method can not only realize the classification of power patent, but also has good classification effect.
Parameter Anomaly Identification Model About Supervisory Control And Data Acquisition
Authors: Ping Guo 
Published: April 2021

Abstract
With the rapid progress of sensor technology, state monitoring of wind turbines is more comprehensive, the amount and type of the sensor used will be more, the state parameters of wind turbines will be more. According to the status indicators to parameter index of target parameters selector model is established in this paper, and the number of input parameters are reduced on the basis of the guarantee accuracy. The two sub-models constitute the abnormal identification model of the working condition index. Finally, the model is analyzed by an example to verify the accuracy and effectiveness of the model. The research in this paper on abnormal discrimination of working condition parameters and evaluation methods of wind turbines is an important premise and basis for making scientific and reasonable operation and maintenance decisions of wind farms, and it has important academic significance.
Applications of Markov Chain in Forecast
Authors: Yutong Xia 
Published: April 2021

Abstract
The article is going to introduce Markov chain and its application into business. Two main daily life examples are applied for the explanation of this theory. In the Markov process, the probability of one state only depends on the next previous state. A mathematical model of market forecast and weather forecast is able to build through constructing transition probability matrix, analysising and computing with Markov chain. According to the data and calculation verification, we can see Markov Chain is a scientific, effective and convenient method for prediction and can be used for solving daily issues.
Research on watermark printing technology of STL model based on Menger curvature
Authors: Zhang Changhao , Li Hu , Chen Xianggang , Shi Xiaolei 
Published: April 2021

Abstract
In order to solve the data protection problem of STL model, this paper proposes a 3D printing watermarking method based on Menger curvature and K-means clustering algorithm. By comparing the research contents of 3D mesh model watermarking algorithm and two 3D printing watermarking methods, the 3D printing watermarking method is applied based on Menger curvature and K-means clustering algorithm. The method flow, watermark embedding and watermark extraction are discussed. Finally, the experimental results show that this method can not only improve the invisibility of model watermark, but also be robust to geometric attacks such as rotation, scaling and translation.
Research on marginal cost model of electric vehicle charging and swapping facilities based on queuing theory
Authors: Hu Lanxin , Yue Tyler 
Published: April 2021

Abstract
Research on the configuration of charging and switching facilities for electric vehicles is an important part of the development of related industries. In this paper, based on the analysis of charging behaviour of electric vehicles, the mathematical model is established by using queuing theory, and on this basis, the marginal cost theory of microeconomics is introduced to analyse the number of charging facilities under different utilization rates. The marginal cost model of charging and switching facilities is established with the objective of minimizing the total marginal cost of charging and switching facilities service system, and is verified by an actual example. The results show that through the rational allocation of the number of facilities, the marginal cost under different utilization rates can be optimized, which can provide strong support for decision makers to make operation plans and promote industrial development.
Space Robot Target Intelligent Capture System Based on Deep Reinforcement Learning Model
Authors: Liang Binyan , Chen Zhihong , Guo Meishan , Wang Yao , Wang Yanbo 
Published: April 2021

Abstract
There are many on-orbit capture tasks for space robots. At present, most space robots capture methods are based on the trajectory planning of robot kinematics. This kind of method has poor control precision in space environment. The intelligence degree of robot capture task is very low. We built a simulation environment for robot space target capture task based on physics engine. A real-time online simulation training platform is established in the simulation environment. We design a robot deep reinforcement learning motion control model based on Actor-Critic algorithm. We optimize the reward function of the DRL model. The reward function shortens the training time and improves the score performance of the model. The experiment data show that the DRL model converges in 800 steps. The average score and standard deviation of the model indicate that the model has successful completed the capture task of space robot.
Image Acquisition Time Series Prediction Method Based on Deep Learning
Authors: Mo Zhisong , Li Yifei 
Published: April 2021

AbstractAt present, in-depth learning can be said to be a learning method based on neural network, and time series can be used to achieve prediction results. Therefore, a time series prediction method for image collection based on deep learning is proposed. Based on time series prediction, the image collection in deep learning is analyzed, and the DBN model is combined with GCRBM model to train the model, identify the time series category, and reconstruct the sequence in order to achieve complete time series prediction. The experimental results show that the time series prediction method works well.
Application of Mathematical Model Based on Optimization Theory and Particle Swarm Algorithm in Radar Station Layout Optimization
Authors: Xuefeng Wang , Chen Mimi 
Published: April 2021

Abstract
The optimization problem of radar station layout was derived through its mathematical description to explore the application of the optimization theory and particle swarm optimization (PSO) in this optimization problem; after the optimization problem was solved, a disturbance factor was introduced to present the improved PSO algorithm (PSO-DF); in the end, the PSO-DF algorithm was used to construct an radar station layout optimization model. The simulation results of the algorithm performance and the radar station layout optimization model show that in comparison with the basic PSO algorithm, the improved PSO-DF algorithm has better local search performance; corresponding to different classical functions, the optimal value, the worst value, mean value and variance of the PSO-DF algorithm are all superior to those of the basic PSO algorithm; the fitness value corresponding to the simulation via the constructed radar station layout optimization model is about 0.8, and the network shows satisfying effects in the monitoring of different areas of responsibility. This research provides a certain reference for the application of the optimization theory and PSO algorithm in the field of radar station layout optimization.
Research on 3D Object Detection Method Based on Convolutional Attention Mechanism
Authors: Yong Zhang , Xiaoxia Zhang , Nana Da 
Published: April 2021

Abstract
In 3D object detection, the illumination and occlusion of the input point cloud data lead to inaccurate feature extraction, and the maximum pooling method destroys the information structure of the point cloud, leading to the problem of weak local feature expression. This paper proposes a 3D object detection method based on Convolutional Attention Mechanism (CAM). CAM first adds an attention mechanism to the first and last layers of the traditional feature extraction network structure, then fuses the feature information of different layers, and finally performs normalization operations. Experimental results show that this method has achieved better results on the KITTI and SUN-RGB data sets compared with mainstream algorithms DoBEM, MV3D, DSS, COG, 2D-driven, and FPNet. The mAP index has increased by 0.6%-12.5%. While CAM realizes the fusion of local and global information, it significantly improves the accuracy of object detection in illuminated and occluded scenes.
An improved U-Net method with High-resolution Feature Maps for Retinal Blood Vessel Segmentation
Authors: Dongye Chang-lei , Ma Yanyan 
Published: April 2021

Abstract
The accurate segmentation of retinal blood vessels is of great significance for the diagnosis of diseases such as diabetes, hypertension, microaneurysms and arteriosclerosis. However, manual segmentation of retinal blood vessels is time-consuming and laborious. This paper proposes a convolutional network structure based on U-Net for retinal vessel segmentation. First, a new convolution block, which makes full use of shallow high-resolution feature maps to minimize the information loss caused by downsampling, is added to the network. Second, the network was downscaled. Particularly, this network application conducts downsampling twice to reduce the complexity of the network and the number of parameters during training. In addition, we retain the original short connection, which merges the feature information of the shallow and deep networks. Therefore, this network can capture the details of blood vessels more effectively. We tested the work on the DRIVE data set and evaluated the accuracy, sensitivity, specificity and AUC, which were 0.9552, 0.7603, 0.9839, and 0.9789, respectively. A comprehensive comparison between the proposed algorithm and the existing algorithms shows that the various indicators of the algorithm perform well.
Research on the Fusion Model of Sports and Medicine Based on Artificial Neural Network Health Analysis and Forecast — —Take atherosclerosis as an example
Authors: Yu Wang , Jinghong Wang , Pan Deng , Jinying Li , Yiting Wang 
Published: April 2021

Abstract
Purpose: With the increasing application of artificial neural network technology in medical diagnosis and health risk prediction, the model of sports medicine integration based on ANNs health risk prediction will provide an operable new idea for the development of sports medicine integration in China. Method: This study takes the more common atherosclerosis as an example and uses the artificial neural network analysis technology to predict the disease risk of 1109 patients, explores the effect of physical activity in predicting models, and explores new ideas for the integration of sports and medicine. Results: The model based on the LSTM network has a good predictive effect in realizing the level of physical activity and the risk of atherosclerosis. Therefore, it is recommended to include indicators such as physical activity level and exercise habits to predict and manage disease risks in the development of the integration of sports and medicine.
Discussion on Computer Network Security Prevention Based on Big Data Era
Authors: Sun Lixian 
Published: April 2021

Abstract
With the continuous development of reform and opening up and the continuous development of science and technology, information and communication industry has also achieved rapid progress. Certain problems will also be encountered in the development of computer technology. The most important problem is about computer network security. Some people are likely to be attacked by viruses and Trojan horses while using computers, leading to a lot of core damaging information. Some network hackers may also use computer loopholes to steal relevant information. Strengthening computer network security management is very important. This article will focus on computer network security in the era of big data as the main topic and discuss the relevant content.
Data Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network
Authors: Zhang Qianyi , Zhang Chi , Ni Jiaming , Wang Xuqiang , Zhang Yao 
Published: April 2021

Abstract
For the problems of privacy data protection caused by massive data sharing in the construction of power Internet of things, a data sensitivity measurement and classification model based on information entropy and BP neural network is proposed. Firstly, a recognition matching algorithm is proposed to identify the sensitive level of attributes in the dataset, and the information entropy is used to determine the weight of attributes sensitivity level, so as to calculate the sensitivity measurement value of records in the dataset; finally, the BP neural network is used to output the data classification results. The experimental results show that the model can achieve accurate measurement and classification of data, with low incorrect judgment rate and error rate.
End-to-end drug entity recognition and adverse effect relation extraction via principal neighbourhood aggregation network
Authors: Kong Luyue , Lai Qinghan , Liu Song 
Published: April 2021

Abstract
Drug entity and adverse effect relation extraction is a critical task that aims to recognize drug entity and extract adverse effect relation from the unstructured medical text. Many works have used statistical learning approach, traditional or new deep learning approach to solve similar pharmacovigilance problem. Recent works tended to employ the graph convolutional network to enhance the ability of drug and adverse effect information extraction. However, the injective problem generated by a single aggregator and the weak generalization of the summation aggregator cause the graph neural network lack the ability to extract sufficient relation information. To solve these problems, we propose a new end-to-end model named recognizing Drug entities and extracting Adverse effect relations via the Principal Neighbourhood Aggregation network (DAPNA). Moreover, we compared the DAPNA with baseline models on the Adverse Drug Effect (ADE) dataset using multiple metrics. The experimental results demonstrate the method proposed in this paper achieves state-of-the-art results and can be applied to other drug and adverse effect information extraction tasks.
Human Error Evaluation Model of Civil Aviation Maintenance Based on Grey Wolf Optimizer
Authors: Ma Ying , Wang Rui 
Published: April 2021

Abstract
Today, with the rapid development of science and technology, human error has already surpassed mechanical failure and become the most important factor in safety accidents. In order to reduce the occurrence of human error in civil aviation maintenance, and to improve the quality of maintenance production, four levels and 18 factors affecting human error in civil aviation maintenance are proposed in this paper,Taking Hongqiao base of Eastern Airlines as an example, a questionnaire survey was used to collect data,and an Inertial Adaptive Hybrid Gray Wolf Optimization (IAHGWO) was proposed,which combined with particle swarm optimization (PSO) and three improved strategies,and an evaluation model was constructed to use IAHGWO to train BP neural network. The results show that the evaluation model has good practicability and accuracy.
One-Shot Image Segmentation with U-Net
Authors: Zhao Guanyi , Zhao He 
Published: April 2021

Abstract
Image segmentation is an important task in the field of computer vision research. Many applications need accurate and efficient segmentation mechanisms. However, most existing methods need a large support set and have struggled with dealing with new classes in solving computer vision problems. In order to deal with the problem, this paper modified the traditional model to adapt it to the task of few-shot segmentation. Especially, the part of encoders was changed into a Siamese neural network for support branch and query branch. Then, the decoded query image is compared with different levels of features from the encoded support image. Experimental results show that this proposed model achieved the best performance compared with several baseline methods, and the improvement is more than 8%.
Fault Diagnosis Method of Power Transformer Based on Improved PNN
Authors: Tang Diankui 
Published: April 2021

Abstract
Power transformer is a core hub of power transmission in power grid, and its operation state will affect the effective operation of power grid. The existing power transformer fault diagnosis methods mainly use the traditional dissolved gas analysis (DGA) method combined with artificial neural network. Probabilistic neural network (PNN) has a wide range of applications in the field of power transformer fault diagnosis because of its good fault tolerance and high training efficiency. In PNN, the smoothing factor has a great influence on the output, but the parameter is lack of efficient selection method, which makes the classification efficiency of PNN not high. Aiming at this problem, a diagnosis method based on improved PNN is proposed for power transformer faults in this paper. Bat algorithm is used to optimize the smoothing factor of PNN to determine the optimal value. Based on the optimal results, the network model is trained to obtain the optimal power transformer fault diagnosis model. The experiment fully proves that the improved PNN has higher diagnosis accuracy than other methods.