bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feature Selection and Reduction of Lower Limb Activity Recognition Based on Surface Electromyography and Motion Data
Authors: Wei Chunfeng , Wang Hong , Hu Fo , Chen Jichi , Lu Yanzheng , Qi Yangyang 
Published: September 2021

Abstract
Daily activity recognition of lower limbs is of great significance to the health care of the elderly and patients with hemiplegia. Surface electromyography (sEMG) signal can directly reflect neuromuscular activity and is an important method for non-invasive monitoring of muscle activity on the body surface. In this paper, a novel method based on sEMG signal and inertial measurement unit (IMU) data to recognize daily activities of lower limbs is proposed. Record sEMG signals and IMU data of fifteen subjects using wearable sensor devices. After preprocessing such as filtering and sliding windows on the data, we extracted seventeen features. A feature selection method based on maximal relevance and minimal redundancy maximal relevance (mRMR) to select representative features. The selected features are input into four machine learning classifiers to classify four daily activities. The performance of the classifier is evaluated using accuracy and receiver operating characteristic curve-area under curve (ROC-AUC) score. The results show that the support vector machine has excellent performance in recognizing the daily activities of human lower limbs.
Data Augmentation with Illumination Correction in Sematic Segmentation
Authors: Liu Honghui , Wang Changjian , Peng Yuxing 
Published: September 2021

Abstract
In the training process of the deep learning network, data augmentation can effectively improve accuracy. Presently, the commonly used data augmentation methods have been mainly divided into space transformation and color transformation. However, these methods do not take into account the effects of the image’s illumination factor. Under natural conditions, the color, angle, and strength of the light will cause the color of the object to change, which is likely to cause segmentation errors. In this article, we start from the angle of illumination and perform data augmentation on the image. By applying the illumination correction algorithm based on Retinex theory, we are able to enhance the data and achieved good results in the experiment. However, there still remains some problems within the current algorithms based on Retinex theory. Especially for images with uniform illumination conditions, which can be overexposed after processed by these algorithms. In response to this problem, we have improved the relevant algorithm. The improved algorithm further improves the accuracy of semantic segmentation on the original basis.
A Recommended Method Based on the Weighted RippleNet Network Mode
Authors: Luo Yutai , Sha Baocheng , Xu Tao 
Published: September 2021

Abstract
User preferences were modeled by the RippleNet network and successfully applied in the recommender systems, but the weight of the entity was not considered. This paper proposes a RippleNet model incorporating the influence of complex network nodes. After the construction of complex networks based on knowledge Graphs, we build the maximum subnet model and calculate the influence of nodes in the graph network. We added it to the RippleNet as the weight of entities. The experimental results showed that new method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, solve the problem that entity influence was not considered in the RippleNet network, and made the recommended results more in line with users’ expectations.
Network Anomaly Traffic Detection Method Based on Multi-SAE and LSTM
Authors: Guo Shuai , Chen Bowei , Su Yang 
Published: September 2021

Abstract
For the purpose of faster and more accurate anomalous traffic detection with increasing classes of data traffic in the network, this paper proposes a new anomalous traffic detection method based on stacked auto-encoders and a long short-term memory network model. The method uses Multi-SAE to extract the effective features of sequential traffic, which is obtained by concatenating multiple stacked auto-encoders, and a long short-term memory network to extract the temporal structure of the effective features, with the Multi-SAE and the long short-term memory network in a back-and-forth tandem structure. To further improve the efficiency of the detection, redundant MAC addresses are also removed in the pre-processing. From the experimental results, this paper achieves effective detection of twenty types of data traffic with an accuracy rate of 98.25%, which is higher than that of the same category of research by nearly 2 percentage points, and the parameters of precision, recall and f1-score also reach over 96%, improving the detection results.
Distance of Single-Valued Neutrosophic Set and Its Application in Pattern Recognition
Authors: Wang Tongjuan 
Published: September 2021

Abstract
Single-valued neutrosophic set (SVNS) is an extension of fuzzy set, which combines the truth, indeterminacy, and falsity information. The measurement of distance of single-valued neutrosophic set will bring new ideas to pattern recognition. This paper introduces the distance definitions and properties of SVNS, and proposes the improved distance definition of single-valued neutrosophic set based on decision maker attitude towards indeterminacy information and applied them to pattern recognition. By assigning a value to decision-maker’s attitude towards indeterminacy information, this attitude comes from the historical experience and risk preference etc. The distance calculated in this way can often get a matching result closer to the reality. The results show that the improved distance formula of SVNSs is more effective and more practical for pattern recognition.
Research on Bi-objective Vehicle Routing Problem Considering Empty Loading Ratio
Authors: Sun Lulu , Jia Wei , Jiang Qi , Shi Weixuan , Zhang Meng 
Published: September 2021

Abstract
The vehicle routing plan in logistic distribution minimizing the transportation cost merely may lead to high empty loading ratio and a waste of logistic resources. This research studies the bi-objective vehicle routing problem considering empty loading ratio. The definitions about the empty loading ratio are proposed firstly. A bi-objective vehicle routing model is developed, minimizing the empty loading ratio and transportation cost simultaneously. Then, the solution method based on ɛ constraint method is provided to compute the Pareto optimal solutions. Finally, numerical experiments are reported, and the results show that the ɛ constraint method is better than the weighting method for the proposed problem.
Intelligent Video Surveillance System Based on Cloud Network
Authors: Li Liwei , Xie Haibin , Li Peng 
Published: September 2021

Abstract
There are two shortcomings in traditional video surveillance systems: First, the amount of data is so huge that the processing, analysis, and viewing of massive data requires a lot of manpower, material resources and financial resources. Second, it is difficult to store and transmit video data. Mass video data often needs to be stored and passed to the backstage for analysis, and huge data makes data storage and transmission difficult. In view of the above trouble, this paper proposes a set of intelligent video surveillance system based on cloud network. It contains modules such as face detection, quick retrieval, etc., and uses the cloud network as a medium to transmit data and information. It can complete the automatic detection, capture, enhancement, and coding processing of the human face in the video, search and compare with the background database automatically and quickly.
Business Anomaly Detection Method of Power Dispatching Automation System Based on Clustering Under-Sampling in the Boundary Region
Authors: Li Junliang , Xu Jianhang , Huang Xu , Ren Bing , Dai Tianqi , Zhang Zemin , Su Rui 
Published: September 2021

Abstract
Timely detecting business anomaly in the power dispatching automation system is significant for the steady operation of the power grid. Though the imbalanced binary classification method in machine learning is an effective way to achieve the business anomaly detection of the system, the overlap of boundary samples is an urgent issue affecting the classification effect. An under-sampling method by removing the clustering noises of the majority samples in the boundary region is proposed. Firstly, KNN is used to search adjacent points of the majority class, and the boundary region and the safety region are divided according to the proportion of the majority samples in adjacent points. Secondly, DBSCAN is used to cluster the majority samples in the boundary region, and noise points are removed. Finally, it’s combined with the method based on model dynamic selection driven by data partition hybrid sampling (DPHS-MDS). The purpose of reducing the overlap degree of boundary samples, balancing the dataset and improving the classification effect is achieved. Experimental results show that the proposed method is superior to the relevant mainstream methods under F-measure and G-mean.
Optimizing the Weights and Thresholds in Dendritic Neuron Model Using the Whale Optimization Algorithm
Authors: Xu Weixiang , Li Cunhua , Dou Yuxiang , Zhang Mengnan , Dong Zihao , Jia Dongbao , Ban Xinxin 
Published: September 2021

Abstract
In recent years, with the great success of dendritic neuron model (DNM) in various fields, the application of intelligent optimization algorithms in dendritic neuron model has attracted increasing attention of researchers. The training process of neural network is regarded as one of the great challenges of machine learning because of its non-linear nature and unknown optimal parameters. The traditional training algorithm of DNM is prone to fall into local optimum and speed of convergence slowly and so on, resulting in the problem of accuracy and low efficiency. In this paper, for solving the classification problem of dendritic neural model, an innovative intelligent optimization algorithm which named whale optimization algorithm (WOA), is applied to the training of DNM for the first time. Compared with six traditional and classic intelligent optimization algorithms in four classic datasets, the results indicate that WOA-DNM has good performance in various aspects, and its advantage is remarkable.
BERT Pre-training Acceleration Algorithm Based on MASK Mechanism
Authors: Hu Puzhao , Dong Lanfang , Zhan Yanyan 
Published: September 2021

Abstract
With the development of natural language processing (NLP) science, machines can understand human language better and better. Especially with the emergence of pre-training models in recent years, such as ELMO, GPT, BERT, etc. Researchers can easily transfer model knowledge and apply them to related fields, but at the same time they often ignore the pretraining process. Due to the huge number of parameters and calculations of the pre-training model, it often takes a lot of resources and much time to get a good pre-training model. So many people are daunted by the pre-training process. From a different perspective, this article mainly focuses on the pre-training process of BERT, and conducts in-depth research and related improvements on its own mask mechanism, so as to accelerate the training process. The experimental results show that our algorithm can get better result than the original scheme which the BERT use. Also it is better than the original BERT in terms of training speed and can better understand the training text input. What is more, we have also conducted a detailed analysis of the experimental result.
Research on Optimization of Main Engine Speed of Inland Ship Based on Genetic Algorithm
Authors: Peng Zhongbo , Tan Jianping , Shi Liyong 
Published: September 2021

Abstract
This paper aims at reduced total fuel consumption of voyage by obtaining navigation data of inland ships, and established the fuel consumption prediction model of the main engine in the segment through studying the navigation route by sections according to the characteristics of the navigation environment, and solved the problem with the help of genetic algorithm to obtain the best main engine for each segment main engine speed. The results show that: (1) The error of the neural network model verification data set is mostly within 6%, and the model performance is good; (2) The experimental ship from Nanjing to Jingzhou from June to September adopts the optimized main engine speed to save 0.6232t of fuel, which is 4.06% lower than the original total fuel consumption. This method has practical and guiding significance for reducing the cost of inland ship navigation and improving the ecological environment of the Yangtze River.
An OSM Contributors Classification Method Based on WPCA and GMM
Authors: Zhao Yijiang , Wei Xingcai , Liu Yizhi , Liao Zhuhua 
Published: September 2021

Abstract
Contributors have a significant impact on data quality of OpenStreetMap (OSM) because most of them are the non-professional, so clustering analysis of contributors based on different experiences has practical significance. Firstly, this paper obtained 31 behavioural characteristics of contributors from OSM historical data. Then, a weighted principal component analysis (WPCA) method was used to reduce the dimensions of the contributors’ behaviour in the selected region. By using an unsupervised prototype-based Gaussian mixture model (GMM) clustering algorithm, contributors with similar contribution attributes in the London area were clustered into four groups. Finally, the characteristics of four different types of contributors are analysed, and two types of experienced and professional contributors are found, who contribute a large amount of high-quality data.
Design and Implementation of a Cross-Media Image to Sound Algorithm and Objective Evaluation Algorithm
Authors: Liu Zihao , Wang Botao 
Published: September 2021

Abstract
With the joining of digital media and Internet, the application of the conversion of cross-media information becomes wider and wider, which makes the integration of multimedia information possible. The transformation of image information to sound information is also in the stage of exploration. It can be applied to all aspects of the industry. Most importantly, it can be used to guide the blind. In this paper, an improved image-to-sound algorithm based on original method is proposed to judge whether there are obstacles in front of the road and test the sound segment through human ears. Among different mapping methods, the highest accuracy can reach 84.2%, which is much higher than the original method. At the same time, an objective evaluation algorithm is designed based on the neural network, which can replace the human ear to judge a large number of image information, and the matching degree of this model can reach 68%.
Robust Fuzzy Clustering Algorithm Based on Adaptive Neighbors
Authors: Miao Kehua , Wang Jialin , Gao Yunlong , Cao Chao , Xie Youwei , Gao Peng 
Published: September 2021

Abstract
In pattern recognition, fuzzy logic is widely used in unsupervised classification or clustering methods. Fuzzy c-means (FCM) clustering algorithm is a typical dynamic clustering algorithm of the fuzzy c-means algorithm based on the error square sum criterion. It introduces fuzzy membership and optimizes the objective function to obtain each sample point for all classes. The membership degree of the center is used to automatically classify the data sample. However, the FCM algorithm is susceptible to the noise points and outliers, and the unbalanced data structure reduces the generalization performance of the algorithm. In this paper, we propose a fuzzy c-means clustering algorithm with adaptive neighbors weight learning. Through adaptive neighborhood robust weight learning, an adaptive weight vector with robustness and sparsity is obtained. During the optimization, we only activate the k samples with the shortest distance to the cluster center and eliminate extreme noise samples to improve the global robustness and sparsity of the algorithm. Finally, empirical analysis verifies the superiority and effectiveness of the proposed clustering method.
Optimizing the Flight Route of UAV Using Biology Migration Algorithm
Authors: Zhang Q Y , He X Y , Dong Y Q , Jiang S Y , Song H 
Published: September 2021

Abstract
Optimizing the flight trajectory unmanned aerial vehicles (UAVs) is always a popular optimization problem in computation intelligence field, which aims to search optimal flight path for avoiding detection and complementing some highly difficult missions in complex military environments. This paper mainly utilizes a recent proposed biology migration algorithm (BMA) inspired by the species migration mechanism for dealing with the UAV trajectory optimization problem. The main goal of the UAV model is to search feasible parameters including the flight angle, coordinates and distance for minimizing the flight price computed based on the threat and fuel costs. As one of swarm intelligence techniques, BMA has the characteristics of self-organization, fast convergence and self-adaption in the optimization process, So, it is able to find a safe flight route between the start point and target point while avoiding the dangerous regions and minimum cost. Simulation experiments show that BMA can generate promising and better results with respective to other compared algorithms.
Motion Target Detection and Recognition Based on YOLOv4 Algorithm
Authors: Yu Hailan , Chen Weili 
Published: September 2021

Abstract
With the application of artificial intelligence more and more widely, the target detection of artificial intelligence “eyes” is becoming more and more important, which can give the machine the ability to detect and recognize image content in the environment. In this paper, YOLOv4 deep learning motion target detection algorithm is used to achieve the positioning and recognition of motion targets, to identify and mark the location and type of objects contained in the image, to achieve the detection of pictures, videos and camera realtime image detection, so that the machine has the most basic visual ability. Using YOLOv4 algorithm, the speed requirement of real-time detection can be realized with certain precision, and the model mAP value reached 69.48%.
Optimization Method of Segmented Grain Structure Based on Shortest Distance Proxy Model
Authors: Zhao Jiuling 
Published: September 2021

Abstract
The structural integrity of the grain is very important for the grain with complex geometry. Large section column structure has its particularity, differs from that of a single column, its length to diameter ratio is large and absolute meat the increase of the thickness, particles of geometric shape and size of the key factors to determine the gas production rate and its change rule of the engine, at the same time, the engine thrust and pressure change with time, particle size determines the volume and weight of the combustion chamber,Therefore, the particle design of solid rocket motor determines the quality design index of the engine to a large extent.In order to improve the efficiency of model optimization, an agent model is established in this paper. The prediction accuracy of the model depends on the distance between prediction points and sample points. The shortest distance hierarchical clustering algorithm is used to improve the prediction accuracy.Through the research, the comprehensive effect of the key factors affecting the structural integrity of the propellant and the law of the influence on the structure and safety margin of the propellant are obtained. The research method and law can provide support for the large solid motor grain design, and provide theoretical reference for the solid rocket motor grain structure multi-parameter optimization design.
Site Location Optimization Based on Genetic Algorithm
Authors: Zuo Yahui , Huang Shan , Feng Zijian , Dou Yajie , Xu Xiangqian 
Published: September 2021

Abstract
With the goal of providing food to people in need, the FBST serves nearly 4,000 square miles in six counties in New York State. The MFP program is among the main activities of FBST, using an MFP truck loaded with food to serve 70 sites in New York State. An effective and fair schedule for visiting all 70 sites is necessary to optimize the MFP plan. In order to describe the problem, a triad {G, R, C} of goal, resource and constraint is established according to the decision theory. Based on the established triad, this paper establishes a multi-objective optimization model with the goal of improving validity and fairness, a genetic algorithm has been adopted to solve the problem. The time schedule for visiting sites is finally obtained after the iteration of 2000 times, which took 156.40 seconds. Considering that customers can go to further sites, the number of service sites and the location of sites can be optimized. The K-means clustering algorithm based on the improved demand centroid solution method is used to solve the site location selection. After optimizing the location of the site, the performance of seniorsite and regular-site systems is improved by 30.755% and 36.413% respectively.
Image Super-Resolution Based on Additional Self-Loop Supervision
Authors: Chen Hong , Xu Li , Luo Bing , Du Shuai , Xiao Kunshu , Li Siwei 
Published: September 2021

Abstract
Relying on the prevalence of deep neural networks, only one image super-resolution (SR) research has taken a big step forward. Many network models use the difference between the real image and the generated super-resolution image as a loss function, ignoring the difference between the small-scale image and the super-resolution generated image. We adopt an end-to-end network model with two-way supervision, which can ensure that the image content is similar in both large and small scales. Only real natural images are used for supervision. As the scale of the image becomes larger, the difference becomes larger, and the effect of correcting image details becomes smaller. Adding the loss between the input image and the degraded image of the generated image can not only keep the content of the image similar on a small scale, but also ensure the image details. Compared with several state-of-the-arts, our method obtains the best results.
Research on Floating Object Ranging and Positioning Based on UAV Binocular System
Authors: Guo Peng , Zhang Nengwen , Jiang Bing , Qin Fatao , Zhou Zhiheng 
Published: September 2021

Abstract
Aiming at the ranging and positioning problem of floating objects polluted by water surface, the binocular system of UAV is designed to obtain the parallax and distance information of target objects in the scene. Firstly, Zhang Zhengyou calibration method is used to calibrate binocular camera; Secondly, stereo correction is carried out on the image to obtain a standard binocular stereo vision model; Finally, the depth information of the target object is obtained by stereo matching algorithm. The simulation results show that the depth error is less than 5%, and the actual physical coordinate position of the floating object is obtained by combining the GPS information of the UAV itself.