bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Partheno-Genetic Algorithm for Open Path Multi-Depot Multiple Traveling Salesmen Problem
Authors: Lou Ping , Xu Kun , Yan Junwei , Xiao Zheng
Published: April 2021

The Multiple traveling salesmen problem (MTSP) is a complex combinatorial optimization problem, which is an extension of the well-known traveling salesmen problem (TSP). Compared to TSP, MTSP is more suitable to model real-life problems. In this paper, an open path multi-depot multiple traveling salesmen problem (OPMDMTSP) is studied. For the problem studied, two different objectives are considered: minimizing the total cost of all sales staff and minimizing the longest travel length. For the OPMDMTSP, an improved partheno-genetic algorithm (IPGA) is proposed in this paper. In IPGA, a new selection operator that combining roulette selection and elitist selection is implemented. In addition, a more comprehensive mutation operation that introduces the propagation mechanism of invasive weed optimization algorithm is used. Extensive experiment that compares the proposed method with some state of the art methods shows that the IPGA is outperform other methods in terms of both solution quality and convergence ability.
Efficient Object Detection based on Deep Feature Fusion Network
Authors: Zhao Bo , Liu Shuyun , Liu Guizhong , Yang Zhonglin , Ma Zhiyang , Fu Huini
Published: April 2021

Real-time object detection is crucial for many applications, such as automatic driving, security monitoring. It is vital for these application to perform accurate detection while keeping real-time performance. This paper propose an efficient object detection method based on deep feature fusing strategy. The feature extraction network employs deep convolution neural network to fuse multi-channel input, including colored image, infrared image and motion image. Multiple sources of images can provide complementary information, which is beneficial to accurate object detection. The backbone network is based on darknet-53 while integrating with feature aggression modules to capture features of shallow and deep activation maps. Our network is first trained on large-scale IMAGENET and COCO datasets, and then fine-tuned on small-scale datasets collected in real world. A series of quantitatively and qualitatively experiments are conducted to show superiority and efficiency of our method.
Physical therapy scheduling of inpatients based on improved Genetic Algorithm
Authors: Li Xin , Chen Haibin
Published: April 2021

In recent years, the physical therapy has become more and more extensive. How to schedule corresponding treatments efficiently in rehabilitation hospitals has attracted the attention of many scholars. A typical physical therapy treatment can be divided into three stages, i.e., the beginning, the treating and the ending. Moreover, only the beginning and the ending stages require a therapist. The treating stage does not. In addition, a physical therapy treatment requires to occupy a bed and an instrument related simultaneously. In order to solve challenging scheduling problems, a genetic algorithm based on Waiting Time Priority Algorithm (WTPA) is designed. Finally, an instance from the rehabilitation department in a hospital is tested. By comparing with the classical simulated annealing algorithm (SA), it verifies the effectiveness and efficiency of the genetic algorithm designed.
Offline Handwritten Text Recognition Using Deep Learning: A Review
Authors: Wang Yintong , Xiao Wenjie , Li Shuo
Published: April 2021

The area of offline handwritten text recognition(OHTR) has been widely researched in the last decades, but it stills an important research problem. The OHTR system has an objective to transform a document image into text data. Compared with online handwriting recognition, the dynamic information about the writing trajectories in OHTR is not available. Many advancements have been proposed in the literature, most notably the application of deep learning methods to OHTR. In this paper, we introduced how this problem has been handled in the past few decades, analyze the latest advancements and the potential directions for future research in this field.
Weak atomic decompositions of B-valued weak Orlicz quasi-martingale spaces
Authors: Zhang Chuanzhou , Wang Jiufeng
Published: April 2021

In this paper, we define the B-valued weak Orlicz quasi-martingale spaces and three types of weak atoms. Three types of weak atomic decompositions of the weak Orlicz quasi-martingale spaces are proved by the geometric properties of Banach spaces.
Layout optimization of workshop equipment based on WITNESS
Authors: Wu Rongjie , Huang Zhonghua , Xie Ya
Published: April 2021

According to the production order, the engine parts workshop of a company mainly produces four parts: valve hydraulic tappet, camshaft, camshaft gear and bearing cover. With the increasing demand of production, the production capacity brought by the original workshop equipment layout gradually cannot meet the increasing demand. In this paper, based on the WITNESS simulation software, we are preparing to collect the data of engine parts and components production workshop in order to get the data information of each element by simulation, and reasoning the unreasonable parts where the busy rate is too high and put forward several improvement methods. Through WITNESS software simulation, respectively, we can analyse whether there will have improvement and conclude the optimal improvement project. As the busy rate of lathe group and milling machine group of the original workshop layout is more than 80%, while the statistics are less than 60% by using the optimal plan to improve the existing layout, reaching a reasonable busy rate. It can be concluded that it is feasible to solve the problem of high work step busy rate and improve the overall production efficiency of the workshop through reasonable equipment layout optimization.
Correlation analysis of College students’ achievement based on improved Apriori algorithm
Authors: Wang Hongqin
Published: April 2021

A large amount of student achievement information has been accumulated in the teaching information management system of colleges. It is of great significance to use data mining technology to analyze the data and find out the potential value of the data.The basic idea and defects of the Apriori algorithm are described in this paper, an improved Apriori algorithm is proposed,which is used to analyze the course scores of all students in the software engineering major of a university in 2018, and dig out the relationship between scores and courses, so as to provide learning guidance for students, provide teaching reference for teachers, and have certain guiding significance for the quality of talent cultivation in colleges and universities.
A deep learning method for classification of chest X-ray images
Authors: Zhao Jiashi , Li Mengmeng , Shi Weili , Miao Yu , Jiang Zhengang , Ji Bai
Published: April 2021

Deep learning techniques have provided new research methods for computer-aided diagnosis, allowing researchers to use deep learning methods to process medical imaging data. Chest X-ray examinations are widely used as a primary screening method for chest diseases. Therefore, it is of great importance to study diagnosis of 14 common pathologies in chest X-ray images using deep learning methods. In this paper, we propose a deep learning model named AM_DenseNet for chest X-ray image classification. The model adopts a dense connection network and adds an attention module after each dense block to optimize the model’s ability to extract features, and finally a Focal Loss function is applied to solve the data imbalance problem. The experiments used chest X-ray images as model input and were trained to output the probabilities of 14 chest pathologies. The Area under the ROC curve (AUC) was used to measure the classification results, and the final average AUC was 0.8537. The experimental results show that the AM_DenseNet model could complete the pathology classification of the chest X-ray images effectively.
Deep Learning based Privacy Information Identification approach for Unstructured Text
Authors: Ning Yichen , Wang Na , Liu Aodi , du Xuehui
Published: April 2021

Data sharing sometimes brings the privacy disclosure risk. Anonymization methods such as k-anonymity, l-diversity prevent privacy disclosure, but such methods are suitable for structured text. There are a lot of unstructured texts in people’s lives (such as social network texts, clinical texts), and identifying and structuring the private information(PI) of unstructured texts is a problem. Based on this, we propose a deep learning-based unstructured text PI identification approach, which can extract PI in unstructured text, associate the PI with the corresponding subject, and organize it into structured data, to support follow-up anonymization. This approach is divided into two tasks: PI identification and PI association. we respectively propose a sequence labeling model based on the RoBERTa-BiLSTM-CRF hybrid neural network and a PI association method based on the RoBERTa-HCR hybrid neural network to identify PI and organize it into structured data. The experimental results show that, compared with the benchmark model, RoBEERTa-BiLSTM-CRF has better performance; compared with the current Chinese coreference resolution model, the average F1-score value of RoBERTa-HCR is increased by 6%.
A Closed-Form Localization Algorithm in Scan-Based Sonar
Authors: Sun Ping , Rong Yingjiao
Published: April 2021

Multi-station TDOA positioning is a more accurate positioning method, which can locate the acoustic emitter by processing the arrival time of signals collected by three or more measuring stations. This paper presents a closed-form localization algorithm using the angle-of-arrival and difference time of scan time measurements from the scan-based sonar (SBS).The basic principle of multi station TDOA passive location is proposed. Location scheme based on the combination of different platforms is used. The basic principle of TDOA passive location is discussed, as well as the realization scheme of the main problems such as the synchronization of observation station and acoustic target, the selection of ground observation station, error correction and so on.
Domain Adaptive Chinese Word Segmentation Method for power data security classification
Authors: Dong Yang , Zheng Yang , Zhang Xu , Gao Jing , Fan Zheng
Published: April 2021

Chinese word segmentation (CWS) is an important task for Chinese NLP, and also an essential pre-processing step to establish a word-root database for security classification of power data, covering different domains such as laws & regulations, power. It is impracticable to label a large number of training corpus for each domain, which brings great challenge to the supervised statistical learning method to carry out effective CWS. Therefore, a Chinese word segmentation approach based on dictionary and semi-supervised conditional random field (SS-CRF ) is presented. At first, a CRF model for CWS is trained with self-training and active learning algorithms and used to conduct CWS task. Then the dictionary features are introduced to correct the result of CRF based segmentation by adopting RMM algorithm. Experiments on a cross domain segmentation task show that the proposed method can effectively improve the domain-adaptive performance of CWS.
Feasibility of applying computed tomography angiography in diagnosing and classifying aortic intramural hematoma
Authors: Yuan Biying , Liu Jianhua , Tian Jiale , Xu Nannan
Published: April 2021

IMH is a common manifestation of aortic disease, which is also a common lesion in acute aortic syndromes. It is often considered as a precursor of AD. Paying close attention to the development of IMH is important for the prognosis of patients. CTA can effectively determine the location and range of IMH and whether it is associated with AD. At present, post-processing techniques of computed tomography angiography include MPR, CPR, VR. These methods are commonly used to evaluate IMH.
Study on Dynamic Monitoring Technology of Soil and Water Conservation in Construction Projects using Multi-source Remote Sensing Information
Authors: Xitao Huang , Siyu Chen , Yu Zhang , Han Zhang , Xiaofeng Yang , Pengtao Jiao , Liyuan Xiao
Published: April 2021

It is the legal duty of water administrative departments to monitor and supervise construction projects, but the single image source and interpretation technology have stunted supervision efficiency improvement. By utilizing the characteristics and advantages of multi-source remote sensing information, we constructed dynamic monitoring technology of soil and water conservation in construction projects based on multi-source remote sensing data, and used Mi Zhi County and Zi Zhou County in Yu Lin City as the experimental area to extract new disturbance maps from 2017 to 2018. The results show that coordinated application of multi-source remote sensing information can effectively improve the monitoring frequency up 64%, and the accuracy up 25%, which can provide reference for the informatization of soil and water conservation supervision in construction projects.
An inner full-state feedback control model for traffic flow system
Authors: Zhang Lidong , Li Xiaowei , Wang Jian
Published: April 2021

In order to discover the compound effect of traffic system’s state variables on its stability properties and inner mechanism, we presented a type of inner full-state feedback control strategy based on optimal velocity model of traffic flow system. We took the car’s velocity and headway as the inner feedback control input parameters and car’s acceleration as the system’s output parameter. With Lyapunov’s direct method, we deduced the new state space equation and its stability condition. Under its stable conditions, we studied the effect of full-state feedback control strategy on traffic flow and the jamming transition by use of numerical and analytical methods. The new system’s controllability and observability were also studied. Numerical simulations verified our analytical results’ correctness.
Overall structure design of model management and data protection system based on STL
Authors: Zhang Changhao , HuLi  , Yu Tianbiao , Shi Xiaolei
Published: April 2021

This paper realizes the development of STL model management and data protection system for 3D printing cloud platform. Firstly, the 3D printing cloud platform is briefly introduced, and the system requirements analysis, system technical architecture construction, system flow and system database design are completed. Then, based on ASP.NET, database and other technologies, combined with C# language, Web development is carried out. Finally, from the perspective of tourist users, registered users and background administrators, the functions of their respective modules are realized.
Longitudinal aerodynamic coefficients modeling from simulated flight data of fixed wing aircraft based on three methods
Authors: Wang Zhigang , Li Aijun , Wang Lihao , Ma Shichao
Published: April 2021

The equation error method, output error method, and neural network method are used to model the aircraft longitudinal aerodynamic coefficients from simulated flight data. The equation error method and the output error method are based on postulated mathematical models of the longitudinal aerodynamic coefficients, and the least squared principle and the maximum likelihood principle are used to estimate the model parameters respectively. The neural network method uses the BP network output to fit the aerodynamic coefficients without assumptions of mathematical model of aerodynamic coefficients. Two sets of simulation tests were conducted, one set of data is used for model identification, and the other set is used for model validation.
An ensemble approach for art face recognition problem
Authors: Wang Cunzheng
Published: April 2021

In recent years, with the development of the digitalization of cultural relics, many cultural relic restoration models based on different calculation methods have been proposed. Although the models for feature classification (such as VGG16, Xception) or ensemble models (such as SVM and ANN) are relatively mature, multiple classification problems and accuracy problems are still far from ideal. This paper proposes an ensemble method for artistic face recognition based on multiple models. Six convolutional neural network models are used as basic models to compare their performance in gender and status classification. Based on the KaoKore data set, after training them to obtain the corresponding characteristics, the ensemble model is used for weight combination to obtain a more accurate model. It has achieved 98.10% and 90.13% accuracy on gender and status classification respectively, which is the best in the current literature.
Credit risk assessment in bank based on SMEs using PCA
Authors: Huang Zhou , Yan Zifeng , Zhao Qingzhu , Ma Keke
Published: April 2021

In recent years, small and medium-sized enterprises (SMEs) have gradually become the backbone of China’s national economic development due to their strong innovation ability and high operational efficiency. However, such enterprises have poor anti risk ability in the early stage of development and need banks to lend to them. In order to help banks quickly and accurately evaluate the ability of small and medium-sized enterprises to determine the amount of loans, this paper proposes a comprehensive ability evaluation model based on PCA algorithm. Considering the strong impact of the new COVID-19 on SMEs in 2020, we introduce this sudden factor into the model to improve the adaptability of the model and help banks to accurately evaluate the enterprise capacity under the COVID-19.
Countermeasures of Computer Security Storage and Cloud Computing Technology
Authors: Jin Wei
Published: April 2021

With the development and application of the Internet, it has greatly changed the way people live, produce and learn, and has been widely used in all fields of society. In the process of computer operation, massive data information is generated, which provides important technical support for computer storage for the effective processing of these data information. The Internet is a double-edged sword, which provides convenience, and is threatened by computer virus, illegal access, hardware damage and so on at the same time in the process of computer information storage, which leads to the collapse of computer system to bring huge economic losses for enterprises and individuals. This paper first expounds the security problems of computer security storage and the characteristics of cloud computing technology, and combines the characteristics of cloud computing technology to explain the application of cloud computing technology in computer security storage.
Collaborative Attention Network for Person Re-identification
Authors: Li Wenpeng , Sun Yongli , Wang Jinjun , Cao Junliang , Xu Han , Yang Xiangru , Sun Guangze , Ma Yangyang , Long Yilin
Published: April 2021

The quality of visual feature representation has always been a key factor in many computer vision tasks. In the person re-identification (Re-ID) problem, combining global and local features to improve model performance is becoming a popular method, because previous works only used global features alone, which is very limited at extracting discriminative local patterns from the obtained representation. Some existing works try to collect local patterns explicitly slice the global feature into several local pieces in a handcrafted way. By adopting the slicing and duplication operation, models can achieve relatively higher accuracy but we argue that it still does not take full advantage of partial patterns because the rule and strategy local slices are defined. In this paper, we show that by firstly over-segmenting the global region by the proposed multi-branch structure, and then by learning to combine local features from neighbourhood regions using the proposed Collaborative Attention Network (CAN), the final feature representation for Re-ID can be further improved. The experiment results on several widely-used public datasets prove that our method outperforms many existing state-of-the-art methods.
Research on the Recognition of Abnormal Behaviors in the Elderly Based on Wi-Fi Signals
Authors: Li YiFan , Cao YongChun , Lin Qiang , Wang WeiQiong
Published: April 2021

Wandering behaviour is an important diagnostic indicator for patients with Alzheimer’s disease, and falls are the main threat to the health of the elderly. Effective detection of these two types of abnormal behaviours of the elderly and timely intervention are important for improving the quality of life of patients with Alzheimer’s disease and related diseases significance. This study proposes and studies a method for detecting and identifying abnormal behaviours of the elderly based on Wi-Fi signals. First, the correlation between Wi-Fi signal changes and abnormal behaviours of the elderly was analyzed; secondly, this study applied general Wi-Fi equipment to obtain the status data of the abnormal behaviours of the elderly and preprocess the data through Hampel filtering; then, the effective features of the CSI sequence fragments were designed and extracted, a data set of 2243 samples including 7 types of abnormal behaviours of the elderly was constructed; finally, a BiLSTM-Based abnormal behaviour recognition model for the elderly was constructed, and the average classification accuracy reached 95.95%, which proved this experiment is feasible and effective.
Research on Weapon Equipment Storage Structure Optimization Model Based on Improved ABC Classification Method
Authors: Li Xiaosong , Li Zenghua , Peng Xinran , Xiao Zhenhua
Published: April 2021

Weapon equipment storage is an important part of weapon equipment supply support. In this paper, we analysed the related concepts of weapon equipment storage, introduced the ABC classification method, proposed the steps to construct the optimization model of the weapon equipment storage structure, studied the influencing factors of the weapon equipment storage structure, and constructed a storage structure optimization model based on the improved ABC classification. The optimal weapon equipment storage structure and strategy were obtained, and a case analysis was carried out. The research conclusions can provide reference for the practical work of weapon equipment storage.
Analysis of Cheating in Exams Based on Difference Equation Model
Authors: Wu Chaolin , Maalla Allam
Published: April 2021

The problem of cheating in exams is a serious social problem, and cheaters have done huge damage to the social public order. How to reduce or even eliminate cheaters? Researchers have analyzed from different angles, most of the research is from the perspective of game theory or evolutionary game theory. However, game theory and evolutionary game theory have the disadvantage of not being able to quantitatively examine the changes in the number of cheaters and supervision. Aiming at the deficiencies of game theory and evolutionary game theory, this paper establishes the evolution model of the cheating problem of the differential equation model, analyzes the balance point and characteristics of the coexistence evolution model of different actors of cheaters and regulators, and examines the behavior of cheaters along with supervision. Changes in the number; the influence of different factors on the changes in the number of cheaters is analyzed, and on this basis, a comparison of anti-cheating strategies is made. After comparing the effects of the two different anti-cheating strategies of ‘blocking’ and ‘blocking’, it is concluded that ‘blocking’ can reduce cheaters more than ‘blocking’, and corresponding policy recommendations are given. Considering that it is difficult to obtain the data of cheaters and supervisory behaviors, the model was simulated using MATLAB, and the simulation results verified the correctness of the analysis.
Extraction and construction algorithm of traffic road network model based on OSM file
Authors: Wang Hangong , Wang Jiakang , Yu Peiyao , Chen Xudong , Wang Ziyang
Published: April 2021

In order to solve the problems that are not conducive to the automatic analysis and processing of related applications in the initial data of the road network, the main data is extracted from it to construct a traffic road network model. We designed an algorithm for extracting and constructing a traffic network model based on OSM files. The algorithm is mainly divided into preprocessing of OSM geographic data, road network extraction, road recognition, intersection recognition and merging, providing data basis for traffic simulation needs.
GDC block: gradient-guided direction-aware convolution block for image classification
Authors: Peng Jinye , Liu Maomei , Tang Lei , Zhong Sheng , Luo Hangzai
Published: April 2021

Convolutional Neural Network (CNN) has achieved great success in visual applications. In the field of image classification, researchers usually customize CNN models to meet the needs of different real-world applications. It consumes a lot of human labor and computing resources but only achieves slight performance improvement. Besides, some recent works try to integrate prior knowledge into classic CNN models to improve their accuracy, but they are usually only effective for special applications. In this paper, we propose a gradient-guided direction-aware convolution (GDC) block. It can be used to replace low-level convolutions of existing CNN without changing the off-the-shelf architecture. The gradient priors provide object shapes that CNN’s low-level convolution requires. And the direction-aware mechanism expands the receptive field size. This scheme is a trade-off between model size and model accuracy. Experimental results show it can moderately reduce the size of any CNN models while enhancing their performance.
A New Speech Enhancement Method Based on Nonnegative Low-rank and Sparse Decomposition
Authors: Sun Jiayi , Sun Chengli , Hong Yi
Published: April 2021

Enhancement of speech degraded by strong noises is a highly difficult task. In this paper, a nonnegative low-rank and sparse matrix decomposition (NLSMD) based speech enhancement method is given to address this problem. The proposed method is motivated with assumptions that in time-frequency (T-F) domain, since power spectrum of many types of noise with different frame are often correlative, noise can be assumed with a low-rank structure, while speeches are often sparse in T-F units. Based on these assumptions, we formulate the speech enhancement as a NLSMD problem, and design an objective function to recover speech component. Compared with traditional methods, the NLSMD-based method does not require a speech activity detector for noise density estimation. Experimental results show the proposed method can achieve better performance over many traditional methods in strong noise conditions, in terms of yielding less residual noise and lower speech distortion.
Heterogeneous Image Template Matching Based on Region Proposal Network
Authors: Yue Sha , Wang Shengzhe , Cui Yuyong , Guan Wei , Gao Xinyi
Published: April 2021

Heterogeneous template matching is important to disaster relief. The method of traditional heterogeneous template matching in real-time performance is so weak and usually has a lower accuracy than deep learning method. A heterogeneous template matching method based on region proposal network is proposed in this paper. Specifically, deep features between the optical images and SAR images are extracted by using residual network, and MRPN, a suitable region proposal network for matching work, is proposed to complete precise positioning of the template region. Finally, the channel fusion is performed on the matching results to get a better result and confidence. It is shown that the real-time performance and the accuracy performance have been greatly improved, which is reaching 97%.
An improved model of document retrieval efficiency based on information theory
Authors: Xiaoli Li , Xiaokai Yin , Kan Li
Published: April 2021

Literature retrieval is an important auxiliary work of scientific research. The form of literature retrieval is becoming more and more abundant, the number of literature is huge, and the storage set is complex. The algorithm of retrieval tools directly affects the accuracy and efficiency of retrieval. Based on the analysis of the problems existing in various literature retrieval technologies, an improved TF-IDF literature retrieval efficiency model is proposed. It provides a new idea for the design and implementation of literature retrieval system.
Prediction model of physical activity level and hypertension based on artificial neural network
Authors: Hua Liang , Yu Wang , Jinying Li , Wanjun Zhang , Yiting Wang
Published: April 2021

Purpose: Taking the population of Henan Provincial People’s Hospital Chronic Disease Control Center from July 1, 2020 to December 31, 2020 who came to the hospital for physical examination as the research object, analyze and explore the role and contribution of physical activity level in the prediction of hypertension by using the LSTM network model, which can provide references for the clinical diagnosis of hypertension. Methods: Randomly select 2000 physical examination data, remove missing and invalid data, and preprocess them. Finally, select factors such as gender, age, body mass index, weight grade, waist-to-hip ratio, physical activity level, body fat percentage and other factors to establish a neural network prediction model. Then test and study the model, focusing on exploring the contribution of physical activity level to prediction. Result: The level of physical activity has certain advantages in predicting the prevalence of hypertension, but the predictive ability in the later stage is insufficient
Handwritten dynamics classification of Parkinson’s disease through support vector machine and principal component analysis
Authors: Xu Zhifei , Zhu Zhaohui
Published: April 2021

Parkinson’s disease is a chronic neurodegenerative disease that affects the daily lives of tens of thousands of middle-aged and elderly people. The intelligent classification method of Parkinson’s disease has received extensive attention in recent years. The paper proposes a new auxiliary classification model of Parkinson’s disease based on principal component analysis and support vector machine. The model first samples and preprocesses the collected handwritten sensor data, then performs dimensionality reduction by principal component analysis, and finally inputs the dimensionality reduction data into a linear kernel support vector machine for Parkinson’s disease classification and prediction. The experiment uses 5-fold cross-validation for dataset segmentation and performance verification. The average performance results obtained on the Meander r dataset are: accuracy is 70.86%, specificity is 67.23%, sensitivity is 75.98%, and F1-Score is 69.72%, and the average performance results obtained on the Spiral dataset are: accuracy is 77.45%, specificity is 70.26%, sensitivity is 85.58%, and F1-Score is 77.10%.
Loop Closure Detection based on Regional Weighted and Siamese Network
Authors: Li Wenju , Ma Qianwen , Cui Liu , Dong Tianzhen
Published: April 2021

Loop closure detection (LCD) is essential for the Simultaneous Localization and Mapping (SLAM) system of an autonomous robot. Aiming at the problem of false positive in traditional loop closure detection methods, in this paper we propose an end-to-end siamese network model. Firstly, the siamese network with two same branches is designed to learn the characteristics of each image pairs. Secondly, the multi-region feature weighting method is introduced to fuse saliency regions of convolutional feature map, which well reflect structured information of the environment. In the end, the geometric consistency verification is used on the candidate convolution feature map to determine the true loop. As a result, experiments on several public datasets have illustrated the superiority of our approach. Compared with traditional methods, under the precision of 100% accuracy, the recall rate is increased by 15%. Our model is more stable in different scenarios, which can achieve robustness loop closure detection.
Research on virtual scene design Based on 3D vision
Authors: Ziyan Zhang
Published: April 2021

In order to meet the design realism and high efficiency requirements of product display scenes, C4D is used to complete 3D virtual scene modeling and virtual design. Use C4D software to make a three-dimensional model of the scene, first make part of the sub-model of the display area, increase the realistic effect of the scene design through material editing and adding textures (UV, normal, Alpha, etc.), combine multiple sub-models for appropriate layout and adjustment acquisition Complete three-dimensional scene model; after completing the three-dimensional virtual scene modeling, in the rendering of the stage environment, add effects through regional lighting and environmental scenes to realize the three-dimensional scene design. Experiments show that the fidelity of 3D virtual scene design is higher than that of similar methods, and the 3D modeling performance is outstanding. This method is suitable for 3D virtual scene design and special effect scene production. The modeling method in 3D virtual scene design is proposed, which takes virtual character painting and 3D model design as the core to complete character painting and modeling, and realize the interaction and display with users.
Research on Multi-objective Optimization Model of the Planting Structure Based on TOPSIS
Authors: Ye Mao , Wang Lu , Zhang Huapeng
Published: April 2021

The problem of planting structure optimization is a multi-objective optimization decision problem. To solve this problem, this paper proposed a bi-level multi-objective optimization model of planting structure based on TOPSIS to optimize the agricultural planting structure. The optimal planting structure could be obtained by integrating the decision from both the upper-level and lower-level in the study area. The upper-level DM maximum total planting income with the minimum of total irrigation water could be obtained and the lower-level DMs maximum planting income with the minimum planting cost in sub-region. Finally, the constructed model was applied to optimize ternary planting structure of “food, cash and feed” in a part of the “Bohai Granary” of Science and Technology Demonstration Areas in Shandong Province in China. The optimal results of planting structure were obtained, and the comparison between the optimal results and the actual results was also made. It shows that the optimized planting structure has a better performance in terms of the increase in total planting income and the decrease in total irrigation water.
Control strategy of hybrid energy storage system based on virtual DC generator to suppress DC bus voltage fluctuation
Authors: Yang Haitao , Yin Zhiyong , Shao Tianzhang
Published: April 2021

In order to eliminate the impact of sudden changes in light intensity and load on the DC microgrid system, based on the analysis of the principles and advantages of the virtual DC generator (VDG), the application of the virtual DC generator to the battery of the hybrid energy storage system is adopted. In the control strategy, it improve the ability of the hybrid energy storage system to suppress DC bus fluctuations. The Matlab/Simulink simulation results show that when the light intensity and load change suddenly, the DC microgrid bus voltage fluctuation amplitude of the virtual motor droop control is 6.1V, 39V, which is significantly smaller than the conventional droop control DC bus voltage fluctuation amplitude 8.7 V, 50.6V. The simulation results show that the control strategy proposed in this paper can improve the ability of the hybrid energy storage system to suppress DC microgrid bus voltage fluctuations.
Retrospective motion correction for Fast Spin Echo based on conditional GAN with entropy loss
Authors: Chen Yalei , Li Pingan , Liu Kewen , Bao Qingjia , Li Zhao , Li Xiaojun
Published: April 2021

We proposed a new end-to-end motion correction method based on conditional generative adversarial network (GAN) and minimum entropy of MRI images for Fast Spin Echo (FSE) sequence. The network contains an encoder-decoder generator to generate the motion-corrected images and a PatchGAN discriminator to classify an image as either real (motion-free) or fake(motion-corrected). Moreover, the image’s entropy is set as one loss item in the cGAN’s loss as the entropy increases monotonically with the motion amplitude, indicating that entropy is a good criterion for motion. The results show that the proposed method can effectively reduce the artifacts and obtain high-quality motion-corrected images from the motion-affected images in both pre-clinical and clinical datasets.
Disturbance-Observer-Based Sliding-Mode Attitude Control of Rotation-Isolating Rudder with Ultrasonic Motor
Authors: Zhang Shihao , Ding Libo , Wang Chen
Published: April 2021

Rotation-isolating rudder is a type of correction mechanism widely used in two-dimensional trajectory correction projectile. In this paper, we focus on its attitude servo system, develop a disturbance-observer-based sliding-mode control method, and prove the asymptotic stability of the control method. Based on the single-axis turntable established, we test the rolling attitude control performance of the rotation-isolating rudder, and the experimental result shows the effectiveness of the control method developed.
Research on the Application of Children’s Reading Analysis Based on Artificial Intelligence——Take Small “raccoon Reading” and “Jiao Jiao Reading” as examples
Authors: Nini Huang , Kong Dehua
Published: April 2021

Artificial intelligence technology is widely used in the field of education, and the development of children’s Chinese reading field is increasingly mature. Using the boundary awareness of AI algorithm for reference, the Chinese reading class should also awaken the boundary awareness of Chinese subject. In the AI algorithm, only the annotated data are valid data. The reading text can become effective learning materials through natural language processing only when it is “annotated” out of ability points. Through machine learning, AI constantly generates self-learning data sets based on trial-and-error feedback. The feedback mechanism, trial-and-error opportunity and Chinese teaching design are transformed into each other to build student portrait and specifically improve students’ independent Chinese learning ability. This article analyzes the application of artificial intelligence technology in the development of children’s Chinese reading services by taking the examples of [[CHECK_DOUBLEQUOT_ENT]] Small raccoon Reading” and “Jiao Jiao Reading.
Research on personalized recommendation method based on social impact theory
Authors: Cheng Huang , Gan Baiqiang , Zhang Chi
Published: April 2021

With the rapid development of computer technology, the personalized needs of users become more and more prominent. The complexity of online learning resources and social networks lead to sparse data sets and low recommendation efficiency. In this paper, the existing collaborative filtering recommendation algorithm does not distinguish the degree of user’s trust when integrating the social impact theory. Therefore, in order to establish more accurate characteristics of user’s social interaction, this paper integrates the trust mechanism in the social impact theory, brings the establishment, dissemination and socialization factors of trust into the research scope, and introduces them into personalized recommendation in the recommendation process of recommendation model, it is used to solve the problem of low recommendation effect and low quality of recommendation system on sparse data set. Experimental results show that the proposed personalized recommendation method can improve the recommendation effect and quality on sparse data sets.
Research on Integrated Safety Assessment in the Process of Electric Vehicle Charging
Authors: Duan Haowei , Gao Hui , Liang Huamin , Li Huailiang , Zhou Damou
Published: April 2021

With the increasing number of electric vehicles, the problem of charging reliability and safety has become the focus of foreign scholars and industry. By analyzing the charging safety characteristics of power battery, charge and discharge equipment and power supply equipment of electric vehicles, the safety mechanism of integrated electric vehicles is established in this paper. Based on the research of integrated safety warning and control, the integrated online warning module and intelligent protection module of electric vehicle charging are constructed. Based on the failure statistics of the integrated safety early warning system and combined with the integrated safety assessment index system of EV charging process, the regional EV charging safety can be assessed, so as to promote the healthy development of EV charging.
Study on Routing Optimization of Fresh Meat Joint Distribution System
Authors: He Kailun , Yang Chunmeng
Published: April 2021

The path optimization problem of fresh meat joint distribution system is a multi-center distribution problem.Based on the shortest distance principle,the service scope and customer group of each distribution center can be determined to convert the multi-center optimization problem into several single-center optimization problems,and the joint distribution scheme can be obtained with genetic algorithm.Taking a fresh meat distribution system in the core area of Chongqing City as an example,this paper compares and analyzes the operation status of the system before and after the implementation of joint distribution.The case analysis shows that carrying out the mode of joint distribution can significantly improve the efficiency of fresh meat distribution.
Finite-time Stabilization of Switched Positive Systems with Time-varying Delays and Actuator Saturation
Authors: Wang Xuefei , Wen Jiwei
Published: April 2021

In this paper, the finite-time stabilization for a class of switched positive systems with time-varying delays and actuator saturation under average dwell time switching is investigated. Through multiple copositive Lyapunov-Krasovskii functionals method, the sufficient conditions for finite-time bounded of continuous-time case are given, and the appropriate switching rules and state feedback controllers are presented together. Moreover, the convex hull technique is employed to deal with actuator saturation. Finally, an illustrative example is given to show the effectiveness of the proposed method.
Development of an Intelligent Control System for Spacecraft Vacuum Thermal Test
Authors: Zhang Jun , Lu Tong , Ye Tianyuan , Li Fangyong , Zhang Yusen , Liu Xusheng
Published: April 2021

This article develops a spacecraft component-level vacuum thermal test intelligent control system, which can control the thermal vacuum chamber to automatically complete the entire test process including establishing a vacuum environment, setting temperature control parameters, switching test conditions, and executing shutdown procedures. It is the first to complete the one-key boot function in similar domestic laboratories. This system combines thermostat, parameter identification and self-tuning control technology to complete the temperature control of the test piece. It solves the technical problems of complex thermal characteristics of test piece, large time delay, and overshoot in temperature control. The system also has the function of automatic conversion of test condition and provides a guarantee for the smooth development of the vacuum thermal test of the spacecraft and its components.
Hardware design of PMSM control system based on DSP
Authors: Shi JiTe , Gu HuiHui , Ge Lixin
Published: April 2021

Compared with traditional motor, permanent magnet synchronous motor has the characteristics of strong anti-interference ability, high starting torque and power density, and good speed regulation performance.Permanent magnet synchronous motor servo system has become a research hotspot in the field of servo drive.In this paper, with DSP28335 as the main control chip to give a permanent magnet synchronous motor AC servo control hardware system design scheme, the paper introduces in detail the system core control circuit, main power circuit, detection circuit design and the choice of main components.The hardware system designed has been applied to engineering practice, and has achieved good control effect.
Research on the system and mechanism of investigation and treatment of port accidents
Authors: Geng Jiezhe , Hu Yuchang
Published: April 2021

Based on the analysis of the current situation of investigation and treatment of port accidents, this paper puts forward some suggestions, such as improving relevant laws and regulations, establishing an independent third-party investigation organization and consultation mechanism, and formulating investigation procedures, so as to promote the modernization of port accident investigation and management system and capacity.
Based on the single-layer contour pattern, the comparison of the zigzag parallel hatch and the contour parallel hatch
Authors: Zhao Jiali
Published: April 2021

Laser marking refers to marking on the surface of a product with a laser mark, text, symbol, image, etc. The hatch tool of laser marking machine can be used to hatch specified 2D-compound curve graph. The direction parallel hatch and the contour parallel hatch are two basic ways of hatching. Under the single-layer contour pattern, for the zigzag parallel hatch, we established an improved area Parity Check Filling Model. For the contour parallel hatch, we established an equidistant line generation model based on the Regular Bezier curve, and used the De Casteljau algorithm to generate equidistant lines with the given hatching line spacing. By running the relevant programs, we get the total length of hatch curves and the average elapsed time of the two hatch algorithms. According to the analysis of the result, the performance of the zigzag parallel hatch algorithm is higher than that of the contour parallel hatch algorithm.
Research on the design factors of H-shaped women’s trousers based on eye-tracking technology
Authors: Chen Jun Jun , Li Yue , Wang Xiao Gang
Published: April 2021

In order to accurately study the silhouette of clothing, this paper adopts eye- tracking technology to conduct in-depth analysis on the influencing factors of the silhouette of H-shaped women’s trousers, obtain the tracking data of eye movement controlling the silhouette, and finally obtain the pattern-making variables controlling the H-shaped women’s trousers through statistical analysis. By eye-tracking experiments, we used image recognition technology to record experimental data, dividing the interest area of H-shaped women’s trousers, analyzing the eye movement indicators such as the total fixation duration, visit count in the interest area, and using SPSS software to perform multi-factor of variance and mean value analysis.Finally the key parts that affect the shape of H-type trousers are determind, which has high accuracy and practicality. The research adopts scientific and rigorous methods, which is the basis for the preliminary research of intelligent pattern-making and profile control.
Research on Automatic Control System of MR Damper Based on Neural Network Algorithm
Authors: Gan Yunhan , Zhong Yi
Published: April 2021

The use of MR dampers can achieve the absorption of harmful vibrations such as seismic waves that affect the structure of the building itself. The current control of the MR damper generally uses a pulse width modulation system to control the current input to the MR damper, thereby controlling the MR damper. But this is a semi-automatic control method, and the error is relatively large. This article introduces an MR damper control system that introduces a neural network algorithm. The system uses a half-bridge drive circuit for output current control in the circuit part and uses the Shen BP network algorithm for numerical prediction and fitting. The experimental research and tests conducted in the project show that the use of this fuzzy algorithm can make the MR damper achieve good damping effects under different conditions, and can well improve the original semi-automatic control method.