bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical study of growth competition between twin grains during directional solidification by using multi-phase field method
Authors: Zhu Chang-Sheng , Wang Ting , Feng Li , Lei Peng , Ma Fang-Lan
Published: June 2021

A multi-phase field model is established to simulate the growth competition and evolution behavior between seaweed and columnar dendrites during directional solidification. According to the effects of surface tension and interfacial energy, we quantitatively analyze the influences of factors such as inclination angles, pulling velocity, and anisotropic strength on twin growth. The results demonstrate that the pulling velocity and anisotropic strength have an important influence on the morphology and evolution of the seaweed and dendritic growth. The low pulling velocity and anisotropic strength are both key parameters for maintaining the stable morphology of seaweed during competitive growth in a bicrystal, showing that the lateral branching behavior is the root of the dendrites that can ultimately dominate the growth. And it is clarified that the lateral branching behavior and lateral blocking are the root causes of the final dominant growth of dendrites. With the increase of anisotropy strength, the seaweed is eliminated fastest in case 1, the seaweed is transformed into degenerate dendritic morphology, and eliminates the seaweed by promoting the generation and lateral growth of the lateral branches of the dendrites. The increase of pulling velocity is to increase the undercooling of favorable oriented grain and accelerate the growth rate of dendrites, thus producing more new primary dendrites for lateral expansion and accelerating the elimination rate of unfavorable oriented grain.
Do not waste your electrodes—principles of optimal electrode geometry for spike sorting
Authors: Tóth Róbert , Miklós Barth Albert , Domonkos Andor , Varga Viktor , Somogyvári Zoltán
Published: June 2021

Objective. This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance. Approach. The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semi-artificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampus in vivo. Main results. Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance. Significance. Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one- and two-dimensional electrode systems, targeting specific brain areas.
Research on the impact of artificial intelligence recommendation on academic procrastination under the background of big data——The mediating role of mobile phone addiction
Authors: Shu Xiajun , Miao Yunting
Published: June 2021

With the increase in the number of “head-down people”, the phenomenon of mobile phone addiction has attracted much attention, especially for college students. Through empirical analysis, this article surveyed 400 college students, of which 315 valid questionnaires. Under the premise of controlling the individual’s academic self-efficacy, this research explores and proves the mediating role of mobile phone addiction in artificial intelligence recommendation and academic procrastination behavior. Artificial intelligence recommendation positively affects the individual’s academic procrastination behavior, and works by influencing the individual’s mobile phone addiction, that is, mobile phone addiction plays a complete mediating role between the artificial intelligence recommendation and the individual’s academic procrastination behavior.
Research on Information Reference Service System Based on AI
Authors: Tian Cao
Published: June 2021

In the era of artificial intelligence, the library information service changes the traditional mode, and the upgrade of the new intelligent intelligence system based on artificial intelligence is an important research direction. This paper proposes a deep recommendation information recommendation service solution based on convolutional neural network, analyzes the key technologies and solutions of artificial intelligence-based information services, and explores ways to use big data to innovate library knowledge services.
Implementing Q-learning on Simple Vehicle: Learning Obstacle Avoidance in Different Environments
Authors: Lu Peiheng
Published: June 2021

Q-learning has been applied to many research projects in the past few decades. Its simple logic of mapping a corresponding action to each possible state has made it one of the most popular methods in reinforcement learning. In this implementation of Q-learning, a simple controlling scheme was used. The percept of the robot is taken as the state, and the actions give instruction to the actuating device of the robot. The goal is for this robot to pick up the obstacle avoidance skill after series of training. A sanity check for such an idea was provided in this essay. The basic disc robot from Robotics Playground was used, and the basic form of Q-learning was utilized. Our vehicle’s obstacle avoidance ability was assessed in environments with different levels of complexity using the Collision-Time graphs produced. The results showed very limited improvements in terms of reducing the number of collisions in all of the environments, and some fluctuations in the number of collisions were shown in the diagram. Combining the fluctuations with observations during the experiment process, we came to a conclusion that using Q-learning to navigate an arbitrary environment, the state space, and action space could be too large for any efficient training to be done.
Defect reconstruction of magnetic flux leakage measurements based on single dimension PSO algorithm
Authors: Fei Shuguo , Liu Guangheng , Fei Jiyou , Huang Chen
Published: June 2021

Magnetic flux leakage (MFL) detection is widely used in the technology of oil and gas pipeline intelligent detection at present. For the defect reconstruction problem in MFL detection, a new improved particle swarm optimization (PSO) algorithm, called SDPSO, is introduced to enhance the accuracy of defect profile reconstruction in this paper. In order to reduce the interaction between multidimensional information, dimensional coupling will deteriorate the search speed and convergence accuracy of the algorithm, a single dimension evolution strategy is complemented to optimize the tradition PSO algorithm. Each generation of evolution only updates a single dimension of the objective function. The dimension with the maximum error between the candidate signal and reference signal is selected as the one which need to be updated in next generation. In order to testify the performance of the proposed method, the simulated signals are utilized to compare with PSO algorithm. The experiment results verify that the proposed method has a higher adequacy of the performance in terms the quality of the solution and robustness for noise.
Image classification of cashmere and wool fiber based on LC-KSVD
Authors: Sun Chunhong
Published: June 2021

The identification of cashmere and wool fibers has always been a key issue in the textile industry. The classification method of cashmere wool based on microscope image processing technology mainly studies fiber structure characteristics, and the steps are complicated. Affected by changes in breeding methods, breed improvements and climate change, the output of cashmere has decreased, and cashmere fibers have more and more variations, which makes the classification of cashmere wool more difficult. In order to accurately identify cashmere and wool fibers, this paper proposes a multi-feature fusion cashmere wool classification algorithm based on the LC-KSVD algorithm. According to the characteristics of the microscope fiber image, the key information and texture features of the fiber image are extracted, and then the multiple types of texture features are merged into Multi-dimensional feature matrix vector, and finally realize the classification of cashmere wool through the LC-KSVD algorithm. Experiments show that compared with other classification algorithms, the classification accuracy of this method can reach up to 90%, which can be used for subsequent cashmere wool fiber classification and identification.
Image-text discourse coherence relation discoveries on multi-image and multi-text documents
Authors: Zheng Hanzhong , Liu Zhexiong , Shi Dejia
Published: June 2021

The occurrences of the image-text pairs are common in social media such as image captions, image annotations, and cooking instructions. However, the links between images and texts as well as the coherence relationship among them are always implicit. We developed a multi-modal approach by firstly establishing the links between image and text in an unsupervised way, then discovered their coherence relations through computational models of discourse to improve the consistency and quality of the images and their corresponding texts and also explore a dynamic and flexible image-text coherence relation.
Distributed Multi-robot Collision Avoidance Using the Voronoi-based Method
Authors: Ding Yiran
Published: June 2021

Collision avoidance is a fundamental problem in multi-robot motion planning, enabling robots to coordinate in a safe and efficient manner. In this paper, we introduce several best-known distributed collision avoidance algorithms and present the Voronoi-based method in detail, which assigns optimal dominance regions for robots using the Voronoi diagram, constrains robot motions within the buffered Voronoi diagram, and follows right-hand rule when deadlock happens during navigating to the goals. A series of simulations are conducted to demonstrate the efficacy of collision avoidance guarantee under robot localization uncertainty while avoiding deadlock using our presented algorithm. We claim that such a method is superior to most of the existing distribution methods in a known environment in efficiency of implementation.
Radar Life Signal Processing Algorithm Based on Improved EEMD
Authors: Xinlei Wang , Jingfeng Zang , Yuhuan Shi
Published: June 2021

Non-contact measurement of human vital signs is of great significance to the construction of smart homes and hospitals. The vital signal extracted from the FMCW radar contains a lot of noise. In order to reduce the noise impact and improve the life signal detection results accuracy, Ensemble Empirical Mode Decomposition (EEMD) algorithm is studied, and a new method based on the combination of EEMD and Translation Invariant Wavelet Transform (TIWT) is proposed. This method firstly decomposes the original signal using EEMD to obtain several Intrinsic Mode Function (IMF) components, and then changes the arrangement order of IMF by Translation Invariant Wavelet Transform to change the position of the singularity in the whole signal to reduce or eliminate the oscillations. Finally, signal screening is completed and life signals are reconstructed. Experiments show that, the proposed method has better detection accuracy in detecting vital signs collected by radar.
Research on Forecast of Daily Electricity Consumption of Household Air Conditioning Based on Improved Long-short Memory Network
Authors: Xia Guo-Fang , Tian Zheng-Qi , OuYang Zen-Kai
Published: June 2021

Daily electricity consumption forecasting of home appliances can improve the accuracy and efficiency of the operation of home energy management systems. In this paper, an improved bidirectional long short memory network (BILSTM) model for predicting daily electricity consumption of household air conditioning is proposed. Firstly, and the “mutual information” is used to analyze the correlation between the daily electricity consumption of air conditioning and some environmental factors. Second, the environmental factors with strong correlation with the daily electricity consumption of air conditioning are selected as the influence factors, and these influence factors and the electricity data are taken as the characteristic input of the network. Finally, the improved bidirectional LSTM load prediction model which has been trained is used to forecast the daily electricity consumption of air conditioning. The experimental results show that the improved bidirectional LSTM network proposed in this paper can predict the daily electricity consumption of air conditioning in short term, and the maximum relative error of the predicted result is less than 5%.
A Neural Network Based Defect Prediction Approach for Virtual High Pressure Die Casting
Authors: De-Jian Xu , Yong-Peng Yu
Published: June 2021

Prediction of defects is important to effective process planning for high pressure die casting (HPDC). Current computer aided engineering (CAE) methods of defect prediction are widely used by experienced engineer in industry. However, it is hard for novices to image and understand the underlying relationship between the process and defects. To bridge the gap between training and onsite applications, this paper present a neural network based defect prediction approach (DPA) for virtual HPDC, and details of the DPA development and its implementation in VR are explained. Moreover, a Virtual HPDC Lab is developed as the case study to demonstrate the functionality of DPA proposed, and the result survey verified that the virtual lab with DPA is very much effective for learning and training of HPDC.
Speed Regulation System of Permanent Magnet Synchronous Motor Field Weakening Control Based on Model Predictive Control
Authors: Wang Hui , Zhang Jiujiang , Liu Hongxia , Wang Fei , Shi Guoning
Published: June 2021

The paper is based on the theoretical analysis of motor field weakening control, design field weakening control system based on voltage feedback. In this research, MPC control algorithm is introduced into speed loop. Under field weakening control strategy, the designed MPC controller is applied to motor control system. Finally, the simulation results show: under field weakening control, MPC controller has better robustness and stronger anti-interference ability than PI controller, which improves system stability.
Deep learning and Its Development
Authors: Zhao Qianji , Shang Zequn
Published: June 2021

Deep learning is a promising branch of machine learning. It is an algorithm that uses artificial neural networks as the architecture to characterize and learn data. In recent years, many companies, for example, Google, Microsoft and Baidu, have become interested in the field of deep learning and have set up many large-scale projects, such as Google’s Deepmind project, including alphago, which has achieved success in Go and e-sports. This article analyzes and summarizes each current research direction and approach of deep learning, with prospection about the future research direction and development of deep learning expounded. An overview of the three basic models of deep learning is given, namely multilayer perceptrons and perceptrons, convolutional neural networks and recurrent neural networks. The benefits and superiority of the deep learning algorithm are illustrated and compared with the conventional methodology used in the common applications. Further research on emerging types of convolutional neural networks and recurrent neural networks are introduced. An overview of the three basic models of deep learning is given, namely multilayer perceptrons and perceptrons, convolutional neural networks and recurrent neural networks. The current application of deep learning in various fields is summarized, such as artificial intelligence, computer vision and natural language processing applications, and some open problems for future research are also analyzed. Finally, the significance and purpose of deep learning are discussed.
Design and Implementation of Real Time Monitoring System of Textile Equipment Based on MQTT
Authors: Han Shaoheng , Chen Yiran , Ye Songlin
Published: June 2021

At present, with the progress of network information technology, the idea of intelligent factory and intelligent manufacturing is imperative. It is imperative to realize intellectualization and informatization in traditional manufacturing plants. According to the intelligentized demand of textile equipment, the real-time monitoring of the spindle status of the textile machine is designed and realized, and the functions of the real-time statistics of the operation efficiency, the number of broken ends, the spindle status of the textile machine, the statistical report and so on are made in real time. It has achieved remote and efficient production management and fault detection. It effectively reduces the cost of manual operation and maintenance, and improves the intelligence level of textile mills.
Identify important users on social networks–Cases from Sina Weibo
Authors: Ma S , Ma Y H
Published: June 2021

In recent years, more and more studies on the strength of interpersonal relationship show that there are some positive social connections in online social networks. In addition, in the process of information dissemination, there may be some key nodes. In order to determine the key nodes of online social networks, we propose a method of edge weight recognition based on historical interaction records. According to the forwarding quantity, the influence of nodes is quantified, and the top node is selected as the key node. We apply our method to a real network and get good results.
Super-resolution reconstruction of unmanned aerial vehicle image based on deep learning
Authors: Wan Lixuepiao , Zhang Gongping
Published: June 2021

Due to the influence of weather, air flow and other environmental factors, unmanned aerial vehicle (UAV) aerial photography often leads to problems such as blur, defocus and so on. In order to solve the problem of low definition of UAV aerial images, this paper proposes a super-resolution reconstruction algorithm based on improved residual learning blocks (IRB) to reconstruct aerial images to achieve high definition. This method can effectively solve the problem of gradient explosion which is often encountered in deep learning network. At the same time, it can suppress the learning of useless information and make full use of important feature information. Through the establishment of UAV aerial data set for cross training of the network, the network can better adapt to the UAV aerial environment. The experimental results show that, compared with the existing neural network algorithm, the improved residual network algorithm can reconstruct UAV aerial image well, and the subjective visual effect of the reconstructed image is better while the edge information is greatly preserved.
Nonlinear Signal Classification based on Wavelet Transform and Deep Belief Network
Authors: Hu Kejia , Li Hongyi , Xu Shaofeng , Tian Keyu , Xu Tianshi , Zhao Di
Published: June 2021

In recent years, the analysis of EMI (Electromagnetic interference) signals has become a hot research topic in the signal processing field. Particularly, EMI signal classification has attracted more and more attention. Conventional signal classification methods usually select features by experience or implicitly by the shallow artificial neural network, and always results in bad performance on high-dimensional and nonlinear EMI signals. This paper proposed a novel classification method based on wavelet transform and deep belief network, while wavelet transform method is used to reduce the dimension of high-dimensional signals and deep belief network can extract nonlinear features from EMI signals. Then we apply BP neural network as the classifier. Results on the benchmark dataset have shown the superiority of the proposed method compared with the state-of-the-art approaches.
Short text filtering based on gradient boosting decision tree
Authors: Zhu Zikun , Yu Xiaodan
Published: June 2021

With the rapid development of the Internet, event detection from massive data has become a research hotspot. However, the existing event detection methods for social networks rarely consider the noise data in short text data in detail. Therefore, there is a lot of noise in the input of event detection, resulting in a large number of false-positive events in the event detection results, which affects the efficiency and accuracy of event detection. In this paper, three types of features are proposed to mine text features, and the gradient boosting decision tree algorithm is used. Experiments show that the algorithm has good filtering performance and interpretability for short texts in social networks.
Path Planning with Q-Learning
Authors: Hu Yuepeng , Yang Lehan , Lou Yizhu
Published: June 2021

As science and technology advance rapidly, the scope of mobile robot applications continues to expand. In mobile robotics, path planning, which has formed a relatively complete theoretical system, is one of the most momentous and challenging tasks, especially in uncertain environments. Path planning has been a popular research problem in recent years, and it has been widely utilized in various fields, mainly including robotic surgery, logistics transportation, and self-driving automobile. To solve path planning, this paper presents an approach that uses Q-learning Algorithm to find multiple feasible paths within obstacle environments. First, the algorithm of Q-learning was pre-trained to make it suitable for path planning. Then an obstacle environment map was modeled and a path planning program was compiled by applying the state action-value function. Finally, the experimental results were collected, including the path planning maps and corresponding graphs of training progress. To guarantee the effectiveness of the final paths, all the parameters in the function are set to be constant. The experimental results show that the Q-learning algorithm can succeed in solving the problem of multi-path planning.
Machine learning accelerates the technological development of brain science
Authors: Chen Mengya , Li Longfei
Published: June 2021

The development of machine learning provides a strong research foundation for the field of brain science. Compared with traditional research methods, the strategy based on machine learning is more suitable for the big data background of the current development of the field, and has a broad application prospect. This paper introduces the application status of machine learning in brain science research from three aspects: neuron reconstruction, neuron classification, neuron synthesis, and summarizes the development direction of machine learning technology in the field.
Research on identification algorithm of railway freight train number based on GSO-BP algorithm
Authors: Dong Y , Zhang X L , Xiang J
Published: June 2021

In the stage of character recognition, it is necessary to select the algorithm that matches the character features of train number. The train number characters of railway freight trains are all printed in straight style. Compared with handwritten characters, each character has relatively fixed features. However, due to the complex operation environment of railway freight trains, the character regions are cracked, tilted and partially missing. Therefore, it is very important to select an algorithm with good robustness. In view of the above analysis, this paper proposes a research on railway freight car number recognition algorithm based on GSO-BP algorithm, the experimental results show that the proposed method is effective in character recognition.
Energy Levels Based Graph Neural Networks for Heterophily
Authors: Wang Qian , Liu Youfa
Published: June 2021

The representation power of graph neural networks (GNNs) under heterophily has drawn much attention recently, in which some connected nodes do not have common labels or similar features. Previous approaches typically incorporate high-order neighbourhoods’ information so as to reinforce aggregation operation. However, they do not directly face with heterophily in first-order neighbours. To deal with this problem, we propose the energy levels guided GNNs in which energy levels of classes are incorporated for better expressiveness. We prove that the label with higher energy level would imply higher energy in the spectrum of the normalized graph Laplacian. Experiments on common benchmark datasets show the effectiveness of the proposed approach.
Improvement based on the proof-of-work consensus algorithm
Authors: Meng Liu , Xuebo Sun
Published: June 2021

Among the Bitcoins using Proof-of-work consensus algorithm, handling capacity is seven second each sum, which is less inefficient. But the Proof-of work depends on the computer function. It is beneficial to the higher-counting point, but unfair to he normal point. Make the computing power centralized. Aiming at the above-mentioned the waste and centralization of the Proof-of-work synchronic arithmetic, as well as the transaction-processing efficiency, we do the research and improvement.
Mobile robot path planning compares wavefront and A* algorithms
Authors: Zhang Siyan , Liao Zehan , Wu Yichen
Published: June 2021

In This study, the A * algorithm and wavefront algorithm are adopted to simulate the motion of the robots using Matlab. And a complex Maze with different complexity is created to compare the time and path that taken by the robot using the A * algorithm and wavefront algorithm. Before testing, we have determined the types of wavefront algorithms. The wavefront algorithms are divided into 4-Sector Connectivity and 8-Sector Connectivity. After summarizing and comparing the previous research results, we decide to use the 8-Sector Connectivity wavefront algorithm and the A* algorithm for testing. After that, the maze (environment) is established by Matlab simulations, and the corresponding algorithm, A* algorithm and wavefront (8-Sector Connectivity) algorithm are assigned to the robot. Finally, the robot moving to the specified position in the maze is tested. We use the built-in function in Matlab to record the time and distance of the robot moving to the specified position in the maze. The data are collected and analysis to compare the A * algorithm and wavefront algorithm. The results show that the A* algorithms achieve better results in a simpler environment, that is, it takes less time to get the goal in the maze. The wavefront algorithm has better performance in some complex environments. In general, we think the A* algorithm better than the wavefront algorithm.
Impact of Route Choice on Pedestrian Casualties in Car Bomb Terrorist Attacks
Authors: Fan Zhenyu , Che Junlin , Zhai Yiming , Ding Ning , lu Yang
Published: June 2021

Current situation of international violent terrorism is severe. Public places with high density crowd are more likely to be attacked by violent terrorists. Terrorist attacks such as car bombs are one of the very common and harmful types. “10•28” Beijing Golden Water Bridge Attack is a very typical example. Based on the case analysis of the Beijing Golden Water Bridge Attack, this paper established a simulation model of a car bomb terrorist attack on the basis of the social force model of the Anylogic software platform and conducted simulation experiments on the impact of route choice on pedestrian casualties in the car bomb violent terrorist attack. The simulation results show that: there is minimal damage to pedestrians under a specific route. These conclusions are of great practical significance for the placement and arrangement of safety protection facilities for large-scale event venues.
Fast update method of new equipment status in IMS network based on ant colony optimization algorithm
Authors: Sun Lili , Zhai Hongting , Zhai Qi , Li Liang , Zhang Qingrui
Published: June 2021

In order to update the status of new equipments in IMS network quickly, a new method based on ant colony optimization algorithm is studied. This method is to lock the new status of IMS network and establish the location model of new equipment in IMS network. For the new equipment in IMS network that has been located, the load balancing method of new equipment in IMS network based on ant colony optimization algorithm is adopted to obtain the optimal operation state of new equipment in IMS network, and realize the rapid update of new equipment status in IMS network. The experimental results show that: the proposed method can accurately locate the coordinates of new equipments in IMS network when the node regularity is significant or not; it can effectively improve the update speed of new equipments in IMS network, and realize the rapid update of new equipment status in IMS network; the negative impact on IMS network is smaller, and under the premise of small and large load of IMS network, the proposed method can improve the performance of IMS network. When the new IMS network adds new equipment status, the false alarm rate is small and the update result is reliable.
Ammunition Scheduling Method in Airborne Weapon Depot Based on Improved Genetic Algorithm
Authors: Liu Min , Li Guanfeng
Published: June 2021

The scheduling of the carrier-based aircraft weapon involves many resources and is subject to many constraints. The design of the scheduling scheme belongs to a type of typical NP-Hard problem. In this paper, genetic algorithm is selected as the optimization algorithm, and a two-layer real-value coding method is designed, and the selection, cross, and mutation operations of the algorithm are improved by using the elite strategy and other methods. The improved algorithm was used to simulate the established mathematical model to verify the effectiveness and superiority of the improved algorithm.
Multi-threshold channel pruning method based on L1 regularization
Authors: Han Fu Xin , Li Yang , Wang Chen Song
Published: June 2021

Aiming at the problem of single detection scene and poor detection speed, a multi-threshold channel pruning method based on L1 regularization is proposed. L1 regularization is applied to the scaling factor of the BN layer to push the scaling factor of the BN layer to zero, thereby identifying unimportant channels, and determining the initial threshold of channel pruning according to the scaling factor after sparse training; The most suitable pruning threshold of the convolutional layer is obtained through minimum error reconstruction. The least square method and the method of weighting the channel are used to reconstruct the error before and after pruning, and the most suitable pruning threshold is obtained by minimizing the error. Through the data set simulation experiment, the YOLOv3 network is pruned, so that the network has a smaller network structure and faster detection speed, which is suitable for deployment on mobile devices.
Remote control and data analysis system based on Alibaba Cloud
Authors: Zhai Yuyi , Jiang Tao , Liu Yunjia , Wang Le , Jiang Houyong
Published: June 2021

In order to eliminate the digital gap between equipment control and production information management, a smart factory remote control and data analysis system based on a cloud-fog hybrid computing model is proposed. Alibaba Cloud Server serves as the cloud computing layer to provide data storage and services, and the Internet of Things platform serves as the fog terminal. The computing layer completes data filtering and system early warning. The system consists of five parts: perception layer, gateway layer, platform layer, application layer and persistence layer. Each layer is designed and analyzed. The dead zone-adaptive swing door trending algorithm is used for data compression, and the system pressure test experiment and the data compression accuracy test experiment are completed. The experimental results show that this system improves the control of data in the production process, reduces the amount of data storage, and has high system compatibility to meet actual production needs.
Reconstructions for Analytic Signals Based on Greedy Algorithm
Authors: Shuang Li
Published: June 2021

In this paper, we utilize greedy algorithm to approximate analytic signals and transfer functions sparsely. Based on reproducing kernels of Hardy space, a redundant dictionary is constructed. The approximating strategy relies on matching pursuit which is an implementation of greedy algorithm. A numerical example and Python codes are provided to illustrate the idea.
Foreign body detection of urban rail train roof based on deep learning
Authors: Wang Chen Song , Cui Wei , Wang Xiao Zhu
Published: June 2021

The identification of foreign body on the roof of urban rail train is an important link to ensure train safety. The foreign body on the roof has the characteristics of small size and irregular shape. In this paper, this paper proposes an improved YOLOV4 foreign body recognition method based on deep learning. It expands the sample of foreign body data through data enhancement to enhance the robustness of foreign body detection; DCN (Deformable Convolution) was added to the backbone network CSPDarkNet53 to enhance the receiver field of the feature extraction network and enhance the detection ability of irregular objects. The experimental results show that the improved YOLOV4 can obtain 94.5% MAP on NVIDIA RTX2080Ti. It can accurately and quickly detect the roof foreign body.
Housing rental system based on blockchain Technology
Authors: Xue Qingshui , Hou Zongyang , Ma Haifeng , Zhu Haozhi , Ju Xingzhong , Sun Yue
Published: June 2021

As for the housing rental market, there are many landlords, arbitrary charges, false rental information and so on, and it is difficult for the government to record and supervise the housing rental situation. Based on the block chain technology of encryption algorithm, this paper constructs a housing leasing Alliance chain. The landlord and tenant sign lease agreements through smart contracts, determine the lease relationship, pay and collect rent automatically on a regular basis, and return the rental right when due. In the design of the Alliance chain network, the certification department node and the record managerment node are introduced to authenticate the authenticity of the house source and personal identity information, and backup the lease contract information, register the housing rental situation, and achieve the effect of safe leasing. This method eliminates the mediation, less cost, clear housing rental information, and is conducive to government market supervision.
Research on UAV path planning obstacle avoidance algorithm based on improved artificial potential field method
Authors: BinKai Qi , Mingqiu Li , Yang Yang , XiYang Wang
Published: June 2021

In this paper, we study the path planning obstacle avoidance problem of UAV based on improved artificial potential field method (APF). By introducing dynamic adjustment coefficients, the gravitational force and repulsive force functions in the traditional APF are improved to make the obstacle avoidance safety factor higher and the final path smoother; for the target unreachability problem, a new attractive potential field is built in the gravitational force function to balance the changes of the traditional attractive force and repulsive force; a longitudinal random factor is used to solve the problem of getting into local minima. Through simulation comparison with other methods, this method can solve the path planning obstacle avoidance problem of UAV more efficiently.
A Research on An Intelligent & Automatic Help Seeking System
Authors: Jing Chen , Maoyan Zeng , Jinhua Zhang , Long Chen
Published: June 2021

The safety of all the factories and families should not be ignored. In this paper, in case of emergency, people in factories or at home can still send out a distress signal through voice without mobile phone, so as to ensure the safety of people and facilities. The authors have developed a smart home help for the elderly device based on communication, intelligent manufacturing, materials and digital simulation technology. The main purpose is to solve the problem that when the elderly are alone at home, they can make an emergency call for help through this system when they encounter sudden accidents. This technology is also very suitable for the safety of factories and other scenes.
Numerical simulation of sintering bellows under multi-point leakage
Authors: Yu Mingzhu , Cai Jin , Kong Xianghu , Kong Xiangwei
Published: June 2021

In order to study the influence of multi-point leakage on the flow field in the bellows and the quantitative evaluation method of air leakage based on temperature, the Fluent software was used to consider the fluid-solid coupling heat transfer in the bellows and the convection and radiation heat dissipation on the outer wall of the bellows. The multi-point leakage environment was set up, and the numerical simulation model of single-side sintered bellows-branch pipe section was established. The temperature distribution and flow field distribution of the fluid on the outer wall and inside the bellows are solved. Infrared thermal imaging was used to extract the temperature trend of the local leakage point of 5# bellows, and compared with the simulation results, it was found that the closer to the leakage point, the faster the temperature dropped, and the two had a strong correlation, which proved that it was feasible to use numerical simulation to study the air leakage of bellows. Through the analysis of different leakage forms, it is found that the influence range and intensity of single-point leakage and multi-point leakage on the field quantity are different. The research method lays a solid foundation for the in-depth study of temperature-based detection algorithm.
A numerical investigation on factors affecting in-plane deformation modes of honeycomb
Authors: Cui C B , Zhai J Y , Li M , Li Z G
Published: June 2021

The main factors affecting honeycomb in-plane deformation modes are researched using Patran/LS-DYNA in this paper. All changes in deformation modes can be summarized as changes in constraints. The main factors affecting the deformation modes include the material constitutive models, the boundary constraints, the cell wall thickness-to-length ratio h/l and the aspect ratio of honeycomb. Inner cells’ constraints and shape of crush bands are affected by the material constitutive models. The sliding difficulty of boundary cells and the initiation of crush bands are influenced by the boundary constraints especially the horizontal frictions and the cell wall thickness-to-length ratio h/l. The number and region of crush bands are affected by the aspect ratio.
Analysis on dynamic precision degradation mechanism of high-speed precision press
Authors: Chen Hao , Zhang Yongqi , Zhang Wei
Published: June 2021

With the increase of service time, the dynamic precision of high-speed precision press is getting lower and lower. In order to study the precision degradation mechanism, a dynamic model of main transmission system of high-speed precision press was established based on ADAMS, and the influence of different bearing clearance on the dynamic precision was studied. The simulation results show that the dynamic precision of bottom dead center (BDC) decreases with the increase of joint clearance. And the increase of the joint clearance between main linkage and main slider has the greatest impact on the dynamic accuracy of BDC, the impact of the joint clearance between crankshaft and major linkage is smaller, and the impact of the joint clearance between crank shaft and bearing is the smallest. Finally, the field test was carried out. The analysis of the test data shows that the wear of the bearings in the main drive system leads to the increase of the clearance, which leads to the degradation of the dynamic precision.
Numerical Study on the Gas-Particle Two-Phase Jet Flow during Canister Launching Process
Authors: Liang Xiao-yang , Zhou Zhi-tan , Yu Qi-rui , Le Gui-gao
Published: June 2021

The Euler-Lagrange model of Jet-gas and AL2O3
is established to study the impact of gas-particle jet flow during canister launching process. The gas phase is the Navier-Stokes equation of the two-component transport of gas and air, and the turbulence model is the realizable two-equation model. The airflow flux is numerically discretized via AUSM format, and the viscous dissipation flux is a second-order central difference. The movement trajectories of particles with different diameters ranging from 1 to 100 μm and the distribution characteristics of jet flow dynamic parameters in the launching box are studied. The jet impingement flow field results of gas-particle two-phase flow are compared with those without particle phase. The results show that the particle phase has a retardation and heat transfer effect on the expansion of jet flow, which affects the distribution of jet flow parameters in the launch canister. Furthermore, compared with the pure jet flow impingement effect, the velocity of the two-phase jet flow along the central axis decreases and the temperature increases. Meanwhile, the trajectory of the particle phase is related to the particle diameter. As the diameter of the particles becomes smaller, the flow ability is better and affected by turbulence, and a random movement area in the downstream area of the jet is formed. As the diameter of the particles becomes larger, a particle aggregation area is formed on the jet axis due to inertial force.
Vibration monitoring of wind turbine tower based on XGBoost
Authors: Su Liancheng , Guo Gaoxin , Li Yingwei
Published: June 2021

A tower vibration monitoring method based on XGBoost is proposed to predict the tower vibration trends under different operating conditions. Firstly, the wind turbine operating conditions are classified based on the Kmeans clustering algorithm. Secondly, the impact of state parameters of the wind turbine on the tower vibration is analyzed, and the tower vibration monitoring model is established based on XGBoost algorithm. Finally, the actual SCADA data of the wind farm is used to verify the proposed method. The results show that the vibration monitoring accuracy of tower is effectively improved by considering the operating conditions of the wind turbine.
The Kinematic and Dynamic Analysis of a Bionic Manipulator
Authors: Ji Zhifei , Hong Hualin , Yuan Xiaodong , Fan Jianyu , Lin Min
Published: June 2021

Bionic manipulators can help people with disabilities to improve their quality of life, they thus became a focus researched by many scholars. In this paper, the kinematic and dynamic analysis of a bionic manipulator was conducted. Firstly, the kinematic model of the bionic manipulator was developed. The relationships between input and out variables were obtained. Then, the dynamic model of the bionic manipulator was established by using the Lagrangian method. The dynamic parameters including the angular displacement, angular velocity and angular acceleration under the action of torque were analyzed. The results indicated that the angular displacements of the three angles are increasing with time. With the increasing of time, the angular velocity first increased and then decreased. The angular accelerations are oscillation with an increase in time. The work in this paper lays the groundwork for future applications of bionic manipulators.
Dynamic Analysis of a 6-DOF Wheeled Mobile Robot
Authors: Ji Zhifei , Yuan Xiaodong , Lin Min , Huang Xiaoqing
Published: June 2021

The Mobile robot is a hot research topic in the research field of robotics. Combined robotic arms and wheeled mobile platforms, a novel mobile robot of six degrees of freedom is researched in this work. On the basis of Lagrange formulations, the dynamic model of the robot is developed. By employing the Adams software, the simulations of the direct and inverse dynamics of the robot are completed. The results indicate that under the conditions of constant driven forces or torques, the upper arm, forearm and rotational platform have the same motion period. In the first five seconds, the accelerations of upper arm, forearm and rotational platform research their peak values respectively, when the time equals 4.63s. Influenced by the motions of upper arm and forearm, the translational platform will obtain kinetic energies in the horizontal plane. Exerting periodic motions on the upper arm and forearm, the corresponding drive forces or torques have the same period with the motions of the upper arm and forearm. The driven forces and torques of the upper arm and forearm have multiple extreme points in one motion period. The motion laws of the robot is of positive significance for its design of controller and path planning.
A Sigma-Delta modulator circuit using correlated double sampling
Authors: Zhou Xirui , Duan Quanzhen , Huang S.
Published: June 2021

This paper presents a 14-bit ΔΣ analog-to-digital converter (ADC) with 2.35 kHz BW, while operating from a 3.3 V supply. Correlated double sampling (CDS) are used to mitigate the ADC’s offset and 1/f noise, especially at low frequencies.
Research on Customized Method of Post-processing of DMU125P Five-axis Machine Tool
Authors: Hao Zheng
Published: June 2021

With the increasing improvement of China’s industrial production technology, more and more attention is paid to the application of new five-axis CNC machine tools, and it is also urgent to strengthen the post-processing design. However, there are still many problems in the post-processing of DMU125P five-axis machine tools at this stage. The method is analyzed by combining the post-processing requirements of the DMU125P five-axis machine tool. At the same time, most studies in recent years have found that the use of post-processing application design is critical to improving the processing of complex mechanical parts. This study will analyze the post-processing customization method of the DMU125P five-axis machine tool, and provide a basis for the next step of work.
Analysis of and Solution to the Slippage of the Torque Limiter of Wind Turbine
Authors: Qi Liao Shu
Published: June 2021

As part of the high-speed shaft coupling, the torque limiter used by the wind turbine generator is installed near the generator side. It has the function of overload protection, which can effectively cut off the great torque generated when the low voltage ride through and the motor are short-circuited, and reduce the damage to the transmission system. In actual operation, especially when running at full power, the torque limiter will often slip, which will increase the damage to the transmission system and reduce the power generation efficiency. This paper analysis the principle of vector control on the machine side of the unit, and finds the cause of the slip of the torque limiter and the solution.
Investigation of dynamic characteristics for road vehicle based on multiple-input multiple-output vibration test
Authors: Yang Liwei , Wang Youjie , Liu Dejun , Liu Zhongping
Published: June 2021

This study is intended to investigate dynamic characteristics of a heavy truck. An experimental setup is designed for full vehicle vibration test consisting of four electro-mechanical shakers with high force rating (35000kgf). Transfer matrix is taken into account for multiple-input multiple-output (MIMO) vibration control. Vibration inputs under the tires can simulate both vertical and rotational excitations of the road. Furthermore, transfer function estimate (TFE) can be obtained based on the known road inputs and measured dynamic responses. Operational modal analysis has been carried out to identify the rigid mode shapes pitch, bounce, roll in low frequency range (1-10Hz). Based on experimental results, vibration response is higher on seat, which results in an uncomfortable level.