bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

AbstractIn 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.