bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Innovation and Application of Multi-dimensional Interactive Teaching Mode in Smart Classroom: Taking Junior Physics Applied Research as an Example
Authors: Dai Yi , Liu Zhen , Luo Dan 
Published: July 2020

Abstract
There are many phenomena such as the interaction between teachers and students is not smooth and rich in the current physics teaching of junior high school. With the help of “Guangdong Education Cloud” platform and the Digital Textbook, this paper mainly discussed multi-dimensional interactive and collaborative learning strategies, and built a multi-dimensional interactive cloud classroom model and application model. The implementation process of cloud interactive classroom teaching of junior middle school in physics was introduced as an example, and the validity of the model was verified according to the contrastive analysis of the data before and after the implementation of the multi-dimensional interactive cloud classroom.
Allometric Growth Patterns of Taimen Larvae and Its Ecological Significance
Authors: Yang Guiqiang , Wang Zhanquan , He Jie , Li Wentong , Yuan Ding 
Published: July 2020

Abstract
Ecological experiment was conducted to study the allometric growth of taimen yolk-sac larvae and its significance in adaptation in early life stage. The results indicated organs such as feeding, breathing and swimming of larvae in the yolk sac stage are rapidly differentiated, and all key organs are allomorphic. In all parts of the body, the head length and body height are growing at a positive allometric speed, the trunk and tail fin length are growing at a slow speed; in the head organs, the rostrum length, eye diameter and head length are growing at a positive allometric speed; in the swimming organs, tail fin length is negative allometric growth; yolk sac length, yolk sac width and yolk sac height are negative allometric growth consumption. The fast development of feeding, breathing, swimming and other organs in yolk-sac larvae of taimen, increases larval survival ability at the early stage in the shortest possible time, and there were important ecological significance to adapt the complex and changing external environment.
State of Charge Estimation for Lithium-ion Battery Based on Cubature Kalman Filter with Fading Square-foot Factor
Authors: Zhang Yulong , Zhou Sida , Hua Yang , Zhou Xinan , Yang Shichun , Liu Xinhua 
Published: July 2020

Abstract
The state of charge (SOC) is the key issue to ensure the safe operation of the battery, but the battery system’s state estimation often has low accuracy and robustness under complex conditions or uneven measurement problem. To solve this problem, this paper proposes an SOC estimation method based on the combination of cubature Kalman filtering, fading factor and square root filtering. In addition, the second-order equivalent model is adopted, and parameters are identified. To verify the success of the algorithm, various operation tests were conducted. The simulation results agree well with the experimental results, and the maximum error can reach 3%. Numerical analysis shows that the method has good convergence and robustness under the initial error disturbance.
Design of Equipment Online Monitoring and Fault Diagnosis Platform Based on IOT
Authors: Yang Luxia , Zhang Guihua , Wu Peng 
Published: July 2020

Abstract
Complex equipment in the process industry is the core productivity of automated manufacturing. Fault prediction, diagnosis, and health maintenance of such equipment are important factors to ensure continuous and reliable operation of production. However, how to adjust the fault judgment rules in time according to the changes in the operating conditions and process of the equipment is the core design content of the fault diagnosis platform. The platform of this paper presented is mainly based on stress wave condition monitoring technology, which combined with process monitoring to form an online monitoring data set of the equipment; Then use the data fusion technology to fuse the collected signals at the decision-making level, and finally combine principal component analysis and the BP neural network to predict the equipment’s health trend and fault type.
Structural Design and Vibration Simulation of A New Type of Hollow High Precision Reducer with Integrated Structure
Authors: Zhao Honghua , Lin Yifei , Lu Shouyin 
Published: July 2020

Abstract
A new type of hollow and high precision reducer for robot is designed. By machining the track in the outer ring of input shaft and cycloid wheel, the traditional structure of crankshaft and bearing is replaced. The 4-DOF dynamic model is used for modeling, and the fixed step and 4-order Runge Kutta method of MATLAB software is used for solving, and the accuracy simulation of the reducer is completed. Compared with the existing reducer, the vibration and comprehensive performance of the new hollow high-precision reducer with integrated structure is better than the existing reducer.
Application of Artificial Intelligence in Civil Engineering Education
Authors: Xie Ming , Meng Fanbing , Zou Jingya , Feng Wei , Ma Shaohua 
Published: July 2020

Abstract
Artificial intelligence technology plays a very critical role in many fields in China. The technology can also realize its due value in the field of education. Taking civil engineering as an example, teachers can use artificial intelligence technology and civil engineering. The combination of professional course content helps students’ in-depth understanding of the actual construction of civil engineering projects and the application of some professional mechanical equipment in the classroom. This is also the main content that the author will discuss in the paper.
Quasi-Convex Functions and Their Inequalities Based on Fractional Integrals
Authors: Yin Hongping , Wang Jingyu 
Published: July 2020

Abstract
In the article, on the basis of Katugampola fractional integrals

K

ρ

u
+

α

h

and

K

ρ

ν
−

α

h

with

h
∈

X
c
p

(

u
,
v

)

, the quasi-convex function is discussed and a few inequalities are derived.
SSM-Seq2Seq: A Novel Speaking Style Neural Conversation Model
Authors: Wang Boran , Sun Yingxiang 
Published: July 2020

Abstract
Open domain personalized dialogue system has attracted more and more attention because of the ability of generating interesting and personalized responses. To incorporate speaking style, the existing methods first train respectively a response generator over a non-personalized conversational dataset and a speaking style extractor over a personalized non-conversational dataset, and then generate personalized responses by the parameter sharing mechanism. However, the training datasets’ speaking styles of the response generator and speaking style extractor is totally different, which makes the performance of the existing methods be not optimal. Intuitively, it will improve the performance by decreasing the gap between two training datasets’ speaking styles. Thus, in this paper, we propose a novel speaking style memory sequence-to-sequence (SSM-Seq2Seq) model, which incorporates the speaking style information from personalized non-conversational dataset into the training dataset of response generator to eliminate the gap. Extensive experiments show that the proposed approach yields great improvement over competitive baselines.
Deep Hashing with Attribute Guidance for Image Retrieval
Authors: Lu Yao , Huang Heyan 
Published: July 2020

Abstract
Similarity-preserving hashing is an important method to solve approximate nearest neighbour search problem for image retrieval. Lots of works have been proposed for supervised hashing and image category labels are utilized as supervised information. However, current works ignore a fact that images can be described by a set of attributes. Intuitively, images of different categories with close visual features can be separated by their attribute features because attributes keep more detailed information. In this paper, we propose a novel supervised deep hashing method with image attribute guidance. Specifically, hash codes are learnt through image visual features and guided by image attributes by maintaining pair wise similarities between images as well as the corresponding attribute descriptions. Extensive experimental results on two benchmark datasets show that our proposed method achieves better performance compared with the state of the art hashing methods.
Anthropometric Parameter Measurement from Equivariant Multi-view Images
Authors: Deng Lijiao , Yan Tingman , Zhao Qunfei 
Published: July 2020

Abstract
In this paper, we propose an anthropometric parameter measurement method that any customized parameter can be measured online by the pre-selected endpoints on the reconstructed 3D body models of equivariant multi-view images. The method includes 3D body model reconstruction, anthropometric parameter measurement, and parameter modification. In 3D body model reconstruction, we detect and segment the human body from its background and reconstruct a generative 3D body model from the segmented image with deep learning. And then we measure anthropometric parameter on the reconstructed 3D body model of each view. Before parameter measurement, we manually pre-select endpoints associated with all anthropometric parameters on the reconstructed 3D body model since all vertices of the reconstructed body model are ordered. However, the information of a single-view image is insufficient and the measurement result is varied regularly by the view changes. To improve the measurement accuracy, we design a convolutional neural network in the last step which can regress more accurate anthropometric parameters from equivariant multi-view measurements. Experimental results on the representative dataset demonstrate that the proposed method can measure planar and spatial anthropometric parameters automatically with comparable performance.
Automatic Attribute Learning for Person Re-Identification
Authors: Li Yang , Xu Huahu , Bian Minjie , Xiao Junsheng 
Published: July 2020

Abstract
Attribute based person re-identification is more robust to image appearance changes than low-level visual feature based methods. However, manual person attribute annotation has low efficiency, poor scalability and poor discrimination. Therefore, a scalable automatic attribute learning method for person re-identification is proposed. Firstly, the mapping matrix of person attributes is designed and generated automatically according to the principles of discernibility, sharing and low redundancy. Then the visual features are extracted using convolutional neural network and attribute classifiers are trained to detect person attributes, and the attribute based representation of person is generated. Finally, the person re-identification is carried out by comparing the similarity of the attribute based representations. Extensive experiments have been carried out on two common datasets Market-1501 and Duke MTMC-reID with the comparison to the Sate-of-the-Arts methods, the method achieved the best performance, which proves the superiority and effectiveness of the method.
Deep Reinforcement Learning based Path Planning for Mobile Robot in Unknown Environment
Authors: Wang Yang , Fang Yilin , Lou Ping , Yan Junwei , Liu Nianyun 
Published: July 2020

Abstract
It is a trend for robots to replace human in industrial fields with the increment of labor cost. Mobile robots are widely used for executing tasks in harsh industrial environment. It is an important problem for mobile robots to plan their path in unknown environment. The ordinary deep Q-network (DQN) which is an efficient method of reinforcement learning has been used for mobile robot path planning in unknown environment, but the DQN generally has low convergence speed. This paper presents a method based on Double DQN (DDQN) with prioritized experience replay (PER) for mobile robot path planning in unknown environment. With sensing its surrounding local information, the mobile robot plans its path with this method in unknown environment. The experiment results show that the proposed method has higher convergence speed and success rate than the normal DQN method at the same experimental environment.
Side Scan Sonar Image Geocoding based on Carrier Velocity Coarseness Correction
Authors: Kai Li 
Published: July 2020

Abstract
Gross error may occur in Geocoded side scan sonar image due to complicated water bottom environments and AUV maneuver. This paper presents a side-scan sonar image geocoding method based on carrier speed gross error correction. This method uses the side scan sonar image imaging principle and SIFT feature matching algorithm to calculate the AUV lateral velocity. This lateral velocity is used to detect and eliminate the gross speed error of DVL in underwater integrated navigation, and to perform gross error correction on the side-scan sonar image geocoding. This method can improve the performance of AUV underwater integrated navigation, and significantly improve the accuracy of side-scan sonar image geocoding.
Design and Implementation of a Desensitization Method of CT Medical Image based on DICOM
Authors: Yuan Jiantin , Wu Wei , Zhang Wenqiang , Lv Xiaoyi , Chen Chen 
Published: July 2020

Abstract
Medical big data, medical image processing and other fields are based on a large number of clinical data. Data desensitization is of great significance to the privacy protection of patients. CT image desensitization based on DICOM standard protocol only uses the strategy of hiding or deleting the data on the image, which greatly weakens the value of data use and cannot meet the normal value demand. Data desensitization should consider that it can still meet the needs of data mining and other applications on the basis of ensuring the privacy security of users. Based on the above application requirements, a strategy to anonymize patient identity is developed. Based on this strategy, a desensitization method that can meet the actual desensitization standards and analysis application requirements at the same time is proposed. This method can not only protect the privacy of patients to a great extent, but also ensure the availability and authenticity of desensitized data. In the future, this method has great potential in the application of CT image desensitization.
Prediction of Maximum Deformation of Single Nail Riveting Based on RBF Neural Network
Authors: Gu Jinyu , Yin Junqing , Chen Yongdang , Zhao Chengcheng , Cheng Yunfei 
Published: July 2020

Abstract
The influencing factors of riveting deformation are more complicated, and the specific relationship between the amount of deformation and each factor is difficult to express with general expressions, which is a non-linear problem. Aiming at this problem, this study uses RBF neural network to establish a model of the relationship between the maximum deformation of single nail riveting and various factors. Then, 1000 sample sizes were designed using the LHS method, with 90% of the sample size as training and 10% as testing. Secondly, the secondary development of the finite element software is carried out by using Python language to realize parametric modeling and batch processing. Finally, using the RBF neural network model to predict the maximum deformation of a single nail riveting, the maximum relative error and the average relative error were 8.43% and 2.948%, respectively. The results show that the RBF neural network can be applied in the field of prediction of maximum deformation of riveting and has high prediction accuracy.
From Artificial Neural Networks to Deep Learning: A Research Survey
Authors: Zhang Zhongwei 
Published: July 2020

Abstract
Deep learning (DL) is one of the hot topics in the field of artificial intelligence. As an extension of artificial neural network (ANN), deep learning models have more powerful learning and adaptation capabilities and they develop more and more rapidly. At present, because of the great advantages compared with the traditional methods, the development of convolutional neural network (CNN) is the hottest and it is widely used. This article first introduces the development of DL in chronological order and then analyses and summarizes artificial neural networks, and then uses the example of the LeNet-5 to explain the basic network structure of CNN and describe its training methods comprehensively. Based on the CNN model, many new convolutional neural networks with improved structure are analysed, such as AlexNet, ZFNet, VGGNet, GoogleNet and so on. The principle of each method is introduced in detail and the accuracy is compared with each other to conclude the correlation between the depth and the accuracy of DL. Then, for each method, it discusses some current problems existing in the corresponding method of DL, and points out future research and application directions worth exploring of each method. Finally, it summarizes the history of DL and the application of specific methods and the existing problems and solutions.
Design and Implementation of a Multi-scale Object Detection Algorithm on TensorFlow
Authors: Yanbin Chen , Huai Wang , Zhuo Han 
Published: July 2020

Abstract
The technology of object detection is a very important subject in computer vision task. It has been widely used in intelligent transportation, face detection, aerospace and medical image equipment. In this paper, a kind of object detection algorithm based on regression and region proposals is studied. We use a popular deep learning framework—TensorFlow, as the experimental platform. We propose a multi-scale object detection algorithm based on RPN region proposal network, which can extract the features of large and small objects by using the level of feature map. In addition, this paper also improves the classification regression model and proposes a two-dimensional loss function, which makes the region proposals closer to the groundtruth boxes in the final training, which makes the training process of the improved network easier. The experimental data set used in this paper is PASCAL VOC data set, the accuracy and speed of each category in 20 detection objects are calculated and analyzed. A number of experiments have proved that the proposed multi-scale object detection algorithm based on RPN have improved in accuracy and speed. The average detection precision on the PASCAL VOC dataset increased to 74.4%.
Design of Trajectory Tracking Algorithm of Isolated Observation Point Automatic Distribution Vehicle based on Visual Servo
Authors: Zhang Tao , Xu Ren-Ren , Zhang Jiu-Shi , Li Kun-Jie 
Published: July 2020

Abstract
The medical isolation observation point for epidemic prevention and control is an important part of the epidemic prevention and control link. With the attention paid by all countries to the epidemic prevention and control, the design and construction of the medical isolation observation point for epidemic prevention and control have put forward higher requirements. This design uses a visual servo-based automatic delivery car for trajectory tracking and research on the tracking algorithm to realize the automatic delivery car for the regular delivery of medicines, food and other daily necessities for isolated personnel. The application of automatic trolleys in the epidemic prevention and control medical isolation observation point can effectively alleviate the pressure on staff, avoid infection, and improve the efficiency of epidemic prevention. Aiming at the complexity of its motion trajectory, a tracking algorithm of vision and information fusion is used to implement the trajectory tracking of the smart car. The trend of the target trajectory is determined by the least square method. The image information collected by the image binarization algorithm is added to a certain anti-jamming program. The PID algorithm is used to optimize the control of the steering gear and the drive motor, improve the operating efficiency and system stability, and meet the actual application.
Trajectory Planning of Manipulator based on Improved Genetic Algorithm
Authors: Qiao Tieliang , Yang Daoguo , Hao Weidong , Yan Jingsen , Wang Ruiqing 
Published: July 2020

Abstract
Laser cladding technology has been used in many applications, and repair of high-value broken drills is one of them. Aiming at the problem of trajectory planning of cladding repair access sequence in multiple damaged areas of the drill bit, the trajectory planning of the cladding path is based on an improved genetic algorithm. Firstly, the modified D-H representation was used to model the manipulator, and the kinematics analysis was performed. Based on this, an improved genetic algorithm is used to select the optimal order of multiple damaged areas for the damaged parts that need to be repaired. In order to avoid falling into the local optimum, adaptive genetic operators are used, and an evolution reversal step operation is added to improve the search ability. Finally, the fifth-order polynomial interpolation method is used in joint space for trajectory planning. After simulation analysis, the path points obtained through genetic algorithm optimization can smoothly perform cladding repair operations in various areas.
Data Analysis of MYO Signals during Upper Limb Movements of Enhanced Exoskeleton
Authors: Lei Sun , Jialong Gao , Honglei An , Hongxu Ma 
Published: July 2020

Abstract
Equipped with enhanced exoskeleton in the individual combat system can effectively enhance the soldiers’long-term load-bearing capacity and enhance the combat effectiveness of the army. In the exoskeleton control system, accurate recognition of human movement intentions plays an important role as upper-level control. This paper uses MYO armband as a sEMG sensor to collect 8 channels sEMG signals. Neural network method is applied to estimate the joint angles during arm movement. Motion capture system is used to verify the estimation accuracy. PCA (Principle Component Analysis) method is performed on the 8 channels data collected by MYO and processed dimensions were selected by experiments to make the estimation accuracy the highest. The results show that when the principal component dimension is selected as 8, which makes the estimation accuracy of the joint angles the highest.
A Fault Diagnosis Model of Marine Diesel Engine Fuel Oil Supply System Using PCA and Optimized SVM
Authors: Hou Liangsheng , Zhang Jundong , Du Baisong 
Published: July 2020

Abstract
The fuel oil supply system of the marine diesel engine contains many components, which fits plenty of sensors to monitor the condition of all components. A fault sample consists of data collected from all the sensors at certain time, which lead the dimension of the fault sample is very high. When the ship is sailing, there is a randomness in fault categories and fault duration, which leads the fault data unbalanced. This paper proposes an appropriate combinational approach to address the above problems. First, to reduce computational complexity, the high dimensional fault samples are converted into the low dimensional ones using the principal component analysis (PCA). Second, a sample size optimization (SSO) strategy is proposed to address the problem of the learning from the imbalanced datasets, which improve the classification performance of support vector machine (SVM). Third, a three-dimensional Arnold mapping is introduced into the particle swarm optimization (PSO) algorithm to improve its generalization capability. Finally, the SVM optimized by the improved PSO is trained as the classifier to identify the ten faults in the fuel oil supply system. Results demonstrate that the average correct diagnosis ratio can be as high as 93.9%.