bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Study on the Settlement of Gravel cushion and DCM Piled Compound Foundation
Authors: Song Shenyou , Xu Guoping , Jin Wenliang , He Xiao , Kou Xiaoqiang , Fu Baiyong 
Published: June 2020

Abstract
The DCM pile is applied in immersed tunnel foundation improvement first time at home in the mega project of Shenzhong Link with improved ground areas over 700000 m3. The compound foundation composed of gravel cushion and DCM piled foundation settlement law is not definite. To explore this law loading plate test in place on site is carried out and the test results show : (1)under the first order of load effect the settlement amount of the compound foundation accounts for over 60% of the total settlement, the modulus of compression of the gravel cushion is 1.5MPa being approximately 10%∼20% of normal modulus of compression; (2)under other loading effects the final deformation of compound foundation settlement curve shows stable state with the modulus of compression of the gravel cushion in range of 4.5∼12MPa which is equivalent to the normal value. The numerical analysis for purpose of the testing shows that the gravel cushion settlement accounts for larger proportion being 58.57% of the total settlement and the gravel cushion thickness, density, residual layers fallen in DCM dredging, all may be factors affecting settlement. Hence it is suggested to enhance study on the workmanship of the tunnel bed formation in order to improve the bed rigidity and anti-deformation ability.
Construction and Empirical Analysis of Information Management System of Engineering Investigation Quality
Authors: Zhou Chang’an , Chen Can , Ren Hong , Zhang Lichu 
Published: June 2020

Abstract
The information management of engineering investigation quality is actually the process of producing, collecting, inputting, sharing and applying engineering investigation parameter data and management information. Using the theory of total quality management and the information technology such as blockchain, big data and cloud computing, this paper explores the possibility of constructing the information management system of engineering investigation quality from “data standard of engineering investigation quality information”, “information management platform of engineering investigation project” and “geological information database of Engineering Investigation”, and takes Chongqing as an example to carry out the engineering investigation quality. The construction and analysis of information management system verify the effectiveness of the information management system of engineering investigation quality.
Study on the Application of Computer Simulation Technology in the Cultivation of Professional Skills
Authors: He Zhizhou 
Published: June 2020

Abstract
In view of the characteristics of computer simulation technology, the training program of professional talents is studied. Summarizing the problems existing in the training of professional skilled personnel, the aim is to perfect the method of cultivating professional skills in order to show the training value of computer simulation technology and provide reference for the development of the industry.
A Security-aware Workflow Scheduling Strategy for Edge-cloud Computing used in UVA Delivery System
Authors: Shi Wenyu , Zhang Rui 
Published: June 2020

Abstract
With the rapid development of e-commerce, the “last mile” of logistics delivery has the problems of low efficiency and high cost. UVA delivery system can overcome this bottleneck whose system is a business process system which requires massive resources and real-time response. Edge-cloud computing integrates the massive processing power of cloud computing with the advantages of low latency and localization of edge computing. However, energy of UVAs is limited and UVAs are vulnerable to attacks. To address such issues, this paper proposes a secure model based on edge-cloud computing. Second, a new fitness function is proposed to evaluate the energy consumption and the security of task execution. Finally, a security-aware energy-efficient multi-source workflow task scheduling strategy within response time constraint is proposed. The results show that our strategy can achieve the security requirement and has less energy consumption under the limitation of response time.
Design and Implementation of Children’s Book Management System Based on Asp.Net Technology
Authors: Yan Zhao 
Published: June 2020

Abstract
with the Rapid Development and Popularization of Computer and Network Technology, Today’s Society is Developing in an All-Round Way. in Today’s Society, the Amount of Information is Increasing Rapidly in Several Stages Every Day. in This Information Explosion Society, Information Automation Management Has Become the Trend of Today’s Society. Books as an Important Carrier of Information, with the Development of Information Technology, the Types and Quantity of Books Have Also Changed Greatly, and Growing At a Very Fast Speed. How to Find, Borrow and Return Books More Efficiently and Quickly Has Become a Difficult Problem in Book Management. the Popularization of Computer and the Development of Network Technology Have Brought an Opportunity for This Problem. This Paper Analyzes the Design of the Management System Based on the Physical Structure of B/s and Introduces in Detail the Development Tools, System Function Requirements and System Software and Hardware Requirements for the Realization of the Web Management System of Children’s Books. the System Has Been Realized by Developing Technology and Tools, Which Ensures the Normal Operation of the System, Realizes the Integrated Function of Information Service of the Library, Increases the Working Efficiency of the Management Personnel, Facilitates the Reading Demand of the Majority of Children, Reduces the Maintenance Cost of the Library, and Makes the Library Develop Towards the Modernization of Information Technology.
Research on Application of Deep Learning Based on Mobile Learning in Smart Grid
Authors: Huang Wen , Zhang Haibiao 
Published: June 2020

Abstract
The rapid economic and social development cannot be separated from the important contribution and guarantee of the power industry. In the development of power industry, substation technology plays a very important role. The development of in-depth learning based on mobile learning provides an effective means for data value extraction. Therefore, it is of great significance to guide power enterprises to make correct decisions by making full use of these data based on the actual power grid and analyzing them in depth to discover and utilize the hidden information. Firstly, the basic framework of in-depth learning is elaborated, and the theoretical basis and technical system of mobile learning are summarized. According to the actual demand of power system, this paper summarizes the key application fields and values of mobile learning-based deep learning in power system data processing based on image data and spatio-temporal data, and puts forward relevant technical development suggestions.
Research on Design and Implementation of Knowledge Map Storage Access System Based on Ontology Reasoning
Authors: Li Jian , Ma Yanzhou 
Published: June 2020

Abstract
In order to support the intelligent combat capability of future wars, the network information system needs in-depth research and exploration in the field of artificial intelligence. This paper designs a distributed aggregation storage mode with the characteristics of balanced storage load distribution and local node aggregation storage under Big Table model. A distributed parallel query engine that uses the Group-By mode to distribute parallel computation query trees. A knowledge map completion method based on Bayesian inference is proposed. Bayesian probabilistic inference theory and RDF implication inference rules are used to jointly infer the potential relationships between entity nodes to predict the relationship between new nodes and original nodes, thus improving the mining efficiency of potential factors in the model and the accuracy of prediction of unknown relationships. Based on Big Table model, the entity sets stored row by row are evenly divided and stored through random prefix and pre-partition operations to realize load balancing. At the same time, random prefixes can be uniformly distributed to nodes for storing entities of the same type, and aggregated by entity category on a single node. With the continuous development of knowledge map technology, future knowledge map learning and reasoning technology can be integrated with new fields such as machine depth learning, cloud computing, block chain, big data, biological genetic engineering, etc. to play an important social value.
Advances in Automatic Bird Species Recognition from Environmental Audio
Authors: Dong Xueyan , Jia Jingpeng 
Published: June 2020

AbstractBioacoustics has recently become one of the “big data” research topics since many bird monitoring projects have collected terabytes of audio using remote sensors. The challenge in recent years is to develop algorithms to realize fully automatic recognition of bird species through analysing environmental recordings. A number of approaches directly draw on experience of effective algorithms in signal processing and image processing areas. They seem working well for small data or lab data, however, the outcomes for large-scale environmental data shows a big gap between theoretical experiments and real applications. To provide possible clues for future research, we review the state-of-art development in automated bird species recognition, and identify wide range of algorithms on noise removal, bird call detection, feature extraction for classification. The significant software tools and publicly available datasets for the task are presented. This survey can be valuable for new researchers who are about to start the journey with birdsong analysis.
Design of Audio Signal Analyzer Based on MCU and FPGA
Authors: Lv Meimei , Gao Tongguo , Yan Ding , Qiao Lufeng 
Published: June 2020

Abstract
This audio signal analyzer is based on a microcontroller (MCU) and a field programmable gate array (FPGA). It consists of a controllable gain amplifier circuit, an active filter circuit, A / D conversion and D / A feedback circuits, and a main control circuit and liquid crystal display module. Through AD conversion, the audio signal is sampled, the continuous signal is discretized, and then the FFT fast Fourier transform operation is performed to analyze and process each frequency component and power of the audio signal in the time and frequency domain. By controlling the gain of the amplifier to expand the dynamic range of the input signal and improve the sensitivity, it can also complete the corresponding human-computer interaction control based on the PS2 keyboard input, while using the high-resolution LCD to display the signal spectrum. The audio signal frequency range that this system can accurately measure is 20Hz-10KHz, its amplitude range is 5mVpp-5Vpp, and the resolution is divided into 20Hz and 100Hz. The measured power accuracy is as high as 1%, and the period of the periodic signal can be accurately measured, which is the ideal solution for audio signal analyzers.
Multi-type web image text detection based on the improved EAST algorithm
Authors: Xiang Yuanji , Luo Fei 
Published: June 2020

Abstract
Web image character recognition has important application value in the commercial field and text detection is the basis of extracting this text information. Among many text detection algorithms, EAST (efficient and accurate scene text detector) algorithm has a good performance in natural scenes, but it is ordinary in web pictures. Based on this, aiming at the problem that the number of channels in the QUAD output layer of EAST algorithm is redundant, which leads to the low accuracy of web image text detection, it is modified as the distance from the point marked as a positive sample to the four vertices of the text box where it is located instead of the coordinate offset to the vertices. The improvement reduces from the original 8 channels to 4 channels, which restricts the optimization direction of the model, and then by setting a new loss function in order to improve the loss value of the edge area of the text box and the pixels in the image that are difficult to detect, the model is more suitable for the text detection of the web image. The experimental results show that the improved EAST algorithm significantly improves the accuracy of text detection in web images.
Research on Cellular Automata Network Public Opinion Transmission Model Based on Combustion Theory
Authors: Chen Hongzhou , Song Yinghua , Liu Dan 
Published: June 2020

Abstract
With the continuous development of social networks and the continuous emergence of public opinion on the Internet, the government’s emergency response is facing tremendous pressure. In this paper, cellular automata is used to compare the social relationships between individuals in the network. Combustion propagation models are used to compare the network masses to several types of burning substances according to their propagation characteristics. The influencing factors among individuals are determined. A network public opinion propagation model is established to explore the rationality of its propagation.
Indoor Semantic Mapping with Efficient Convolutional Neural Networks for Resource-constrained SLAM System
Authors: Zhang Jiahao , Wang Zhiqiang , Zhu Qing 
Published: June 2020

Abstract
Scene parsing and robotic manipulation using visual sensing are of significant importance for mobile and robotics related applications. And increasing efforts are being made to associate semantic concepts to geometric entities in robot’s surroundings. However, they are not well suitable for resource-constrained and mobile platforms. Toward an efficient semantic mapping solution, we propose a specifically light-weight segmentation network for building a dense 3D semantic map in real-time. The propose architecture is based on depth-wise convolution and channel shuffle to reduce computation cost. An improved Atrous Spatial Pyramid Pooling (ASPP) and encoder-decoder structures are presented and multi-scale features are merged for better prediction. Experiments are compared with other methods to prove the effectiveness and superiority.
Study of simulating human intelligent control
Authors: Zhao Jing 
Published: June 2020

Abstract
Because the temperature control system of glass furnace has the characteristics of non-linear, time-varying, large amount of interference, large lag and so on, the traditional PID control is difficult to ensure the temperature control accuracy and can not achieve the expected effect, the artificial intelligent control is proposed for the temperature control system of glass furnace. Humanoid intelligent control is to make the controller imitate the control behavior of human. It uses control theory and computer technology to act on the actual control system. Through the analysis of the simulation curve of the artificial intelligent control of glass furnace temperature, the superiority of the artificial intelligent control is verified.
Automatic defect detection based on improved Faster RCNN for substation equipment
Authors: Shouguo Lv , Liu Kai , Qiao Yaohua , Wang Qi , Yang Sun , Li Zhenyu 
Published: June 2020

Abstract
Defect detection methods based on machine learning extremely accelerate the substation routine inspection process. In this paper, we propose an automatic defect detection method based on improved Faster RCNN. For one thing, random feature pyramid (RFP) structure is introduced for the highly discriminative feature map construction; for another thing, we execute the detection boxes selection by soft non-maximum suppression (SNMS), keeping the detection of defects which distribute densely. Finally, online hard example mining (OHEM) is employed to deal with the imbalance problem. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.
Face Recognition Technology Analysis Based on Deep Learning Algorithm
Authors: Liang Li 
Published: June 2020

Abstract
with the Explosive Development of Deep Learning Technology Face Recognition and Other Recognition Technologies Mostly Adopt Deep Learning Algorithm for Recognition. Although the Deep Learning Algorithm Has High Recognition Accuracy, It Has a Huge Demand for Computing. in the Mobile Terminal, We Can Use Artificial Intelligence Chips That Can Accelerate Deep Learning Operations to Complete Relevant Operations. Deep Learning Has Fixed Modes, Like Convolution. Ai Chips Can Significantly Improve the Efficiency of Deep Learning Operations by Optimizing the Corresponding Operation Modes. in This Way, Mobile Terminals Can Quickly Implement Complex Deep Learning Operations, Such as Face Recognition Based on Deep Learning. One Representative of Ai Chips is the Tensor Processing Unit of Google, Which is Able to Accelerate the Tensor Flow of the Deep Learning System, Which is Far More Efficient Than Gnus. the Tpu Provides 1,530 Times the Performance Improvement and 3,080 Times the Efficiency (Performance/Watt) Improvement over the Same Cpu and Cpu. Traditional Face Recognition Algorithms Include Face Recognition Technology Based on Pca(Principal Components Analysis) and Face Location Technology Based on Ad Boost. Although the Traditional Face Recognition Technology is Fast, the Detection Effect is Much Different from the Deep Learning Technology. on the One Hand, the Accuracy of the Traditional Face Recognition Methods Represented by Pca is Far Lower Than That of the Deep Learning Algorithm. on the Other Hand, for the Recognition of Massive Users, the Traditional Pca Face Recognition Technology is Not Competent.
Distributed Partial Discharge Detection Technology for High Voltage Cable Based on Ac Withstand Voltage
Authors: Liu Dawei , Liu Yi , Li Fang , Wang Chen 
Published: June 2020

Abstract
with the Development of Urbanization, It is More and More Urgent to Improve the Reliability and Stability of Power Supply. Due to the Consideration of Municipal Planning, Environment, Personal Safety and Other Factors, the Utilization Rate of Power Cables in Urban High-Voltage Transmission Channels Has Increased Dramatically. Accidents Such as Fire, Large-Area Power Failure and So on, Which Are Caused by Internal Insulation Defects of Cables, Defects in Joint Position Fabrication and Installation Process, Occur from Time to Time. This Paper Studies the Detection Method of Potential Insulation Defects in the Cable Interior and Joint Position Based on the Ac Withstand Voltage Partial Discharge Detection Technology, and Designs a Set of Distributed Partial Discharge Test System Based on the High Frequency Pulse Method to Detect the Interior, Middle Indirect Head and Terminal Head of the Cable. in the Field, the Distributed Pd Detection Technology Based on Ac Withstand Voltage Designed in This Paper Can Effectively Detect the Internal and Joint Insulation Defects of Power Cables, and Realize Fault Location, Which Has the Value of Practical Application and Promotion.
Mp-Matt:a Time Series Prediction Method with Mine Gas Sensor Data
Authors: Wang Weihan 
Published: June 2020

Abstract
Based on the Encoder-Decoder Framework, Combined with the Pooling Preprocessing and the Mupli-Attention Mechanism, This Paper Proposed the Mp-Matt Model for the Prediction of Mine Gas Time Series Data. the Model Had a Good Predictive Accuracy in the Data Set of Oxygen, Carbon Monoxide and Methane in a Mine. the Accuracy of the Best Baseline Comparison Model Had Increased by 10.30%, 22.26% and 20.02%, Respectively. This Study Compared the Effects of Each Component in the Model. the Comparison Showed That the Multi-Attention Layer Had the Most Obvious Effect. the Increase of the Metic Pooling Layer Was Lower, and the Effect of Adding the Fatt Layer Was Slightly Better Than That of Adding the Hatt Layer.
Consumer Behavior Analysis in the Offline Retail Stores based on convolutional neural network
Authors: Wang Mingxu 
Published: June 2020

Abstract
Pedestrian attributes recognizing (PAR) is an important task in computer vision area due to it plays an important role in video surveillance. On the other hand, pedestrian visual attributes are treated as middle-level semantic features which can provide calibration information for high- level human related visual tasks in order to improve the discriminative ability of the models, such as pedestrian detection, people tracking, person re-identification, action recognition and scene understanding. However, the most study of PAR is based on single person. In this work, I implements a multiple pedestrian attributes recognition model based on the offline retail scenes. This model combines object detection and multitask classification techniques and can be trained end-to-end directly for back propagation. This paper also demonstrates the performance of the final model through a series of experiment.
Semantic Parsing of Action Text for Text-to-Scene Conversion
Authors: Yang Fuping , Wang Linna 
Published: June 2020

Abstract
Understanding the action text is the main problem of dynamic scene generation in text-to-scene conversion. However, the existing basic research about it is still lacking, and its semantic parsing method is not perfect. Therefore, based on the idea of action chain structure, the formal expression of it is established on the basis of defining action text, classifying action text and analysing the features of action text. The method of action text semantic parsing is proposed combing with the action text semantic dictionary. The experimental results show that the method completes the semantic parsing for the action text to a certain extent and provides a reference for computer to understand the semantic of action text.
Economic Analysis of Information Transmit Mechanism in the Electronic Supply Chain Finance Service
Authors: Lei Su , Haiying Wang , Weiyu Tian , Haiyue Li 
Published: June 2020

Abstract
As many finance services are made through supply chain system, it needs the stability and accuracy in information transmit process. Supply chain finance service online should be built on the system, including open API, basic database, and progress standardization. The paper focuses on related processes on the SCM platform. Then it discusses how to strengthen the determination of legal rights and responsibilities and provides business scenario of planning electronic warehouse receipt. It shows that the information transmit mechanism can get the economic profit with the help of system.
Discussions About Supply Logistics in Automobile Industry of China
Authors: Wang Xueliang , Le Xiaobing 
Published: June 2020

Abstract
Based on the research of lean production, the lean supply theory is a development of the lean concept from the production field to the supply field. After that, it is developed integrated lean supply which is a kind of collaboration in the first level and provides components with higher quality and lower cost for assembly plants in larger size, higher level, shorter time and enterprises in the supply chain for a long term and it could finish the process of supply with higher lean requirement on the premise of highly integration. The supply chain system in auto-mobile industry is very complicated. At the same time the operational efficiency of supply logistics will directly influence the final profit of enterprises in the supply chain. However, in the process of implementing lean supply logistics, due to the lack of specific and systematic theoretical guidance, the research results are not satisfactory. This article base on the perspective of collaborative innovation, and through collaborative innovation of supply-chain, production and distribution logistics in different stages of logistics operation to realize logistics integration of a enterprise successfully.