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development Strategy and Countermeasures of China's CBM Industry under the Goal of “Carbon Peak and Neutrality”

Journal of Earth Science 2023年第4期34卷 975-984页

作者： Fengyin Xu Xia Yan Fenglin Wang Xinyuan Ma Jian Yun Hongna Wang Borui Xu Shuangyuan Zhang Delei Mao China United Coalbed Methane National Engineering Research Center Co. Ltd.Beijing 100095China Research Institute of Petroleum Exploration&Development PetroChina Coalbed Methane Co.Ltd.Beijing 100028China Research management department CNPC Research Institute of Safety and Environment Technology Co.Ltd.Beijing 102249China Exploration technical R&D Center CNPC Exploration Software Co.Ltd.Beijing 100080China

China’s coalbed methane(CBM) industry is in an extremely important “climbing period” and “strategic opportunity period”,which can be generally characterized by “three low and one small”:low degree of exploration and development,low adaptability of main technology,low return on investment and small development *** the “carbon peak and neutrality” background,the development status of CBM industry is systematically *** resources,technology,management problems and reasons are *** and countermeasures to accelerate the industrial development are put forward according to the factors such as efficient development of resources,major technical breakthrough,talent team training,policy formulation and implementation,return on investment and so *** existing problems are as follows:(a) The overall occurrence conditions in China are complicated and the development is difficult compared with the other countries.(b) The research precision accuracy of CBM resource conditions is not enough.(c) The adaptability of technology is poor.(d) The management mode is not *** view of these problems,this paper puts forward the “two steps” development strategy and the technical and management countermeasures of “five in one”.The corresponding “five in one” technology and management countermeasures are the formulation and implementation of relevant safeguard measures in accordance with the principle of collaborative innovation in five aspects:resources,technology,talents,policies and *** the above measures,the dream and grand blueprint of CBM industry shall be realized.

关键词： coalbed methane CBM industry carbon peak carbon neutrality development strate-gy countermeasures.

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Method of adaptive learning feature model fusion and verification based on metamodels 3

Method of adaptive learning feature model fusion and verific...

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3rd International Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Wang, Chenyu Zhu, Yi Wu, Zixin Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Xuzhou221000 China Jiangsu Province Education Informatization Engineering Technology Research Center Jiangsu Normal University Xuzhou221000 China The Key Laboratory of the Software Development and Verification Technology The Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9781510681316

As the advancement of computer technique and artificial intelligence technology, education based on computer technology has become a hot topic, and the wave of educational informatization is rising. Among them, adaptive learning has received widespread attention as a type of educational informatization technology. However, the current adaptive learning feature models use different semantics to describe each other, and the models lack meta model level explanations, resulting in a lack of further operability of the models and difficulty in integrating with each other. In order to solve these problems, our paper proposes a fusion and verification method for learning feature adaptive models based on meta models. Above all, an adaptive learning feature meta model is proposed, which defines the construction rules of the adaptive learning feature model at meta model level, so that different models can be fused using the same set of semantic rules. Then, at the meta model level, transform the learning model into a requirement model. Finally, using formal methods, validate the fused feature model, which can make sure of the semantic consistency between the previous and subsequent models. This article demonstrates the entire process through a model fusion experiment. The experimental results indicate that this method is correct and feasible in promoting feature model fusion without adaptive learning. © 2024 SPIE.

关键词： Informatization

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Optimal Sharding for Dynamic Throughput Optimization in Blockchain Systems with Deep Reinforcement Learning

Optimal Sharding for Dynamic Throughput Optimization in Bloc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yao, Bingbing Wan, Jianxiong Jaffry, Shan Li, Leixiao Ma, Zhiqiang Liu, Chuyi Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Republic of China China Syntronic Research and Development Canada Inc. Canada

ISBN: (纸本)9798350337020

The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput, which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like Vehicle-to-vehicle (V2V) communication. To address the above issue, the sharding technology, which divides the network into multiple disjoint groups so that transactions can be processed in parallel, is applied to the blockchain systems as a promising solution to improve TPS. This paper considers the Optimal Blockchain Sharding (OBCS) problem which is formulated as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL) based methods where the action space has to be discretized such that it is not too large for tractability. However, the discretization degrades the solution quality since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and propose a sharding control algorithm based on Parametrized Deep Q-Networks (P-DQN) to efficiently handle the discrete-continuous hybrid action space without the scalability issue. Experimental results show that our Parametrized Deep Q-Networks Blockchain Sharding (P-DQNBS) method can effectively improve the TPS by up to 20%. © 2023 IEEE.

关键词： blockchain deep reinforcement learning hybrid action space sharding

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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Consistency Check for Multimodal Transportation Data: A Case Study of Vision and LiDARA 9

Consistency Check for Multimodal Transportation Data: A Case...

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9th International Conference on Information Management and technology, ICIMTech 2024

作者： Liu, Zhengyu Chen, Zhenwu Yang, Xiangmin Peng, Lei Software Engineering Institute University of Science and Technology of China HeFei230026 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Research Center for Automotive Electronics ShenZhen518055 China Shenzhen Urban Transport Planning Center Co. Ltd. Innovation Center for ITS ShenZhen518063 China Shenzhen Shenzhentong Co. Ltd. Dept. Research and Development ShenZhen518131 China

ISBN: (纸本)9798350390025

This study proposes a novel consistency verification method for multimodal transportation data, targeting spatial feature issues caused by multisource heterogeneous data in the transportation field. We unify different sources of data and point cloud data into a single 3D space through image data dimensionality increase and point cloud registration, addressing data inconsistencies caused by perspective differences. Through the application of data dimensionality augmentation and point cloud registration, our method effectively resolves spatial feature discrepancies between various data sources, significantly improving the accuracy of consistency verification. Experimental results indicate that the model trained with our method exhibits superior precision in consistency verification of multisource heterogeneous data compared to traditional approaches. © 2024 IEEE.

关键词： Spatio-temporal data

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Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 307-315页

作者： Wang, Wei Chen, Jian Hu, Yuzhu Liu, Han Chen, Junxin Gadekallu, Thippa Reddy Garg, Lalit Guizani, Mohsen Hu, Xiping Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518000 China School of Software Dalian University of Technology Dalian116621 China Division of Research and Development Lovely Professional University Phagwara India Center of Research Impact and Outcome Chitkara University Punjab India Faculty of Information and Communication Technology University of Malta Msida Malta United Arab Emirates

The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently. © 2024 The Authors

关键词： Internet of things

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Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

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Computers, Materials & Continua 2024年第9期80卷 4475-4496页

作者： Xiwang Guo Liangbo Zhou Zhiwei Zhang Liang Qi Jiacun Wang Shujin Qin Jinrui Cao College of Information and Control Engineering Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Research Center of the Economic and Social Development of Henan East Provincial Joint Shangqiu Normal UniversityShangqiu476000China Department of Computer Science New Jersey City UniversityJersey CityNJ 07305USA

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle *** on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is *** enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular *** established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,*** a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

关键词： Parallel disassembly line balancing problem multi-product multiskilled workers discrete chemical reaction optimization algorithm

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Identification of Electrical Equipment Based on V-I Trajectory, Odd Harmonic Currents 8

Identification of Electrical Equipment Based on V-I Trajecto...

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8th International Conference on Smart Grid and Smart Cities, ICSGSC 2024

作者： Fu, Yan Chen, Shengze Zhao, Fang Shi, Yvhan Ye, Siwen Jiang, Jianfeng Wang, Lei Yang, Xijun Electric Power Science Research Institute Shanghai Electric Power Company Shanghai China Software Development Center Shanghai China Shanghai Jiao Tong University Dept. of Electrical Engineering Shanghai China Beijing Zhixiang Technology Co. Ltd Beijing China

ISBN: (纸本)9798350377408

Non-intrusive load monitoring (NILM) and identification are important to smart grid construction and safe operation. A method combining mixed feature matrix and convolutional neural network based on V-I trajectory and odd harmonic components is proposed and proved for non-intrusive electrical load monitoring technology. In the paper, the specific contents are to analyze the basic characteristics of electrical equipment, including steady-state and transient characteristics of input current. Six simulation circuits of single-phase AC power supply powered electrical equipment are established by means of MATLAB/Simulink using Electrical or Electronic blockset, including heater, hair dryers LED string, adapter, variable frequency air conditioner (air-CON), and variable frequency refrigerators, as the electrical equipment to be analyzed and identified. The grid side current waveforms and spectrums, V-I trajectories, and odd harmonic current components of these six load models are extracted as basic features. The mixed feature matrix using V-I trajectory and odd harmonic features, as well as convolutional neural network (CNN), are proposed for the analysis and identification of electrical equipment. After training and testing on the PLAID dataset, the system can effectively identify electrical equipment under steady-state operation. In addition, three different detection methods are compared and analyzed to verify the superiority of the proposed method for analyzing and identifying electrical equipment based on mixed feature matrix and convolutional neural network. © 2024 IEEE.

关键词： Smart power grids

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Tightly-Coupled Lidar-Based Lifelong Mapping using Wheels and a MEMS Gyroscope for Mobile Robots in Dynamic Environment 24

Tightly-Coupled Lidar-Based Lifelong Mapping using Wheels an...

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3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering, RAIIE 2024

作者： Hu, Lizhi Chen, Huilin Li, Xiang Lu, Wei Zhao, Yifan Hu, Songyu Navigation and Localization Algorithm Department Zhejiang HuaRay Technology Co. Lt. Hangzhou China Software Research and Development Center Zhejiang HuaRay Technology Co. Lt. Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798400718311

Localization and mapping technology of mobile robots is important in production and warehousing. The changing warehousing environment always result in poor accuracy and robustness of the mobile robot's localization. It is necessary to detect the degree of environmental changes and update the map. This paper proposes a novel lifelong mapping method fusing measurements from the lidar, wheels and a MEMS gyroscope, which can create a reliable map corresponding to the current working environment. Firstly, we use the wheels and gyroscopes measures for pre-integration to provide relative motion constraints, while integrating wheels and gyroscopes to provide initial pose for lidar matching. We also establish lidar odometry by submaps to provide another type of relative motion constraints. The absolute constraints are obtained by matching the lidar measurements with prior map. These three types of constraints are optimized in a factor graph. At the same time, during the process of updating the map, detect newly added artificial markers such as reflective columns or strips, and update them into the map timely. Then, in order to delete redundant lidar data keyframes for building map, we propose a determine redundancy method based on submaps. Finally, we propose a method for calculating the degree of map changes, which can update the map timely. Experimental results prove that our method can establish higher quality maps, optimize lidar external parameters timely during the mapping process, delete redundant lidar keyframes more efficiently, and update the current environmental map more accurately and timely. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Gyroscopes

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Using analytic hierarchy process to evaluate deep learning for infrared target recognition

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Multimedia Tools and Applications 2024年第38期83卷 86229-86245页

作者： Xu, Laixiang Cao, Bingxu Wei, Xinhong Li, Bei Su, Jingfeng Ning, Shiyuan Zhao, Junmin School of Computer and Data Science Henan University of Urban Construction Pingdingshan467036 China School of Information Engineering Luohe Vocational Technology College Henan University of Technology Luohe Institute of Technology Luohe462000 China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Pingdingshan467036 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan467036 China Department of Software Information China Electronics Technology Group Corporation 36-th Research Institute Jiaxing314033 China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan467036 China

In order to promote the evaluation performance of deep learning infrared automatic target recognition (ATR) algorithms in the complex environment of air-to-air missile research, we proposed an analytic hierarchy process (AHP) evaluation system consisting of the target recognition ability, the generalization recognition ability, the anti-interference ability, the background suppression ability, and the cost metrics ability. According to detailed quantitative analysis and experimental results, we establish probability evaluation models and get their probability values. Compared with traditional evaluation systems, our proposed method implementation is showing high reliability. Similar superior performances are also achieved, such as quick, comprehensive, and scientific. Through the above research, the key technology of deep learning performance evaluation in automatic target recognition has been broken through, which provides some basis and guidance for the analysis of infrared imaging-guided weapon systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Infrared imaging

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