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Geological modeling of coalbed methane reservoirs in the tectonically deformed coal seam group in the Dahebian block,western Guizhou,China

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Frontiers of Earth Science 2024年第1期18卷 44-67页

作者： Yong SHU Shuxun SANG Xiaozhi ZHOU Key Laboratory of Coalbed Methane Resources and Reservoir Formation Process(Ministry of Education) China University of Mining and TechnologyXuzhou 221116China Carbon Neutrality Institute China University of Mining and TechnologyXuzhou 221008China School of Resources and Geosciences China University of Mining and TechnologyXuzhou 221116China Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization China University of Mining and TechnologyXuzhou 221008China

The widely spread Carboniferous-Permian coal seam group in southern China has great potential for coalbed methane resources,but the extensively developed tectonically deformed coal seriously restricts its *** the Dahebian block in western Guizhou as the study area,the geological model of coalbed methane reservoirs in the tectonically deformed coal seam group was established,and the spatial distribution pattern of model parameters was clarified by clustering algorithms and factor *** facies model suggests that the main coal body structures in Nos.1,4,and 7 coal seams are cataclastic coal and granulated coal,whereas the No.11 coal seam is dominated by granulated coal,which has larger thicknesses and spreads more *** in situ permeability of primary undeformed coal,cataclastic coal,granulated coal,and mylonitized coal reservoirs are 0.333 mD,0.931 mD,0.146 mD,and 0.099 mD,respectively,according to the production performance analysis *** property model constructed by facies-controlled modeling reveals that Nos.1,4,and 7 coal seams have a wider high-permeability area,but the gas content is lower;the high-permeability area in the No.11 coal seam is more limited,but the gas content is *** results of the self-organizing map neural network and K-means clustering indicate that the geological model can be divided into 6 clusters,the model parameter characteristics of the 6 clusters are summarized by data analysis in combination with 6 factors extracted by factor analysis,and the application of data analysis results in multi-layer coalbed methane co-development is *** study provides ideas for the geological modeling in the tectonically deformed coal seam group and its data analysis.

关键词： geological modeling tectonically deformed coal coal seam group clustering algorithm Dahebian block,western Guizhou

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Resource Allocation for Joint Communication and Positioning in mmWave Ad Hoc Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第2期73卷 2187-2201页

作者： Luo, Xueni Lu, Xiaofeng Yin, Benquan Yang, Kun Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England

Joint communication and positioning will be a critical driver in future wireless networks for emerging application areas. Supporting mobility, Ad-hoc networks can freely and dynamically self-organize an arbitrary and temporary network topology without any pre-existing infrastructure. Combined with millimeter-wave (mmWave), Ad-hoc networks can construct communication links with less time and higher directivity due to directional antennas and building blockage. The wide spectrum of mmWave could provide a high-oriented channel for positioning, which is significant for multi-user conditions. In this article, we concentrate on high-efficiency algorithms to allocate spectrum and power to different services and achieve a performance tradeoff between the communication and positioning process. Besides, the severe interference between users would degrade the actual system performance. To address these challenges, this article proposes an optimal clustering algorithm based on the mmWave Line of Sight (LoS) probability to form two different sub-nets for communication and positioning services, respectively. Then, the available spectrum resources are divided into two parts for the above sub-nets under the Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM) technique, which could design sub-bands independently. Finally, we proposed an optimal algorithm to allocate sub-bands and power to improve the performance of the communication sub-net while guaranteeing the positioning performance in the corresponding sub-net. Numeric simulation results demonstrate that the proposed resource allocation algorithm could achieve better performance both in the communication and position process.

关键词： Millimeter wave communication Ad hoc networks Resource management OFDM Interference clustering algorithms Linear antenna arrays Joint communication and positioning millimeter-wave clustering algorithm resource allocation Ad hoc network

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AWeb Application Fingerprint Recognition Method Based on Machine Learning

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 887-906页

作者： Yanmei Shi Wei Yu Yanxia Zhao Yungang Jia School of Integrated Circuit Science and Engineering Tianjin University of TechnologyTianjin300382China College of International Business Zhejiang Yuexiu UniversityShaoxing312030China School of Public Administration Zhejiang Gongshang UniversityHangzhou310018China Tianjin Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China Tianjin300100China

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and *** fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web *** response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering *** method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality *** core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated *** transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint *** experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad *** novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

关键词： Web application fingerprint recognition unsupervised learning clustering algorithm feature extraction automated testing network security

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A clustering Approach to Approximate the Timed Reachability Graph for a Class of Time Petri Nets

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第7期67卷 3693-3698页

作者： Zhou, Jiazhong Lefebvre, Dimitri Li, Zhiwu Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Normandie Univ GREAH F-76600 Le Havre France Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Timed extended reachability graphs (TERG) of time Petri nets abstract the temporal specifications and represent the feasible trajectories under the earliest firing policy. One drawback of such graphs is the rapid increase in the number of states with respect to time specifications. For this reason, approximations of TERG that remove some states have been studied in recent works. In this article, we improve the approximation of a TERG. New objects-called vertices-are defined to manipulate the time constraints and algorithms are proposed to cluster nearby vertices. A metric based on the time constraints is defined for this purpose and a cluster TERG of reduced size is obtained.

关键词： Approximation clustering algorithm discrete event system reachability graph time Petri net (TPN)

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Sparse Bayesian Learning-Based 3-D Radio Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction, and Sparse Recovery

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第1期10卷 80-93页

作者： Wang, Jie Zhu, Qiuming Lin, Zhipeng Wu, Qihui Huang, Yang Cai, Xuezhao Zhong, Weizhi Zhao, Yi Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Nanjing 211106 Peoples R China

The radio environment map (REM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. In view of a limited number of spectrum sensors and constrained sampling time, this paper presents a new three-dimensional (3D) REM construction scheme based on sparse Bayesian learning (SBL). Firstly, we construct a scenario-dependent channel dictionary matrix by considering the propagation characteristic of the interested scenario. To improve sampling efficiency, a maximum mutual information (MMI)-based optimization algorithm is developed for the layout of sampling sensors. Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D REM. We finally use the simulation data of the campus scenario to construct the 3D REMs and compare the proposed method with the state-of-the-art. The recovery performance and the impact of different parameters on the constructed REMs are also analyzed. Numerical results show that the proposed scheme can reduce the required spectrum data and has higher accuracy under the low sampling rate.

关键词： Three-dimensional displays Sensors Sparse matrices Dictionaries Transmitters Optimization Data models 3D radio environment map sparse Bayesian learning mutual information channel propagation model clustering algorithm

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Research on frequency-domain characteristics of acoustic emission signals generated during wood damage and fracture process

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WOOD MATERIAL SCIENCE & ENGINEERING 2024年

作者： Du, Kun Wang, Youli Zhang, Zhiheng Li, Ming Fang, Saiyin Huang, Changlin Zhao, Yue Mao, Feilong Qin, Gezhou Southwest Forestry Univ Coll Mech & Transportat Kunming Yunnan Peoples R China Yunnan Vocat Inst Energy Technol Coll Mech & Elect Engn Qujing Yunnan Peoples R China Anhui Polytech Univ Beijing Middle Rd Wuhu Anhui Peoples R China Minist Educ Key Lab Adv Percept & Intelligent Control High End Wuhu Anhui Peoples R China

This research conducts three-point bending tests on air-dried beech, elm, pine, and cypress specimens to research their damage and fracture behaviour. Wavelet transform is utilized to decompose original acoustic emission signals generated during the damage and fracture process into five layers. Then, based on the principle of signal correlation, the wavelet-decomposed detail signals are adaptively reconstructed. The K-means algorithm is used to classify reconstructed AE signals into different groups based on their frequency-domain characteristic. Research results indicate that the peak frequency of low-frequency AE signals generated during the wood damage and fracture process is approximately 21 kHz. These AE signals may be generated by damage, delamination, bucking, and collapse of the cell-wall layer. When wood specimens are in the later stage of nonlinear deformation and in the macroscopic fracture stage, in addition to the mentioned microscopic damage above, there will also be formation and expansion of microcrack damage zones, as well as cell-wall rupture and macroscopic fracture of wood fibres. In addition to the low-frequency AE signals mentioned above, these AE signals are medium-frequency signals with peak frequency in the range of 25-60 kHz, and high-frequency signals with a peak frequency of approximately 165 kHz.

关键词： Acoustic emission wavelet transform clustering algorithm frequency-domain characteristic

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ISFO-CS: An Improved Sailfish Optimization algorithm for Controller Selection in SDWSN

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WIRELESS PERSONAL COMMUNICATIONS 2025年第1-2期140卷 299-331页

作者： Rajoriya, Manisha Kumari Gupta, Chandra Prakash Rajasthan Tech Univ Dept Comp Sci & Engn Kota 324010 Rajasthan India

Software-defined wireless sensor networks (SDWSNs) have recently been added to networking, increasing scalability and performance. Choosing SDN controllers is a critical issue for network administrators in SDNs. The control plane in SDN is a separate procedure that operates on the control layer. In terms of applications and services, the controller provides a comprehensive view of the entire network. The three key factors examined when selecting a controller are open source, campus network, and productivity. An appropriate device for the prompt processing of all switch requests is required for SDN to function properly and the network to behave properly. To determine the optimum controller for the given parameters, decision logic that allows controller comparison must be developed. As a result, an improved Sailfish Optimization based controller selection (ISFO-CS) method is proposed in this study to ideally take the best controller node (CN) from a group of sensor nodes. The ISFO selects the best CN by considering a multi-objective fitness function incorporating distance, residual energy, node coverage, and sensor node communication cost. After selecting CN, the Fuzzy C-means (FCM) clustering algorithm executes the subsequent data transmission process. The Matlab program is used to execute the simulation, and the performance of the proposed ISFO-CS methodology is assessed using several performance criteria. The proposed ISFO-CS model accomplishes a 95% packet delivery ratio for 500 rounds, consumes less energy of 0.13 mJ, and takes 1.8 s for 500 rounds of node to controller latency.

关键词： Software defined wireless sensor network (SDWSN) Controller selection problem (CSP) Controller nodes (CNs) Wireless sensor network (WSN) clustering algorithm Data transmission Multi-objective fitness function

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A Two-Stage Multi-Parameter-Based Sorting Method for Ensuring Consistency Between Parallel-Connected Lithium-Ion Batteries

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WORLD ELECTRIC VEHICLE JOURNAL 2025年第3期16卷 125-125页

作者： Hong, Hanchi Chen, Xiangxin d'Apolito, Luigi Ye, Yangqi Shen, Shuiwen Xiamen Univ Technol Sch Mech & Automot Engn Xiamen 361000 Peoples R China

Lithium-ion power battery pack life, capacity and safety depend primarily on consistency between battery cells. However, inconsistencies between battery cells are inevitable due to the inherent variability in production processes and operational environments. In parallel circuits, battery management systems can usually only monitor the total module current and terminal voltage, which results in limitations that lead to inter-unit inconsistency, reducing overall safety and energy efficiency. The conventional method of battery sorting involves analyzing static parameters such as capacity, internal resistance and voltage to ensure static consistency between cells. Nonetheless, cell-to-cell variations are more pronounced during dynamic and complex operations. The direct integration of static and dynamic features may result in data scale discrepancies and redundant information. Thus, the present study proposes a two-stage multi-parameter clustering method based on static and dynamic features. Initially, Density-Based Spatial clustering of Applications with Noise (DBSCAN) was applied to sort abnormal batteries and identify the number of subsequent clusters, using discharge capacity, internal resistance and open-circuit voltage (OCV) as inputs. Then, a Principal Component Analysis (PCA) was used to downscale and extract features from the discharge voltage profile. The principal component data were used as inputs to the Self-Organizing Map (SOM) clustering algorithm, which uses its self-organized and unsupervised learning characteristics to mine more dynamic time-series features and complete the final clustering and sorting. Finally, the effectiveness of the two-stage sorting method in parallel circuits was verified by determining clustering evaluation indexes, as well as the cycle life and discharge curves of batteries reassembled in parallel.

关键词： lithium-ion battery two-stage sorting principal component analysis clustering algorithm parallel connection

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Design of Online Monitoring Method for Distribution IoT Devices Based on DBSCAN Optimization algorithm

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第5期49卷 181-194页

作者： Hou, Chaofan Xu, Nan Liu, Siyu State Grid Beijing Electric Power Company Information and Communication Branch Beijing China

In response to the data mutation problem caused by equipment failures in the distribution Internet of Things, this study proposes a density-based clustering optimization algorithm for online monitoring of equipment data anomalies. This method considers the local and global similarity of high-dimensional measurement data and constructs a composite time series similarity measurement criterion. Improvements are made to the density-based clustering algorithm, which combines with preprocessed device historical measurement data to adaptively generate global density parameters. Through clustering training, core data points are obtained to detect abnormal changes in data. The experiment showed that compared to traditional density-based clustering algorithms, the improved algorithm had good clustering performance, with standardized mutual information and adjusted mutual information increased by about 2%. Compared to anomaly detection algorithms, the density-based clustering optimization algorithm for anomaly detection of equipment data in the distribution Internet of Things increased the detection rate by 38% and reduced the false detection rate by 65%. Therefore, the proposed online monitoring method for data anomalies can improve the data detection rate and has high practical value for the reliable operation of distribution Internet of Things systems. © 2025 Slovene Society Informatika. All rights reserved.

关键词： abnormal data detection clustering algorithm distribution internet of things parameter adaptation time correlation

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BLOCKCHAIN ENHANCED STUDENT PHYSICAL PERFORMANCE ANALYSIS USING MACHINE LEARNING-IOT AND APRIORI algorithm IN PHYSICAL EDUCATION NETWORK TEACHING

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第3期25卷 1478-1491页

作者： Li, Jianing Quan, Zheping Song, Weijia Taiyuan Normal Univ Coll Phys Educ Jinzhong 030619 Peoples R China Cavite State Univ Wesleyan Coll Manila 0900 Philippines

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students' physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K -means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923.

关键词： Apriori Relevance clustering algorithm Network Teaching Behavior Analysis College Student

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