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Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering

ENERGY 2024年 299卷

作者： Shi, Xueli Liang, Bingchen Li, Shaowu Zhao, Jianchun Wang, Junhui Wang, Zhenlu Tianjin Univ State Key Lab Hydraul Engn Intelligent Construct & Tianjin 300072 Peoples R China Ocean Univ China Coll Engn Qingdao 266100 Shandong Peoples R China Power China Huadong Engn Corp Ltd Hangzhou 311122 Zhejiang Peoples R China

This study based on the 25-year wave hindcast database of the western Pacific and used three unsupervised learning clustering algorithms to classify the wave energy resources in the China East Adjacent Seas. Five wave energy characteristic parameters are comprehensively considered in the calculation process of the clustering algorithm. According to the analysis of the classification results, it can be seen that the Class IV is the most suitable for wave energy development in the China East Adjacent Seas, followed by the Class III and Class V. The Class VI is too far away from the coast to be used as the intended area for wave energy development. The Class I is mostly located in inland seas and harbors, which are not suitable for wave energy development. By analyzing the annual average captured power, it can be seen that the optimal capture interval of the existing wave energy converters with mature technology is too large compared with the wave conditions in the China East Adjacent Seas. We should vigorously develop wave energy converters that are more suitable for the wave conditions of Class III, Class IV and Class V, and improve the capture efficiency of the wave energy converters.

关键词： Wave energy classification clustering algorithm Wave energy converter Captured power

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A novel method of quantitative evaluation and comprehensive classification of low permeability-tight oil reservoirs: A case study of Jidong Oilfield, China

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Petroleum Science 2022年第4期19卷 1527-1541页

作者： Dong-Liang Jiang Hao Chen Jian-Peng Xing Lin Shang Qun-Hui Wang Yan-Chun Sun Yao Zhao Jian Cui Ian Duncan State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing102249China Key Lab of Petroleum Eng.of MOE China University of Petroleum(Beijing)Beijing102249China Postdoctoral Station of PetroChina Jidong Oilfield Company TangshanHebei 063000China Postdoctoral Program of PetroChina Research Institute of Petroleum Exploration and Development Beijing100083China Exploration and Development Research Institute PetroChina Jidong Oilfield CompanyTangshanHebei063000China Bureau of Economic Geology The University of Texas at Austin78705United States

The classification of low permeability-tight reservoirs is the premise of development. The deep reservoir of Shahejie 3 member contains rich low permeability-tight reserves, but the strong heterogeneity and complex micro pore structure make the main controlling factors subjective and the classification boundaries unclear. Therefore, a new indicator considering the interaction between fluid and rock named Threshold Flow Zone Indicator(TFZI) is proposed, it can be used as the main sequence of correlation analysis to screen the main controlling factors, and the clustering algorithm is optimized combined with probability distribution to determine the classification boundaries. The sorting coefficient, main throat radius, movable fluid saturation and displacement pressure are screened as the representative parameters for the following four key aspects: rock composition, microstructure, flow capacity and the interaction between rock and fluid. Compared with the traditional probability distribution and clustering algorithm, the boundary of the optimized clustering algorithm proposed in this paper is more *** classification results are consistent with sedimentary facies, oil levels and oil production *** method provides an important basis for the development of low permeability-tight reservoirs.

关键词： Low permeability-tight reservoir Classification boundary Correlation analysis Probability distribution clustering algorithm

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An Improved Fast Search clustering algorithm Based on Kernel Density

An Improved Fast Search Clustering Algorithm Based on Kernel...

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IEEE International Conference on Smart City

作者： Ruisheng Zhang Huiyi Ma Qidong Liu Zhili Zhao School of Information Science and Engineering Lanzhou University

ISBN: (纸本)9781509018949

clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density data sets. In order to solve that problem, we propose an improved algorithm KFSC in this paper by dynamically controlling of the width of the kernel function. KFSC uses the idea of attractor of the DENCLUE and can customize their own personalized attraction for each point. The experimental results on synthetic data sets show that KFSC has a better performance on uneven density data sets than FSC and RFSC.

关键词： clustering algorithm Density-based clustering Kernel function Uneven density

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A Case Study of Accurate and Fair Classification 13th

A Case Study of Accurate and Fair Classification

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Liu, Xiaoqian Chao, Zemin Jiangsu Police Inst Dept Comp Informat & Cyber Secur Nanjing 210031 Peoples R China

ISBN: (纸本)9789819992454;9789819992430;9789819992423

As artificial intelligence and human life become increasingly inseparable, the legal or ethical issues faced by artificial intelligence systems in autonomous decision-making are also increasing. During the training process, the algorithms may be influenced by human biases such as gender, race, and other factors, leading to discrimination and affecting fairness. To establish secure intelligent systems, fair machine learning has become a popular research direction. This work demonstrates the existing definitions of fairness and designs experiments to show that combining clustering algorithms into the data handling process can effectively improve the classification accuracy and fairness on bank loan dataset. In the case study, K-means clustering, hierarchical clustering and Gaussian Mixture Model are used, proving that the clustering algorithms can significantly improve the accuracy of the model and ensure the relative fairness of the classification results.

关键词： Fairness clustering algorithm Machine learning

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Mobile Networks Classification Based on Time-Series clustering 5

Mobile Networks Classification Based on Time-Series Clusteri...

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IEEE 5th International Conference on Electronics and Communication Engineering (ICECE)

作者： Lu, Shun Qian, Bing Zhao, Long-Gang Sun, Qiong China Telecom Corp Ltd Res Inst AI R&D Ctr Beijing Peoples R China China Telecom Corp Ltd Network Management & Cloud Comp Dept Res Inst Beijing Peoples R China

ISBN: (纸本)9781665487894

With the increasing complexity of network architecture, the classification of mobile network cells become more important in network operation and maintenance. However, the previous classification method based on manual annotation of scene labeling is inefficient and biased. In this paper, we focus on proposing a data-driven classification method to eliminate the drawbacks of manual annotation. The proposed method extracts the patterns of mobile network on temporal shape and statistical features, and then calculates the fused distance matrix from these two feature sets, K-medoids is leveraged to get the classification labels. We design a series of experiments and analyses to demonstrate the validity of the proposed method, which is based on hourly real data sampled from 9454 mobile cells. The experiments demonstrate that the proposed method achieves good performance on the cell classification of two O&M (Operations and Maintenance) scenarios, and significantly improves the work efficiency.

关键词： mobile network cells clustering algorithm time-series data

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Optimized YOLOv7 for Small Target Detection in Aerial Images Captured by Drone

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 70-79页

作者： Liu, Yanxin Chen, Shuai Luo, Lin Liaoning Petrochem Univ Sch Informat & Control Engn Fushun Peoples R China

It is challenging to detect small targets in aerial images captured by drones due to variations in target sizes and occlusions arising from the surrounding environment. This study proposes an optimized object detection algorithm based on YOLOv7 to address the above-mentioned challenges. The proposed method comprises the design of a Genetic Kmeans (1IoU) clustering algorithm to obtain customized anchor boxes that more significantly apply to the dataset. Moreover, the SPPFCSPC_group structure is optimized using group convolutions to reduce model parameters. The fusion of Spatial Pyramid Pooling-Fast (SPPF) and Cross Stage Partial (CSP) structures leads to increased detection accuracy and enhanced multi-scale feature fusion network. Furthermore, a Detect Head is incorporated into the classification phase for more accurate position and class predictions. According to experimental findings, the optimized YOLOv7 algorithm performs quite well on the VisDrone2019 dataset in terms of detection accuracy. Compared with the original YOLOv7 algorithm, the optimized version shows a 0.18% increase in the Average Precision (AP), a reduction of 5.7 M model parameters, and a 1.12 Frames Per Second (FPS) improvement in the frame rate. With the above described enhancements in AP and parameter reduction, the precision of small target detection and the real-time detection speed are increased notably. In general, the optimized YOLOv7 algorithm offers superior accuracy and real-time capability, thus making it well-suited for small target detection tasks in real-time drone aerial photography.

关键词： Small target detection drone aerial photography YOLOv7 clustering algorithm spatial pyramid pooling

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A clustering-based Location Allocation Method for Delivery Sites under Epidemic Situations

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系统仿真学报 2024年第12期36卷 2782-2796页

作者： Zhou Yaqiong Chen Junqi Li Weishi Qiu Sihang Ju Rusheng College of System Engineering National University of Defense TechnologyChangsha 410073China

To address the poor performance of commonly used intelligent optimization algorithms in solving location problems—specifically regarding effectiveness,efficiency,and stability—this study proposes a novel location allocation method for the delivery sites to deliver daily necessities during epidemic *** establishing the optimization objectives and constraints,we developed a relevant mathematical model based on the collected data and utilized traditional intelligent optimization algorithms to obtain Pareto optimal *** on the characteristics of these Pareto front solutions,we introduced an improved clustering algorithm and conducted simulation experiments using data from Changchun *** results demonstrate that the proposed algorithm outperforms traditional intelligent optimization algorithms in terms of effectiveness,efficiency,and stability,achieving reductions of approximately 12%and 8%in time and labor costs,respectively,compared to the baseline algorithm.

关键词： location problem clustering algorithm intelligent optimization algorithm Pareto front

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Medical Image clustering algorithm Based on Graph Entropy

Medical Image Clustering Algorithm Based on Graph Entropy

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International Conference on Fuzzy Systems and Knowledge Discovery

作者： Yu Zhan Haiwei Pan Qilong Han Xiaoqin Xie Zhiqiang Zhang Xiao Zhai Coll. of Comput. Sci. & Technol. Harbin Eng. Univ. Harbin China

ISBN: (纸本)9781467376839

Recently, a variety of medical imaging technologies have been used widely in clinical diagnosis. As a large number of medical images are produced everyday, it becomes a hot issue of data mining on medical image in current that how to make full use of these medical images and cluster efficiently to help doctors to diagnose. In this paper, we propose a medical image clustering method. Firstly, medical image dataset is represented as a weighted, undirected and completed graph. Secondly, the graph is sparsified and pruned. This model can describe the similarity between medical images very well. Last, weighted and undirected graph clustering method based on graph entropy is proposed to cluster these medical images. The experimental results show that this method can cluster medical images efficiently and run well in time complexity and clustering results.

关键词： component medical image entropy-based sparsification clustering algorithm

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Multi-scale deep learning and clustering-based tabletop object instance segmentation for robot manipulation

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2024年第5期21卷

作者： Jiang, Zhihong Xue, Yongrui Zhao, Yan Huang, Xiao Li, Hui Beijing Inst Technol Sch Mechatron Engn Beijing Peoples R China Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing Peoples R China Minist Educ Key Lab Biomimet Robots & Syst Chinese Beijing Peoples R China

3D object instance segmentation plays a vital role in various applications such as autonomous driving, robotics and virtual reality. However, tabletop scenes exhibit diverse object complexities and size variations. The challenge is to enhance the accuracy of segmenting these scenes for multiple object instances. This limitation directly impacts robots' capabilities to effectively grasp and manipulate objects. In this paper, we propose a multi-scale deep learning and clustering-based approach for object instance segmentation in tabletop scenes. Our approach incorporates a multi-scale neighborhood feature sampling (MNFS) module specifically designed to extract local features, and a clustering algorithm to eliminate noise and preserve instance integrity. Furthermore, we combine the strength of both methods through ScoreNet and non-maximal suppression. We conducted extensive experiments on TO-Scene, the first large-scale dataset of 3D tabletop scenes, and observed an average mIoU improvement of approximately 4.07% compared to existing methods. This highlights the superior performance of our proposed method. In addition, we tested our algorithm on a real-scene robotics platform and showed that it has good performance and generalization capabilities to support future applications such as robot grasping.

关键词： 3D point cloud instance segmentation deep learning clustering algorithm robot grasping

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Distributed Task Allocation algorithm for Heterogeneous UAV Cluster Based on Game Theory

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Guidance, Navigation and Control 2024年第4期4卷 58-85页

作者： Shuxin Xue Yajie Ma Bin Jiang Xuan Lyu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 P.R.China

This paper proposes a distributed task allocation algorithm based on game theory to solve the complex task allocation optimization problem when UAV clusters carry heterogeneous resources and tasks have heterogeneous demands. Considering the heterogeneity of resources,two pre-processing methods are proposed: one is the grouping algorithm that combines greedy algorithm with simulated annealing algorithm, and the other is the improved K-medoids clustering algorithm based on heterogeneous resources. These pre-process methods, through grouping and clustering, can reduce the complexity of task allocation. The entropy weight method is utilized to prioritize tasks based on multiple metrics. Considering task demands,airborne resources and path cost, a coalition formation game model is established, which is proved to be a potential game. Then a distributed task allocation algorithm based on coalition formation game is designed to address the task allocation problem. Finally, the simulation involving 30 tasks with heterogeneous requirements assigned to 100 UAVs validates the effectiveness of the proposed algorithm, showing that it can achieve good task allocation results with great real-time performance.

关键词： Task allocation heterogeneous UAV cluster clustering algorithm coalition formation game potential game

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