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SOFTWARE-PRACTICE & EXPERIENCE 1992年第10期22卷 849-862页

作者： YAUNG, AT RAZ, T Westlake Programming Laboratory IBM Corporation 5 West Kirkwood Boulevard Roanoke TX 76299–0001 U.S.A.

This paper presents a methodology for the analysis of the linkage among the processes in an organization. The methodology has three main steps: model construction;connectivity analysis;and structure analysis. The model construction step generates a linkage matrix which is used in the following steps. The connectivity analysis is based on linkage consistency and interprocess coupling metrics, which are defined for each process individually and for all the processes in the organization as a group. The clustering algorithm is based on a linkage intensity measure derived from the linkage matrix. Following a detailed description of the methodology and its metrics and algorithms, the results obtained in a medium-size software development organization are presented.

关键词： PROCESS MANAGEMENT PROCESS ANALYSIS LINKAGE clustering algorithm

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Cooperative caching strategy based on cluster and social interest in mobile edge network

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DIGITAL SIGNAL PROCESSING 2022年第0期127卷

作者： Wang, Weiguang Li, Hui Liu, Yang Cheng, Wei Liang, Rui Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

Mobile edge caching can deliver contents directly without the backhaul link, which can effectively solve the problem of spectrum scarcity caused by huge mobile data traffic. In this paper, different from the existing user-centric clustering algorithms, a distributed caching algorithm is proposed based on content providers (CPs), which can form a CPs cluster as large as possible to satisfy the UE requirements. The cache capacity in the cluster formed by this algorithm is collectively used to provide higher content hit probability and diversity. Furthermore, considering the impact of social interests on the performance of caching strategies, a closed-form expression of the network hit ratio of the entire cache is derived on the basis of random geometry theory. Then, a network hit ratio maximization optimization problem is constructed and solved. The simulation results show that the proposed strategy has superior data offloading performance than other cooperative caching strategies. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Mobile edge caching network Cooperative caching clustering algorithm Social interest

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Pre-selection of monitoring stations for marine water quality using affinity propagation: A case study of Xincun Lagoon, hainan, China

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年第Pt B期325卷 116666页

作者： Fang, Xin Luo, Chengshu Zhang, Dongrong Zhang, Haifeng Qian, Jian Zhao, Canghai Hou, Zonghao Zhang, Yifei Minist Nat Resources Inst Oceanog 2 Hangzhou 310012 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Nanjing 210093 Peoples R China Key Lab Nearshore Engn Environm & Ecol Secur Zhej Hangzhou 310000 Peoples R China Zhejiang Dev & Planning Inst Hangzhou 310030 Peoples R China

The development, protection, and restoration of bays require works in scientific research and applications, and the success of which depends on a well deployment of monitoring stations for marine water quality. However, for bays without historical data, it is difficult to carry out related research on deployment of the monitoring stations, resulting in very few research works. This paper has introduced the affinity propagation (AP) clustering algorithm and achieved good results by correcting the preferences. The results show that under the given preference, that is, when the value of M is -6800, the number of monitoring stations in the Xincun lagoon area is 24. Simultaneous the sensitivity analysis of preferences shows that the number of exemplars decreases with lower preferences, that is, when M decreased from -4000 to -12000, the number also decreased from 70 to 36. However, some exemplars remain unchanged or being changed to adjacent positioning. This shows the stability of computation results and the rationality of AP. The research results can be well applied to other bays, even open waters.

关键词： Environmental investigation station clustering algorithm Layout optimization Bias parameter selection Coastal waters

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Research on Production Profiling Interpretation Technology Based on Microbial DNA Sequencing Diagnostics of Unconventional Reservoirs

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ENERGIES 2023年第1期16卷 358-358页

作者： Yang, Haitong Wang, Lei Qiang, Xiaolong Ren, Zhengcheng Wang, Hongbo Wang, Yongbo Wang, Shuoliang China Univ Geosci Beijing Sch Energy Resources Beijing 100190 Peoples R China China Univ Petr Sch Petr Engn Beijing 102249 Peoples R China Greatwall Drilling Co China Natl Petr Corp CNPC Geol Res Inst Beijing 100101 Peoples R China PetroChina Changqing Oilfield Co Gas Prod Plant 2 Yulin 719000 Peoples R China

Production profiling technology is an important method for monitoring the dynamics of oil and gas reservoirs which can effectively improve the efficiency of oil recovery. Production profiling is a technique in which a test instrument is lowered from the tubing to the bottom of the well to measure flow, temperature, pressure, and density in a multi-layer section of a producing well. Normal production profiling process needs to stop production, operate complex, consume long time and high cost. Furthermore, the profile cannot be continuously monitored for a long time. To address these limitations, this paper proposes a production profiling interpretation method based on reservoir primitive microbial DNA sequencing. The microbial stratigraphic baseline with high-resolution features is obtained by sampling and DNA sequencing of produced fluid and cuttings from different wells. Specifically, the random forest algorithm is preferred and improved by comparing the accuracy, precision, recall, F1-score, and running time of three clustering methods: Naive-Bayes classifier, random forest classifier, and back-propagation classifier. Constructing PSO-random forest model is based on stratigraphic records and produced fluid bacteria features. The computational accuracy and efficiency of this method allows it to describe the production profile for each formation. Moreover, this test process does not need to stop production with simple operation and does not pollute the formation. Meanwhile, by sampling fluid production at different stages, it can achieve the purpose of long-term effective dynamic monitoring of the reservoir.

关键词： DNA extraction DNA sequencing production profiling dynamic monitoring clustering algorithm

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A Signal Characteristic Based Cluster Scheme for Aeronautical Ad Hoc Networks

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2014年第10期8卷 3439-3457页

作者： Tian, Yu Ma, Linhua Ru, Le Tang, Hong Song, Bo Air Force Engn Univ Sch Aeronaut & Astronaut Engn Xian 710038 Peoples R China

clustering is an effective method for improving the performance of large scale mobile ad hoc networks. However, when the moving speed is very fast, the topology changes quickly, which leads to frequent cluster topology updates. The drastically increasing control overheads severely threaten the throughput of the network. SCBCS (Signal Characteristic Based Cluster Scheme) is proposed as a method to potentially reduce the control overheads caused by cluster formation and maintenance in aeronautical ad hoc networks. Each node periodically broadcasts Hello packets. The Hello packets can be replaced by data packets, which preserve bandwidth. The characteristics of the received packets, such as the Doppler shift and the power of two successive Hello packets, help to calculate the relative speed and direction of motion. Then, the link connection lifetime is estimated by the relative speed and direction of motion. In the clustering formation procedure, the node with the longest estimated link connection time to its one-hop neighbors is chosen as the cluster head. In the cluster maintenance procedure, re-affiliation and re-clustering schemes are designed to keep the clusters more stable. The re-clustering phenomenon is reduced by limiting the ripple effect. Simulations have shown that SCBCS prolongs the link connection lifetime and the cluster lifetime, which can reduce the topology update overheads in highly dynamic aeronautical ad hoc networks.

关键词： Mobile Ad Hoc Networks clustering algorithm Mobility Doppler shift Power

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Impacts of climate change on the wetlands in the arid region of Northwestern China over the past 2 decades

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ECOLOGICAL INDICATORS 2023年 149卷

作者： Wang, Ruimei Ding, Jianli Ge, Xiangyu Wang, Jinjie Qin, Shaofeng Tan, Jiao Han, Lijing Zhang, Zhe Xinjiang Univ Coll Geog & Remote Sensing Sci Urumqi 800017 Peoples R China Xinjiang Univ Xinjiang Key Lab Oasis Ecol Urumqi 830017 Peoples R China Xinjiang Univ Higher Educ Inst Key Lab Smart City & Environm Modelling Urumqi 830017 Peoples R China

Climate change has caused inland wetlands shrinkage and exacerbated problems, such as sustainable development and ecological security, for years. These issues are mainly pronounced in the inland arid area. The ecological environment's deterioration is especially severe in the drylands of the interior. However, dryland wetland changes and their response to climate are poorly understood. This study uses the K-means algorithm in Google Earth Engine (GEE) to classify two typical dryland wetlands (Ebinur and Bosten Lakes) for rapidly and accurately detecting dryland wetland changes. Moreover, it explores the long-term spatial-temporal variation in wetland distribution. In addition, it investigates the response of various lakes to climate change in northern and southern Xinjiang using wavelet analysis. The study's results showed that K-means clustering in the GEE platform has a high classification accuracy (Kappa > 0.8) in wetland classification, making it a feasible approach. The terminal lake wetland types, represented by the Ebinur Lake, changed significantly between 2001 and 2021. In contrast, the inflow-outflow lake wetland types, represented by the Bosten Lake, perform more consistently. Significant spatial-temporal variation is observed at Ebinur Lake, with the lake gradually shrinking and trans-forming into a marsh, where the largest marsh proportion degrades into non-wetland during the year. Bosten Lake experienced frequent conversions between marsh and non-wetland throughout the year. Furthermore, the responses of various dryland lakes to climate change are consistent, and a low precedes precipitation and follows evapotranspiration. However, their sensitivity to climate response varies, with the terminal lake being most affected by climate change. Mastering the dynamic changes and climate response of dryland wetlands achieves the sustainable development goals of drylands, including carbon neutrality and peak carbon dioxide emissions.

关键词： Dryland wetland clustering algorithm Wetland condition Delayed response

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Pythagorean fuzzy information processing based on centroid distance measure and its applications

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Sun, Gang Wang, Mingxin Guizhou Univ Commerce Comp & Informat Engn Coll Guiyang 550014 Peoples R China Hunan Inst Technol Sch Math & Phys Hengyang 421002 Peoples R China

By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages.

关键词： Pythagorean fuzzy number (PFN) Centroid distance measure clustering algorithm Ranking method Multi criteria decision making (MCDM) method

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Refined seismic fragility curves of substation equipment considering ground motion classifications

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2024年 187卷

作者： Zhu, Wang Bian, Xiaoxu Xie, Qiang Tongji Univ Dept Struct Engn Siping Rd 1239 Shanghai 200092 Peoples R China

Seismic fragility of substation equipment is vital in risk analyses, as well as post-earthquake prediction of failure probabilities of multiple equipment for deciding the inspection order. However, the existing fragility curves of equipment are commonly generated without adequately considering intensity measures (IMs) of ground motions. During the post-earthquake use, they are deemed applicable to any earthquake occurring in the region, which is insufficiently precise. Thus, this paper employs a new method to derive more refined fragility curves for substation equipment based on ground motion classifications. It uses principal component analysis to convert IMs into a small number of principal components (PCs). Taking the PCs as indices, the selected ground motions are clearly categorized into distinct classes using the K-means clustering algorithm. Then, a simulation model of equipment is developed and the time-history dynamic analyses are performed to calculate seismic responses under different classes of ground motions. Finally, the refined fragility curves are derived via multiple stripe analysis for each ground motion class. A 500 kV power transformer is used as a case to implement the method. The results show that a small number of PCs can effectively reflect the multiple ground motion characteristics. Different ground motion classes have distinct distributions of IMs and PCs. There are notable discrepancies in the fragility curves of the power transformer when subjected to different ground motion classes. The significant disparities between the refined and original unclassified fragility curves highlight the importance of considering ground motion classifications.

关键词： Seismic fragility curves Substation equipment Ground motion classification Principal component analysis clustering algorithm Power transformer

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TransFNN: A Novel Overtemperature Prediction Method for HVDC Converter Valves Based on an Improved Transformer and the F-NN algorithm

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SENSORS 2023年第8期23卷 4110页

作者： Zhou, Sihan Qin, Liang Sun, Hui Peng, Bo Ruan, Jiangjun Wang, Jing Tang, Xu Wang, Xiaole Liu, Kaipei Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Elect Power Res Inst State Grid Anhui Elect Power Hefei 230601 Peoples R China

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer-FCM-NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters;(ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared;with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.

关键词： HVDC converter valve cooling system time series prediction Transformer clustering algorithm

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Random-forest-based failure prediction for hard disk drives

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2018年第11期14卷 1-1页

作者： Shen, Jing Wan, Jian Lim, Se-Jung Yu, Lifeng Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Informat Engn Hangzhou Zhejiang Peoples R China Zhejiang Univ Sci & Technol Sch Informat & Elect Engn Hangzhou Zhejiang Peoples R China Chonnam Natl Univ Dept Comp Engn Yeosu South Korea Hithink RoyalFlush Informat Network Co Ltd Hangzhou Zhejiang Peoples R China

Failure prediction for hard disk drives is a typical and effective approach to improve the reliability of storage systems. In a large-scale data center environment, the various brands and models of drives serve diverse applications with different input/output workload patterns, and non-ignorable differences exist in each type of drive failures, which make this mechanism much challenging. Although many efforts are devoted to this mechanism, the accuracy still needs to be improved. In this article, we propose a failure prediction method for hard disk drives based on a part-voting random forest, which differentiates prediction of failures in a coarse-grained manner. We conduct groups of validation experiments on two real-world datasets, which contain the SMART data of 64,193 drives. The experimental results show that our proposed method can achieve a better prediction accuracy than state-of-the-art methods.

关键词： Failure prediction random forest clustering algorithm hard disk drives

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