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13th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Tan, Wenan Fang, Xi Zhao, Lu Tang, Anqiong Nanjing Univ Aeronaut & Astronaut Sch Comp & Technol Nanjing 210016 Peoples R China Shanghai Polytech Univ Sch Comp & Informat Shanghai 2012209 Peoples R China

ISBN: (纸本)9789811330445;9789811330438

To address the issue that the K-means algorithm chooses and determines the initial cluster center in a random way, which would fall into the local optimal clustering result, a way towards choosing the initial clustering center using information entropy is proposed. This proposed method divides the dataset evenly into data blocks with more than K, and then uses the entropy method to obtain the value of target function of each data block, as well as selects the centroid corresponding to the data block with the smallest value function of the first k target as the initial cluster center. By using entropy method to ensure the efficiency of the initial clustering center selection, an anomaly detection method is proposed. The result of the experiment show that this method performs better than the traditional K-means algorithm both in clustering effect and anomaly detection ability.

关键词： Anomaly detection Information entropy cluster algorithm cluster analysis

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Nonlinear Strength Reduction Method of Rock Mass in Slope Stability Evaluation

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MATERIALS 2023年第7期16卷 2793-2793页

作者： Chen, Yifan Chen, Yizhou Lin, Hang Hu, Huihua Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Survey & Design Inst Hunan Prov Commun Planning Changsha 410200 Peoples R China

As the strength parameters of rock mass degrade differently during slope instability, different factors should be considered in the strength reduction method. Previous nonlinear reduction methods were essentially implemented based on the Mohr-Coulomb criterion, which was reported not to reflect the nonlinear performance of rock mass. To address this deficiency, in this study, the Hoek-Brown criterion was combined with a nonlinear reduction technique for slope stability evaluation. Firstly, based on the classical definition of safety factors, the relationships that should be satisfied by each parameter of the critical slope were derived. The critical curve of the slope regarding the Hoek-Brown constant m(b) and the uniaxial compressive strength of rock mass s(cmass) was then obtained. On the assumption that the slope parameter deterioration conforms to the shortest path theory, the reduction ratio of s(cmass) to m(b) was determined. The more objective k-means algorithm was employed to automatically search the potential sliding surface, on which the slope safety factor was calculated as the ratio of sliding resistance to sliding force. Finally, the slopes in published literature were adopted for verification, and the calculated safety factors were compared with those by other methods, which showed better efficacy.

关键词： slope stability nonlinear criterion strength reduction method cluster algorithm

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A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

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ENERGIES 2023年第23期16卷 7693页

作者： Wei, Chih-Chiang Yang, Yen-Chen Natl Taiwan Ocean Univ Dept Marine Environm Informat & Ctr Excellence Oce Keelung 20224 Taiwan

One of the most important sources of energy is the sun. Taiwan is located at a 22-25 degrees north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long-short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model.

关键词： solar radiation prediction cluster algorithm neural network

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A method for calculating two-dimensional spatially extension distances and its clustering algorithm

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Procedia Computer Science 2023年 221卷 1187-1193页

作者： Yaocong Qin Xingsen Li Research Institute of Extenics and Innovation Methods GuangZhou 510006 China

This paper presents a novel method for calculating the extension distance between a point and a two-dimensional convex set. The proposed method builds upon existing techniques by employing a line traversal approach. By traversing the convex set with a line originating from the point of interest, the extension distance is computed based on the intersection points between the line and the set along the two coordinate axes. The weights assigned to the intersection points play a crucial role in the distance calculation. By summing the extension distances in both directions, the overall extension distance of the point with respect to the set is obtained. Furthermore, a clustering algorithm is introduced, which incorporates the concept of an interaction factor commonly used in engineering experiments. The multidimensional data is projected onto feature planes created by combining pairs of features. The calculation method for extension distances between points and convex sets in two dimensions is applied to determine the distances between points and cluster classes (or cluster centers). The distances on each feature plane are then weighted, summed, and analyzed to establish the relationships between points and cluster classes, thereby achieving clustering. The feasibility of the proposed algorithm is validated through comparative experiments k-means algorithm on the iris dataset and the wine dataset. The experimental results confirm the effectiveness and viability of the proposed approach.

关键词： extenics entenics distance cluster algorithm

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Flow field characterization and evaluation method based on unsupervised machine learning

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartA期215卷

作者： Li, Shanshan Feng, Qihong Zhang, Xianmin Liu, Haicheng Liu, Lijie Huang, Yingsong China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Shengli Oilfield Co Res Inst Explorat & Dev SINOPEC Dongying 257015 Shandong Peoples R China

Long-term water flooding can be inefficient due to imbalanced displacement, resulting in sub-optimal recoveries. Therefore, the quantitative characterization and evaluation method of the flow field is significant for further development and adjustment. In this study, based on streamline simulation results, decoding rules were formulated to extract streamline data. Considering the attribute feature of each node along the streamline, flow field characterization indices were constructed. And combining the fast density peaks clustering (FDPC) algorithm and silhouette coefficient (SC), a novel flow field evaluation method based on streamline clustering was proposed. Results indicated that the invalid water circulation channels and areas with developing potential in reservoirs can be identified. Besides, the distribution of flow field can also be observed, and the characteristics of the identified region can be analyzed, which provides a basis for the subsequent flow field adjustment. Meanwhile, streamlines between injection and production wells can be subdivided to describe the distribution of water displacement capacity. Based on the evaluation results of streamline clustering, Gudao oilfield adjusted the flow field through injection-production adjustment, which effectively improved the development effect of the block and increased the recovery degree from 50.3% to 52.4%. This work provides an effective evaluation method to directly characterize the displacement changes for underground flow field, and it is of great significance for the decision-making of water flooding structure adjustment and optimization.

关键词： Water flooding Flow field characterization and evaluation Streamline characteristic Streamline clustering cluster algorithm Flow field adjustment Streamline simulation

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Research on Mining of Government Data Based on Enhanced-Object Exchange Model 21

Research on Mining of Government Data Based on Enhanced-Obje...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Sa, Kejin Bai, Yu Wang, Chenggang Big Data Applicat Improving Govt Governance Capab Guiyang 550022 Peoples R China Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Southwest China Inst Elect Technol Chengdu 610036 Peoples R China

ISBN: (纸本)9781665458139

In order to acquire the required knowledge from the vast amounts of government affairs data, accurate data mining has important practical significance. Therefore, a precision mining method of government affairs big data based on E-OEM model is studied. The method firstly uses web crawlers to capture the vast amounts of government data. Government data then performs data cleaning, pattern normalization, format standardization, index information collection and preprocessing, and finally, according to the purpose of data mining, the Kirkpatrick model is used to complete the mining effect evaluation. Experimental comparison specifically includes correlation analysis, cluster analysis, classification analysis, exception analysis and evolution analysis as well as five others. The results show that: compared with the three methods in previous studies, the Fl-score obtained by the application of the research method is higher (0.852), which proves that the quality of government affairs big data mining is better.

关键词： government affairs big data the data mining Kirkpatrick model cluster algorithm association rules

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Spatio-temporal hierarchical cluster analysis of mining-induced seismicity in coal mines using Ward's minimum variance method

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JOURNAL OF APPLIED GEOPHYSICS 2021年 184卷 104249-104249页

作者： Lurka, Adam Cent Min Inst GIG Lab Min Geophys Katowice Poland

Mining-induced seismicity is one of the most dangerous physical phenomenon's accompanying coal and ore extraction in underground mines and often resulting in rock burst. In many cases, this seismicity can be grouped together, forming spatial clusters of events related to some structures in mines such as faults or pillars. However, as shown in this paper, the usual seismicity in coal mines is mainly related to current mining process and there are no distinct spatial or temporal groups of seismic events, but rather they form continuous cloud of events related to mining activity. Therefore, we introduce new spatio-temporal metric together with Ward's minimum variance method to obtain hierarchical clustering analysis of mining-induced seismicity. Constructing metric in this way made it possible to assess seismic hazard concurrently in space and time. The clustering method is utilized to a case study of mining-induced seismicity from coal mine with high seismic hazard and where high energy mainshock occurred. In each cluster, magnitude distribution of seismic activity is calculated, showing in some cases, a significant departure from Gutenberg-Richter distribution of seismic events, which can be important in seismic hazard analysis. As a measure of accuracy of each cluster locations we have introduced bootstrapping procedure. We have shown that location errors of seismic activity affect each cluster center location of the order of a few tens of meters. The presented cluster analysis of mining seismicity provides new measures to determine group of seismic sources and high stressed areas in the rock mass in mines allowing for determination of seismic and rockburst hazard. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mining induced seismicity cluster cluster algorithm Magnitude distribution Seismic event Seismic hazard Seismic event location Bootstrapping

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Spatio-Temporal Dynamics of European Innovation—An Exploratory Approach via Multivariate Functional Data cluster Analysis

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第1期8卷

作者： Rhoden, Imke Weller, Daniel Voit, Ann-Katrin Institute of Energy and Climate Research—Systems Analysis and Technology Evaluation Forschungszentrum Jülich Jülich 52425 Germany Institute for International Law of Peace and Armed Conflict Center for Research Methods Ruhr-Universität Bochum Bochum 44801 Germany FOM Hochschule für Oekonomie und Management Essen 45127 Germany

We apply a functional data approach for mixture model-based multivariate innovation clustering to identify different regional innovation portfolios in Europe, considering patterns of specialization among innovation types. We combine patent registration data and other innovation and economic data across 225 regions, 13 years, and eight patent classes. The approach allows us to form several regional clusters according to their specific innovation types and captures spatiotemporal dynamics too subtle for most other clustering methods. Consistent with the literature on innovation systems, our analysis supports the value of regionalized clusters that can benefit from flexible policy support to strengthen regions as well as innovation in a systematic context, adding technology specificity as a new criterion to consider. The regional innovation cluster solutions for IPC classes for ‘fixed constructions’ and ‘mechanical engineering’ are highly comparable but relatively less comparable for ‘chemistry and metallurgy’. The clusters for innovations in ‘physics’ and ‘chemistry and metallurgy’ are similar;innovations in ‘electricity’ and ‘physics’ show similar temporal dynamics. For all other innovation types, the regional clustering is different. By taking regional profiles, strengths, and developments into account, options for improved efficiency of location-based regional innovation policy to promote tailored and efficient innovation-promoting programs can be derived. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： cluster algorithm European innovation Functional data analysis (FDA) Innovation concentration Multivariate cluster analysis Spatio-temporal cluster modeling

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Predicting Wafer-Level Package Reliability Life Using Mixed Supervised and Unsupervised Machine Learning algorithms

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MATERIALS 2022年第11期15卷 3897-3897页

作者： Su, Qing-Hua Chiang, Kuo-Ning Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 300 Taiwan

With the increasing demand for electronic products, the electronic package gradually developed toward miniaturization and high density. The most significant advantage of the Wafer-Level Package (WLP) is that it can effectively reduce the volume and footprint area of the package. An important issue in the design of WLP is how to quickly and accurately predict the reliability life under the accelerated thermal cycling test (ATCT). If the simulation approach is not adopted, it usually takes several ACTCs to design a WLP, and each ACTC will take several months to get the reliability life results, which increases development time considerably. However, simulation results may differ depending on the designer's domain knowledge, ability, and experience. This shortcoming can be overcome with artificial intelligence (AI). In this study, finite element analysis (FEA) is combined with machine learning algorithms, e.g., Kernel Ridge Regression (KRR), to create an AI model for predicting the reliability life of electronic packaging. Kernel Ridge Regression (KRR) combined with the K-means cluster algorithm provides a highly accurate and efficient way to obtain AI models for large-scale data sets.

关键词： Wafer-Level Package (WLP) Finite Element Analysis (FEA) machine learning Kernel Ridge Regression (KRR) cluster algorithm

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Knowledge guided Bayesian classification for dynamic multi-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2022年第0期250卷

作者： Ye, Yulong Li, Lingjie Lin, Qiuzhen Wong, Ka-Chun Li, Jianqiang Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Dynamic multi-objective optimization problems (DMOPs) typically contain multiple conflicting objectives that vary over time, requiring the optimization algorithms to quickly track the changing Pareto-optimal solutions (POS). Recently, prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) using transfer learning ideas have been considered promising, which could accelerate the search towards the POS by transferring information from previous environments to estimate the location of the POS in the next environment. However, most of the existing methods only transfer the search experiences from one or two previous environments to construct the prediction model for the next moment, which may ignore the effective information in the earlier search process thereby degrading the prediction accuracy in some cases. In this paper, a knowledge-guided Bayesian classification for DMOEA, called KGB-DMOEA, is proposed, which aims to achieve a robust prediction by fully exploiting the information from all historical environments. Particularly, when an environmental change is detected, a knowledge reconstruction-examination strategy is designed to divide all historical optimal solutions into useful and useless ones, which are used as positive and negative samples, respectively, for training the prediction model in subsequent steps. Then, a non -linear probabilistic classifier, i.e., naive Bayesian classifier, is constructed by using the above training samples, which can fully mine the effective knowledge from all historical environments and predict a high-quality initial population for the new environment. Experimental results on multiple DMOPs test suits demonstrate that KGB-DMOEA is superior to several state-of-the-art DMOEAs in solving various DMOPs. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Dynamic multi-objective optimization Naive Bayesian classification Evolutionary algorithm cluster algorithm

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