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Demand-response oriented multi-dimension refined portrait of adjustable resources based on load and survey data fusion

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Chen, Shaocong Gan, Lei Chen, Canyang Yu, Kun Pi, Huimin Qian, Zi'ang Dai, Ran Hohai Univ Coll Energy & Elect Engn Nanjing Jiangsu Peoples R China

Adjustable resources on the demand side of power system plays a vital role to improve operational flexibility of future low-carbon power system integrated with high-penetration renewable generations. While, these demand-side resources may underperform their expected potentials, due to the lack of understanding on consumers' refined behaviors. Facing the flexibility improvement of future power system, refined portrait structure of single user combining load characteristics and subjective behavior, is constructed with multi-dimension label system from 4 aspects, including energy consumption and load characteristics, adjustable potential, behavioral awareness and user's nature. Aiming at supplying demand response service, several key indexes are selected and further evaluated here, via data-driven load character analysis and social-survey-driven user's subjective consciousness mining based on comprehensive evaluation with combination weighting approach. For practical application to demand response decision making, large-scale user adjustable resource is evaluated and classified based on multivariate density-based clustering algorithm. Numerical results show the feasibility and rationality of the proposed assessment method.

关键词： adjustable resource assessment demand response combined operation and survey data combination weighting approach density-based clustering algorithm refined portrait

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Frequency stability-based controlled islanding scheme based on clustering algorithm and electrical distance using real-time dynamic criteria from WAMS data

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 30卷

作者： Mahdavizadeh, S. F. Aghamohammadi, M. R. Ranjbar, S. Shahid Beheshti Univ Elect Eng Dept Tehran Iran Velayat Univ Dept Elect Engn Iranshahr Iran

This paper presents online criteria of frequency stability-based controlled islanding scheme based on the density-based clustering algorithm and the system electrical distance from wide-area measuring system (WAMS) data. For this issue, at each time window, based on evaluating the correlation coefficient criteria between all two pairs of synchronous generators (SGs), the possible coherent generator groups are identified. Next, by using clustering algorithm, the identified coherent SGs are clustered and sorted through online working mode. In this case, an online index is proposed which and developed index in the form of mixed-integer linear programming (MILP) which through real-time evaluations, the system frequency stability is evaluated. In the use of MILP processes, the network electrical distance is considered as the main objective function (OF) which by solving the proposed OF through minimum criteria, proper islanding locations are identified. Finally, at the post-islanding period, a nonlinear programming (NLP) procedure is performed through each individual islands where corresponding proper operational feasibilities are satisfied. In this case of transient stability criteria, based on the coherent SGs located at each island, the stability criteria is preserved through coherent groups as the core of islands. Also, by allocating proper load buses based on the SGs governor capacities, the islands frequency stabilities are controlled through acceptable regions. The effectiveness of the proposed scheme is carried through two IEEE 39-bus test system and Iran practical power grid. Results present the effectiveness of the proposed approach through different islanding conditions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Controlled islanding Frequency stability Electrical distance density-based clustering algorithm MILP WAMS

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Multi-object Cosegmentation Using density-based clustering

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电脑学刊 2020年第1期31卷 148-165页

作者： Tzu-Chiang Wang I-Cheng Chang Chun-Man Lin

Cosegmentation is one of the interesting and popular topics in computer vision. The goal of cosegmentation is to extract the common foreground objects from an image set with minimum additional information. The existing cosegmentation algorithms could be classified into two categories. One is to extract one kind of foreground objects in the image set under unsupervised approaches; the other one is to find different kinds of common foreground objects in the image set under supervised approaches of which the number of kinds should be predefined. In this paper, we propose an unsupervised cosegmentation method for multiple foreground objects, which need not preset the number of object kinds. Moreover, most of the existing cosegmentation algorithms assume that the common foreground objects should appear in all images of the image set. However, if the foreground object only appears in a few images, the object is often misclassified. Our proposed algorithm can segment different kinds of common objects and have a higher segmentation rate for some foreground objects not appearing in all images. In the proposed work, an image is considered as the combination of several objects, and each object is composed of object elements. The image set could be decomposed into lots of object elements, and then object elements with similar features could be clustered into one sub-object class representing one part of an object. According to the class distribution of elements, common objects are extracted by the selection criteria. The concept of independent object elements is also proposed to increase the segmentation rate. In the experimental results, we demonstrate that the proposed approach could get better segmentation results compared with other methods.

关键词： cosegmentation algorithm density-based clustering algorithm multi-class cosegmentation algorithm multiple object segmentation unsupervised clustering algorithm

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The Study of density-based Classification through Hyper-spectrum Image Data of Paddy Rice with Considering Attribute Reduction

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航测及遥测学刊 2020年第3期25卷 181-192页

作者：萬絢王依蘋鄭育欣

In the past, it draws the great attraction of using Data Mining approaches and geostatistics analysis through remote sensing data which are well-accepted. The goal of this study is decided to extract the core spectral information through hyperspectral vs. multispectral imaging. More specifically, the paddy-field remote sensing image is applied with a supervised learning linear discriminant analysis and unsupervised learning density-based clustering algorithm in this study. The pre-processing is used the Principal Component Analysis (PCA) to design parallel study for four case studies: (1) hyper-spectrum versus multi-spectrum with linear discriminant analysis (2) hyper-spectrum versus multi-spectrum density-based clustering algorithm (3) hyper-spectrum versus multispectrum principal component analysis + linear discriminant analysis (4) hyper-spectrum versus multi-spectrum by principal component + density-based clustering algorithm. The DBSCAN with hyper-spectrum image data has an overall accuracy rate of 86.85% which is higher than those of DBSCAN with multi-spectrum (79.45%). The results are presented by error matrix (accuracy rate) and the thematic maps are drawn.

关键词： Image Classification Data Mining density-based clustering algorithm

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Extending a Tandem Mass Spectral Library to Include MS² Spectra of Fragment Ions Produced In-Source and MSⁿ Spectra

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017年第11期28卷 2280-2287页

作者： Yang, Xiaoyu Neta, Pedatsur Stein, Stephen E. NIST Mass Spectrometry Data Ctr Mail Stop 8362 Gaithersburg MD 20899 USA

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS2 spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS3 and MS4 spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS1 scans for an analyte acquired during an infusion experiment. The MS2 spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS2 spectra of the original precursors and of the in-source fragments as well as the MSn spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before.

关键词： Tandem mass spectral library In-source fragmentation MSn density-based clustering algorithm Top-down hierarchical divisive clustering algorithm Metabolite identification

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Study on Sitting Postures Recognition in Pressure Cushion

Study on Sitting Postures Recognition in Pressure Cushion

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International Conference on Sensors, Measurement and Intelligent Materials (ICSMIM 2012)

作者： Bao, Junrong Bao, Chongzhi Li, Jian Wuhan Univ Technol Wuhan 430063 Peoples R China Lanzhou 11 Middle Sch Lanzhou 730000 Peoples R China Pilot Phys Examinat Ctr Lanzhou 730020 Peoples R China

ISBN: (纸本)9783037856529

Sitting postures affect their safety in wheelchair for persons with the disability or the ender people. In this paper, it is explored the relationship between the pressure distributions and sitting posture;using density-based clustering methods establish the evaluation model. Real instance verify that the model can accurately predict the relations between pressure distribution and sitting postures.

关键词： Human Sitting Gesture density-based clustering algorithm Pressure Sensor Body Pressure Distribution

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An algorithm to cluster data for efficient classification of support vector machines

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第3期34卷 2013-2018页

作者： Li, Der-Chiang Fang, Yao-Hwei Natl Cheng Kung Univ Dept Ind & Informat Engn Tainan 701 Taiwan

Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computational complexity. The computational experiments presented in this paper show that the clustering algorithm significantly advances SVM learning efficiency. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： density-based clustering algorithm machine learning support vector machines computational complexity

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A new clustering validity index for evaluating arbitrary shape clusters

A new clustering validity index for evaluating arbitrary sha...

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6th International Conference on Machine Learning and Cybernetics

作者： Liu, Shang Huang, Ya-Lou Tanjin Univ Finance & Econ Informat Sci & Technol Dept Tianjin 300222 Peoples R China Nankai Univ Lab Intelligent Informat Proc Tianjin 300222 Peoples R China

ISBN: (纸本)9781424409723

When doing clustering analysis it always needs a clustering validity index to evaluate if the present clustering scheme can reflect the real natural structure of the dataset. The clusters founded by the clustering algorithm can be of arbitrary shape, but the exiting validity indices can only assess the validity of convex clusters. To solve this problem a new validity index Comp_Sepa is proposed in this paper, which can evaluate a cluster scheme including both non-convex and convex clusters, and the validity index Comp_Sepa is computed by the minimum-cost spanning tree (MST) of the objects of clusters. Experiments show that the new validity index can evaluate the clustering scheme correctly and effectively.

关键词： validity index density-based clustering algorithm MST clustering analysis

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A NEW clustering VALIDITY INDEX FOR EVALUATING ARBITRARY SHAPE CLUSTERS

A NEW CLUSTERING VALIDITY INDEX FOR EVALUATING ARBITRARY SHA...

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： SHANG LIU YA-LOU HUANG Information Science & Technology Department Tanjin University of Finance and Economics Laboratory of Intelligent Information Processing NanKai University Tianjin 300222 P.R.CHINA

When doing clustering analysis it always needs a clustering validity index to evaluate if the present clustering scheme can reflect the real natural structure of the dataset The clusters founded by the clustering algorithm can be of arbitrary shape, but the exiting validity indices can only assess the validity of convex *** solve this problem a new validity index Comp_Sepa is proposed in this paper, which can evaluate a cluster scheme including both non-convex and convex clusters, and the validity index Comp_Sepa is computed by the minimum-cost spanning tree (MST) of the objects of *** show that the new validity index can evaluate the clustering scheme correctly and effectively.

关键词： Validity index density-based clustering algorithm MST clustering analysis

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