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Robust Static Transmission Expansion Planning Considering Contingency and Wind Power Generation

JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 461-470页

作者： de Paula, Arthur N. de Oliveira, Edimar J. de Oliveira, Leonardo W. Honorio, Leonardo M. Univ Fed Juiz de Fora Rua Jose Lourenco Kelmer S-N BR-36036900 Juiz De Fora MG Brazil

This paper proposes a framework based on the Benders decomposition to obtain a scenario-based robust static transmission expansion planning by considering N-1 security criterion, transmission losses and uncertainties in wind power generation. The model is solved by a bi-level approach that seeks to minimize investment cost as well as penalty costs of wind spill and load curtailment. The wind uncertainty is modeled by grouped historical wind series through k-means clustering technique maintaining the wind correlation between different geographic regions. Case studies are performed in the well-known power systems: IEEE-RTS 24-bus test system and an equivalent Brazilian Southern 46-bus system. In addition, a detailed tutorial case is also presented with a modified version of Garver 6-bus test system.

关键词： Transmission expansion planning Transmission active losses Benders decomposition N-1 security criterion Wind power uncertainty k-means clustering algorithm

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Application of an unsupervised artificial neural network technique to multivariant surface water quality data

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ECOLOGICAL RESEARCH 2009年第1期24卷 163-173页

作者： Cinar, Oezer Merdun, Hasan Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Dept Environm Engn TR-46060 Kahramanmaras Turkey Kahramanmaras Sutcu Imam Univ Fac Agr Dept Agr Engn TR-46060 Kahramanmaras Turkey

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies-Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, k, Cl, NH4-N, NO2-N, o-PO4, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.

关键词： kohonen self-organising feature maps clustering k-means clustering algorithm Surface water quality Variable dependencies

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Task scheduling scheme based on resource clustering in desktop grids

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2014年第6期27卷 918-930页

作者： Gil, Joon-Min kim, Sungsuk Lee, JongHyuk Catholic Univ Daegu Sch Informat Technol Engn Gyongsan 712702 Gyeongbuk South Korea Seokyeong Univ Dept Comp Sci Seoul 136704 South Korea Samsung Elect Software R&D Ctr Suwon 443742 Gyeonggi South Korea

Desktop grids are platforms for grid computing that incorporate desktop resources into a grid infrastructure. The purpose of this computing paradigm is to process a massive computational tasks by exploiting the donated resources connected over the Internet. In desktop grids, it is important to guarantee fast turnaround time in the presence of dynamic properties, such as volatility and heterogeneity. To achieve this objective, we propose a task scheduling scheme based on resource clustering that can selectively allocate tasks to those resources that are most suitable for the current situation of a desktop grid environment. As a classifier of resources, the k-means clustering algorithm is introduced to classify resources according to their own task execution availability and result-return probability. The experimental results show that our scheduling scheme is more efficient than existing scheduling schemes with respect to reducing both the turnaround time and the quantity of resources consumed. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： task scheduling resource classification k-means clustering algorithm desktop grids

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MULTIMODAL OPTIMIZATION OF JOB-SHOP SCHEDULING PROBLEMS USING A clustering-GENETIC algorithm BASED APPROACH

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2019年第5期26卷 651-662页

作者： Zou, Pan Rajora, Manik Liang, Steven Y. Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA Donghua Univ Coll Mech Engn Shanghai Peoples R China

Though ample work has been performed on the optimization of job-shop scheduling problems (JSSPs), very few techniques can satisfy the requirement of modern-day workshops, i.e., providing multiple schedules that achieve the desired goal. These techniques also do not provide a good balance between converging to the optimal solution while finding multiple optimal solutions. To overcome this obstacle, a new algorithm is proposed by combining the k-means clustering algorithm and genetic algorithm (GA) for multimodal optimization of JSSPs. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters based on some machine sequence-related features under the assumption that different global optima will have different features. Next, the adapted genetic operators are applied to the individuals belonging within the same cluster with the aim of independently searching for global optima within each cluster. The performance of the proposed algorithm is measured by its application to the multimodal optimization of benchmark JSSPs and comparing its performance against other multimodal optimization algorithms. The results of the case studies show that the algorithm has a better average optimal value and is also capable of finding multiple optimal solutions.

关键词： multimodal optimization job-shop scheduling problem (JSSP) k-means clustering algorithm genetic algorithm

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The FRCk clustering algorithm for determining cluster number and removing outliers automatically

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2021年第5期24卷 485-494页

作者： Guo, Yubin Wu, Yuhang Zhang, Xiaopeng Bo, Aofeng Li, Ximing South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China South China Agr Univ Coll Software Engn Guangzhou 510642 Peoples R China South China Agr Univ Informat & Network Serv Ctr Guangzhou 510642 Peoples R China Guangzhou HolandAI Technol Co Ltd Guangzhou 510006 Peoples R China

clustering algorithm is one of the most popular unsupervised algorithms for data grouping. The k-means algorithm is a popular clustering algorithm for its simplicity, ease of implementation and efficiency. But for k-means algorithm, the optical cluster number is difficult to predict, while it is sensitive to outliers. In this paper, we divide outliers into two types, and then prompt a clustering algorithm to remove the two-type outliers and calculate the optimal cluster number in each clustering iteration. The algorithm is a fusion of rough clustering and k-means, abbreviated as FRCk algorithm. In the FRCk algorithm, outliers are removed precisely, therefore the optical cluster number can be more accurate, and the quality of clustering result can be improved accordingly. And this algorithm is proven effective by experiment.

关键词： clustering k-means clustering algorithm optical cluster number outlier

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Research on the Relationship between Fuel Economy and Driving Behavior of Heavy-Duty Vehicles 23

Research on the Relationship between Fuel Economy and Drivin...

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23rd COTA International Conference of Transportation Professionals (CICTP) - Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation

作者： Zhang, Shuai-Qi Xu, Ting Peng, Chong Wang, Jing-Tao Wang, Shu-Zhen Changan Univ Xian Coll Transportat Engn Xian Peoples R China Changan Univ Xian Coll Transportat Engn Dept Transportat Xian Peoples R China

ISBN: (数字)9780784484869

ISBN: (纸本)9780784484869

In order to study the influence of heavy-duty vehicle driving behavior on fuel economy, this paper collected the on-board diagnostics (OBD) data of vehicle driving on a circular urban arterial road. After data preprocessing, the kinematic segments were divided, and the characteristic parameter values were defined. Principal component analysis was used to reduce the dimension of the values. Then k-means clustering algorithm was carried out to classify driving behavior based on traffic and operating conditions. The cumulative fuel consumption and running time under the two conditions were analyzed. Finally, the abnormal driving behavior is defined, and the regression analysis of its impact on fuel consumption was carried out. The research shows that different traffic and operating conditions have a direct impact on vehicle fuel consumption per hundred kilometers, which can comprehensively evaluate the relationship between vehicle driving behavior and fuel economy.

关键词： Driving behavior Fuel economy kinematic segment characteristic value Principal component analysis k-means clustering algorithm

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An adaptive segmentation method combining MSRCR and mean shift algorithm with k-means correction of green apples in natural environment

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Information Processing in Agriculture 2019年第2期6卷 200-215页

作者： Sashuang Sun Huaibo Song Dongjian He Yan Long College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light *** enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent *** that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold ***,the poorly segmented images were corrected using the k-means algorithm according to the shape,color and texture *** users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this *** with the manifold ranking algorithm,the k-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,***,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared *** proposed method accurately recognized the green apples under complex illumination conditions and growth ***,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

关键词： Green fruit Adaptive segmentation MSRCR algorithm Mean shift algorithm k-means clustering algorithm Manifold ranking algorithm

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Development of Digital Evidence Collector and File Classification System with k-means algorithm 5

Development of Digital Evidence Collector and File Classific...

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IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob)

作者： Ruriawan, Muhammad Faris Anggono, Bintaran Siahaan, Isaac Anugerah Purwanto, Yudha Telkom Univ Sch Elect Engn Bandung Indonesia

ISBN: (纸本)9781728148656

Digital forensic is a branch of forensic science that focuses on research on the usual storage media that electronic users use such as hard disks, flash drives or other devices used on computers. The output is called digital evidence. The purpose of doing digital forensics is to find an evidence that can be used in the investigation of a case, until the evidence becomes valid and could be used as evidence in court. In this research, we implement a system of digital evidence collection, recovery, and file classification application. The classification was done by k-means clustering algorithm. The system could detect the storage media, duplicate the content, and classify the output using k-means algorithm. It can help a forensic examiner in the collection, examination, analysis, and reporting phase in accordance with NIST SP 800-86. The application also can assist investigators in managing files in the storage media as digital evidence so that outputs are obtained in accordance with applicable law.

关键词： digital forensic digital evidence k-means clustering algorithm

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Image Segmentation using Firefly algorithm

Image Segmentation using Firefly Algorithm

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International Conference on Information Technology (InCITe) - The Next Generation IT Summit on the Theme - Internet of Things - Connect Your Worlds

作者： Sharma, Akash Sehgal, Smriti Amity Univ Dept CSE Noida India

ISBN: (纸本)9781509026128

Image segmentation is an important step in the domain of image processing in which we segment the image into several parts which carry certain type of information for the user. Image segmentation is very difficult step in the processing of the image which aims at extracting the information from image. clustering is used to segment the image. clustering algorithms are part of data mining algorithm that groups the data into various number of given clusters. All the data points in one cluster have similar properties based on which they are clustered i.e. each cluster has minimum difference between its points and maximum difference from other cluster data points. The proposed algorithm uses k-mean algorithm and firefly to cluster image pixels into k cluster for segmentation. Since k-mean clustering algorithm is gets trapped in local optima it is optimized using firefly algorithm. Swarm intelligence based algorithms forms the basis of the firefly algorithm which has several application and used to solve optimization problems. Firefly algorithm has been applied in many research and optimization areas. Firefly algorithm and its hybridized version have been used to solve various problems successfully. To apply firefly algorithm to wide areas of problem the firefly algorithm must be modified or integrated with other algorithms. Presently metaheuristic nature of algorithm plays an important role and current optimization algorithm include this nature and are very efficient in solving NP-hard problems.

关键词： k-means clustering algorithm Firefly Optimization Image Segmentation Correlation

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Visual based laser speckle pattern recognition method for structural health monitoring

Visual based laser speckle pattern recognition method for st...

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Conference on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems

作者： Park, kyeongtaek Torbol, Marco Ulsan Natl Inst Sci & Technol Sch Urban & Environm Engn 50 UNIST Gil Ulsan 44919 South Korea

ISBN: (数字)9781510608221

ISBN: (纸本)9781510608221

This study performed the system identification of a target structure by analyzing the laser speckle pattern taken by a camera. The laser speckle pattern is generated by the diffuse reflection of the laser beam on a rough surface of the target structure. The camera, equipped with a red filter, records the scattered speckle particles of the laser light in real time and the raw speckle image of the pixel data is fed to the graphic processing unit (GPU) in the system. The algorithm for laser speckle contrast analysis (LASCA) computes: the laser speckle contrast images and the laser speckle flow images. The k-mean clustering algorithm is used to classify the pixels in each frame and the clusters' centroids, which function as virtual sensors, track the displacement between different frames in time domain. The fast Fourier transform (FFT) and the frequency domain decomposition (FDD) compute the modal properties of the structure: natural frequencies and damping ratios. This study takes advantage of the large scale computational capability of GPU. The algorithm is written in Compute Unifies Device Architecture (CUDA C) that allows the processing of speckle images in real time.

关键词： System identification structural health monitoring speckle contrast imaging k-means clustering algorithm parallel computing

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