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5th International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Awal, Md. Abdul Rabbi, Jakaria Hossain, Sk. Imran Hashem, M. M. A. Khulna Univ Engn & Technol Dept Comp Sci & Engn Khulna 9203 Bangladesh

ISBN: (纸本)9781509012695

With the rapid development of information, communication and transportation systems, the number of tourists is increasing all over the world. Most of the tourists are facing problems when they visit unfamiliar places. That's why it is often difficult for an individual tourist to make an optimal sightseeing itinerary plan during a tight schedule. To help tourists in this situation, a hybrid approach has been proposed in this paper to recommend an optimal itinerary plan. In this approach, k-means clustering algorithm has been used to cluster various tourist spots based on the geographic location and preferences of the tourists. Then the individual cluster is treated as a travelling salesman problem. To find the optimal route in each cluster greedy and 2-opt algorithms are applied. To switch from one cluster to the next cluster, a route is established from the last spot of one cluster to the centroid of the next cluster. Thus, all the tourist spots will be visited efficiently.

关键词： Itinerary Planning k-means clustering algorithm Travelling Salesman Problem Greedy algorithm 2-opt algorithm

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A Method for Coherency Identification Based on Singular Value Decomposition

A Method for Coherency Identification Based on Singular Valu...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Zhu, Qiaomu Chen, Jinfu Duan, Xianzhong Sun, Xin Li, Yinhong Shi, Dongyuan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9781509041688

A novel method to identify coherent generator groups based on singular value decomposition (SVD) in power system is presented in this paper. Via the SVD algorithm, the high-dimensional real-time rotor-angle data of generators is projected into low-dimensional subspace, and the key characteristic coefficients indicating the information of coherency is obtained. Then, the coherency identification is realized by running kmeans clustering algorithm on the coefficients. The dimensionality reduction for clustering algorithm by SVD speeds up the coherency identification. Using the real-time rotor-angle data, the proposed method has the potential to be applied to measured data to implement coherency identification. The effectiveness of this method is demonstrated by the simulation results on IEEE 39-bus system.

关键词： coherency identification k-means clustering algorithm real-time rotor angles singular value decomposition (SVD)

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Complexity Analysis of Lumped Parameter Models: Development of Complexity Reduction algorithm

Complexity Analysis of Lumped Parameter Models: Development ...

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作者： YUXI LI Reykjavik University

学位级别：硕士

Lumped parameter models have been shown to be a useful tool for geothermal reservoir analysis and production planning. Tank models are a common form of lumped parameter models, incorporating tanks of given capacitance partially filled with fluid. Between the tanks are connections with given conductance, that allow fluid to flow between connected tanks with different fluid levels. In this thesis, we analyze how tank models of varying complexity compare in terms of accuracy and utility. An algorithm called Complexity Reduction algorithm (CRA) is developed that automatically finds those models that are likely to be the best by choosing a certain path through the model space. Since in general, it is reasonable to expect that a complex model is able to give an accurate fitting result and the optimum model indicated by CRA only has a medium complexity, a switch-back method is developed to decrease the training error of the complex models. Also, in some cases, there is a large number of production wells that are producing hot water, which will lead to a situation where very many parameters needed to be estimated, since the number of parameters grows quadratically in terms of the number of tanks. The k-means clustering algorithm is shown to be suitable for finding an initial production tank configuration under such situations. Real data from the Laugarnes geothermal field and Reykir geothermal area in Iceland is shown in the thesis. The results show that the newly developed algorithm provides insights into model selection for lumped parameter models. The accuracy of both history-matched and predicted drawdown for lumped parameter models of varying complexity and the results by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as an indicator for model selection have been shown.

关键词： Geothermal reservoir modeling Lumped-parameter models Complexity analysis Information criterion Complexity Reduction algorithm Switch-back method Model selection k-means clustering algorithm

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Application Of Improved k-means clustering algorithm In Transit Data Collection

Application Of Improved K-means Clustering Algorithm In Tran...

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2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010)

作者： Wu, Xueying Yao, Chunlong Dalian Polytech Univ Informat Sci & Engn Coll Dalian Peoples R China

ISBN: (纸本)9781424464968

Timely, accurate and complete transits data are the prerequisite of improving public transportation query system service level. It will generate a lot of redundant data by using the GPS terminal to collect transit site data, due to differences in the location of the same name site and the existing GPS system deviation. Therefore an improved k-means clustering algorithm was proposed, which was applied into clustering analysis of transit data with the same site name but different location. Experimental results show that the algorithm is effective and clustering results accord with the actual situation.

关键词： k-means clustering algorithm GPS Transit Site Public Transportation Query System

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Face Detection using Image Morphology - A Review

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INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS 2016年第4期10卷 89-93页

作者： Mandhala, Venkata Naresh Bhattacharyya, Debnath kim, Tai-hoon KL Univ Dept Comp Sci & Engn Vaddeswaram 522502 AP India Bharati Vidyapeeth Univ Coll Engn Dept Informat Technol Pune 411043 Maharashtra India Sungshin Womens Univ Dept Convergence Secur 249-1Dongseon Dong 3 Ga Seoul 136742 South Korea

This paper presents an approach by various algorithms stage by stage for Face Detection. It first detects the face portion, removing all other portion from an image. First it will remove the background and then the body or cloths portion of the image. To achieve this we propose an algorithm based on k-Mean clustering, Bresenham's algorithm, Graham Scan algorithm. With the help of image morphology, algorithm will detect the skin texture of the face. Image opening and closing will help to get the skin texture. Using a nose template algorithm will find the nose tip by template matching method. Feature vectors are calculated with respect to the nose tip as the origin of 8 octants. Image moment provides a measure for variation in the skin. This total process is done by the feature extraction algorithm.

关键词： k-means clustering algorithm Bresenham's algorithm Graham scan algorithm Image Morphology Template Matching

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Outliers Detection Method Using clustering in Buildings Data 41

Outliers Detection Method Using Clustering in Buildings Data

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41st Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Habib, Usman Zucker, Gerhard Bloechle, Max Judex, Florian Haase, Jan AIT Austrian Inst Technol Dept Energy Vienna Austria Helmut Schmidt Univ Hamburg Hamburg Germany

ISBN: (纸本)9781479917624

To achieve energy efficiency in buildings, a lot of raw data is recorded, during the operation of buildings. This recorded raw data is further used for the analysis of the performance of buildings and its different components e.g. Heating, Ventilation and Air-Conditioning (HVAC). To save time and energy it is required to ensure resilience of the data by detecting and replacing outliers (i.e. data samples that are not plausible) in the data before detailed analysis. This paper discusses the steps involved for detecting outliers in the data obtained from absorption chiller using their On/Off state information. It also proposes a method for automatic detection of On/Off and/or Missing Data status of the chiller. The technique uses two layer k-means clustering for detecting On/Off as well as Missing Data state of the chiller. After automatic detection of the chiller On/Off cycle, a method for outlier detection is proposed using Z-Score normalization based on the On/Off cycle state of chillers and clustering outliers by Expectation Maximization clustering algorithm. Moreover, the results of filling the missing values with regression and linear interpolation for short and long periods are elaborated. All proposed methods are applied to real building data and the results are discussed.

关键词： adsorption chillers physical rules k-means clustering algorithm Outliers Z-Score Normalization Expectation Maximization clustering algorithm (EM) Heating Ventilation and Air-Conditioning(H-VAC) Fault detection and diagnosis (FDD) Machine learning regression linear interpolation

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k-means clustering for Adaptive Wavelet Based Image Denoising 2

K-Means Clustering for Adaptive Wavelet Based Image Denoisin...

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Agrawal, Utkarsh Roy, Soumava kumar Tiwary, U. S. Prashanth, D. S. IIIT Allahabad Dept Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9781467369114

clustering algorithms are used for systematic retrieval of data by organizing them into several clusters. k-means is one such algorithm which partitions data into groups based on distance metric in an unsupervised way. clustering is used to organize data for efficient retrieval. In this paper, we study Denoising of images corrupted with variable Gaussian noise spread across the images (dataset). The dataset was made by applying k-means grouping statistical parameters of the training images which are present in wavelet domain. Adaptive Soft thresholding of noisy images is done, selecting the best parameter based on the cluster. After applying inverse wavelet transform PSNR of the denoised image is calculated. Impressive results are obtained by applying this technique.

关键词： k-means clustering algorithm Image Denoising Soft Thresholding Discrete Wavelet Transform

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Data clustering Approaches Survey and Analysis 1

Data Clustering Approaches Survey and Analysis

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International Conference On Futuristic Trends in Computational Analysis and knowledge Management

作者： Ahalya, G. Pandey, Hari Mohan Amity Univ ASET CSE Dept Noida India

ISBN: (纸本)9781479984336

In the current world, there is a need to analyze and extract information from data. clustering is one such analytical method which involves the distribution of data into groups of identical objects. Every group is known as a cluster, which consists of objects that have affinity within the cluster and disparity with the objects in other groups. This paper is intended to examine and evaluate various data clustering algorithms. The two major categories of clustering approaches are partition and hierarchical clustering. The algorithms which are dealt here are: k-means clustering algorithm, hierarchical clustering algorithm, density based clustering algorithm, self-organizing map algorithm, and expectation maximization clustering algorithm. All the mentioned algorithms are explained and analyzed based on the factors like the size of the dataset, type of the data set, number of clusters created, quality, accuracy and performance. This paper also provides the information about the tools which are used to implement the clustering approaches. The purpose of discussing the various software/tools is to make the beginners and new researchers to understand the working, which will help them to come up with new product and approaches for the improvement.

关键词： clustering Expectation maximization clustering algorithm Hierarchical clustering k-means clustering algorithm Self-organization maps algorithm density based clustering algorithm

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Blind continuous hidden Markov model-based spectrum sensing and recognition for primary user with multiple power levels

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IET COMMUNICATIONS 2015年第11期9卷 1396-1403页

作者： Liu, Boyang Li, Zan Si, Jiangbo Zhou, Fuhui Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China

Spectrum sensing has been well studied because of its significance in cognitive radio. Different from the existing works which a primary user (PU) is assumed to have only one constant transmit power, a more practical scenario that the PU transmitting with multiple power levels is considered. A continuous hidden Markov model (CHMM)-based blind algorithm for not only detecting the presence of PU but also recognising the transmit power level of the PU is proposed. The training problem of CHMM is solved by combining the wavelet singularity detection with k-means clustering algorithm. An effective method for estimation of the number of power levels is proposed. Two different strategies are designed to perform spectrum sensing. Simulation results show the efficiency of the proposed algorithm.

关键词： hidden Markov models radio spectrum management cognitive radio wavelet transforms signal detection pattern clustering blind continuous hidden Markov model-based spectrum sensing primary user multiple power level spectrum recognition cognitive radio CHMM-based blind algorithm PU transmit power level recognition wavelet singularity detection k-means clustering algorithm

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A Method for Coherency Identification Based on Singular Value Decomposition

A Method for Coherency Identification Based on Singular Valu...

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IEEE Power and Energy Society General Meeting

作者： Qiaomu Zhu Jinfu Chen Xianzhong Duan Xin Sun Yinhong Li Dongyuan Shi State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781509041695

A novel method to identify coherent generator groups based on singular value decomposition (SVD) in power system is presented in this paper. Via the SVD algorithm, the high-dimensional real-time rotor-angle data of generators is projected into low-dimensional subspace, and the key characteristic coefficients indicating the information of coherency is obtained. Then, the coherency identification is realized by running k-means clustering algorithm on the coefficients. The dimensionality reduction for clustering algorithm by SVD speeds up the coherency identification. Using the real-time rotor-angle data, the proposed method has the potential to be applied to measured data to implement coherency identification. The effectiveness of this method is demonstrated by the simulation results on IEEE 39-bus system.

关键词： Coherency identification k-means clustering algorithm Real-time rotor angles Singular value decomposition (SVD) Singular value decomposition Swine vesicular disease clustering algorithms k-means algorithm dimensionality reduction angular data Authentication dynamos power systems

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