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Utilizing Clonal Selection Theory Inspired algorithms and K-Means clustering for Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption 2nd

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2nd International Conference on Soft Computing and Data Mining (SCDM)

作者： Lasisi, Ayodele Ghazali, Rozaida Chiroma, Haruna Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Fed Coll Educ Tech Sch Sci Dept Comp Sci Gombe Gombe State Nigeria

ISBN: (纸本)9783319512815;9783319512792

The prediction of carbon dioxide (CO2) emissions from petroleum consumption inspired and motivated this research. Over the years, the rate of emissions of CO2 continues to multiply, resulting in global warming. This paper thus proposes the use of clonal selection theory inspired algorithms;CLONALG and AIRS to forecast global CO2 emissions. The K-means algorithm divides the data into groups of similar and meaningful patterns. Comparative simulations with multi-layer Perceptron, IBk, fuzzy-rough nearest neighbor, and vaguely quantified nearest neighbor reveal that the CLONALG and AIRS produced outstanding results, and are able to generate highest detection rates and lowest false alarm rates. As such, gathering useful information with the accurate prediction of CO2 emissions can help to reduce the emission of CO2 contributions to global warming which assist in policies on climate change.

关键词： Clonal selection algorithm Artificial immune recognition system clustering algorithm Carbon dioxide emissions

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A new T-S fuzzy model predictive control for nonlinear processes

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 88卷 132-151页

作者： Boulkaibet, Ilyes Belarbi, Khaled Bououden, Sofiane Marwala, Tshilidzi Chadli, Mohammed Univ Johannesburg Inst Intelligent Syst Johannesburg South Africa Univ Constantine 1 Dept Elect Engn Constantine Algeria Univ Abbes Laghrour Fac Sci & Technol Khenchela Algeria Univ Picardie Jules Verne Amiens Amiens France

In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Generalized Predictive Control Takagi-Sugeno fuzzy system Kernel ridge regression clustering algorithm Particle Swarm Optimization Takagi-Sugeno system based Kernel ridge regression Sliding-window Kernel Recursive Least squares

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Energy Balance Based clustering algorithm

Energy Balance Based Clustering Algorithm

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2012 2nd International Conference on Advanced Materials and Information Technology Processing(AMITP 2012)

作者： Chengbing Huang Xingjiang Yang Computer Science Department of Aba Teachers College

According to the shortcomings of clustering algorithms commonly seen in Ad Hoc Network, energy balance based clustering algorithm is proposed in this paper. Through the establishment of the clustering algorithm, the times of information interaction are reduced as much as possible, and the node with the largest energy is selected as cluster head for achieving energy conservation and keeping the cluster structure unchanging for the longest time.

关键词： clustering algorithm Energy Balance Mobile Ad Hoc Networks Ad Hoc

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IRPL: An energy efficient routing protocol for wireless sensor networks

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JOURNAL OF SYSTEMS ARCHITECTURE 2017年 75卷 35-49页

作者： Zhang, Wenbo Han, Guangjie Feng, Yongxin Lloret, Jaime Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Peoples R China Hohai Univ Dept Informat & Commun Engn Changzhou 213022 Peoples R China Univ Politecn Valencia Integrated Management Coastal Res Inst Valencia Spain

This study aims to overcome the disadvantages of the original RPL (IPv6 Routing Protocol for Low power and Lossy networks) routing protocol (RPL including problems with energy consumption and energy load balance). We developed a relatively balanced RPL - the improved protocol (IRPL). This protocol is based on an efficient clustering algorithm and an effective topology control model of the loop domain communication route. The clustering algorithm can be used to calculate the optimal number of cluster heads by assumption of the network model. Combined with the clustering probability model and the node competition mechanism, the cluster head node in the wireless sensor network was used to complete the clustering process. In the topology control model, the wireless sensor network was divided into concentric rings with equal areas. Nodes determined the best network route, depending on different levels of ring domain and the optimal forwarding communication area defined in this study. Simulation results indicate that the IRPL routing protocol can reduce overall network energy consumption, balance network energy consumption, and prolong network lifetime. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Wireless sensor network IPRL clustering algorithm Topology control model Ring domain Optimal forwarding communication area

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Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation

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IET IMAGE PROCESSING 2017年第9期11卷 777-785页

作者： Namburu, Anupama Samayamantula, Srinivas Kumar Edara, Srinivasa Reddy Jawaharlal Nehru Technol Univ Dept CSE Kakinada India Jawaharlal Nehru Technol Univ Dept ECE Kakinada India Acharya Nagarjuna Univ Dept CSE Guntur India

Intuitionistic fuzzy sets (IFSs), rough sets are efficient tools to handle uncertainty and vagueness present in images and recently are combined to segment medical images in the presence of noise and intensity non homogeneity (INU). These hybrid algorithms are sensitive to initial centroids, parameter tuning and dependency with the fuzzy membership function to define the IFS. In this paper, a novel clustering algorithm, namely generalized rough intutionistic fuzzy c-means (GRIFCM) is proposed for brain magnetic resonance (MR) image segmentation avoiding the dependency with the fuzzy membership function. In this algorithm, each pixel is categorized into three rough regions based on the thresholds obtained by the image data by minimizing the noise. These regions are used to create IFS. The distance measure based on IFS eliminate's the influence of noise and INU present in the image producing accurate brain tissue segmentation. The proposed algorithm is evaluated through simulation and compared it with existing k-means (KM), fuzzy c-means (FCM), Rough fuzzy c-means (RFCM), Generalized rough fuzzy c-means (GRFCM), soft rough fuzzy c-means (SRFCM) and rough intuitionistic fuzzy c-means (RIFCM) algorithms. Experimental results prove the superiority of the proposed algorithm over the considered algorithms in all analyzed scenarios.

关键词： rough set theory fuzzy set theory biomedical MRI brain image segmentation medical image processing image denoising pattern clustering biological tissues neurophysiology magnetic resonance brain image segmentation generalised rough intuitionistic fuzzy c-means Intuitionistic fuzzy set IFS rough set medical image segmentation hybrid algorithm initial centroids parameter tuning fuzzy membership function dependency clustering algorithm FCM noise minimisation intensity non-homogeneity brain tissue segmentation cluster standardisation uncertainty handling noise influence elimination

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Research on Classifying Interim Products in Ship Manufacturing Based on clustering algorithm

Research on Classifying Interim Products in Ship Manufacturi...

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2012 IEEE 19th International Conference on Industrial Engineering and Engineering Management(IE&EM 2012)

作者： Lin Gong Yan Song Hao Lin Zi-xu Chen School of Mechanical Engineering Beijing Institute of Technology

This paper first reviews some methods of classifying interim products during shop manufacturing. Then, the general cluster analysis is present. The progress of classifying interim products based on cluster analysis is given, and seven features of interim products are present to analysis. Finally, a case is calculated to show the effective of the method, which provides a useful way for effective classification of the intermediate products in the shipbuilding process.

关键词： Classifying clustering algorithm interim products ship manufacturing

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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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Automatic text classification algorithm based on Gauss improved convolutional neural network

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年 21卷 195-200页

作者： Du, Jian-hai Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China

The traditional KNN query is a kind of algorithm with good stability and accuracy performance. However, when the sample size is too large, the computational efficiency of the algorithm is affected greatly. Therefore, a kind of parallel MKNN text classification algorithm based on clustering center text series has been proposed. Firstly, the effective dimensionality reduction of similarity calculation amount of the algorithm is realized based on the clustering center, and the original large-scale document samples are replaced with a relatively small number of clustering sample centers to realize improvement of the KNN query process. Secondly, MapReduce parallel framework is used to meet real-time demand of large-scale text classification and calculation combined with features of text classification, and to effectively overcome slow speed of the KNN query process and ensure accuracy of text classification as higher as possible. Finally, the classification speed of proposed algorithm can be effectively improved under the premise of ensuring sufficient accuracy through comparison in experiment of text classification accuracy and algorithmic efficiency with the similar single-threaded algorithm. (C) 2017 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Text classification Parallel computation Natural language Neural network

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Cluster Optimization Based on Metaheuristic algorithms in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2017年第2期97卷 2633-2647页

作者： Mekonnen, Melaku Tamene Rao, Kuda Nageswara Andhra Univ Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

Partition of networks into optimal set of clusters is the prominent technique to prolong the network lifetime of energy constrained wireless sensor networks. Enumeration search method cannot find optimal clusters within polynomial bounded time for large scale networks since the computational complexity of problem grows exponentially with the dimension of networks. Optimal cluster configuration in sensor networks is known to be Non-deterministic Polynomial (NP)-hard optimization problem and for that reason we have applied polynomial time metaheuristic algorithms to find optimal or near-optimal solutions. In this paper, we present clustering algorithms based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO) to find optimal set of cluster heads in the network. The optimization problem consists of finding optimal configuration of clusters such that the communication distance per cluster is not only minimized but the cluster balance and energy efficiency is also maintained in the network. The SA and PSO toolboxes are developed in C++ and integrated with OMNeT++ simulation environment to implement the proposed clustering algorithms. The performance of algorithms with respect to network lifetime, load balance and energy efficiency of network is examined in the simulation.

关键词： Wireless sensor networks Simulated annealing Particle swarm optimization Network lifetime Load balance clustering algorithm

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Heuristic planning for dynamic VAR compensation using zoning approach

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第11期11卷 2852-2861页

作者： Guan, Lin Wu, Liang Li, Feng Zhao, Qi South China Univ Technol Sch Elect Power Guangzhou Guangdong Peoples R China

Dynamic VAR compensation (DVC) is gaining increasing attentions with growing construction of high-voltage DC systems and renewable power plants in modern power system. This study proposes a novel zoning-based heuristic planning method to reduce the sites and capacity of DVC installation. clustering algorithm based on buses' dynamic behaviour correlation and voltage control ability is developed to partition the system into several control zones. Then, candidate DVC sites are determined by zones, which narrow the searching space and avoid excessive installation of DVCs on adjacent buses. The voltage control index is proposed to find proper DVC installation locations by zones and capacity optimisation is then carried out to reduce investment. The proposed methodology is demonstrated on a modified New England 39-bus test system with wind farm integration. Performance comparison with conventional planning scheme confirms the advantages of the new heuristic planning scheme in practical applications.

关键词： static VAr compensators pattern clustering wind power plants power generation planning voltage control dynamic VAR compensation zoning approach high-voltage DC systems clustering algorithm renewable power plants modern power system heuristic planning method DVC installation dynamic behaviour correlation voltage control ability capacity optimisation modified New England 39-bus test system wind farm integration

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