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An adaptive neuro-fuzzy approach for system modeling

An adaptive neuro-fuzzy approach for system modeling

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chen-Sen Ouyang Wan-Jui Lee Shie-Jue Lee Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan

ISBN: (纸本)0780375084

In this paper, a novel adaptive neuro-fuzzy modeling system is proposed for solving system modeling problems. Two phases are included in our approach.. In the first phase, a merge-based fuzzy self-clustering algorithm is used to automatically partition the sample data set into fuzzy clusters. Initial clusters are generated rapidly and similar clusters are merged together gradually based on similarity and distortion measures. TSK-type fuzzy rules associated with generated clusters are extracted. Then, the obtained rules are refined by a fuzzy neural network in the second phase. To speed up the convergence of learning, we develop a hybrid learning algorithm which combines a recursive SVD-based least squares estimator and the gradient descent method. Experimental results have shown that our method is more efficient than other methods.

关键词： Modeling Fuzzy sets Clustering algorithms Adaptive systems partitioning algorithms Distortion measurement Data mining Fuzzy neural networks Convergence Least squares approximation

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Improved MapReduce k-Means Clustering Algorithm with Combiner

Improved MapReduce k-Means Clustering Algorithm with Combine...

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International Conference on Computer Modeling and Simulation, UKSIM

作者： Prajesh P. Anchalia Department Of Computer Science and Engineering R V College Of Engineering Bangalore India

Data Mining and High Performance Computing are two broad fields in Computer Science. The k-Means Clustering is a very simple and popular data mining algorithm that has its application spread over a very broad spectrum. MapReduce is a programming style that is used for handling high volume data over a distributed computing environment. This paper proposes an improved and efficient method to implement the k-Means Clustering Technique using the MapReduce paradigm. The main idea is to introduce a combiner in the mapper function to decrease the amount of data to be written by the mapper and the amount of data to be read by the reducer which has considerably reduced the redundant Map-Reduce calls that have resulted in a significant reduction in the time required for clustering as it has decreased the read/write operations to a large extent. The implementation of Improved MapReduce k-Means Clustering has been clearly discussed and its effectiveness is compared to the regular implementation in an experimental analysis. The results consolidate this research by concluding that the Improved MapReduce Implementation of k-Means Clustering Algorithm out performs the regular implementation by over 300 seconds.

关键词： Clustering algorithms partitioning algorithms Algorithm design and analysis Programming Data mining Convergence Computational modeling

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A hierarchical clustering algorithm based on density for data stratification

A hierarchical clustering algorithm based on density for dat...

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International Conference on Systems and Informatics (ICSAI)

作者： Zhiwei Sun College of Computer Science and Information Engineering TianJin University of Science and Technology TianJin China

Cluster analysis is a primary method for data mining. Existing cluster approaches use global input parameters. But the real data can't be described by them, and each input parameter will have a significant effect to the result. A new algorithm named HDBSCAN will be introduced for the purpose of cluster analysis which can produce nature stratification. The algorithm have a preprocess procedure which use graph to express the structure of neighborhood, thus the parameters can be easily set, this is important for determination of input parameters; and then a hierarchical approach based on density clustering algorithm is used to analysis the data with the different Eps-neighborhood. At last the relationship among the cluster results will be got by scanning the cluster results above. We show how to get the intrinsic clustering structure and show the results. Both theory analysis and experimental results confirm the approach can cluster data with automatic setting different parameters in different partitions.

关键词： Clustering algorithms Algorithm design and analysis partitioning algorithms Classification algorithms Complexity theory Spatial databases

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Efficient Retrieval from Large-Scale Egocentric Visual Data Using a Sparse Graph Representation

Efficient Retrieval from Large-Scale Egocentric Visual Data ...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Vijay Chandrasekhar Wu Min Xiao Li Cheston Tan Bappaditya Mandal Liyuan Li Joo Hwee Lim Institute for Infocomm Research

We propose representing one's visual experiences (captured as a series of ego-centric videos) as a sparse-graph, where each node is an individual frame in the video, and nodes are connected if there exists a geometric transform between them. Such a graph is massive and contains millions of edges. Autobiographical egocentric visual data are highly redundant, and we show how the graph representation and graph clustering can be used to exploit redundancy in the data. We show that popular global clustering methods like spectral clustering and multi-level graph partitioning perform poorly for clustering egocentric visual data. We propose using local density clustering algorithms for clustering the data, and provide detailed qualitative and quantitative comparisons between the two approaches. The graph-representation and clustering are used to aggressively prune the database. By retaining only representative nodes from dense sub graphs, we achieve 90% of peak recall by retaining only 1% of data, with a significant 18% improvement in absolute recall over naive uniform subsampling of the egocentric video data.

关键词： Videos Clustering algorithms Visualization Databases partitioning algorithms Q measurement Image edge detection

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A design methodology for system level synthesis of multi-core system architectures

A design methodology for system level synthesis of multi-cor...

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Electronics, Communications and Photonics Conference (SIECPC), Saudi International

作者： Karim Yehia Mona Safar Hassan Youness Mohamed AbdElSalam Ashraf Salem Department of Electronics and Communications Engineering Cairo University Cairo Egypt Department of Computers and Systems Engineering Ain Shams University Cairo Egypt Mentor Graphics Egypt Cairo Egypt

A multi-core system is an integrated circuit containing multiple processor cores that implements most of the functionality of a complex electronic system and some other components like FPGA/ASIC on a single chip. In this paper, we present a novel approach to synthesize multi-core system architectures from Task Precedence Graphs (TPG) models. The front end engine applies efficient algorithm for scheduling and communication contention resolving to obtain the optimal multi-core system architecture in terms of number of processor cores, number of busses, task-to-processor/channel-to-bus mapping, optimal schedule, and HW/SW partition. The back end engine generates a SystemC simulation model using a well-known commercial tool model generation library. The viability and potential of the approach is demonstrated by a case study.

关键词： Optimal scheduling Processor scheduling Multicore processing partitioning algorithms Scheduling Engines

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Resource allocation and request handling for user-aware content retrieval in the cloud

Resource allocation and request handling for user-aware cont...

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Conference on Local Computer Networks (LCN)

作者： Boyang Yu Jianping Pan Department of Computer Science University of Victoria Victoria BC Canada

The user-aware content retrieval services are always data-intensive and require much resource to satisfy the user demand, which incurs the high cost of implementation. Considering that they could largely exploit the pay-as-you-go paradigm and the almost unlimited resource pool of the cloud, we investigate the design issues of deploying the services to the cloud in a cost-effective way. We formulate the resource allocation and request handling problem which aims at lowering the deployment cost and guaranteeing the service quality simultaneously. Due to the hardness of obtaining an optimal solution, we design two approximate algorithms with different points of emphasis and analyze their approximation ratios as well. In addition, we discuss the implementation issues in applying the proposed algorithms to the practical systems. Finally, the algorithms are evaluated and validated through both trace-based and synthesized simulations where they show a large improvement in terms of the total system cost.

关键词： Resource management Approximation methods Approximation algorithms Algorithm design and analysis Quality of service Indexes partitioning algorithms

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A novel semi-supervised approach for network traffic clustering

A novel semi-supervised approach for network traffic cluster...

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International Conference on Network and System Security, NSS

作者： Yu Wang Yang Xiang Jun Zhang Shunzheng Yu School of Information Technology Deakin University Melbourne Australia Department of Electronic and Communication Engineering Sun Yat-Sen University Guangzhou China

Network traffic classification is an essential component for network management and security systems. To address the limitations of traditional port-based and payload-based methods, recent studies have been focusing on alternative approaches. One promising direction is applying machine learning techniques to classify traffic flows based on packet and flow level statistics. In particular, previous papers have illustrated that clustering can achieve high accuracy and discover unknown application classes. In this work, we present a novel semi-supervised learning method using constrained clustering algorithms. The motivation is that in network domain a lot of background information is available in addition to the data instances themselves. For example, we might know that flow f 1 and f 2 are using the same application protocol because they are visiting the same host address at the same port simultaneously. In this case, f 1 and f 2 shall be grouped into the same cluster ideally. Therefore, we describe these correlations in the form of pair-wise must-link constraints and incorporate them in the process of clustering. We have applied three constrained variants of the K-Means algorithm, which perform hard or soft constraint satisfaction and metric learning from constraints. A number of real-world traffic traces have been used to show the availability of constraints and to test the proposed approach. The experimental results indicate that by incorporating constraints in the course of clustering, the overall accuracy and cluster purity can be significantly improved.

关键词： Clustering algorithms Protocols Classification algorithms partitioning algorithms Payloads Correlation Measurement

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Fast, automatic, procedure-level performance tuning

Fast, automatic, procedure-level performance tuning

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International Conference on Parallel Architecture and Compilation Techniques (PACT)

作者： Zhelong Pan Rudolf Eigenmann Purdue University School of ECE West Lafayette IN

ISBN: (纸本)9781509030224

This paper presents an automated performance tuning solution, which partitions a program into a number of tuning sections and finds the best combination of compiler options for each section. Our solution builds on prior work on feedback-driven optimization, which tuned the whole program, instead of each section. Our key novel algorithm partitions a program into appropriate tuning sections. We also present the architecture of a system that automates the tuning process; it includes several pre-tuning steps that partition and instrument the program, followed by the actual tuning and the post-tuning assembly of the individually-optimized parts. Our system, called PEAK, achieves fast tuning speed by measuring a small number of invocations of each code section, instead of the whole-program execution time, as in common solutions. Compared to these solutions PEAK reduces tuning time from 2.19 hours to 5.85 minutes on average, while achieving similar program performance. PEAK improves the performance of SPEC CPU2000 FP benchmarks by 12% on average over GCC O3, the highest optimization level, on a Pentium IV machine.

关键词： Tuning Optimization Context Program processors partitioning algorithms Instruments Benchmark testing

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Parallelism and Auto-Tuner of Irregular Iteration in Reservoir Simulation

Parallelism and Auto-Tuner of Irregular Iteration in Reservo...

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International Symposium on Computer, Consumer and Control (IS3C)

作者： Ji-Lin Zhang Li-Ting Zhu Jian Wan Cong-Feng Jiang Department of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

In the field of reservoir numerical simulation, irregular iteration problem is one of the most important reasons to affect the efficiency of large-scale computing. Memory access patterns can't be determined at compiler-time, which brings us difficulties to parallel. This library uses Inspector/Executor model based on distributed clusters, and improves parallel efficiency utilizing alternate tiling technology. Besides, we get the optimal efficiency by auto-tuner, which can find the right parameters. Finally, numerical results on CLUSTER show that the method has a good parallel efficiency as well as scalability, and the auto-tuner possesses practicality to a certain extent.

关键词： Tiles Parallel processing Algorithm design and analysis Clustering algorithms Schedules partitioning algorithms Program processors

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EMPAR: an interactive synthesis environment for hardware emulations

EMPAR: an interactive synthesis environment for hardware emu...

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Asia and South Pacific Design Automation Conference

作者： Tsing-Gen Lee Wen-Jong Fang A.C.-H. Wu Department of Computer Science National Tsing Hua University Hsinchu Taiwan

In this paper, we present EMPAR, an interactive synthesis environment for hardware emulations. EMPAR provides an open-ended design environment for the development of hardware emulators, which is capable of supporting: (1) a variety of EM architectures (2) a variety of EM synthesis algorithms, (3) interactive control by the user, and (4) design quality analysis. An X-window based graphical user interface has been developed to support a variety of interactive design tasks. The key features in EMPAR are: (1) it is open-ended so that arbitrary algorithms and tools can be built on top of it and (2) it is fully interactive and leaves the control to the designer. EMPAR can be used as a design environment for existing hardware emulators as well as a test bed for the evaluation and development of new hardware-emulator architectures and synthesis algorithms.

关键词： Hardware Emulation Algorithm design and analysis Logic Process design partitioning algorithms Application specific integrated circuits Field programmable gate arrays Silicon Design automation

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