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检索条件"主题词=Selection Algorithm"

共 252 条记录，以下是11-20 订阅

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An Area-Overhead-Oriented Monitoring-Path selection algorithm for Suspicious Timing Error Prediction

An Area-Overhead-Oriented Monitoring-Path Selection Algorith...

引用

IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)

作者： Yoshida, Shinnosuke Shi, Youhua Yanagisawa, Masao Togawa, Nozomu Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan Waseda Univ Waseda Inst Adv Study Tokyo Japan

ISBN: (纸本)9781479952304

As process technologies advance, the importance of timing error correction techniques is increasing as well. In this paper, We propose an area-overhead-oriented monitoring-path selection algorithm for suspicious timing error prediction circuits (STEPCs). STEPC predicts timing errors by monitoring the middle points of several speed-paths in a circuit. However, we need many STEPCs with a high area overhead to predict timing errors in an overall circuit. Our proposed method moves the STEPC insertion positions to minimize the number of inserted STEPCs. We apply a max-flow and min-cut approach to determine the optimal positions of inserted STEPCs. Our proposed algorithm reduces the required number of STEPCs to 1/19 and their area to 1/5 compared with a naive algorithm. Furthermore, our algorithm realizes 2.25X overclocking compared with just inserting STEPCs into several speed-paths.

关键词： error correction graph theory timing circuits STEPC insertion positions area-overhead-oriented monitoring-path selection algorithm max-flow approach min-cut approach naive algorithm speed-paths suspicious timing error prediction circuits timing error correction techniques algorithm design and analysis Clocks Delays Logic gates Monitoring Registers algorithm design and analysis timing circuits selection algorithm Registers error forecasting Timing devices Logic gates error correction Graph theory TIMING ERROR Maximum Flow dividing and cutting machine delay

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Long Step User selection algorithm for Unitary Beamforming

Long Step User Selection Algorithm for Unitary Beamforming

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IEEE 75th Vehicular Technology Conference (VTC Spring)

作者： Li, Yan Sun, Yi Zhou, Feng Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian Peoples R China

ISBN: (纸本)9781467309905;9781467309882

This paper focuses on the design of user selection algorithm for Unitary Beamforming (UBF) in multi-user multiple-input multiple-output (MIMO) systems. A long step user selection (LSUS) algorithm is proposed, and is verified to be rather robust when combined with Givens rotation-based iterative optimization UBF. For most of existing user selection algorithms, only the best user is chosen at each selection, we regard the step length as one. However, because members of the optimal user set must both have high channel gains and low inter-user interference, if only one user is considered each step, the user that will be selected at next step can not be guaranteed to have both high channel gain and low inter-user interference with the just selected users. Therefore, an algorithm that two users are considered at each selection is proposed in this paper, the step length is regarded as two. It will in some extent help find the favorable user set and finally lead to a higher capacity. Moreover, Gram-Schmidt orthogonalization is used in our proposed algorithm to select the favorable users and at the same time generate the initial unitary beamforming matrix according to the channel state information (CSI). Two other beamforming schemes are simulated as references to demonstrate the good performance of the proposed algorithm even under low signal-to-noise-ratio (SNR) scenario.

关键词： selection algorithm step size Beamforming Unitary multiple input multiple output systems

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A Novel Adaptive Weight selection algorithm for Multi-Objective Multi-Agent Reinforcement Learning

A Novel Adaptive Weight Selection Algorithm for Multi-Object...

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International Joint Conference on Neural Networks (IJCNN)

作者： Van Moffaert, Kristof Brys, Tim Chandra, Arjun Esterle, Lukas Lewis, Peter R. Nowe, Ann Vrije Univ Brussel Dept Comp Sci Brussels Belgium Alpen Adria Univ Klagenfurt Klagenfurt Austria Lakeside Labs Klagenfurt Austria Univ Oslo N-0316 Oslo Norway Aston Univ Birmingham B4 7ET W Midlands England

ISBN: (纸本)9781479914845

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

关键词： learning (artificial intelligence) selection algorithm Reward Learning Smart cameras monodromy Self tuning hypervolume

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Boosting Variable selection algorithm for Linear Regression Models 10

Boosting Variable Selection Algorithm for Linear Regression ...

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10th International Conference on Natural Computation (ICNC)

作者： Zhang, Chun-Xia Wang, Guan-Wei Xi An Jiao Tong Univ Fac Math & Stat Xian 710049 Shaanxi Peoples R China Xian Technol Univ Sch Mechatron Engn Xian 710032 Shaanxi Peoples R China

ISBN: (纸本)9781479951512

With respect to variable selection for linear regression models, this paper proposes a novel boosting learning method based on genetic algorithm. Its main idea is as follows: each training example is first assigned to a weight and genetic algorithm is adopted as the base learning algorithm of boosting. Then, the training set associated with a weight distribution is taken as the input of genetic algorithm to do variable selection. Subsequently, the weight distribution is updated according to the quality of the previous variable selection results. Through repeating the above steps for multiple times, the results are then fused via a weighted combination rule. The performance of the proposed method is investigated on several simulated data sets. The experimental results show that boosting can significantly improve the variable selection performance of a genetic algorithm and can accurately identify the relevant variables.

关键词： Boosting Electronics packaging Genetic algorithms Input variables Linear regression Prediction algorithms Training Input variables Genetic algorithm (GA) Linear Regression Boost Electronics packaging Prediction algorithms Functional training Training selection algorithm learning algorithms

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Clustering and Retrieval Method of Immunological Memory Cell in Clonal selection algorithm

Clustering and Retrieval Method of Immunological Memory Cell...

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6th International Conference on Soft Computing and Intelligent Systems (SCIS) / 13th International Symposium on Advanced Intelligence Systems (ISIS)

作者： Ichimura, Takumi Kamada, Shin Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku 1-1-71 Ujina Higashi Hiroshima 7348558 Japan Prefectural Univ Hiroshima Grad Sch Comprehens Sci Res Minami Ku Hiroshima 7348558 Japan

ISBN: (纸本)9781467327435;9781467327428

The clonal selection principle explains the basic features of an adaptive immune response to a antigenic stimulus. It established the idea that only those cells that recognize the antigens are selected to proliferate and differentiate. This paper explains a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. Antibodies generated by the clonal selection algorithm are clustered in some categories according to the affinity maturation, so that immunological memory cells which respond to the specified pathogen are created. Experimental results to classify the medical database of Coronary Heart Disease databases are reported. For the dataset, our proposed method shows the 99.6% classification capability of training data.

关键词： Clonal selection selection algorithm Immunologic Memory immunocytes Medical databases Adaptive Immune Response Cluster Analysis

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Antenna selection algorithm with Improved Channel Predictor for Vehicular Environment 6

Antenna Selection Algorithm with Improved Channel Predictor ...

引用

6th IEEE International Symposium on Wireless Vehicular Communications (WiVeC)

作者： Shemshaki, Mona Mecklenbraeuker, Christoph Vienna Univ Technol Inst Telecommun Vienna Austria

ISBN: (纸本)9781479944521

Multiple Input Multiple Output (MIMO) systems have become state-of-the-art in Wireless Local Area Networks due to the utilization of spatial diversity and spatial multiplexing. In such systems, the implementation cost imposed by increased hardware and software complexity is not negligible. Antenna selection is a potential low-cost MIMO solution. In this work, we analyse and discuss receive antenna selection in combination with channel prediction for Vehicle-to-Infrastructure (V2I) scenarios in which the IEEE 802.11p frame structure is extended by additional preambles and a postamble.

关键词： wireless local area network (WLAN) multiple input multiple output systems selection algorithm space division multiplexing spatial diversity ANTENNA antennae Receiving antennas FRAMED STRUCTURES

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APPLICATION OF AN ADVANCED META MODEL selection algorithm ON THE SENSITIVITY ANALYSIS OF A COOLED TURBINE BLADE

APPLICATION OF AN ADVANCED META MODEL SELECTION ALGORITHM ON...

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67th ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) on Road Mapping the Future of Propulsion and Power

作者： Diermeier, Florian Voigt, Matthias Mailach, Ronald Meyer, Marcus Tech Univ Dresden Inst Fluid Mech Chair Turbomachinery & Flight Prop D-01069 Dresden Germany Rolls Royce Deutschland Ltd & Co KG D-15827 Blankenfelde Mahlow Germany

ISBN: (纸本)9780791886076

Probabilistic methods are growing more important in the aerospace industry due to the ability to describe the behaviour of complex systems in the presence of input parameter variance. Sensitivity analysis based on meta models can be utilized for this purpose. The reliability of the results is dependent on the surrogate model quality, which in turn depends on the available data. A priori the appropriate meta model type is not known. An approach to automatically select the best fitting model for a given data set is presented in this paper. For comparison, polynomial regression with least squares fitting, moving least squares, radial basis functions, and support vector regression are used as candidate types. The selection of the best meta model type is based on two quality criteria utilizing a cross-validation (CV) scheme. The developed approach is demonstrated on the sensitivity analysis of a cooled turbine blade.

关键词： Turbine blades selection algorithm Sensitivity analysis Fittings Polynomials Aerospace industry Support vector machines Complex systems Reliability

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A generic moduli selection algorithm for the Residue Number System 10

A generic moduli selection algorithm for the Residue Number ...

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10th IEEE International Conference on Design and Technology of Integrated Systems in Nanoscale Era (DTIS)

作者： Dasygenis, Minas Petrousov, Ioannis Univ Western Macedonia Dept Informat & Telecommun Engn Kozani 50100 Greece

ISBN: (纸本)9781479919994

Heterogeneous architectures require custom intellectual property (IP) blocks, named 'accelerators', that perform an operation efficiently. Residue Number System (RNS), a non binary compatible arithmetic system, can be used in such accelerators. One fundamental challenge in the design of RNS circuits is the selection of the moduli set. Here, we present our algorithm that results in very good moduli set for the given requirements. Experimental results and comparisons with similar algorithms, prove the efficiency of it. The algorithm has been implemented into a public web tool and is available via a web browser.

关键词： residue number systems Web Browser Accelerators selection algorithm algorithms

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Fast Mode selection algorithm Based on Derived Layer

Fast Mode Selection Algorithm Based on Derived Layer

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IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Liang, Yingyi He, Zhenyu Li, Yi Harbin Inst Technol Sch Comp Sci Shenzhen Grad Sch Harbin Peoples R China

ISBN: (纸本)9781479953530

Scalable Video Coding (SVC), provides different resolutions, different video quality and different video streaming rate after once compression according to various requirements of users. The characteristic performance can solve a series of video transmission problems encountered in the current complex and heterogeneous network environment conveniently and effectively, and provide a highly efficient solution for the new video network. Because of problems such as the SVC coding efficiency in multilayer and the coding cost, the research on SVC is mainly focused on how to improve the coding speed of the algorithm (fast SVC algorithm). For the macroblock mode selection in H.264/SVC, the paper selects the fast algorithm based on the macroblock in derived layer.

关键词： Correlation Cost function Encoding Prediction algorithms Standards Static VAr compensators Streaming media Static VAr compensators Streaming Video Prediction algorithms Cost functions User needs selection algorithm macro block coding

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Recommendation Method for Service selection algorithm Based on User Preference 18

Recommendation Method for Service Selection Algorithm Based ...

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2nd International Conference on Computer Science and Application Engineering (CSAE)

作者： Takahashi, Ryuichi Nishida, Kazuma Fukazawa, Yoshiaki Ibaraki Univ Coll Engn 4-12-1 Nakanarusawa Hitachi Ibaraki Japan Waseda Univ Sch Fundamental Sci & Engn Shinjuku Ku 3-4-1 Okubo Tokyo Japan

ISBN: (纸本)9781450365123

A new service can be constructed(1) by composing web services in service-oriented architecture (SOA). Although many selection algorithms have been proposed, a universal one cannot be determined because each algorithm has different characteristics and each situation is unique. Consequently, choosing an algorithm according to the user's requirements leads to selecting the optimum web service. This research develops recommendations of selection algorithms by a quantitative score comparison. We define the quality of algorithm (QoA), which expresses the characteristics of the algorithm, and propose a method to recommend algorithms that satisfy the user's requirements. Our method can compare and select algorithms quantitatively by merely inputting the desired characteristics into the system, reducing the costs of selecting services and improving the user's satisfaction.

关键词： Service oriented computing selection algorithm algorithm recommendation

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