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检索条件"主题词=Selection algorithm"

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Outliers' Effect Reduction of One-Class Neural Networks Classifier

Outliers' Effect Reduction of One-Class Neural Networks Clas...

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International Conference on Control, Engineering & Information Technology

作者： Bilal Hadjadji Youcef Chibani LISIC Lab. Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene (USTHB)

ISBN: (纸本)9781479982134

The One Class Auto Associative Neural Network (AANN) has been investigated for solving various problems. Nonetheless, it is sensitive to the presence of outliers in the training set, which is known problem for one-class classifiers. For this, attempts have been done via proposing the use of efficient kernel and ensemble method to reduce the effect of outliers for one class support vector machine classifier. However, for the AANN, even with ensemble method, the effect of outliers is still maintained. Thus, we propose in this paper the joint use of ensemble method with a selection algorithm to select the appropriate training samples for the AANN, which leads to better reduction of the outliers' effect and therefore improving the AANN ensemble and classification robustness. Experimental results conducted on several real-world datasets prove the effective use of the proposed approach.

关键词： One-Class Auto-Associative Neural Networks Ensemble method selection algorithm Outliers' effect reduction

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Fast algorithm for N-2 Contingency Problem

Fast Algorithm for N-2 Contingency Problem

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Hawaii International Conference on System Sciences

作者： K. S. Turitsyn P. A. Kaplunovich Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge USA

ISBN: (纸本)9781467359337

We present a novel selection algorithm for N-2 contingency analysis problem. The algorithm is based on the iterative bounding of line outage distribution factors and successive pruning of the set of contingency pair candidates. The selection procedure is non-heuristic, and is certified to identify all events that lead to thermal constraints violations in DC approximation. The complexity of the algorithm is O(N~2) comparable to the complexity of N - 1 contingency problem. We validate and test the algorithm on the Polish grid network with around 3000 lines. For this test case two iterations of the pruning procedure reduce the total number of candidate pairs by a factor of almost 1000 from 5 millions line pairs to only 6128.

关键词： algorithm Contingency Problem contingency line outage distribution factor algorithms selection algorithm

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Correlation-aware sparsity-enforcing sensor placement for spatio-temporal field estimation

Correlation-aware sparsity-enforcing sensor placement for sp...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： V. Roy G. Leus Fac. of Electr. Eng. Delft Univ. of Technol. Delft Netherlands

ISBN: (纸本)9781467369985

In this work, we propose a generalized framework for designing optimal sensor constellations for spatio-temporally correlated field estimation using wireless sensor networks. The accuracy of the field intensity estimate in every point of a given service area strongly depends upon the number and the constellation of the sensors along with the spatio-temporal statistics of the field. We formulate and solve a sparsity-enforcing optimization problem to select the best sensor locations that achieve some desired estimation performance. The sparsity-enforcing iterative selection algorithm is aware of the non-separable space-time covariance structure of the field.

关键词： Bayesian framework Wireless sensor network convex optimization field estimation sparsity Wireless sensor networks sensor placement CONSTELLATIONS Sensors service areas Convex function selection algorithm

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Measurement-based Analysis of Spectrum Sensing in Adaptive WSNs under Wi-Fi and Bluetooth Interference

Measurement-based Analysis of Spectrum Sensing in Adaptive W...

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IEEE Vehicular Technology Conference

作者： Federico Penna Claudio Pastrone Maurizio A. Spirito Roberto Garello Politecnico di Torino Diparimento di Elettronica (DELEN) Istituto Superiore Mario Boella (ISMB) TRM Lab - Pervasive Technologies Politecnico di Torino Diparimentodi Elettronica (DELEN)

ISBN: (纸本)9781424425167

As a consequence of the diffusion of wireless systems operating in the 2.4 GHz ISM band, harmful interference among heterogeneous networks is becoming a serious issue for their performance. This paper is focused on IEEE 802.15.4 WSNs undergoing the interference of co-located IEEE 802.11b/g WLANs or Bluetooth piconets. On the basis of energy measurements carried out using a standard-compliant testbed, the statistics of the interfering energy are characterized for an extensive set of traffic conditions. Then the impact of each interference pattern onto a practical WSN application is analyzed in terms of packet loss rate at the application layer. The last part of the paper introduces a channel selection algorithm based on the estimation of the information-theoretic capacity of the considered channels.

关键词： Wi-Fi Bluetooth interference pattern harmful interference Energy measurement selection algorithm CARRIER SYSTEM Heterogeneous networks Packet loss INSTITUTE OF ELECTRICAL AND ELECTRONIC ENGINEERS interference interference Personal area networks

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EMPIRICALLY-ESTIMABLE MULTI-CLASS CLASSIFICATION BOUNDS

EMPIRICALLY-ESTIMABLE MULTI-CLASS CLASSIFICATION BOUNDS

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Alan Wisler Visar Berisha Dennis Wei Karthikeyan Ramamurthy Arizona State University ECEE and SHS IBM Thomas J. Watson Research Center

ISBN: (纸本)9781479999897

In this paper, we extend previously developed non-parametric bounds on the Bayes risk in binary classification problems to multi-class problems. In comparison with the well-known Bhattacharyya bound which is typically calculated by employing parametric assumptions, the bounds proposed in this paper are directly estimable from data, provably tighter, and more robust to different types of data. We verify the tightness and validity of this bound using an illustrative synthetic example, and further demonstrate its value by incorporating it into a feature selection algorithm which we apply to the real-world problem of distinguishing between different neuro-motor disorders based on sentence-level speech data.

关键词： Bayes error rate multi-class classification divergence measures non-parametric estimator Bound Phyla TIGHTNESS Bayes data types multi-class World problems selection algorithm paper Taxonomy

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Statistical on-line signature verification using rotation-invariant dynamic descriptors

Statistical on-line signature verification using rotation-in...

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Asian Control Conference

作者： M. R. Nilchiyan R. Bte Yusof S. E. Alavi Universiti Teknologi Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Center of excellence in Telecommunication

ISBN: (纸本)9781479978632

The main challenges of online signature verification framework can be summarized in 1) accuracy and speed of preprocessing step, 2) the number of extracted features and how informative they are, and 3) complexity of decision making step owing to the less number of input samples for training. In this paper, in order to simplify the preprocessing step, a set of rotation invariant descriptors is presented. The complexity of system because of high dimensionality of signature features is reduced by exploiting the statistical moments of the wavelet coefficients. To improve it further, suggested Fisher-Metric (FM) filtering develops a feature selection algorithm. In the end, taking advantage of artificial neural network on SVC2004 database blow up the performance of the proposed approach with False Acceptance Rate (FAR) of 3% and False Rejection Rate (FRR) of 2.5% with an equal error rate (ERR) of less than 3%. The validation of the proposed framework suggests that the proposed method outperforms the state-of-the-art techniques.

关键词： signature validation Subject Headings selection algorithm Descriptors false rejection rate Artificial neural networks Complex systems False Acceptance Rate

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Rateless Code Based Opportunistic Multicasting over Cognitive Radio Networks

Rateless Code Based Opportunistic Multicasting over Cognitiv...

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IEEE Global Communications Conference

作者： Pavol Polacek Chih-Wei Huang Department of Communication Engineering National Central University

ISBN: (纸本)9781467309202

Cognitive radio (CR) represents an exciting new paradigm on spectrum utilization and potentially more bandwidth for exploding multimedia traffic. We focus on the layer encoded video multicast problem over CR and contribute 1) an opportunistic multicasting framework, based on rateless forward error correction (FEC) codes, 2) a mechanism for adaptation of data fragment size to improve transmission efficiency, 3) a joint secondary channel (SC) and video data selection algorithm. By adapting fragment size, tracking video group receiving rate, and adapting transmission parameters, we are able to realize the opportunistic multicasting advantage in a challenging CR environment. The proposed overall SC and video data selection algorithm finds the best rateless FEC code length, physical layer modulation and coding schemes (MCS), fragment size and SC combination to increase the effective throughput and heuristically reach maximum system utility, Favorable results comparing to other algorithms showcase the improved performance.

关键词： Cognitive radio Multicast multicast communication spectrum utilization efficiency transmission parameter Radio networks transmission efficiency selection algorithm video data chromium Scandium Codes FEC Regimen

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Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision

Iterative Projection and Matching: Finding Structure-preserv...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Alireza Zaeemzadeh Mohsen Joneidi Nazanin Rahnavard Mubarak Shah University of Central Florida

ISBN: (纸本)9781728132945

The goal of data selection is to capture the most structural information from a set of data. This paper presents a fast and accurate data selection method, in which the selected samples are optimized to span the subspace of all data. We propose a new selection algorithm, referred to as iterative projection and matching (IPM), with linear complexity w.r.t. the number of data, and without any parameter to be tuned. In our algorithm, at each iteration, the maximum information from the structure of the data is captured by one selected sample, and the captured information is neglected in the next iterations by projection on the null-space of previously selected samples. The computational efficiency and the selection accuracy of our proposed algorithm outperform those of the conventional methods. Furthermore, the superiority of the proposed algorithm is shown on active learning for video action recognition dataset on UCF-101;learning using representatives on Im-ageNet;training a generative adversarial network (GAN) to generate multi-view images from a single-view input on CMU Multi-PIE dataset;and video summarization on UTE Egocentric dataset.

关键词： images computer vision Dataset Legislators linear complexity selection algorithm Iterative Experiential Learning Projection algorithms UCF 101 iterative methods Subspace

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Dynamic Buffer-aided Distributed Space Time Coding Techniques for Cooperative DS-CDMA Systems

Dynamic Buffer-aided Distributed Space Time Coding Technique...

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WSA 2016;20th International ITG Workshop on Smart Antennas

作者： Jiaqi Gu Rodrigo C. de Lamare

In this work, we propose a dynamic buffer-aided distributed space-time coding (DSTC) scheme for cooperative direct-sequence code-division multiple access systems. We first devise a relay selection algorithm that can automatically select the optimum set of relays among both the source-relay phase and the relay-destination phase for DSTC transmission according to the signal-to-interference-plus-noise ratio (SINR) criterion. Multiple relays equipped with dynamic buffers are introduced in the network, which allows the relays to store data received from the sources and wait until the most appropriate time for transmission. The proposed technique improves the quality of the transmission with an acceptable delay as the buffer size is adjustable. Simulation results show that the proposed dynamic buffer-aided DSTC scheme and algorithm outperforms prior art.

关键词： Direct-sequence code-division multiple access relay space-time coding selection algorithm transmission quality CDMA mobile communications transmission

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Estimating the Uniqueness of Test Scenarios derived from Recorded Real-World-Driving-Data using Autoencoders

Estimating the Uniqueness of Test Scenarios derived from Rec...

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IEEE Intelligent Vehicles Symposium

作者： Jacob Langner Johannes Bach Lennart Ries Stefan Otten Marc Holzapfel Eric Sax FZI Research Center for Information Technology Porsche AG

ISBN: (纸本)9781538644539

Advanced Driver Assistant Systems (ADAS) use a multitude of input signals for tasks like trajectory planning and control of vehicle dynamics provided by a large variety of information sources such as sensors and digital maps. To assure the feature's valid behavior all realistically possible environmental situations have to be tested. The test scenarios used for simulation can be derived from real-world-driving-data. However, the significance of derived scenarios is weakened by repetitive similar situations within the driving data, which increase the test efforts without providing new insights regarding the test of the ADAS. In this contribution, an automated selection algorithm for test scenarios based on relevant environmental parameters is presented. Starting with a randomly selected initial testset, the machine-learning concept of autoencoders is utilized to recognize novel scenarios within the data pool, which are iteratively added to the initial testset. Furthermore, the key parameters for the autoencoder's performance are shown in depths. The approach is fully automated, so that the identified novel scenarios within an entire testset are automatically combined to a reduced testset of unique relevant scenarios. The achieved testset reduction and thereby the saving potential in simulation time is demonstrated on a dataset including several thousand test kilometers.

关键词： Information source data pool uniqueness advanced driver assistance systems (ADAS) Vehicle dynamics trajectory planning selection algorithm environmental parameters

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