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检索条件"主题词=detection algorithms"

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Power System Transient Instability detection Algorithm Based on Real Time Measurement

Power System Transient Instability Detection Algorithm Based...

引用

IEEE Asia-Pacific Conference on Circuits and Systems

作者： Bao-Hui Zhang Huan Xie Guang-liang Yu Peng Li Z.Q. Bo A. Klimek School of Electrical Engineering Xi'an Jiaotong University Xi'an China Automation & Information Systems AREVA T and D Inc. Stanford USA

In this paper, a method is proposed for fast and accurate online detection of transient instability based on real-time measurement. The cluster of critical machines is determined and refreshed at each time. Determination of the irrevocable loss of synchronism is made based on the geometric characteristic of critical machines trajectory. Unlike the previous extended equal-area criterion method, it is unnecessary to calculate the unstable equilibrium point. The speed of algorithm is fast enough to prevent system from transient instability. Simulations are carried out on the IEEE 50 generator test system and a real power system. The results indicate that the proposed method has the features of reliability and effectiveness in identifying the loss of synchronism with inter-area mode.

关键词： Power system transients detection algorithms Real time systems Power measurement Power system stability Power system reliability Open loop systems Communication system control Control systems Electric variables measurement

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Simple Iterative Near-ML detection for D-STTD Systems

Simple Iterative Near-ML Detection for D-STTD Systems

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Namjeong Lee Sujin Kim Jongsub Cha Joonhyuk Kang Youngok Kim School of Engineering Information and Communications University South Korea Department of Electronic Engineering Kwangwoon University South Korea

In this paper, a computationally efficient iterative detection algorithm (IDA) for D-STTD systems is presented. To find an optimal sequence by iterative searching, the proposed scheme consists of two stages. Firstly, the IDA establishes the candidate sequences. Then, it iteratively finds an optimal sequence by examining the candidate sequences until the early termination condition is satisfied. The adaptation of the number of candidate sequences makes the proposed IDA more efficient in computational complexity. The proposed detector is shown to achieve the near-ML performance with significantly reduced complexity.

关键词： Transmitting antennas detection algorithms Detectors Diversity methods MIMO Interference cancellation Bit error rate Matrix decomposition Iterative algorithms Electronic mail

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Robust detection of semantically equivalent visually dissimilar objects

Robust detection of semantically equivalent visually dissimi...

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

作者： Taeil Goh Ryan West Kazunori Okada San Francisco State University San Francisco CA USA

We propose a novel and robust detection of semantically equivalent but visually dissimilar object parts with the presence of geometric domain variations. The presented algorithms follow a part-based object learning and recognition framework proposed by Epshtein and Ullman. This approach characterizes the location of a visually dissimilar object (i.e., root fragment) as a function of its relative geometrical configuration to a set of local context patches (i.e., context fragments). This work extends the original detection algorithm for handling more realistic geometric domain variation by using robust candidate generation, exploiting geometric invariances of a pair of similar polygons, as well as SIFT-based context descriptors. An entropic feature selection is also integrated in order to improve its performance. Furthermore, robust voting in a maximum density framework is realized by variable bandwidth mean shift, allowing better root detection performance with the presence of significant errors in detecting corresponding context fragments. We evaluate the proposed solution for the task of detecting various facial parts using FERET database. Our experimental results demonstrate the advantage of our solution by indicating significant improvement of detection performance and robustness over the original system.

关键词： Robustness Object detection Face detection Voting Bandwidth Gaussian processes detection algorithms Image databases Visual databases Mouth

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Parallelization of the Scale-Invariant Keypoint detection Algorithm for Cell Broadband Engine Architecture

Parallelization of the Scale-Invariant Keypoint Detection Al...

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Bomjun Kwon Taiho Choi Heejin Chung Geonho Kim Software Laboratories Samsung Electronics Company Limited Suwon Gyeonggi South Korea

This paper describes design and implementation of a parallel algorithm that detects scale-invariant keypoints from digital images. Keypoint detection is one of the most important operations in image processing since efficient image matching is possible with it. Image matching is used in many intelligent image processing service, including object/scene recognition, stereo correspondence, motion tracking, and panorama imaging. In this paper, we design a new parallel algorithm of keypoint detection that is suitable for Cell Broadband Engine architecture by converting each subprocess of keypoint detection algorithm into parallel version. The experimental results show that performance of our algorithm increases in proportion to the number of processors utilized, that is, it achieves linear scalability.

关键词： detection algorithms Engines Algorithm design and analysis Parallel algorithms Image processing Image matching Digital images Layout Image recognition Tracking

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New lane detection algorithm for autonomous vehicles using computer vision

New lane detection algorithm for autonomous vehicles using c...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Quoc-Bao Truong Byung-Ryong Lee Department of Mechanical and Automotive Engineering University of Ulsan Ulsan South Korea School of Mechanical and Automotive Engineering University of Ulsan Ulsan South Korea

For navigation tasks it is necessary to determine position of the ego vehicle relative to the road. One of the principal approaches is to detect road boundaries and lanes using a vision system in the vehicle. This paper presents a simple and robust method designed to detect and estimate the curvature of road lane boundaries from images provided by a monocular camera. First, we use vector-lane-concept and non-uniform B-spline (NUBS) interpolation method to construct the boundaries road lane. Based on lane detection result, we estimate the curvature of left and right lane boundaries for autonomous guided vehicle systems application. Some experimental results based on real world road images are presented. These simulation results show the efficiency, feasibility and robustness of the algorithm.

关键词： detection algorithms Remotely operated vehicles Mobile robots Computer vision Road vehicles Vehicle detection Robustness Navigation Machine vision Design methodology

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Detecting Linear Features in Weld Seam Images Based on Beamlet Transform

Detecting Linear Features in Weld Seam Images Based on Beaml...

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9th International Conference on Signal Processing

作者： Deng Shuangcheng Jiang Lipei Xue Long Jiao Xiangdong Beijing Inst Petrochem Technol Optomechatron Equipment Technol Beijing Area Majo Beijing 102617 Peoples R China

ISBN: (纸本)9781424421787

Weld seam images are replete with noise and ordinary line detection algorithms such as Hough Transform is not an ideal approach to extract significant linear features from it. We present a novel line detection algorithm based on beamlet analysis. The algorithm is described in detail after introducing the beamlet dictionary and beamlet transform. Taking into account of some special characteristics of welding image processing, we add an orientation-thresholding step to the standard beamlet-based line detection algorithm. Experiments show that our line detection algorithm is powerful in anti-noising and is especially suitable to detect linear features in heavily noisy weld seam images. We directly extracted one significant linear feature of the weld seam from both the original experimental image and the extra-noised images (SNR >= 6db) exactly and efficiently at scale 0 with the help of orientation threshold. Neither pre-processing nor post-processing of the image was performed This implies that our algorithm can dramatically improve the efficiency of weld seam image processing.

关键词： Computer vision Welding Image processing Image edge detection detection algorithms Image recognition Detectors Laboratories Petrochemicals Feature extraction

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Low-complexity detection and performance in multi-gigabit high spectral efficiency wireless systems

Low-complexity detection and performance in multi-gigabit hi...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Saif K. Mohammed A. Chockalingam B. Sundar Rajan Department of ECE Indian Institute of Science Bangalore India

Recently, we reported a low-complexity likelihood ascent search (LAS) detection algorithm for large MIMO systems with several tens of antennas that can achieve high spectral efficiencies of the order of tens to hundreds of bps/Hz. Through simulations, we showed that this algorithm achieves increasingly near SISO AWGN performance for increasing number of antennas in i.i.d. Rayleigh fading. However, no bit error performance analysis of the algorithm was reported. In this paper, we extend our work on this low-complexity large MIMO detector in two directions: i)We report an asymptotic bit error probability analysis of the LAS algorithm in the large system limit, where N t , N r rarr infin keeping N t = N r , where N t and N r are the number of transmit and receive antennas, respectively. Specifically, we prove that the error performance of the LAS detector for V-BLAST with 4-QAM in i.i.d. Rayleigh fading converges to that of the maximum-likelihood (ML) detector as N t ,N r rarr infin keeping N t = N r . ii)We present simulated BER and nearness to capacity results for V-BLAST as well as high-rate non-orthogonal STBC from Division Algebras (DA), in a more realistic spatially correlated MIMO channel model. Our simulation results show that a) at an uncoded BER of 10 -3 , the performance of the LAS detector in decoding 16 times16 STBC from DA with N t = N r =16 and 16-QAM degrades in spatially correlated fading by about 7 dB compared to that in i.i.d. fading, and b) with a rate-3/4 outer turbo code and 48 bps/Hz spectral efficiency, the performance degrades by about 6 dB at a coded BER of 10 -4 . Our results further show that providing asymmetry in number of antennas such that N r > N t keeping the total receiver array length same as that for N r = N t , the detector is able to pick up the extra receive diversity thereby significantly improving the BER performance.

关键词： Detectors Bit error rate MIMO Rayleigh channels Receiving antennas Degradation Fading detection algorithms AWGN Performance analysis

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Reduced Complexity ML detection for Differential Unitary Space-Time Modulation with Carrier Frequency Offset

Reduced Complexity ML Detection for Differential Unitary Spa...

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IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

作者： Feifei Gao A. Nallanathan Chintha Tellambura Institute for Infocomm Research Singapore Division of Engineering King''s College London UK Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

Recently, a maximum likelihood (ML) detection rule for differential unitary space time modulation (DUSTM) under the existence of unknown carrier frequency offset (CFO) has been derived. However, the ML detection is based on the exhaustive search over all the unitary group codes. In this paper, we design an efficient detection algorithm for newly derived ML rule, by modifying the bound intersection detector (BID). Our proposed algorithm is seen as a generalization of the existing BID that is known to be an optimal detector for the conventional DUSTM. The simulation results show that the proposed algorithm can save a large portion of the computational complexity compared to the naive searching method.

关键词： Frequency modulation Detectors Transmitting antennas Maximum likelihood detection Frequency conversion Educational institutions Maximum likelihood estimation Algorithm design and analysis detection algorithms Computational modeling

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Efficient Clustering-Based Outlier detection Algorithm for Dynamic Data Stream

Efficient Clustering-Based Outlier Detection Algorithm for D...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Manzoor Elahi Kun Li Wasif Nisar Xinjie Lv Hongan Wang Intelligence Engineering Laboratory Institute of Software of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing China COMSATS institute of information Technology Islamabad Pakistan

Anomaly detection is currently an important and active research problem in many fields and involved in numerous applications. Most of the existing methods are based on distance measure. But in case of data stream these methods are not very efficient as computational point of view. Most of the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. In this paper we introduced a clustering based approach, which divide the stream in chunks and cluster each chunk using k-mean in fixed number of clusters. Instead of keeping only the summary information, which often used in case of clustering data stream, we keep the candidate outliers and mean value of every cluster for the next fixed number of steam chunks, to make sure that the detected candidate outliers are the real outliers. By employing the mean value of the clusters of previous chunk with mean values of the current chunk of stream, we decide better outlierness for data stream objects. Several experiments on different dataset confirm that our technique can find better outliers with low computational cost than the other exiting distance based approaches of outlier detection in data stream.

关键词： detection algorithms Data mining Databases Information technology Nearest neighbor searches Fuzzy systems Data engineering Knowledge engineering Application software Computational efficiency

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A Novel Window-Based Corner detection Algorithm for Gray-Scale Images

A Novel Window-Based Corner Detection Algorithm for Gray-Sca...

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Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP)

作者： Ambar Dutta Avijit Kar B.N. Chatterji Department of CSE Birla Institute of Technology and Science Kolkata India Department of CSE Jadavpur University Kolkata India Department of ECE B. P. Poddar Institute of Management and Technology Kolkata India

In computer vision, the corners of an object play an important role in shape representation and analysis. In this paper, we describe a new approach to corner detection in a digital image based on the assumption that corners are image points with high information content, and hence corners in an image exist in the regions having considerably high intensity variations. Consequently, a complex corner response function (CRF) is computed only within the regions with considerable high intensity variation instead of entire image reducing the computational cost of the whole procedure. Experiments are conducted with the help of a few images showed the efficiency of the technique, both in terms of execution time and false positive corners.

关键词： detection algorithms Gray-scale Detectors Computer vision Image edge detection Shape Image segmentation Performance evaluation Pixel Image analysis

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