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检索条件"主题词=Selection Algorithm"

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Analysis of Cluster Head selection Methods In WSN 6

Analysis of Cluster Head Selection Methods In WSN

引用

6th International Conference on Inventive Computation Technologies (ICICT)

作者： Arulmurugan, A. Waris, Saiyed Faiayaz Bhagyalakshmi, N. Vignans Fdn Sci Technol & Res Deemed Univ Dept Comp Sci & Engn Guntur 522213 Andhra Pradesh India

ISBN: (纸本)9781728185019

A wireless network is one of the emerging domains in the field of Computer Science. This domain solves the issue of the cost of laying cables in colleges and organizations. It makes help of radio waves to connect laptops, Smartphones to the Internet. One of the sub-areas of the cellular network is Wireless Sensor Network. As the name suggests wireless sensor network makes use of the sensor. These sensors are used to detect some phenomena and based on the phenomena information will be gathered. Clustering is a process by which sensor nodes in WSN are grouped based on some criteria. In general, each and every cluster will have a Cluster head that is selected based on election algorithms. This paper gives a detailed survey of the list of important CH selection algorithms that are available for selecting a group head in a group and it tries to focus more upon analyzing the drawbacks faced in the literature. This is going to be effective enough in creating a suitable cluster head selection mechanism for enhancing the lifetime of the network.

关键词： Wireless Sensor Networks Clustering Cluster head selection algorithm

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Feature selection for Leaks Detection and Characterization in Diesel air path

Feature Selection for Leaks Detection and Characterization i...

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10th IEEE International Conference on Networking, Sensing and Control (ICNSC)

作者： Benkaci, M. Hoblos, G. Langlois, N. IRSEEM Inst Rech Syst Elect Embarques F-76800 St Etienne France IRSEEM Saint Etienne Du Rouvray France

ISBN: (纸本)9781467352000;9781467351980

Feature selection is an essential step for data classification used in fault detection and diagnosis process. In this work, a new approach is proposed which combines a feature selection algorithm and neural network tool for leaks detection and characterization tasks in diesel engine air path. The Chi(2) is used as feature selection algorithm and the neural network based on Levenberg-Marquardt is used in system behavior modeling. The obtained neural network is used for leaks detection and characterization. The model is learned and validated using data generated by xMOD. This tool is used again for test. The effectiveness of proposed approach is illustrated in simulation when the system operates on a low speed/load and the considered leak affecting the air path is very small.

关键词： Feature extraction leak detection Neural network selection algorithm data classification system behavior Defect detection air

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Characteristic analysis of an access-point selection for user throughput and system optimization based on user cooperative moving

Characteristic analysis of an access-point selection for use...

引用

IEEE Consumer Communications and Networking Conference (CCNC)

作者： Miyata, Sumiko Murase, Tutomu Yamaoka, Katsunori Tokyo Inst Technol Meguro Ku S3-682-12-1 Ookayama Tokyo 1528552 Japan Syst Platforms Res Labs NEC Meguro Ku Kawasaki 1528552 Japan

ISBN: (纸本)9781457720710

We propose an optimal access-point (AP) selection algorithm for maximizing system throughput while preserving newly arrived-user throughput. In our algorithm, newly arrived users cooperate with a WLAN system they are trying to use, i.e., they are willing to move toward an appropriate AP. We discuss the effectiveness of our proposed AP selection algorithm by using numerical analysis. We also clarify and analyze the characteristics of system throughput.

关键词： system optimization Throughput selection algorithm AP Numerical Analysis

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IMU-Assisted Nearest Neighbor selection for Real-Time WiFi Fingerprinting Positioning 5

IMU-Assisted Nearest Neighbor Selection for Real-Time WiFi F...

引用

International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Jin, Myungjun Koo, Bonhyun Lee, Sangwoo Park, Chansik Lee, Min Joon Kim, Sunwoo Hanyang Univ Dept Elect & Comp Engn Seoul South Korea Chungbuk Natl Univ Sch Elect Engn Cheongju South Korea Agcy Def Dev Daejeon South Korea

ISBN: (纸本)9781467380546

This paper presents a nearest neighbor selection algorithm for real-time WiFi fingerprinting positioning with the assist of inertial measurement unit (IMU) measurements. The WiFi fingerprinting positioning using received signal strength (RSS) measurements suffers from the RSS variation problem. Due to this problem, reference points that are irrelevant to the user's position are selected, and the positioning accuracy decreases. To overcome the RSS variation problem, we propose an IMU-assisted nearest neighbor selection algorithm that filters out irrelevant reference points based on the position prediction with IMU measurements. The proposed algorithm was evaluated and compared with the conventional K-nearest neighbors (KNN) selection and the IMU-based dead-reckoning positioning in a real indoor environment. The experimental results showed that the average positioning error of the proposed algorithm was 2.41 m, whereas those of the KNN-based fingerprinting algorithm and the IMU-based dead-reckoning positioning were 3.57 m and 15.27 m.

关键词： reference station Patient Positioning Wi-Fi Fingerprinting positioning accuracy Nearest neighbor RSS gene Really Simple Syndication REACTOR SAFETY STUDY Dead reckoning selection algorithm

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Model selection-Based Blind Adaptive Channel Estimation algorithms for Parsimonious Receivers in CDMA Systems

Model Selection-Based Blind Adaptive Channel Estimation Algo...

引用

International Conference on Intelligent Sensors, Sensor Networks and Information Processing

作者： EL-Sallam, Amar A. Univ Western Australia Sch Elect Elect & Comp Engn Nedlands WA 6009 Australia

ISBN: (纸本)9781424438228

We consider a blind channel estimation and a model selection algorithms for the estimation and identification of significant channel parameters in CDMA systems. In conventional estimation algorithms, channel parameters are usually estimated using a channel length that is related to the maximum delay spread of the channel. However, this can result in estimating too many parameters where many of them are either zero or insignificant. In this work we propose algorithms that estimate then identify only significant channel parameters, leading to parsimonious receivers. First, we use users' spreading codes and the received signal to form a data model for the system. Then, a blind adaptive channel estimation algorithm is derived to estimate the channel response. This is followed by a model selection algorithm to identify significant channel parameters among the estimated ones. Compared with conventional algorithms, our algorithm: (i) estimates the channel response with a faster convergence rate (ii) has lower estimation errors (iii) identifies significant parameters with high probabilities and at low SNRs (iv) has a lower BER performance.

关键词： channel response channel estimation selection algorithm algorithms data models CDMA mobile communications Receivers channel blind estimation parameters bit error rate performance received signal model selection

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Pixel-Pair Features selection for Vehicle Tracking

Pixel-Pair Features Selection for Vehicle Tracking

引用

2nd IAPR Asian Conference on Pattern Recognition (ACPR)

作者： Zhang, Zhibin Li, Xuezhen Kurita, Takio Tanaka, Shinya Hiroshima Univ Grad Sch Engn Higashihiroshima Hiroshima 7398521 Japan Nissan Motor Co Ltd Kanagawa 2430123 Japan

ISBN: (纸本)9781479921904

This paper proposes a novel tracking algorithm to cope with the appearance variations of vehicle in the natural environments. The algorithm utilizes the discriminative features named pixel-pair features for estimating the similarity between the template image and candidate matching images. Pixel-pair features have been proved to be robust for illumination changes and partial occlusions of the training object. This paper improves the original feature selection algorithm to increase the tracking performance in other appearance changes (such as shape deformation, drifting and view angle change). The new feature selection algorithm incrementally selects the discriminative pixel-pair feature whose matching error between the target and the background is lower than a given threshold. Also the roulette selection method based on the edge values is utilized to increase the possibility to select more informative feature points. The selected features therefore are considered to be robust for shape deformation and view angle changes. Compared with the original feature selection algorithm, our algorithm shows excellent robustness in a variety of videos which include illumination changes, shape deformation, drifting and partial occlusion.

关键词： Feature extraction look angles (electronics) selection algorithm Vehicle tracking visual tracking algorithms Videotapes Occlusion

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Stall Model Identification of a Cessna Citation II from Flight Test Data Using Orthogonal Model Structure selection

Stall Model Identification of a Cessna Citation II from Flig...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： van Ingen, Joost B. de Visser, Coen C. Pool, Daan M. Delft Univ Technol Fac Aerosp Engn Control & Simulat Sect POB 5058 NL-2600 GB Delft Netherlands

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Since 2019, a key element of simulator-based training of airline pilots is stall training. A major and still largely open research question is which level of model fidelity is required for effective training. As part of an effort to answer this question, a model of the quasi-steady stall dynamics of a Cessna Citation II aircraft is identified from flight test data that was specifically collected for this research at an altitude of 5,500 m. To ensure any reductions in elevator and aileron effectiveness during stall were also explicitly measured, the test pilots used additional quasi-random flight test inputs. The considered stall model structure is based on Kirchoff's theory of flowseparation. During identification, the nonlinear and linear parameters of the model are estimated in separate, recursively executed, steps. This separation enables the application of a semi-objective model structure selection method using multivariate orthogonal functions for the aerodynamic coefficients included in the model. This approach shows that stall-related effects should be included in the model equations for lift, drag, and pitch moment. Overall, it is found that the model parameters were consistently estimated from the flight test data and that the model accurately describes the aircraft's stall dynamics in the considered flight condition. The developed methodology is concluded to be well-suited for the direct identification of stall models from flight test data.

关键词： Cessna Citation II Flight Test Instrumentation Control Surfaces Aerodynamic Force Coefficients Flow Separation Test Pilots Lift Coefficient Flight Data selection algorithm Flight Envelope

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Sparse Representation Preserving for Unsupervised Feature selection 22

Sparse Representation Preserving for Unsupervised Feature Se...

引用

22nd International Conference on Pattern Recognition (ICPR)

作者： Yan, Hui Jin, Zhong Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781479952083

Recent research has demonstrated that sparse coding (or sparse representation) is a powerful tool for pattern classification. This paper presents a new unsupervised feature selection method, termed Sparse Representation Preserving Feature selection (SRPFS), which aims at minimizing reconstruction residual based on sparse representation in the subspace of the selected features. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed combinatorial optimization formulation. In particular, the latter algorithm incorporates both l(2,1)-norm and l(1)-norm minimization within unsupervised feature selection framework. The experimental results on four real-world datasets demonstrate the improvements brought by our proposed SRPFS with joint selection algorithm.

关键词： Face Feature extraction Joints Laplace equations Linear programming Training Vectors Feature extraction Laplace's equation sparse representation Joints Joints linear programming Face Face selection algorithm Functional training greedy algorithm Sparse coding Pattern recognition Training Cloning Vectors

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A Heuristic QoS-Aware Service selection Approach to Web Service Composition

A Heuristic QoS-Aware Service Selection Approach to Web Serv...

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8th IEEE/ACIS International Conference on Computer and Information Science

作者： Liu, Dongmei Shao, Zhiqing Yu, Caizhu Fan, Guisheng E China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

ISBN: (纸本)9780769536415

Web service composition is a flexible way for resource sharing and application integration. However, how to guarantee quality of service composition remains a challenging problem. This paper proposes a heuristic approach to optimizing service selection for composition so that the user's QoS expectation is satisfied. Optimization is enforced for constraints in both local and global level. For each level, a mathematical model is established, and the corresponding heuristic algorithm to solve Web service selection problem by using convex hull frontiers method is proposed. Furthermore, Multiple Criteria Decision Making (MCDM) technique is applied to merge multiple dimensional resources in the heuristic algorithms. Finally, a case study demonstrates feasibility and efficiency of our method.

关键词： Web service QoS selection algorithm convex hull

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Segment pre-selection in decision-tree based speech synthesis systems 25

Segment pre-selection in decision-tree based speech synthesi...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Donovan, RE IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA

ISBN: (纸本)0780362934

Corpus based approaches to unit selection for concatenative speech synthesis have become popular in recent years due to their improved sensitivity to unit context over their more simple predecessors. These systems usually make use of large speech databases and employ sophisticated search algorithms to determine the optimal unit sequence to use to synthesise each sentence. For many applications it is not possible to have the entire database, which may be as large as several hundred megabytes. available to the synthesiser at runtime. What is required is some form of off-line pre-selection algorithm to determine which subset of the database enables the highest quality speech synthesis to be performed for a given runtime system size. This paper describes a pre-selection algorithm developed at IBM for use with decision-tree-based concatenative speech synthesisers.

关键词： Speech synthesis Primary election Decision Trees Runtime selection algorithm Database SEARCHING algorithm

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