• Title/Summary/Keyword: K-means++ algorithm

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Dynamic Optimization Algorithm of Constrained Motion

  • Eun, Hee-Chang;Yang, Keun-Heok;Chung, Heon-Soo
    • Journal of Mechanical Science and Technology
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    • v.16 no.8
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    • pp.1072-1078
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    • 2002
  • The constrained motion requires the determination of constraint force acting on unconstrained systems for satisfying given constraints. Most of the methods to decide the force depend on numerical approaches such that the Lagrange multiplier method, and the other methods need vector analysis or complicated intermediate process. In 1992, Udwadia and Kalaba presented the generalized inverse method to describe the constrained motion as well as to calculate the constraint force. The generalized inverse method has the advantages which do not require any linearization process for the control of nonlinear systems and can explicitly describe the motion of holonomically and/or nongolonomically constrained systems. In this paper, an explicit equation to describe the constrained motion is derived by minimizing the performance index, which is a function of constraint force vector, with respect to the constraint force. At this time, it is shown that the positive-definite weighting matrix in the performance index must be the inverse of mass matrix on the basis of the Gauss's principle and the derived differential equation coincides with the generalized inverse method. The effectiveness of this method is illustrated by means of two numerical applications.

A Study on Active and Reactive Power Control for Efficient Operations of Wind Farm (유.무효 전력 제어를 통한 풍력발전단지의 효율적인 운전)

  • Jang, Sung-Il;Kim, Ji-Won;Kim, Kwang-Ho
    • Proceedings of the KIEE Conference
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    • 2002.07b
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    • pp.1351-1354
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    • 2002
  • Wind farm which are composed with wind turbine generators can be a good alternatives to solve environmental problem and solutions to cope with energy crisis for using wind energy. Until now, these wind turbine generators have been being studied on the viewpoint of only active power control for reducing the burden of main grid. In this control scheme, we might demand a reactive power compensator in order to make reparation for the reactive power produced from wind turbine generator itself. Therefore, if the reactive power as well as active power of wind turbine generator were controlled according to the demand of reactive power, the installation of a additional reactive power compensator could be reduced. This paper presents the control method of a active and reactive power for wind turbine generators by means of SVPWM(Space Vector Pulse Width Modulation) inverting method and describes a operational coordination of wind turbine generators. The proposed power control algorithm can simply produce the output power of wind turbine generator needed in wind farm, which can reduce the power of main grid more and exclude a supplementary reactive power compensator. We assumed that wind farm are composed with two kinds of wind turbine generators, AC/DC/AC and induction generator types.

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A Study on Three Phase Partial Discharge Pattern Classification with the Aid of Optimized Polynomial Radial Basis Function Neural Networks (최적화된 pRBF 뉴럴 네트워크에 이용한 삼상 부분방전 패턴분류에 관한 연구)

  • Oh, Sung-Kwun;Kim, Hyun-Ki;Kim, Jung-Tae
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.4
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    • pp.544-553
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    • 2013
  • In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

Optimal Configuration Algorithm for ESS with Renewable Energy Resources Considering Peak-shaving Effects (신재생 에너지가 도입된 전력저장장치의 첨두부하절감 효과를 고려한 최적 구성 알고리즘)

  • Lee, Na-Eun;Kim, Wook-Won;Kim, Jin-O
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.63 no.9
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    • pp.1199-1205
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    • 2014
  • A power system configuration has been increasingly advanced with a number of generating units. In particular, renewable energy resources are widely introduced due to the environmental issues. When applying the renewable energy sources with the ESS (Energy Storage System), the ESS is the role of a potential generating resource in the power system while mitigating the output volatility of renewable energy resources. Thus, for applications of the ESS, the surrounding environment of it should be considered, which means that capacity and energy of the ESS can be affected. Moreover, operation strategy of the ESS should be proposed according to the installation purpose as well as the surrounding environment. In the paper, operation strategy of the ESS is proposed considering load demand and the output of renewable energy resources on a hourly basis. Then, the cost of electrical energy is minimized based on the economic model that consists of capital cost, operation cost, fuel cost, and grid cost for a year. It is sure that peak-shaving effects can be achieved while satisfying the minimum cost of electrical energy.

Monitoring of wind turbine blades for flutter instability

  • Chen, Bei;Hua, Xu G.;Zhang, Zi L.;Basu, Biswajit;Nielsen, Soren R.K.
    • Structural Monitoring and Maintenance
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    • v.4 no.2
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    • pp.115-131
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    • 2017
  • Classical flutter of wind turbine blades indicates a type of aeroelastic instability with fully attached boundary layer where a torsional blade mode couples to a flapwise bending mode, resulting in a mutual rapid growth of the amplitudes. In this paper the monitoring problem of onset of flutter is investigated from a detection point of view. The criterion is stated in terms of the exceeding of a defined envelope process of a specific maximum torsional vibration threshold. At a certain instant of time, a limited part of the previously measured torsional vibration signal at the tip of blade is decomposed through the Empirical Mode Decomposition (EMD) method, and the 1st Intrinsic Mode Function (IMF) is assumed to represent the response in the flutter mode. Next, an envelope time series of the indicated modal response is obtained in terms of a Hilbert transform. Finally, a flutter onset criterion is proposed, based on the indicated envelope process. The proposed online flutter monitoring method provided a practical and direct way to detect onset of flutter during operation. The algorithm has been illustrated by a 907-DOFs aeroelastic model for wind turbines, where the tower and the drive train is modelled by 7 DOFs, and each blade by means of 50 3-D Bernoulli-Euler beam elements.

Superpixel-based Vehicle Detection using Plane Normal Vector in Dispar ity Space

  • Seo, Jeonghyun;Sohn, Kwanghoon
    • Journal of Korea Multimedia Society
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    • v.19 no.6
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    • pp.1003-1013
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    • 2016
  • This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

DeepCleanNet: Training Deep Convolutional Neural Network with Extremely Noisy Labels

  • Olimov, Bekhzod;Kim, Jeonghong
    • Journal of Korea Multimedia Society
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    • v.23 no.11
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    • pp.1349-1360
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    • 2020
  • In recent years, Convolutional Neural Networks (CNNs) have been successfully implemented in different tasks of computer vision. Since CNN models are the representatives of supervised learning algorithms, they demand large amount of data in order to train the classifiers. Thus, obtaining data with correct labels is imperative to attain the state-of-the-art performance of the CNN models. However, labelling datasets is quite tedious and expensive process, therefore real-life datasets often exhibit incorrect labels. Although the issue of poorly labelled datasets has been studied before, we have noticed that the methods are very complex and hard to reproduce. Therefore, in this research work, we propose Deep CleanNet - a considerably simple system that achieves competitive results when compared to the existing methods. We use K-means clustering algorithm for selecting data with correct labels and train the new dataset using a deep CNN model. The technique achieves competitive results in both training and validation stages. We conducted experiments using MNIST database of handwritten digits with 50% corrupted labels and achieved up to 10 and 20% increase in training and validation sets accuracy scores, respectively.

Application of artificial neural networks to a double receding contact problem with a rigid stamp

  • Cakiroglu, Erdogan;Comez, Isa;Erdol, Ragip
    • Structural Engineering and Mechanics
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    • v.21 no.2
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    • pp.205-220
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    • 2005
  • This paper presents the possibilities of adapting artificial neural networks (ANNs) to predict the dimensionless parameters related to the maximum contact pressures of an elasticity problem. The plane symmetric double receding contact problem for a rigid stamp and two elastic strips having different elastic constants and heights is considered. The external load is applied to the upper elastic strip by means of a rigid stamp and the lower elastic strip is bonded to a rigid support. The problem is solved under the assumptions that the contact between two elastic strips also between the rigid stamp and the upper elastic strip are frictionless, the effect of gravity force is neglected and only compressive normal tractions can be transmitted through the interfaces. A three layered ANN with backpropagation (BP) algorithm is utilized for prediction of the dimensionless parameters related to the maximum contact pressures. Training and testing patterns are formed by using the theory of elasticity with integral transformation technique. ANN predictions and theoretical solutions are compared and seen that ANN predictions are quite close to the theoretical solutions. It is demonstrated that ANNs is a suitable numerical tool and if properly used, can reduce time consumed.

A Study on the Linear Time-Varying System of MRAC (선형시변 시스템 기준 모델 적응제어에 관한 고찰)

  • Koo, Tak-Mo;Shin, Jang-Kyoo;Kim, Che-Young
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.21 no.4
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    • pp.78-83
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    • 1984
  • A method is proposed for the adaptive control of linear time varying discrete systems. The stability problem of the model reference adaptive control systems is considered by means of the properties of hypergtability, The hyperstability approach also allows for solutions to the adaptation mechanism. Employing the principles of the continuous time case with output feedback renders it to the discrete case which simplified the system design. The system response is obtained by applying the ramp and step input as a reference signal to the system respectively. With checking the adaptation time for ramp and step input the validity of proposed algorithm was confirmed by the computer simulation.

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A Study on the Rule Development for BIM-based Automatic Checking in a Duct System (덕트설비의 BIM 기반 자동검토를 위한 규칙개발에 관한 연구)

  • Song, Jong-Kwan;Cho, Geun-Ha;Ju, Ki-Beom
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.25 no.11
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    • pp.631-639
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    • 2013
  • This study derives quality checking items in Building Mechanical Systems Design Criteria, and suggests quality criteria to review BIM models in the duct system of an air conditioning system for rule-based automatic checking. First, components for the duct system of an air conditioning system were reviewed, and the quality checking items were drawn from Building Mechanical Systems Design Criteria, through assessment according to object, attribute and relationship composing the BIM model. Second, quality checking types were derived, by analyzing the quality checking items and Rule set of the Solibri Model Checker. Finally, methods of algorithm functioning for checking the BIM models for mechanical systems in computers were drawn, and Elements to comprise the quality checking criteria (rule) were suggested. This study means that that checking items are derived from domestic criteria, and a way for the development process of determining quality checking criteria (rules) is suggested.