• Title/Summary/Keyword: Modified k-means algorithm

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Suppression of the Thrust Loss for the Maximum Thrust Operation in the Electric Propulsion Ship (전기추진선의 최대추력 운항을 위한 추력손실 억제에 관한 연구)

  • Kim, So-Yeon;Youn, Young-Doo;Sul, Seung-Ki
    • Journal of the Society of Naval Architects of Korea
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    • v.44 no.3 s.153
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    • pp.238-247
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    • 2007
  • This paper presents the thrust loss suppression algorithm in the electric propulsion ship. The thrust loss due to cavitation can be regarded as the disturbance torque. The disturbance torque is estimated by means of the disturbance observer. Considering the estimated disturbance torque, the speed reference of the propeller is modified to suppress the thrust loss. The experimental results which performed in the cavitation tunnel with electric machine operating system and its controller will be shown to verify the proposed algorithm.

Membership Function-based Classification Algorithms for Stability improvements of BCI Systems

  • Yeom, Hong-Gi;Sim, Kwee-Bo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.10 no.1
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    • pp.59-64
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    • 2010
  • To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

A Study on Pitch Period Detection Algorithm Based on Rotation Transform of AMDF and Threshold

  • Seo, Hyun-Soo;Kim, Nam-Ho
    • Journal of the Institute of Convergence Signal Processing
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    • v.7 no.4
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    • pp.178-183
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    • 2006
  • As a lot of researches on the speech signal processing are performed due to the recent rapid development of the information-communication technology. the pitch period is used as an important element to various speech signal application fields such as the speech recognition. speaker identification. speech analysis. or speech synthesis. A variety of algorithms for the time and the frequency domains related with such pitch period detection have been suggested. One of the pitch detection algorithms for the time domain. AMDF (average magnitude difference function) uses distance between two valley points as the calculated pitch period. However, it has a problem that the algorithm becomes complex in selecting the valley points for the pitch period detection. Therefore, in this paper we proposed the modified AMDF(M-AMDF) algorithm which recognizes the entire minimum valley points as the pitch period of the speech signal by using the rotation transform of AMDF. In addition, a threshold is set to the beginning portion of speech so that it can be used as the selection criteria for the pitch period. Moreover the proposed algorithm is compared with the conventional ones by means of the simulation, and presents better properties than others.

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Fuzzy Identification by Means of an Auto-Tuning Algorithm and a Weighted Performance Index

  • Oh, Sung-Kwun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.8 no.6
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    • pp.106-118
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    • 1998
  • The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

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A Robust Bayesian Probabilistic Matrix Factorization Model for Collaborative Filtering Recommender Systems Based on User Anomaly Rating Behavior Detection

  • Yu, Hongtao;Sun, Lijun;Zhang, Fuzhi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.9
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    • pp.4684-4705
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    • 2019
  • Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

A Study for BMS Operation Algorithm of Electric Vehicles (전기자동차용 전지관리장치의 전지잔존량 연산알고리즘에 관한 연구)

  • Lee J.Moon;Choi Uk-Don;Lee Jong-Phil;Lee Jong-Chan
    • Proceedings of the KIPE Conference
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    • 2001.07a
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    • pp.114-117
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    • 2001
  • In the Electric Vehicle(EV) driving system, the Battery Management System(BMS) is very important and an essential equipment. Particularly, BMS monitors the State of Charge(SOC), voltage, current, and temperature of the battery modules when Electric Vehicle is in the state of motoring or charging. Major roles of BMS are like these the first, estimation of State of Charge(SOC), the second, detection of the unbalance of the voltage between battery modules, the third, control of the available limit of the voltage and temperature of batteries by monitoring the batteries status during motoring or charging. In this research, We have focused on estimating SOC of battery according to the status of Electric Vehicle and the BMS operation algorithm. The result for algorithm of SOC estimation is presented. It have been modified, compensated, and verified by means of the experiment.

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Design of Fingerprints Identification Based on RBFNN Using Image Processing Techniques (영상처리 기법을 통한 RBFNN 패턴 분류기 기반 개선된 지문인식 시스템 설계)

  • Bae, Jong-Soo;Oh, Sung-Kwun;Kim, Hyun-Ki
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.6
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    • pp.1060-1069
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    • 2016
  • In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

A Shortest Path Routing Algorithm using a Modified Hopfield Neural Network (수정된 홉필드 신경망을 이용한 최단 경로 라우팅 알고리즘)

  • Ahn, Chang-Wook;Ramakrishna, R.S.;Choi, In-Chan;Kang, Chung-Gu
    • Journal of KIISE:Information Networking
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    • v.29 no.4
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    • pp.386-396
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    • 2002
  • This paper presents a neural network-based near-optimal routing algorithm. It employs a modified Hopfield Neural Network (MHNN) as a means to solve the shortest path problem. It uses every piece of information that is available at the peripheral neurons in addition to the highly correlated information that is available at the local neuron. Consequently, every neuron converges speedily and optimally to a stable state. The convergence is faster than what is usually found in algorithms that employ conventional Hopfield neural networks. Computer simulations support the indicated claims. The results are relatively independent of network topology for almost all source-destination pairs, which nay be useful for implementing the routing algorithms appropriate to multi -hop packet radio networks with time-varying network topology.

Characteristics of Gas Furnace Process by Means of Partition of Input Spaces in Trapezoid-type Function (사다리꼴형 함수의 입력 공간분할에 의한 가스로공정의 특성분석)

  • Lee, Dong-Yoon
    • Journal of Digital Convergence
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    • v.12 no.4
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    • pp.277-283
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    • 2014
  • Fuzzy modeling is generally using the given data and the fuzzy rules are established by the input variables and the space division by selecting the input variable and dividing the input space for each input variables. The premise part of the fuzzy rule is presented by selection of the input variables, the number of space division and membership functions and in this paper the consequent part of the fuzzy rule is identified by polynomial functions in the form of linear inference and modified quadratic. Parameter identification in the premise part devides input space Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. In this paper, membership function of the premise part is dividing input space by using trapezoid-type membership function and by using gas furnace process which is widely used in nonlinear process we evaluate the performance.

Quantitative impact response analysis of reinforced concrete beam using the Smoothed Particle Hydrodynamics (SPH) method

  • Mokhatar, S.N.;Sonoda, Y.;Kueh, A.B.H.;Jaini, Z.M.
    • Structural Engineering and Mechanics
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    • v.56 no.6
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    • pp.917-938
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    • 2015
  • The nonlinear numerical analysis of the impact response of reinforced concrete/mortar beam incorporated with the updated Lagrangian method, namely the Smoothed Particle Hydrodynamics (SPH) is carried out in this study. The analysis includes the simulation of the effects of high mass low velocity impact load falling on beam structures. Three material models to describe the localized failure of structural elements are: (1) linear pressure-sensitive yield criteria (Drucker-Prager type) in the pre-peak regime for the concrete/mortar meanwhile, the shear strain energy criterion (Von Mises) is applied for the steel reinforcement (2) nonlinear hardening law by means of modified linear Drucker-Prager envelope by employing the plane cap surface to simulate the irreversible plastic behavior of concrete/mortar (3) implementation of linear and nonlinear softening in tension and compression regions, respectively, to express the complex behavior of concrete material during short time loading condition. Validation upon existing experimental test results is conducted, from which the impact behavior of concrete beams are best described using the SPH model adopting an average velocity and erosion algorithm, where instability in terms of numerical fragmentation is reduced considerably.