• Title/Summary/Keyword: Vector Machines

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Patch load resistance of longitudinally stiffened webs: Modeling via support vector machines

  • Kurtoglu, Ahmet Emin
    • Steel and Composite Structures
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    • v.29 no.3
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    • pp.309-318
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    • 2018
  • Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.

Adaptive States Feedback Control of Unknown Dynamics Systems Using Support Vector Machines

  • Wang, Fa-Guang;Kim, Min-Chan;Park, Seung-Kyu;Kwak, Gun-Pyong
    • Journal of information and communication convergence engineering
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    • v.6 no.3
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    • pp.310-314
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    • 2008
  • This paper proposes a very novel method which makes it possible that state feedback controller can be designed for unknown dynamic system with measurable states. This novel method uses the support vector machines (SVM) with its function approximation property. It works together with RLS (Recursive least-squares) algorithm. The RLS algorithm is used for the identification of input-output relationship. A virtual state space representation is derived from the relationship and the SVM makes the relationship between actual states and virtual states. A state feedback controller can be designed based on the virtual system and the SVM makes the controller with actual states. The results of this paper can give many opportunities that the state feedback control can be applied for unknown dynamic systems.

Fault diagnosis of rotating machinery using multi-class support vector machines (Multi-class SVM을 이용한 회전기계의 결함 진단)

  • 황원우;양보석
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2003.11a
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    • pp.537-543
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    • 2003
  • Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

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Intelligent Walking of Humanoid Robot for Stable Walking on a Decent (휴머노이드 로봇의 경사면 내리막 보행을 위한 지능보행 연구)

  • Kim, Dong-Won;Park, Gwi-Tae
    • The Journal of Korea Robotics Society
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    • v.1 no.2
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    • pp.197-202
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    • 2006
  • We present the synergy effect of humanoid robot walking down on a slope and support vector machines in this paper. The biped robot architecture is highly suitable for the working in the human environment due to its advantages in obstacle avoidance and ability to be employed as human substitutes. But the complex dynamics in the robot and ground makes robot control difficult. The trajectory of the zero moment point (ZMP) in a biped walking robot is an important criterion used for the balance of the walking robots. The ZMP trajectory as dynamic stability of motion will be handled by support vector machines (SVM). Three kinds of kernels are also employed, and each result from these kernels is compared to one another.

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Low-Cost SVM-DTC Strategy of Induction Machine Drives Using Single DC-link Current Sensor

  • Wang, Wei;Cheng, Ming;Hua, Wei;Ding, Shichuan;Zhu, Ying;Zhao, Wenxiang
    • Journal of international Conference on Electrical Machines and Systems
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    • v.1 no.3
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    • pp.266-273
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    • 2012
  • In conventional direct torque control (DTC) using space-vector modulation (SVM) of induction machine (IM) drives, at least three current sensors are needed. In this paper, a low-cost SVM-DTC strategy is proposed, in which only a single current sensor is used. The position of the voltage space vector is divided into two areas: effective and non-effective area. If it is located in the non-effective area, the voltage space vector will be shifted into the effective area with minimum distortion. Further, the switching frequency remains constant. The simulation is carried out on a MATLAB/Simulink platform and the simulated results verify the effectiveness of the proposed strategy.

Intelligent 3D Obstacles Recognition Technique Based on Support Vector Machines for Autonomous Underwater Vehicles

  • Mi, Zhen-Shu;Kim, Yong-Gi
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.3
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    • pp.213-218
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    • 2009
  • This paper describes a classical algorithm carrying out dynamic 3D obstacle recognition for autonomous underwater vehicles (AUVs), Support Vector Machines (SVMs). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years. A recognition system is designed using Support Vector Machines for applying the capabilities on appearance-based 3D obstacle recognition. All of the test data are taken from OpenGL Simulation. The OpenGL which draws dynamic obstacles environment is used to carry out the experiment for the situation of three-dimension. In order to verify the performance of proposed SVMs, it compares with Back-Propagation algorithm through OpenGL simulation in view of the obstacle recognition accuracy and the time efficiency.

A Fault Diagnosis Methodology for Module Process of TFT-LCD Manufacture Using Support Vector Machines (SVM을 이용한 TFT-LCD 모듈공정의 불량 진단 방안)

  • Shin, Hyun-Joon
    • Journal of the Semiconductor & Display Technology
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    • v.9 no.4
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    • pp.93-97
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    • 2010
  • Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

A Development of Unknown Intrusion Detection System with SVM (SVM을 통한 미확인 침입탐지 시스템 개발)

  • Kim, Seok-Tae;Han, In-Gyu;Lee, Chang-Yong;Kho, Jeong-Ho;Lee, Do-Won;Oh, Jeong-Min;Bang, Cheol-Soo;Lee, Geuk
    • Convergence Security Journal
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    • v.7 no.4
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    • pp.23-28
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    • 2007
  • In this research, we suggest the unknown intrusion detection system with SVM(Support Vector Machines). At the system, at first, collected training-packets are processed through packet image creating module. And then, it is studied by the SVM module. Finally, the studied SVM module classifies the test-data unsing test-packet-image. This system's stability and efficient characteristic of security is far superior than the existing it.

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The Threat List Acquisition Method in an Engagement Area using the Support Vector Machines (SVM을 이용한 교전영역 내 위협목록 획득방법)

  • Koh, Hyeseung
    • Journal of the Korea Institute of Military Science and Technology
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    • v.19 no.2
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    • pp.236-243
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    • 2016
  • This paper presents a threat list acquisition method in an engagement area using the support vector machines (SVM). The proposed method consists of track creation, track estimation, track feature extraction, and threat list classification. To classify the threat track robustly, dynamic track estimation and pattern recognition algorithms are used. Dynamic tracks are estimated accurately by approximating a track movement using position, velocity and time. After track estimation, track features are extracted from the track information, and used to classify threat list. Experimental results showed that the threat list acquisition method in the engagement area achieved about 95 % accuracy rate for whole test tracks when using the SVM classifier. In case of improving the real-time process through further studies, it can be expected to apply the fire control systems.

Application of Support Vector Machines to the Prediction of KOSPI

  • Kim, Kyoung-jae
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2003.05a
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    • pp.329-337
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    • 2003
  • Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

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