• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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• pp.7-15
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• 2009

• Earthquakes and Structures
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• v.16 no.6
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• pp.679-690
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• 2019

This paper presents machine learning methods using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) to analyze optimal damper distribution for structural vibration control. Regarding different building structures, a genetic algorithm based optimization method is used to determine optimal damper distributions that are further used as training samples. The structural features, the objective function, the number of dampers, etc. are used as input features, and the distribution of dampers is taken as an output result. In the case of a few number of damper distributions, multi-class prediction can be performed using SVM and MLP respectively. Moreover, MLP can be used for regression prediction in the case where the distribution scheme is uncountable. After suitable post-processing, good results can be obtained. Numerical results show that the proposed method can obtain the optimized damper distributions for different structures under different objective functions, which achieves better control effect than the traditional uniform distribution and greatly improves the optimization efficiency.

• Journal of Korean Ophthalmic Optics Society
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• v.12 no.4
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• pp.87-90
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• 2007

To manufacture the lens mold used in producing polycarbonate (PC) lenses, the core manufacturing is needed and this core manufacturing is generally performed by diamond turning machine (DTM) or computer numerical control (CNC) lathe. The numerical data about the lens core feature is necessarily needed for operating of these devices. Therefore, we developed the program which calculate the numerical data about the lens core feature. The program was composed to be able to input aspherical coefficients of lens feature, display the graph of lens feature, and save the numerical data file.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.887-891
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• 1992

In this study, it is developed the interactive DNC(Direct Numerical Control) system, in using RS-232C cable and auxiliary computer, through the diagnosis of planning process and information evaluation. this DNC system recognize the Manufacturing planning and control it. This DNC system has a different notation. It can be done by an operator who hasn't knowledge about personnel computer. It is operated with automatic planning and measurement tec. by operator, using part program on the NC(Numerical Control).

• Structural Engineering and Mechanics
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• v.88 no.6
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• pp.535-549
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• 2023

Deep extreme learning machine (DELM) and multi-verse optimization algorithms (MVO) are hybridized for designing an optimal and adaptive control framework for uncertain buildings. In this approach, first, a robust model predictive control (RMPC) scheme is developed to handle the problem uncertainty. The optimality and adaptivity of the proposed controller are provided by the optimal determination of the tunning weights of the linear programming (LP) cost function for clustered external loads using the MVO. The final control policy is achieved by collecting the clustered data and training them by DELM. The efficiency of the introduced control scheme is demonstrated by the numerical simulation of a ten-story benchmark building subjected to earthquake excitations. The results represent the capability of the proposed framework compared to robust MPC (RMPC), conventional MPC (CMPC), and conventional DELM algorithms in structural motion control.

• 한국기계연구소 소보
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• s.13
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• pp.75-85
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• 1984

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.307-311
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• 1992

종래의 DNC(Direct Numerical Control)에서 가공 Cell의 효율을 높이기 위한 분산 제어 DNC(Distributed Numerical Control)시스템 운용을 위해서는, 운용에 필요한 소프트웨어 외에 공작기계 및 주변기계에 대한 실시간 감시/제어 기능을 가져야 한다. 이를 위해 당 연구실에서는 경제성 및 확장성을 고려 범용 PLC (Programable Logic Controller)와 각 공작기계 및 주변기기를 연결하고, PC(Personal Computer)와 다자간 및 고속 통신이 가능한 전용 통신회선을 이용한 경제성 있는 실시간 가공 Cell 감시/제어시스템 RT-COMOS(Real Time Machine Cell Control and Monitoring System)를 개발했다. 본 논문에서는 이에 대한 연구결과를 소개한다.

• Journal of Drive and Control
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• v.9 no.4
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• pp.26-31
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• 2012

Friction reduction between sliding hydraulic machine components is required to improve efficiency and reliability of hydraulic machineries. It is recently reported that surface texturing on sliding bearing surfaces can reduce the friction force highly. In this paper, numerical analysis is carried out to investigate the effect of dimple numbers and inlet boundary pressures on the lubrication characteristics of a parallel sliding bearing using a commercial computational fluid dynamics (CFD) code, FLUENT. The results show that the pressure distribution, load capacity, dimensionless friction force and leakage with dimple number and their locations, and inlet pressures. The overall lubrication characteristics are highly affected by dimple numbers and boundary pressure. The numerical method adopted and results can be used in design of efficient hydraulic machine components.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.149-152
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• 2004

Speed and torque control of PMSM(Permanent Magnet Synchronous Motor) are usually achieved by using position and speed sensors which require additional mounting space, reduce the reliability in harsh environments and increase the cost of a motor. Therefore, many studies have been performed for the elimination of speed and position sensors. In this paper, a novel speed sensorless control of a permanent magnet synchronous motor based on SVMR(Support Vector Machine Regression) is presented. The SVM regression method is an algorithm that estimates an unknown mapping between a system's input and outputs, from the available data or training data. Two well-known different voltage model is necessary to estimate the speed of a PMSM. The validity and the usefulness of proposed algorithm are thoroughly verified through numerical simulation.