• 전력전자학회논문지
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• 제5권2호
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• pp.140-148
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• 2000

간단한 시간 지연 관측기를 이용한 영구자석 동기전동기의 강인 전류 제어 기법이 제시된다. 전압원 인버터 구동영구자석 동기전동기의 전류 제어 기법 중 전류 제어가 우수한 성능을 주는 것으로 알려져 있지만 이 기법은 전동기 파라미터와 동작 조건에 대한 모든 정보를 필요로 하며 전동기와 제어기의 파라미터가 일치하지 않을 경우 응답 성능이 저하되게 된다. 이러한 제함 점을 극복하기 위해 시간 지연 제어 기법을 사용하여 파라미터 변화에 의한 외란 성분이 추정되고 이는 전향 제어 방식으로 기준 전압의 계산에 이용된다. 이를 통해 제어기 성능이 매우 간단한 방식으로도 상당히 향상됨을 입증한다. 제안된 제어 방식의 타당성이 비교 시뮬레이션과 실험을 통해 입증된다.

• 산업경영시스템학회지
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• 제38권3호
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• pp.100-107
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• 2015

Recently as the manufacturers want competitiveness in dynamically changing environment, they are trying a lot of efforts to be efficient with their production systems, which may be achieved by diminishing unplanned operation stops. The operation stops and maintenance cost are known to be significantly decreased by adopting proper maintenance strategy. Therefore, the manufacturers were more getting interested in scheduling of exact maintenance scheduling to keep smooth operation and prevent unexpected stops. In this paper, we proposedan integrated maintenance approach in injection molding manufacturing line. It consists of predictive and preventive maintenance approach. The predictive maintenance uses the statistical process control technique with the real-time data and the preventive maintenance is based on the checking period of machine components or equipment. For the predictive maintenance approach, firstly, we identified components or equipment that are required maintenance, and then machine parameters that are related with the identified components or equipment. Second, we performed regression analysis to select the machine parameters that affect the quality of the manufactured products and are significant to the quality of the products. By this analysis, we can exclude the insignificant parameters from monitoring parameters and focus on the significant parameters. Third, we developed the statistical prediction models for the selected machine parameters. Current models include regression, exponential smoothing and so on. We used these models to decide abnormal patternand to schedule maintenance. Finally, for other components or equipment which is not covered by predictive approach, we adoptedpreventive maintenance approach. To show feasibility we developed an integrated maintenance support system in LabView Watchdog Agent and SQL Server environment and validated our proposed methodology with experimental data.

• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.116-128
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• 2010

Modeling of the three phase electric arc furnace and its voltage flicker mitigation are the purposes of this paper. For modeling of the electric arc furnace, at first, the arc is modeled by using current-voltage characteristic of a real arc. Then, the arc random characteristic has been taken into account by modulating the ac voltage via a band limited white noise. The electric arc furnace compensation with static VAr compensator, Thyristor Controlled Reactor combined with a Fixed Capacitor bank (TCR/FC), is discussed for closed loop control of the compensator. Instantaneous flicker sensation curves, before and after accomplishing compensation, are measured based on IEC standard. A new method for controlling TCR/FC compensator is proposed. This method is based on applying a predictive approach with closed loop control of the TCR/FC. In this method, by using the previous samples of the load reactive power, the future values of the load reactive power are predicted in order to consider the time delay in the compensator control. Also, in closed loop control, two different approaches are considered. The former is based on voltage regulation at the point of common coupling (PCC) and the later is based on enhancement of power factor at PCC. Finally, in order to show the effectiveness of the proposed methodology, the simulation results are provided.

• 생물정신의학
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• 제25권4호
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• pp.89-100
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• 2018

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation method that delivers 1-2 mA of current to the scalp. Several clinical studies have been conducted to confirm the therapeutic effect of major depressive disorder (MDD) patients with tDCS. Some studies have shown tDCS's antidepressant effect, while the others showed conflicting results in antidepressant effects. Our aim of this review is to understand the biological bases of tDCS's antidepressant effect and review the results of studies on tDCS's antidepressant effect. For the review and search process of MDD treatment using tDCS, the US National Library of Medicine search engine PubMed was used. In this review, we discuss the biological mechanism of tDCS's antidepressant effect and the existing published literature including meta-analysis, systematic review, control trial, open studies, and case reports of antidepressant effects and cognitive function improvement in patients with MDD are reviewed. We also discuss the appropriate tDCS protocol for MDD patients, factors predictive of response to tDCS treatment, the disadvantages of tDCS in MDD treatment, and side effects.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1031-1033
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• 1996

Now is an automation age. Therefore it is required that machine can do work which was done by men. Artificial Neural Network was developed by the necessity of this purpose. This paper shows a Predictive Control with a Neural Network. The Neural Network learns an Inverted Pendulum in various situations. Then, it has a power to predict the next state after accept the current state. And the Neural Network directs the Bang-Bang Controller to give input to a plant. It seems like that a human expert looks the state of a plant and then controls the plant. It is used a Feedforward Neural Network and shown control state according to the learning. We could get a satisfactory results after complete learning.

• Journal of Power Electronics
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• 제12권5호
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• pp.800-812
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• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.368-370
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• 1995

A new three-level push-pull type quantum series resonant rectifier for the power factor correction is proposed. The proposed single phase rectifier enables a zero-current switching operation of all the power devices allowing the circuit to operate at high switching frequencies and high power levels. With the proposed control technique, an unity power factor and greatly reduced line current harmonics can be obtained.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.43.6-43
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• 2001

In this paper, we describe an actor-critic method as a kind of temporal difference (TD) algorithms. The value function is regarded as a current estimator, in which two value functions have different inputs: one is an actual experience; the other is a simulated experience obtained through a predictive model. Thus, the parameter´s updating for the actor and critic parts is based on actual and simulated experiences, where the critic is constructed by a radial-basis function neural network (RBFNN) and the actor is composed of a kinematic-based controller. As an example application of the present method, a tracking control problem for the position coordinates and azimuth of a nonholonomic mobile robot is considered. The effectiveness is illustrated by a simulation.

• Radiation Oncology Journal
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• 제38권2호
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• pp.129-137
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• 2020

Purpose: To identify the clinical usefulness of serum M protein and to establish a rationale for regular follow-up with serum protein electrophoresis in solitary plasmacytoma. Materials and Methods: Sixty-nine patients with solitary plasmacytoma and solitary plasmacytoma with minimal marrow involvement according to the International Myeloma Working Group criteria were retrospectively reviewed. Results: At a median follow-up of 6.2 years, 5-year local control (LC), 5-year multiple myeloma-free survival (MMFS), 5-year failure-free survival (FFS), and 5-year overall survival (OS) were 82.6%, 44.1%, 41.8%, and 85.1%, respectively. Among the patients whose initial serum M protein was present or not evaluated, 37.3% of patients showed disappearance of serum M protein after various treatment. MMFS of these patients were comparable to non-secretory plasmacytoma with undetectable levels of M protein, and significantly better than patients with persistent M protein. Increase of serum M protein ≥0.1 g/dL was most predictive of treatment failure with area under the curve of 0.731. Conclusion: Patients who eventually showed persistence of serum M protein after treatment showed worse MMFS and FFS compared to those whose serum M protein disappeared or who had initially non-secretory disease. The increase of serum M protein level ≥0.1 g/dL from current nadir was predictive of treatment failure. Therefore, regular follow-up with serum M protein is highly recommended especially unless the patient had initially non-secretory disease.

• Nuclear Engineering and Technology
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• 제52권9호
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.