• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.345-351
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• 2001

We are concerned with a multiple replenishment contract with a purchase price discount in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows contracting several firmed orders at a time with a price discount. Due to a larger forecast error of the future demand, the buyer should keep a higher level of safety stock to provide the same level of service of the usual (s, Q) policy but can reduce his purchase cost by placing larger quantity. Thus there exists a trade-off between the price discount and inventory holding cost. We present a model for the contract and an algorithm to find the optimum number of the firmed orders. Computer experiments show that the algorithm finds the global optimum solution very fast.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.3
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• pp.232-238
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• 2015

A sensorless control algorithm of brushless DC (BLDC) motors with a model current based on 120 degree conduction mode is proposed in this paper. The rotor speed and position can be estimated using the current model of BLDC motor, which is a modified version of the conventional current model of permanent magnet synchronous motor. The rotor speed and position can be obtained using the difference of the actual current and the model current. The position error caused by the parameter errors of the model current is compensated using a PI controller and the feedback loop of the real current. The validity of the proposed sensorless control algorithm is verified through simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.319-322
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• 1997

In vector control technique, stator currents of an induction motor are transformed to equivalent d-q currents in reference frame consist of d and q axis, each of which is coincide with flux and torque direction respectively. In this paper, the new algorithm is suggested where the stator current through an induction motor torque is monitored by using a vector control method where an additional equipment is not need. The G-programming is used to apply the suggested algorithm in the experiment and this is applied to an actual system to monitor the torque value of an induction motor on real time. To solve the vibration trouble of estimated torque caused from an unbalanced real rotating speed of an induction motor and measured rotating speed by suggesting the reconstructed in a method based on measurement current signal. This produced system testifies an accuracy of an induction motor through the experiment by comparing the reference value of the control method.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.857-860
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• 1999

It is made possible a removal of the preamble for carrier recovery and symbol-timing recovery by storing a burst in memory with low overhead QPSK demodulation and this demodulation method also effects frame efficiency improved by processed synchronization performance. In this paper, we have proposed that new algorithm for arc-tangent look-up table which transform the input I, Q data by phase. This I, Q data plays an important role in demodulation and makes demodulator with low-overhead by storing a burst in memory. To evaluate proposed new algorithm and symbol-timing recovery method, function simulation and timing verification have been done by using synopsys VHDL tool.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.3-8
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• 1995

Since Interior Permanent Magnet syncronous Motor has a structure whose magnet is inserted in the rotor, d, q inductance is differ and the motor products hybrid torque combined allignment term and reluctance term. Air gap flux density and d, q axis inductances of the Interior Permanent Magnet Synchronous Motor obtained by analytical method are compensated using Finite Element Method. For optimal design, the efficiency of the motor is taken as the objective function, and Genetic Algorithm finds the value of design parameters which maximize the objective function.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1114-1116
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• 2003

Voltage sags are known as a serious problem causing mal-operation of equipment like computers, process controllers and adjustable-speed drives. In this paper, characterization of voltage sag and an overview of methods used in the mitigation of voltage sags are presented. Moreover a fast detection method for voltage disturbances is explored. The algorithm is based on the theory that allows a set of three-phase voltages be converted the d-q value. The utility input voltages are sensed and then converted to some quantities in the d-q transformation. And the difference between reference value and input value are showed that some disturbances happened in the system.

• Structural Engineering and Mechanics
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• v.19 no.2
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• pp.123-139
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• 2005

An automatic modal identification program is developed for continuous extraction of modal parameters of three cable-supported bridges in Hong Kong which are instrumented with a long-term monitoring system. The program employs the Complex Modal Indication Function (CMIF) algorithm for identifying modal properties from continuous ambient vibration measurements in an on-line manner. By using the LabVIEW graphical programming language, the software realizes the algorithm in Virtual Instrument (VI) style. The applicability and implementation issues of the developed software are demonstrated by using one-year measurement data acquired from 67 channels of accelerometers permanently installed on the cable-stayed Ting Kau Bridge. With the continuously identified results, variability in modal vectors due to varying environmental conditions and measurement errors is observed. Such an observation is very helpful for selection of appropriate measured modal vectors for structural health monitoring use.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.533-538
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• 2001

This paper describes a combinational method to compute the global and local solutions of optimization problems. The present hybrid algorithm uses both a genetic algorithm and a local concentrate search algorithm (e. g simplex method). The hybrid algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The present algorithm can be supplied to minimize the resonance response (Q factor) and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.388-394
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• 2001

Reinforcement learning is a kind of unsupervised learning methods that an agent control rules from experiences acquired by interactions with environment. The eligibility is used to resolve the credit-assignment problem which is one of important problems in reinforcement learning, Conventional eligibilities such as the accumulating eligibility and the replacing eligibility are ineffective in use of rewards acquired in learning process, since on1y one executed action for a visited state is learned. In this paper, we propose a new eligibility, called the distributed eligibility, with which not only an executed action but also neighboring actions in a visited state are to be learned. The fuzzy Q-learning algorithm using the proposed eligibility is applied to a cart-pole balancing problem, which shows the superiority of the proposed method to conventional methods in terms of learning speed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.15-16
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• 2018

본 논문에서는 다수의 센서 노드로 구성된 Internet of Things (IoT) 환경에서 새로운 환경에 대해 적응하는데 걸리는 시간을 줄이기 위한 새로운 스케줄링 기법을 제안한다. IoT 환경에서는 데이터 수집 및 전송 패턴이 사전에 정의되어 있지 않기 때문에 기존 정적인 Packet scheduling 기법으로는 한계가 있다. Q-learning은 네트워크 환경에 대한 사전지식 없이도 반복적 학습을 통해 Scheduling policy를 확립할 수 있다. 본 논문에서는 기존 Q-learning 스케줄링 기법을 기반으로 각 큐의 패킷 도착률에 대한 bound 값을 이용해 Q-table과 Reward table을 초기화 하는 새로운 Q-learning 스케줄링 기법을 제안한다. 시뮬레이션 결과 기존 기법에 비해 변화하는 패킷 도착률 및 서비스 요구조건에 적응하는데 걸리는 시간이 감소하였다.