• Proceedings of the Korea Society for Simulation Conference
• /
• 1999.04a
• /
• pp.184-188
• /
• 1999

The performance of an AGV varies with the applied AGV dispatching rule in the operation of AGVS. This study proposes a multi-attribute AGV dispatching rule. The suggested dispatching rule considers the output queue of a workstation, distance between an idle AGV and a workstation to be served, the input queue of the destination and the remaining job process of a part. This study suggests two types of and the remaining job process of a part. This study suggests two types of multi-attribute dispatching rules. One is an one-stage rule which selects the part to be served considering four attributes simultaneously. The other is a two-stage rule by which a workstation is selected and a part is chosen from the selected workstation. The simulation runs were executed under different experimental conditions to obtain preliminary statistics on the several performance measures.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.4
• /
• pp.673-682
• /
• 2010

This paper describes and analyzes a various generation methods of the mutually orthogonal polyphase sequences with low cross-correlation peak sidelobe and low autocorrelation peak sidelobe levels. The mutual orthogonality is the key requirement of multi-static or MIMO(Multi-Input Multi-Output) radar systems which provides the good target detection and tracking performance. The polyphase sequences, which are generated by SA(Simulated Annealing) and GA(Genetic Algorithm), have been analyzed with ACF(Autocorrelation Function) PSL(Peak Sidelobe Level) and CCF(Crosscorrelation Function) level at the matched filter output. Also, the ambiguity function has been introduced and simulated for comparing Doppler properties of each sequence. We have suggested the phase selection rule for applying multi-static or MIMO systems.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2008.05a
• /
• pp.530-531
• /
• 2008

많은 DSP 등에서 Multi-Output Instructions(MOI)를 지원하나 이를 사용할 수 있는 컴파일러가 없다. 그래서 기존연구에서 이 문제를 해결하는 새로운 코드 생성 알고리즘을 개발하여 소개하였다. 하지만, 이 논문에서 제시한 방법은 많은 제약이 있어, 본 논문에서는 기존 논문에서 사용한 MOI를 위한 compiler grammar rule description을 확장하고, 알고리즘을 변경하여 기존에 제안된 방법이 해결할 수 없었던 MOI 들까지 모두 컴파일러에서 처리할 수 있도록 하였다.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.11
• /
• pp.852-858
• /
• 1991

An adaptive hierarchical classifier(AHCL) for Korean character recognition using a neural net is designed. This classifier has two neural nets: USACL (Unsupervised Adaptive Classifier) and SACL (Supervised Adaptive Classifier). USACL has the input layer and the output layer. The input layer and the output layer are fully connected. The nodes in the output layer are generated by the unsupervised and nearest neighbor learning rule during learning. SACL has the input layer, the hidden layer and the output layer. The input layer and the hidden layer arefully connected, and the hidden layer and the output layer are partially connected. The nodes in the SACL are generated by the supervised and nearest neighbor learning rule during learning. USACL has pre-attentive effect, which perform partial search instead of full search during SACL classification to enhance processing speed. The input of USACL and SACL is a directional edge feature with a directional receptive field. In order to test the performance of the AHCL, various multi-font printed Hangul characters are used in learning and testing, and its processing its speed and and classification rate are compared with the conventional LVQ(Learning Vector Quantizer) which has the nearest neighbor learning rule.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.16 no.2
• /
• pp.128-136
• /
• 1991

A neural network of multi layer perception type, learned by error back propagation learning rule, is generally used for the verification or clustering of similar type of patterns. When learning is completed, the network has a constant value of output depending on a pattern. This paper shows that the intensity of neuron's out put can be controlled by a function which intensifies the excitatory interconnection coefficients or the inhibitory one between neurons in output layer and those in hidden layer. In this paper the value of factor in the function to control the output is derived from the know values of the neural network after learning is completed And also this paper show that the amount of an increased neuron's output in output layer by arbitary value of the factor is derived. For the applications increased recognition performance of a pattern than has distortion is introduced and the output of partially damaged neurons are first managed and this paper shows that the reduced recognition performance can be recovered.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.157-157
• /
• 2000

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. The concept of relative gain matrix is used to obtain the input-output pairs. However, among the input/output variables which are not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by tile introduction of a simple compensator. This compensator adjusts the degree of coupling between variables using a neural network. In this proposed neuro-fuzzy controller, the Neural network which is realized by back-propagation algorithm, adjusts the mutual coupling weight between variables.

• The Journal of the Acoustical Society of Korea
• /
• v.13 no.4
• /
• pp.60-68
• /
• 1994

In this paper we propose a new Hybrid learning rule applied to multilayer feedforward neural networks, which is constructed by combining Hebbian learning rule that is a good feature extractor and Back-Propagation(BP) learning rule that is an excellent classifier. Unlike the BP rule used in multi-layer perceptron(MLP), the proposed Hybrid learning rule is used for uptate of all connection weights except for output connection weigths becase the Hebbian learning in output layer does not guarantee learning convergence. To evaluate the performance, the proposed hybrid rule is applied to classifier problems in two dimensional space and shows better performance than the one applied only by the BP rule. In terms of learning speed the proposed rule converges faster than the conventional BP. For example, the learning of the proposed Hybrid can be done in 2/10 of the iterations that are required for BP, while the recognition rate of the proposed Hybrid is improved by about $0.778\%$ at the peak.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.12
• /
• pp.116-123
• /
• 1994

A modified Hybrid learning rule(MHLR) is proposed, which is derived from combining the Back Propagation algorithm that is known as an excellent classifier with modified Hebbian by changing the orginal Hebbian which is a good feature extractor. The network architecture of MHLR is multi-layered neural network. The weights of MHLR are calculated from sum of the weight of BP and the weight of modified Hebbian between input layer and higgen layer and from the weight of BP between gidden layer and output layer. To evaluate the performance, BP, MHLR and the proposed Hybrid learning rule (HLR) are simulated by Monte Carlo method. As the result, MHLR is the best in recognition rate and HLR is the second. In learning speed, HLR and MHLR are much the same, while BP is relatively slow.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.5
• /
• pp.505-508
• /
• 2006

This paper presents a new analytical approach for designing multiloop PI controllers for disturbance rejection in multivariable processes with time delay. The proposed method is based on IMC-PID design approach. To overcome a sluggish load response by dominant pole in the process, the IMC filter is modified to compensate the dominant pole effect. Based on the modified IMC filter, an analytical tuning rule for multiloop PI controller is driven by extending the generalized IMC-PID method for single input/single output (SISO) systems [1] to multi input/multi output (MIMO) systems. Simulation results show that the proposed method gives a satisfactory load performance as well as servo performance in the multiloop system.

• Korean Management Science Review
• /
• v.29 no.1
• /
• pp.1-14
• /
• 2012

The goal of the multi-reservoir operation planning is to provide an optimal release plan that maximize the reservoir storage and hydropower generation while minimizing the spillages. However, the reservoir operation is difficult due to the uncertainty associated with inflows. In order to consider the uncertain inflows in the reservoir operating problem, we present a Stochastic Dynamic Programming (SDP) model based on the markov decision process (MDP). The objective of the model is to maximize the expected value of the system performance that is the weighted sum of all expected objective values. With the SDP model, multi-reservoir operating rule can be derived, and it also generates the steady state probabilities of reservoir storage and inflow as output. We applied the model to the Geum-river basin in Korea and could generate a multi-reservoir monthly operating plan that can consider the uncertainty of inflow.