• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• ETRI Journal
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• v.44 no.4
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• pp.560-572
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• 2022

H²RDC (heuristic handover based on RCC-DTSK-C), a heuristic algorithm based on a highly interpretable deep Takagi-Sugeno-Kang fuzzy classifier, is proposed for suppressing the mobile heterogeneous networks problem of frequent handover and handover ping-pong in the multibase-station scenario. This classifier uses a stack structure between subsystems to form a deep classifier before generating a base station (BS) priority sequence during the handover process, and adaptive handover hysteresis is calculated. Simulation results show that H²RDC allows user equipment to switch to the best antenna at the optimal time. In high-BS density load and mobility scenarios, the proposed algorithm's handover success rate is similar to those of classic algorithms such as best connection (BC), self tuning handover algorithm (STHA), and heuristic for handover based on AHP-TOPSIS-FUZZY (H²ATF). Moreover, the handover rate is 83% lower under H²RDC than under BC, whereas the handover ping-pong rate is 76% lower.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.12-20
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• 1998

As an approach to develope a control system with high robustness in changing control environment conditions, this paper will propose a robust control system, using multilayer neural network and biological immune system. The proposed control system adjusts weights of the multilayer neural network(MNN) with the immune algorithm. This algorithm is made up of two major divisions, the innate immune algorithm as a first line of defence and the adaptive immune algorithm as a barrier of self-adjustment. Using the proposed control system based on immune algorithm, we will work out a design for the controller of a robot manipulator. And we will demonstrate the effectiveness of the control system of robot manipulator with computer simulations.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.137-145
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• 2008

In general, a large-scale wireless sensor network(WSNs) is composed of hundreds of or thousands of sensor nodes. In this large-scale wireless sensor networks, it is required to maintain and manage the networks to lower management cost and obtain high energy efficiency. Users should be provided with sensing service at the level of quality for users through an efficient system. In evaluating the result data quality provided from this network to users, the number of sensors related to event detection has an important role. Accordingly, the network protocol which can provide proper QoS at the level of users demanding quality should be designed in a way such that the overall system function has not to be influenced even if some sensor nodes are in error. The energy consumption is minimized at the same time. The protocol suggested in this article is based on the LEACH protocol and is a role-based self-Organization one that is appropriate for large-scale networks which need constant monitoring.

• The Journal of Korean Association of Computer Education
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• v.9 no.5
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• pp.31-39
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• 2006

The change into the knowledge based information society requires a transformation of educational paradigm. Accordingly, intelligent learning and distance education are attracting a fair amount of attention. To apply the instructional learning method in this field, we need to consider a individualization of learning, as it were, abstraction of fact and path through learning, which is based on learner's traits, this focus entails a argument for individualized reasoning strategy. Therefore, in this paper, we design a learner's cognitive union, which is based on X-Neuronet(eXtended Neuronet), represent learner's hierarchical knowledge is able to self-learn, and grows adaptive union by proprietor. Additionally, we propose a individualized reasoning strategy, which relies upon learner's cognitive union, and verify the validity.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.43-50
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• 2001

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.101-109
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• 2006

We propose an adaptive digital watermarking algorithm using HVS(human visual system) and SOM(Self-Organizing Map) among neural networks. This method adjusts adaptively the strength of the watermark which is embedded in different blocks according to block classification in DCT(Discrete Cosine Transform) domain. All blocks in 3 classes out of 4 are selected to embed a watermark. Watermark sequences are embedded in 6 lowest frequency coefficients of each block except the DC component. The experimental results are excellent.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2129-2137
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• 2015

Lots of research and development works have been actively focused on the self-driving vehicles, locally and globally. In order to implement the self-driving vehicles, lots of fundamental core technologies need to be successfully developed and, specially, it is noted that traffic lights detection and recognition system is an essential part of the computer vision technologies in the self-driving vehicles. Up to nowadays, most conventional algorithm for detecting and recognizing traffic lights are mainly based on the color signal analysis, but these approaches have limits on the performance improvements that can be achieved due to the color signal noises and environmental situations. In order to overcome the performance limits, this paper introduces the morphological analysis for the traffic lights recognition. That is, by considering the color component analysis and the shape analysis such as rectangles and circles simultaneously, the efficiency of the traffic lights recognitions can be greatly increased. Through several simulations, it is shown that the proposed method can highly improve the recognition rate as well as the mis-recognition rate.

• Computers and Concrete
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• v.12 no.3
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• pp.243-259
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• 2013

Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS's results are highly accurate, precise and satisfactory.