• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.257-260
• /
• 1996

This study is about the construction of algorithm for selecting Yongshin of the Four Pillars. To emulate the method the expert uses when he select the Yongshin, we introduce the Hopfield Network. The result of the simulation classified with Yongshin is presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.11a
• /
• pp.180-184
• /
• 1997

This paper presents the fuzzy neural network which utilizes a fuzzified Kohonen learning uses a fuzzy membership value, a function of the iteration, and a intra-membership value instead of a learning rate. The IRIS data set if used to test the fuzzy neural network. The test result shows the performance of the fuzzy neural network depends on k and the vigilance parameter T.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.11a
• /
• pp.393-398
• /
• 2000

In this paper, we propose an Intelligent Monitoring Network System(IMNS) for the truck scale balance system. Truck scale balance system consis of three parts; Load cell part, Indicator part, and Junction box part. IMNS is attached to Junction box in truck scale balance system. Even if Load cell have been some problems, a truck scale balance system still has been run to determine, the values involved error. Therefore prosed system is has concentrated on Load cell part. Other Parts have been changed a portion of circuit for monitoring system.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.101-104
• /
• 1999

본 논문은 서비스 제어 시스템(Service Control Point : SCP), 서비스 교환 시스템(Service Switching System : SSP), 그리고 지능형 정보제공 시스템(Intelligent Peripheral : IP)을 물리적 구성 요소로 하는 차세대지능망 (Advanced Intelligent Network : AIN) 서비스중 주요 서비스인 자동콜렉트콜(Automatic Collect Call : ACC) 서비스에 대한 지능형 정보제공 시스템에서의 자원 제공시간 (점유 시간)을 분석한 것이다. 차세대 지능망 구성요소들이 연동되어 서비스되는 상황에서 지능형정보 제공시스템에서의 서비스 시나리오를 제시하였으며, 특수 자원에 대한 자원 제공 점유 시간을 분석하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1998.11a
• /
• pp.442-445
• /
• 1998

본 논문은 56Kbps 모뎀을 이용하는 전화망 가입자나 터미널 어댑터를 사용하는 ISDN 가입자가 인터넷 정합장치를 이용하여 사용자 ID없이 개방제로 인터넷에 접속하여 서비스를 받고, 유료 정보제공자에 대한 대체인증 기능 및 과금회수대행 기능을 제공하는 인터넷 정합시스템(WNAS: Web Network Access System)의 설계 및 구현에 관한 내용이다. 본 논문에서는 웹 기반의 인터넷 정합장치에 최대 120/60가입자가 동시에 파일 받기/보내기를 했을 경우 시스템의 전송속도를 분석하였다.

• Journal of Communications and Networks
• /
• v.5 no.3
• /
• pp.215-221
• /
• 2003

It is expected that per-user customized services are widely used in next generation Personal Communication Network. To provide personalized services for each call, per-user service profiles are frequently referenced and signaling traffic is considerably large. Since the service calls are requested from the places where user stays, we can expect that the traffic is localized. In this paper, we propose a new service profile replication scheme, named Follow-Me Replication with local Anchor (FMRA). By replicating user's service profile in a user-specific location area, local anchor of each region, the signaling traffic for call and mobility can be distributed to local network. We compared the performance of the FMRA with two typical schemes: Intelligent Network-based !Central scheme and IMT-2000 based full replication scheme, as we refer it to Follow-Me Replication Unconditional (FMRU). Performance results indicate that FMRA lies between Central and FMRU schemes according to call to mobility ratio, and we identified the efficient ranges of CMR for FMRA depending on the various network parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.1
• /
• pp.7-12
• /
• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.8
• /
• pp.701-708
• /
• 2001

Recently, the research on intelligence of computer has actively been under way in various areas and gradually extended to adapt to uncertain and complex environments. In this paper, we propose the MVL-Neural Valued Logic. Also, we verify that the MVL-Automata can be implemented to Neural Network and the MVL-Neural Network Model can be a simulator by MVL-Automata. Therefore, we propose that the MVL-Neural Network Model can be widely used in such area, as intelligent system or modeling of brain. In particular, the MVL-Neural Network is expected to be used as core technology of next generation computer.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1099-1104
• /
• 2003

In this paper, the position control of a car-like mobile robot using neural network is proposed. The positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as references, the robot posture by localization is corrected by a cascaded controller. In order to improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The remotely located neural network filter modifies the reference trajectories to minimize the positional errors by wireless communication. A car-like mobile robot is built as a test-bed and experimental studies of proposed several control algorithms are performed. It turns out that the best position control can be achieved by a cascaded controller with neural network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.419-422
• /
• 2003

To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy Similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed. Minimum, average, and maximum values of fragrance data acquisitions are used to form triangular fuzzy numbers. Then the fuzzy similarity treasure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of input vectors and the connection strengths of hidden units. In experiments, performances of the proposed method is compared with the conventional Self-Organized Network inspired by Immune Algorithm (SONIA), and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9％. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-25％.