• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.411-416
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• 2002

Recently, the trial and success of malicious cyber attacks has been increased rapidly with spreading of Internet and the activation of a internet shopping mall and the supply of an online internet, so it is expected to make a problem more and more. Currently, the general security system based on Internet couldn't cope with the attack properly, if ever, other regular systems have depended on common softwares to cope with the attack. In this paper, we propose the positive selection mechanism and negative selection mechanism of T-cell, which is the biological distributed autonomous system, to develop the self/non-self recognition algorithm, the anomalous behavior detection algorithm, and AIS (Artificial Immune System) that is easy to be concrete on the artificial system. The proposed algorithm can cope with new intrusion as well as existing one to intrusion detection system in the network environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.78-85
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• 2008

In this paper, we propose the extension of cooperation protocol which realizes the autonomous control of QoS and show its effectiveness with implementation and experiments of our agent-based videoconference system. The autonomous QoS control in the agent-based videoconference system means the framework of adjusting its functions/abilities by itself to cope with the changes of user requirements and system resource situations. But, the original cooperation protocol used by the agent-based videoconference system is not enough to perform cooperation closely to use the limited resources effectively. In our proposal, the cooperation protocol is offended in the following points: (1) introducing a cooperation states, (2) extension of performatives, (3) introducing compromise levels. We embedded the proposed protocol to the agent-based videoconference system and experimented the behavior of the system. According to the experiments on this prototype system, we confirmed that the flexibility of the system is improved.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.746-753
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• 2008

In this paper, we introduce the improved control method are communicated between a master and a slave robot in the teleoperation systems. When the master and slave robots are located in different places, time delay is unavoidable under the network environment and it is well known that the system can become unstable when even a small time delay exists in the communication channel. The time delay may cause instability in teleoperation systems especially if those systems include haptic feedback. This paper presents a control scheme based on the estimator with virtual master model in teleoperation systems over the network. As the behavior of virtual model is tracking the one of master model, the operator can control real master robot by manipulating the virtual robot. And LQG/LTR scheme was adopted for the compensation of un-modeled dynamics. The approach is based on virtual master model, which has been implemented on a robot over the network. Its performance is verified by the computer simulation and the experiment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.5
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• pp.26-37
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• 2009

This paper suggests a sensor's placement method for detecting the dilemma zone problem when real-time driver's safety service is provided at signalized intersections by multiple pointed traffic sensors using USN environments. For detecting the dangerous situations from vehicles accelerating through yellow intervals, red-light running and stopping abruptly like as dilemma zone problem, VISSIM(microscopic, behavior-based multi-purpose traffic simulation program) is used to perform a real-time multiple detection situation by changing the input data like as various inflow-volume, design speed change, driver perception and response time. As a result, the optimal interval of traffic sensors is 20~27m, and the initialized sensor location from stop-line is different according to road design speed. Moreover, the pattern of detection about dilemma zone is also different according to inflow-volumes. This paper shows that the method is useful to evaluate the sensor's placement problem based on micro-simulation and the results can be used as the basic research for USN services.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.68-79
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• 2014

This paper analyzes the characteristics of trip chaining activities of elderly and explores temporal and spatial distribution. The research also estimates ordered probit model and binary logistic model to investigate various factors affecting trip chaining and mode choice patterns. We utilized household survey data for elderly conducted in 2006 and 2010 in Seoul metropolitan area. Research results indicate that trip chaining showed an increasing trend and simple trip chaining counts for more than 85%. GIS mapping expressed spatial distribution of trip departure and arrival areas, particularly showing regional changes in job-related trips. We also found that more factors influence trip chaining in 2010, compared with 2006, and travel cost is more sensitive than travel time in determining travel mode. The research contributes to establish transportation policies based on travel behavior of elderly in a upcoming super-aged society.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.179-184
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• 2006

An intuitive understanding of the dynamic pattern generation in asymmetric networks may be considered an essential component in developing models for the dynamic information processing. It has been reported that the neural network with cyclic connections generates multiple limit cycles. The dynamics of discrete time network with cyclic connections has been investigated intensively. However, the dynamics of a cyclic connection neural network in continuous-time has not been well-known due to the considerable complexity involved in its calculation. In this paper, the dynamic behavior of a continuous-time cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$, has been investigated. Furthermore, the dynamics and stability of the network have been analyzed using a piece-wise linear approximation.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.471-475
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• 2008

Microarray technology in biological science enables molecular level observations and analyses on the biological phenomina by allowing to measure the RNA expression profiles in cells. Microarray data analysis is applied in various purposes such as identifying significant genes which react to drug treatment, understanding the genome scale phenomina. In drug response experiments, the microarray-based gene expression analysis could provide meaningful information. It is sometimes needed to identify the genes which shows different expression behavior for treatment group and normal group each other. When the normal group shows the medium level expression, it is not easy to discriminate the group just by expression level comparison. This paper proposes a method which selects group-wise representative values for each gene and sets the value range of the groups in order to filter out the genes with specific pattern. It also shows some experiment results.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.290-295
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• 2011

On the basis of DCPA(Distance to Closest Point of Approach) and TCPA(Time to CPA), the conventional algorithms for collision avoidances have a drawback that the '72 CORLEGs(International Regulations for Preventing Collisions at Sea, 1972) has not taken into account to prevent collisions between ships. In this paper, the proposed algorithm decides whether the own ship is a give-way vessel or a stand-on vessel by observing the relative bearing of the encountered ship. To determine the ship position and time for collision avoidance, the proposed algorithm utilizes the ellipse model for ship safety domain. The computer simulation is done to represent the process of adversive behavior. Using the proposed method, the past maritime accident is analyzed. The proposed method can be effectively applied to collision avoidance by CORLEGs even when the target ship's navigational lights is invisible in poor weather and/or in the restricted visibility.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.595-602
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• 2009

Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.