• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.1-16
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• 2000

This paper analyzes the approaches of Web-based expert systems by comparing their pros and cons. and proposes a methodology of implementing the Web-based backward inference engines with reduced burden to Web servers. There are several alternatives to implement expert systems under the WWW environment : CGI, Web servers embedding inference engines external viewers Java Applets and HTML. Each of the alternatives have advantages and disadvantages of each own in terms of development and deployment testing scalability portability maintenance and mass service. Especially inference engines implemented using HTML possess relatively large number of advantages compared with those implemented using other techniques. This paper explains the methodology to present rules and variables for backward inference by HTML and JavaScript and suggests a framework for design and development of HTML-based Expert System. A methodology to convert a traditional rule base to an Experts Diagram and then generate a new HTML-based Expert System from the Experts Diagram is also addressed.

• Journal of Intelligence and Information Systems
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• v.15 no.3
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• pp.31-51
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• 2009

Research on how to develop information systems efficiently and effectively since early 1960s has resulted in many techniques, methods and methodologies. Only a few of them, however, have been successfully practiced in the field. Model-Driven Development(MDD) is an innovative approach emphasizing the central role of model for development activities, attracting many practitioners' attention as well as researchers'. As MDD matures, many researchers have been trying to establish the evidence of its effectiveness. But many of them only suggest lessons learned or report limited evidence of effectiveness based on isolated case studies. This paper reports the state of the art of Model-Driven Engineering(MDE) and its major issues. We reviewed a number of papers and collected the conceptual definitions of MDE effectiveness from the technological and organizational perspectives. A case study in which MDD technology was adopted has been performed in order to measure the effectiveness of MDD quantitatively and qualitatively. This paper also analyzes and summarizes key considerations and lessons learned for IT organizations to adopt MDE successfully from the case study.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.23-36
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• 2009

Network intrusion detection have been continuously improved by using data mining techniques. There are two kinds of methods in intrusion detection using data mining-supervised learning with class label and unsupervised learning without class label. In this paper we have studied the way of improving network intrusion detection accuracy by using LBG clustering algorithm which is one of unsupervised learning methods. The K-means method, that starts with random initial centroids and performs clustering based on the Euclidean distance, is vulnerable to noisy data and outliers. The nonuniform binary split algorithm uses binary decomposition without assigning initial values, and it is relatively fast. In this paper we applied the EM(Expectation Maximization) based LBG algorithm that incorporates the strength of two algorithms to intrusion detection. The experimental results using the KDD cup dataset showed that the accuracy of detection can be improved by using the LBG algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2355-2362
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• 2016

Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.99-109
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• 2007

Variable message signs (VMS), which are used for providing real-time information on traffic conditions and accident occurrences, are one of the important components of intelligent transportation systems VMS messages need to meet human factor requirements: messages should be readable and understandable while driving. Lab-controlled experiments on VMS messages were conducted to obtain useful data for analyzing drivers' responsive characteristics for VMS messages. Binary logistic regression (BLR) modeling techniques were applied to explore the relationships among drivers' message perceptions, message reading time, and amount of VMS messages. Probabilistic outcomes of the proposed BLR-based perception model could be greatly utilized to design VMS messages considering drivers' legibility performance. The major contribution of this study is to develop invaluable statistical models that can be used in designing VMS messages more effectively from the human factor point of view. The results could be further applied to establish the scheme of VMS message phase and duration.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.391-398
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• 2017

In this paper, we compared the performance of "Network Intrusion Detection System based on attack feature selection using fuzzy control language"[1] and "Intelligent Intrusion Detection System Model for attack classification using RNN"[2]. In this paper, we compare the intrusion detection performance of two techniques using KDD CUP 99 dataset. The KDD 99 dataset contains data sets for training and test data sets that can detect existing intrusions through training. There are also data that can test whether training data and the types of intrusions that are not present in the test data can be detected. We compared two papers showing good intrusion detection performance in training and test data. In the comparative paper, there is a lack of performance to detect intrusions that exist but have no existing intrusion detection capability. Among the attack types, DoS, Probe, and R2L have high detection rate using fuzzy and U2L has a high detection rate using RNN.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.9-18
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• 2020

The research objectives investigates main research topics and trends in the information and communication technology(ICT) field, Korea using LDA(Latent Dirichlet Allocation), one of the topic modeling techniques. The experimental dataset of ICT research and development(R&D) project of 5,200 was acquired through matching with the EZone system of IITP after downloading R&D project dataset from NTIS(National Science and Technology Information Service) during recent five years. Consequently, our finding was that the majority research topics were found as intelligent information technologies such as AI, big data, and IoT, and the main research trends was hyper realistic media. Finally, it is expected that the research results of topic modeling on the national R&D foundation dataset become the powerful information about establishment of planning and strategy of future's research and development in the ICT field.

• Smart Structures and Systems
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• v.20 no.2
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• pp.231-246
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• 2017

A recent work by the authors has demonstrated the feasibility of scour evaluation for Kao-Ping-Hsi Cable-Stayed Bridge simply based on ambient vibration measurements. To further attain the goal of scour monitoring, a key challenge comes from the interference of several environmental factors that may also significantly alter the pier frequencies without the change of scour depth. Consequently, this study attempts to investigate the variation in certain modal frequencies of this bridge induced by several environmental factors. Four sets of pier vibration measurements were taken either during the season of plum rains, under regular summer days without rain, or in a period of typhoon. These signals are analyzed with the stochastic subspace identification and empirical mode decomposition techniques. The variations of the identified modal frequencies are then compared with those of the corresponding traffic load, air temperature, and water level. Comparison of the analyzed results elucidates that both the traffic load and the environmental temperature are negatively correlated with the bridge frequencies. However, the traffic load is clearly a more dominant factor to alternate the identified bridge deck frequency than the environmental temperature. The pier modes are also influenced by the passing traffic on the bridge deck, even though with a weaker correlation. In addition, the variation of air temperature follows a similar tendency as that of the passing traffic, but its effect on changing the bridge frequencies is obviously not as significant. As for the effect from the alternation of water level, it is observed that the frequency baselines of the pier modes may positively correlate with the water level during the seasons of plum rains and typhoon.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.70-77
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• 2017

Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.247-253
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• 2011

In this paper, we propose and implement the automated brightness control system using distance measuring sensor for reducing the power consumption of emotional lighting device. In order to reduce the power consumption of emotional lighting devices which express continuous color changes, the proposed device measures the distance continuously using ultrasonic sensor and by using this, it also performs PWM Dimming control. The lighting device is composed of micro controller, LED driver, ultrasonic sensor, communication module and so on. And the device performs the real time brightness control by adapting the measured distance information from ultrasonic sensor to PWM signals. From this experiment, we implement the active lighting system which minimizes unnecessary power consumption during user's absence by adapting existing energy reducing techniques.