• Composites Research
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• v.14 no.6
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• pp.47-58
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• 2001

This paper describes applications of cure monitoring techniques by using embedded fiber optic strain sensors, which are extrinsic Fabry-Perot interoferometric (EFPI) and/or fiber Bra99 grating (FBG) sensors, to three kinds of molding methods of autoclave, FW and RTM molding methods. In these applications, internal strain of high-temperature curing resin was monitored by EFPI sensors. From theme experimental results, it was shown that strain caused by thermal shrink at cooling stage could be measured well. In addition, several specific matters to these molding methods were considered. As thor an autoclave molding of unidirectional FRP laminates, it was confirmed that off-axis strain of unidirectional FRP could be monitored by EFPI sensors. As for FW molding using room-temperature (RT) cured resin, it was found that the strain outputs from EFPI sensors represented curing shrinkage as well as thermal strain and the convergence meant finish of cure reaction. It was also shown that this curing shrinkage should be evaluated with consideration on logarithmic change in stiffness of matrix resin. As for a RTM melding, both EFPI and FBC sensors were employed to measure strain. The results showed that FBG sensors hale also good potential for strain monitoring at cooling stage, while the non-uniform thermal residual strain of textile affected the FBG spectrum after molding. This study has proven that embedded fiber optic strain sensors hale practical ability of cure monitoring of FRP. However, development of automatic installation methods of sensors remains as a problem to be solved for applications to practical products.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.68-73
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• 2005

Various techniques have been proposed for detection and tracking of targets in order to develop a real-world computer vision system, e.g., visual surveillance systems, intelligent transport systems (ITSs), and so forth. Especially, the idea of distributed vision system is required to realize these techniques in a wide-spread area. In this paper, we develop a ubiquitous vision system for location-awareness of multiple targets. Here, each vision sensor that the system is composed of can perform exact segmentation for a target by color and motion information, and visual tracking for multiple targets in real-time. We construct the ubiquitous vision system as the multiagent system by regarding each vision sensor as the agent (the vision agent). Therefore, we solve matching problem for the identity of a target as handover by protocol-based approach. We propose the identified contract net (ICN) protocol for the approach. The ICN protocol not only is independent of the number of vision agents but also doesn't need calibration between vision agents. Therefore, the ICN protocol raises speed, scalability, and modularity of the system. We adapt the ICN protocol in our ubiquitous vision system that we construct in order to make an experiment. Our ubiquitous vision system shows us reliable results and the ICN protocol is successfully operated through several experiments.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.33-41
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• 2013

Intelligent automobile technology and IT convergence, the development of new imaging technology media applications based on open source Android installed on tracked, wheeled smart phone application technology and the development of intelligent vehicles as a new paradigm a lot of research and development being made. Android-based intelligent automotive applications, technology, and evolved into the center of a set of various multimedia technologies move beyond the limits of the means of each of multimedia platforms, services and applications that have been developed in such a distributed environment, has been developed according to a variety of services through technology mobile terminal device technology is an absolute requirement. In this paper, SVC Codec, real-time video and graphics processing and SoC design intelligent vehicles middleware applications with monolithic system specification through Android-based design of intelligent vehicles dedicated middleware research experiments on open platforms, and provides various terminal services functions SoC based on a newly designed and standardized interface analysis techniques in this study were verified through experiments.

• Journal of Intelligence and Information Systems
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• v.11 no.3
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• pp.57-67
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilized Holland career search test results, TOEIC score, course work list and GPA score as the input for data mining, and we were able to generate knowledge and rules with regard to the college road map advisory service. Factor analysis and AHP(Analytic Hierarchy Process) were the primary techniques deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained from the human student advice experts.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.79-88
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• 2018

With the increasing popularity of electronic toll collection system using 5.8 GHz dedicated short-range communications (DSRC) technology, DSRC-based travel-time collection systems have been deployed on major urban and rural arterial routes in Korea. However, since probe sample sizes are frequently insufficient in probe-based systems, the gathered travel times from probe vehicles fluctuate significantly compared to those of the population; as a result, the accuracy of the collected travel times could decrease. To mitigate the fluctuations (also known as biases), smoothing techniques need to be applied. In this study, some smoothing techniques-moving average, the Loess, and Savitzky-Golay filtering-were applied to probe travel times. Resultantly, the error in the smoothed travel times at the lowest sampling plan (5%) decreased as much as 45% compared to those in non-smoothed travel times. The results of this study can be practically applied to probe-based travel-time estimation systems for providing reliable travel times along the travel corridor.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.1 s.2
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• pp.75-84
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• 2003

Current techniques for road-traffic monitoring rely on sensors which have limited capabilities, are costly and disruptive to install. The use of video cameras coupled with computer vision techniques offers an attractive alternative to current sensors. Video based traffic monitoring systems are now being considered key points of advanced traffic management systems. In this paper, we propose the new method which extract the traffic information using video camera. The proposed method uses an adaptive updating scheme for background in order to reduce the false alarm rate due to various noises in images. also, the proposed extraction method of traffic information calculates the traffic volume ratio of vehicles passing through predefined detection area, which is defined by the length of profile occupied by cars over that of overall detection area. Then the ratio is used to define 8 different states of traffic and to interpret the state of vehicle flows. The proposed method is verified by an experiment using CCTV traffic data from urban area.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.698-703
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• 2003

This paper presents analog secure communication system about safe speech transmission using chaotic signals. We applied various conditions that happen in actuality communication environment modifying chaotic synchronization and chaotic communication schemes and analyzed restoration performance of speech signal to computer simulation. In transmitter, we made the chaotic masking signal which is added voice signal to chaotic signal using PC(Pecora ＆ Carroll) and SFB(self-feedback) control techniques and transmitted encryption signal to noisy communication channel And in order to calculate the degree of restoration performance, we proposed the definition of analog average power of recovered error signals in receiver chaotic system. The simulation results show that feedback control techniques can certify that restoration performance is superior to quantitative data than PC method about masking degree, susceptibility of parameters and channel noise. We experimentally computed the table of relation of parameter fluxion to restoration error rate which is applied the encryption key values to the chaotic secure communication.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.266-273
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• 2005

Data mining techniques enable us to generate useful information for decision support from the data sources which are generated and accumulated in the process of routine organizational management activities. College administration system is a typical example that produces a warehouse of student records as each and every student enters a college and undertakes the curricular and extracurricular activities. So far, these data have been utilized to a very limited student service purposes, such as issuance of transcripts, graduation evaluation, GPA calculation, etc. In this paper, we utilize Holland career search test results, TOEIC score, course work list, and GPA score as the input for data mining and generation the student advisory information. Factor analysis, AHP(Analytic Hierarchy Process), artificial neural net, and CART(Classification And Regression Tree) techniques are deployed in the data mining process. Since these data mining techniques are very powerful in processing and discovering useful knowledge and information from large scale student databases, we can expect a highly sophisticated student advisory knowledge and services which may not be obtained with the human student advice experts.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.372-377
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• 2006

In this paper, we propose sensibility recognition model that recognize user's sensibility using brain waves. Method to acquire quantitative data of brain waves including priority living body data or sensitivity data to recognize user's sensitivity need and pattern recognition techniques to examine closely present user's sensitivity state through next acquired brain waves becomes problem that is important. In this paper, we used pattern recognition techniques to use Multi Layer Perceptron (MLP) that is pattern recognition techniques that recognize user's sensibility state through brain waves. We measures several subject's emotion brain waves in specification space for an experiment of sensibility recognition model's which propose in this paper and we made a emotion DB by the meaning data that made of concentration or stability by the brain waves measured. The model recognizes new user's sensibility by the user's brain waves after study by sensibility recognition model which propose in this paper to emotion DB. Finally, we estimates the performance of sensibility recognition model which used brain waves as that measure the change of recognition rate by the number of subjects and a number of hidden nodes.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.309-320
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• 2007

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. It causes low prediction accuracy of the minority class because classifiers tend to assign instances to major classes and ignore the minor class to reduce overall misclassification rate. In order to solve the data imbalance problem, there has been proposed a number of techniques based on resampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.