• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.105-112
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• 2011

Delay reduction of vehicles at the intersection is highly dependent on the signal operation method. Improper traffic operation causes the violation of the traffic regulations and increasing traffic congestion. Delay because of congestion has contributed to the increase in carbon dioxide in the atmosphere. The focus of this paper is to measure the amount of carbon dioxide when the intersection is changed to roundabout. Even though, Intergovernmental Panel on Climate Change(IPCC) recommends Tier 1 method to measure the amount of greenhouse gas from vehicles, this paper used Tier 3 method because we could use the data of average running distance per each vehicle model. Two signalized intersections were selected as the study area and the delay reductions of roundabout operation were estimated by VISSIM microscopic simulation tool. The control delay for boksu intersection reduced from 28.6 seconds to 4.4 seconds and the KRIBB intersection sharply reduced from 156.4 seconds to 23.6 seconds. In addition, carbon dioxide for two intersections reduced to 646.5 ton/year if the intersection is changed to roundabout. Future research tasks include testing the experiment for networks, as well as for various intersection types.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.15-26
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• 2003

Ultrasonic inspection methods are widely used for detecting flaws in materials. The signal analysis step plays a crucial part in the data interpretation process. A number of signal processing methods have been proposed to classify ultrasonic flaw signals. One of the more popular methods involves the extraction of an appropriate set of features followed by the use of a neural network for the classification of the signals in the feature space. This paper describes an alternative approach which uses the least mean square (LMS) method and expectation maximization (EM) algorithm with the model based deconvolution which is employed for classifying nondestructive evaluation (NDE) signals from steam generator tubes in a nuclear power plant. The signals due to cracks and deposits are not significantly different. These signals must be discriminated to prevent from happening a huge disaster such as contamination of water or explosion. A model based deconvolution has been described to facilitate comparison of classification results. The method uses the space alternating generalized expectation maximization (SAGE) algorithm In conjunction with the Newton-Raphson method which uses the Hessian parameter resulting in fast convergence to estimate the time of flight and the distance between the tube wall and the ultrasonic sensor Results using these schemes for the classification of ultrasonic signals from cracks and deposits within steam generator tubes are presented and showed a reasonable performances.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.19-29
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• 2003

This paper presents a novel hybrid classifier for face recognition with artificially generated virtual training samples. We utilize both the nearest neighbor approach in feature angle space and a connectionist model to obtain a synergy effect by combining the results of two heterogeneous classifiers. First, a classifier called the nearest feature angle (NFA), based on angular information, finds the most similar feature to the query from a given training set. Second, a classifier has been developed based on the recall of stored frontal projection of the query feature. It uses a frontal recall network (FRN) that finds the most similar frontal one among the stored frontal feature set. For FRN, we used an ensemble neural network consisting of multiple multiplayer perceptrons (MLPs), each of which is trained independently to enhance generalization capability. Further, both classifiers used the virtual training set generated adaptively, according to the spatial distribution of each person's training samples. Finally, the results of the two classifiers are combined to comprise the best matching class, and a corresponding similarit measure is used to make the final decision. The proposed classifier achieved an average classification rate of 96.33% against a large group of different test sets of images, and its average error rate is 61.5% that of the nearest feature line (NFL) method, and achieves a more robust classification performance.