• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.128-141
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• 2018

In this paper, a methodology to analyze the potential safe benefit of six cooperative driver assistance systems via V2V (vehicle-to-vehicle) communications is proposed. Although it is quite necessary to assess social impact with respect to new safety technologies for cooperative vehicles with V2V communications, there are few studies in Korea to predict the quantitative safety benefit analysis. In this study, traffic accident scenarios are classified based on traffic fatality between passenger cars. The sequential collision type is classified for a multiple pile-up with respect to collision direction such as forward, side, head-on collisions. Then movement of surrounding vehicle is considered for the scenario classification. Next, the cooperative driver assistance systems such as forward collision warning, blind spot detection, and intersection movement assistance are related with the corresponding accident scenarios. Finally, it is summarized how much traffic fatality may be reduced potentially due to the V2V communication based safety services.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.98-106
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• 1998

This paper proposes an efficient learning algorithm for improving the training performance of the neural network. The proposed method improves the training performance by applying the backpropagation algorithm of a global optimization method which is a hybrid of a stochastic approximation and a conjugate gradient method. The approximate initial point for f a ~gtl obal optimization is estimated first by applying the stochastic approximation, and then the conjugate gradient method, which is the fast gradient descent method, is applied for a high speed optimization. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to those of the conventional backpropagation and the backpropagation algorithm which is a hyhrid of the stochastic approximation and steepest descent method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.367-371
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• 2006

Multilayer percepteon has the advantage of learning their optimal parameters and efficiency. However, MLP shows some drawbacks when dealing with high dimensional data within the input space. Also, it Is very difficult to find the optimal parameters when the input data are highly correlated such as large scale face dataset. In this paper, we propose a novel technique for face recognition based on LDA and local MLP. To resolve the main drawback of MLP, we calculate the reduced features by LDA in advance. And then, we construct a local MLP per group consisting of subset of facedatabase to find its optimal learning parameters rather than using whole faces. Finally, we designed the face recognition system combined with the local MLPs. From various experiments, we obtained better classification performance in comparison with the results produced by conventional methods such as PCA and LDA.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.383-388
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• 2006

SBR is one of the most general sewage/wastewater treatment processes and, particularly, has an advantage in high concentration wastewater treatment like sewage wastewater. A Kernel PCA based fault diagnosis system for biological reaction in full-scale wastewater treatment plant was proposed using only common bio-chemical sensors such as ORP(Oxidation-Reduction Potential) and DO(Dissolved Oxygen). During the SBR operation, the operation status could be divided into normal status and abnormal status such as controller malfunction, influent disturbance and instrumental trouble. For the classification and diagnosis of these statuses, a series of preprocessing, dimension reduction using PCA, LDA, K-PCA and feature reduction was performed. Also, the diagnosis result using differential data was superior to that of raw data, and the fusion data show better results than other data. Also, the results of combination of K-PCA and LDA were better than those of LDA or (PCA+LDA). Finally, the fault recognition rate in case of using only ORP or DO was around maximum 97.03% and the fusion method showed better result of maximum 98.02%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.52-61
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• 2015

PCE(Passenger Car Equivalents) is used to analysis of road capacity and LOS(Level of Service). In this study calculates PCE by number of lane and 12 vehicle type by MOLIT(Minister of Land, Infra Structure and Transport) using individual vehicle data. The results of the calculation, PCEs are increased when high vehicle classification level, many number of lanes and weekend. Heavy vehicle factors are smaller than KHCM on 4, 6 lane. Also, In this study estimates of PCE variation model by heavy vehicle percentage. Impact of Heavy vehicles on PCEs is the most sensitive on 2 lane. The results of the study, heavy vehicles low impact on PCE on multi-lane and business trips are a little in weekend.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.13-29
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• 2020

Automobiles have long been an essential part of daily life, but the social costs of car traffic accidents exceed 9% of the national budget of Korea. Hence, it is necessary to establish prevention and response system for car traffic accidents. In order to present a model that can classify and predict the degree of injury in car traffic accidents, we used big data analysis techniques of K-nearest neighbor, logistic regression analysis, naive bayes classifier, decision tree, and ensemble algorithm. The performances of the models were analyzed by using the data on the nationwide traffic accidents over the past three years. In particular, considering the difference in the number of data among the respective injury severity levels, we used down-sampling methods for the group with a large number of samples to enhance the accuracy of the classification of the models and then verified the statistical significance of the models using ANOVA.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.1-12
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• 2012

This paper aims at developing an active expressway entrance control effectiveness analysis tool which operators can utilize and manage traffic based on current traffic condition. For this, after identifying the current problems of tollgate-based entrance policy being used, a new set of decision element such as congestion index, decision criteria for congestion, and congestion management unit has been proposed together with the procedure of newly developed tollgate control policy. Three key parts developed are traffic condition identification module, tollgate metering module, and travel speed calculation module. Some measures of effectiveness were also identified and the newly developed effectiveness analysis tool produced better result. According to classification of traffic condition by reference speed as 80km/h, the improved tollgate entrance procedure increased 21.5% in average travel speed compared with Do-Nothing case and also increased 8.8% compared with current entrance control method.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.161-165
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• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.361-367
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• 2016

The intruder detection system using digital PIR sensor has the problem that it can't recognize human correctly. In this paper, we suggest a new intruder detection system based on analog PIR sensor to get around the drawbacks of the digital PIR sensor. The analog type PIR sensor emits the voltage output at various levels whereas the output of the digitial PIR sensor is binary. The signal captured using analog PIR sensor is sampled, and its frequency feature is extracted using FFT or MFCC. The extracted features are used for the input of neural networks. After neural network is trained using various human and pet's intrusion data, it is used for classifying human and pet in the intrusion situation.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.887-892
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• 2005

Under pure P2P environments, collaborative filtering must be provided with only a few service items by real time information without accumulated data. However, in case of collaborative filtering with only a few service items collected locally, quality of recommended service becomes low. Therefore, it is necessary to research a method to improve quality of recommended service by users' context information. But because a great volume of users' context information can be recognized in a moment, there can be a scalability problem and there are limitations in supporting differentiated services according to fields and items. In this paper, we solved the scalability problem by clustering context information Per each service field and classifying il per each user, using SOM. In addition, we could recommend proper services for users by measuring the context information of the users belonging to the similar classification to the service requester among classified data and then using collaborative filtering.