• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.145-154
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• 2012

This paper analyzed the characteristics of traffic accidents in all tunnels on nationwide freeways and selected some various independent variables related to accident occurrence in tunnels. The study aims to develop reliable accident forecasting models using the various dependent variables such as the number of accident (no.), no./km, and no./MVK. Finally, reliable multiple linear regression models were proposed in this paper. This study tested the validity verification of developed models through statistics such as $R^2$, F values, multicollinearity, residual analysis. The paper selected the accident forecasting models considering the characteristics of tunnel accidents and two models were finally proposed according to two groups of tunnel length. In the selected models, natural logarithm of ln(no./MVK) is used for the dependent variable and AADT, vertical slope, and tunnel hight are used for the independent variables. The reliability of two models was proved by the comparison analysis between field data and estimating data using RMSE and MAE. These models may be not only effective in evaluating tunnel safety under design and planning phases of tunnel but also useful to reduce traffic accidents in tunnels and to manage the traffic flow of tunnel.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.6
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• pp.133-144
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• 2012

This study focused on estimating influential factors of traffic accidents and analyzing traffic accident severity of elderly and non elderly using traffic accident data. In order to reclassify elderly and non elderly traffic accident by a statistical method from entire traffic accident data, multiple discriminant analysis was applied. Also ordered logit model was applied for analyzing traffic accident severities using traffic accident severities as an independent variable and transportation facilities, road conditions and human characteristics as dependent variables. As results of the comparison between elderly and non elderly traffic accident, the traffic accident severity was affected by the age, types of traffic accidents, human characteristics and road conditions as well. Also, transportation facilities and road conditions affected to more elderly traffic accident than non elderly. Therefore, traffic accident severity would be decreased with the improvement of transportation facilities and road conditions for the elderly.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.153-172
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• 2018

Overloading of Commercial vehicles have been an important area of transportation as one of the main causes of pavement damage, bridge collapse, severe traffic accident, etc. In this study, we analyzed the effects of overweight prevention by analyzing overweight driving patterns and using weight sensors. First, we analyzed relevant literatures of overweight and surveyed the commercial weight sensors. Then we chose the typical type of overweight vehicles based of overweight enforcement data analysis. MEMs inclinometer weight sensor were installed to 10 test vehicles and data was collected by weight sensors and gps in real time. As a result of gross vehicle weight and axle weight analysis, it was found weight sensor could decrease overweight rate. However, since the number of samples of test vehicles is insufficient to represent the whole commercial vehicle, further studies are deemed possible through the extension test.

• Safety and Health at Work
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• v.6 no.2
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• pp.77-84
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• 2015

Background: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. Methods: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. Results: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. Conclusion: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.63-79
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• 2001

Data Envelopment Analysis(DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications and merits, some features of DEA remain bothersome. DEA offers no guideline about to which direction relatively inefficient DMUs improve since a reference set of an inefficient DMU, several efficient DMUs, hardly provides a stepwise path for improving the efficiency of the inefficient DMU. In this paper, we aim to show that DEA can be used to evaluate the efficiency of life insurance companies while overcoming its limitation with the aids of machine learning methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.417-428
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• 2018

The intelligent video surveillance environment is a system that extracts various information about a video object and enables automated processing through the analysis of video data collected in CCTV. However, since the privacy exposure problem may occur in the process of intelligent video surveillance, it is necessary to take a security measure. Especially, video metadata has high vulnerability because it can include various personal information analyzed based on big data. In this paper, we propose a COP-Transformation scheme to protect video metadata. The proposed scheme is advantageous in that it greatly enhances the security and efficiency in processing the video metadata.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.5 no.2 s.10
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• pp.44-54
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• 2006

The share rate of bus mode has been decreasing continuously. To solve this problem, the local governments implement Bus Management System(BMS) or Bus Impormation System(BIS). In operating the public transportation preferential policy, it is very important to verify the effectiveness of the projects. This study performed the effectiveness of BMS through before and after study based on the field data. Moreover, this study analyzed the factors affecting to BMS. We anticipate that the results of this study would be useful reference in local government's BMS planning.

• Smart Structures and Systems
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• v.4 no.3
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• pp.305-318
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• 2008

The development of a digital signal processor based prototype is described in relation to continuing efforts for realizing a fully self-contained active sensor system utilizing impedance-based structural health monitoring. The impedance method utilizes a piezoelectric material bonded to the structure under observation to act as both an actuator and sensor. By monitoring the electrical impedance of the piezoelectric material, insights into the health of the structured can be inferred. The active sensing system detailed in this paper interrogates a structure utilizing a self-sensing actuator and a low cost impedance method. Here, all the data processing, storage, and analysis is performed at the sensor location. A wireless transmitter is used to communicate the current status of the structure. With this new low cost, field deployable impedance analyzer, reliance on traditional expensive, bulky, and power consuming impedance analyzers is no longer necessary. A complete power analysis of the prototype is performed to determine the validity of power harvesting being utilized for self-containment of the hardware. Experimental validation of the prototype on a representative structure is also performed and compared to traditional methods of damage detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.417-422
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• 2000

In this paper, an efficient fuzzy rule generation scheme for adaptive neuro-fuzzy system modeling using the Independent Component Analysis(ICA) as a preprocessing is proposed. Correlation between inputs was not considered in the conventional neuro- fuzzy modeling schemes, such that enormous number of rules and large amount of error were unavoidable. The correlation between inputs is weakened by employing ICA so that the number of rules and the amount of error are reduced. In simulation, the Box-Jenkins furnace data is used to verify the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.