• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.827-833
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• 2022

This study aims to prevent secondary traffic accidents with high severity by overcoming the limitations of existing traffic information collection systems through analysis of traffic information collection detectors and various algorithms used to detect unexpected situations. In other words, this study is meaningful present that analyzing the 'unexpected situation that causes secondary traffic accidents' and 'Existing traffic information collection system' accordingly presenting a solution that can preemptively prevent secondary traffic accidents, intelligent traffic information collection system that enables accurate information collection on all sections of the road. As a result of the experiment, the reliability of data transmission reached 97% based on 95%, the data transmission speed averaged 209ms based on 1000ms, and the network failover time achieved targets of 50sec based on 120sec.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.257-262
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• 2021

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.905-910
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• 2022

Drowsy driving requires a lot of social attention because it increases the incidence of traffic accidents and leads to fatal accidents. The number of accidents caused by drowsy driving is increasing every year. Therefore, in order to solve this problem all over the world, research for measuring various biosignals is being conducted. Among them, this paper focuses on non-contact biosignal analysis. Various noises such as engine, tire, and body vibrations are generated in a running vehicle. To measure the driver's heart rate and respiration rate in a driving vehicle with a piezoelectric sensor, a sensor plate that can cushion vehicle vibrations was designed and noise generated from the vehicle was reduced. In addition, we developed a system for classifying whether the driver is sleeping or not by extracting the model using the CNN-LSTM ensemble learning technique based on the signal of the piezoelectric sensor. In order to learn the sleep state, the subject's biosignals were acquired every 30 seconds, and 797 pieces of data were comparatively analyzed.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.559-566
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• 2021

It is necessary to quickly detect and respond to ship accidents that occur continuously due to the influence of the recently increased maritime traffic. For this purpose, ship detection research is being actively conducted based on satellite images that can be monitored in real time over a wide area. However, there is a possibility that the wake may be falsely detected as a ship because the wake removal is not performed in previous studies that performed ship detection using spectral characteristics. Therefore, in this study, ship detection was performed using SDI (Ship Detection Index) based on the Sentinel-2A satellite image, and the wake was removed by utilizing the difference in the spectral characteristics of the ship and the wake. Probability of detection (POD) and false alarm rate (FAR) indices were used to verify the accuracy of the ship detection algorithm in this study. As a result of the verification, POD was similar and FAR was improved by 6.4% compared to the result of applying only SDI.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• Journal of Advanced Navigation Technology
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• v.24 no.3
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• pp.192-197
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• 2020

Recently, the technologies related to autonomous drive has studying the goal for safe operation and prevent accidents of vehicles. There is radar and camera technologies has used to detect obstacles in these autonomous vehicle research. Now a day, the method for using LiDAR sensor has considering to detect nearby objects and accurately measure the separation distance in the autonomous navigation. It is calculates the distance by recognizing the time differences between the reflected beams and it allows precise distance measurements. But it also has the disadvantage that the recognition rate of object in the atmospheric environment can be reduced. In this paper, point cloud data by triangular functions and Line Regression model are used to implement measurement algorithm, that has improved detecting objects in real time and reduce the error of measuring separation distances based on improved reliability of raw data from LiDAR sensor. It has verified that the range of object detection errors can be improved by using the Python imaging library.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.55-64
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• 2018

In this paper, we propose fully convolutional neural networks based automatic detection of a pothole that directly causes driver's safety accidents and the vehicle damage. First, the training DB is collected through the camera installed in the vehicle while driving on the road, and the model is trained in the form of a semantic segmentation using the fully convolutional neural networks. In order to generate robust performance in a dark environment, we augmented the training DB according to brightness, and finally generated a total of 30,000 training images. In addition, a total of 450 evaluation DB was created to verify the performance of the proposed automatic pothole detection, and a total of four experts evaluated each image. As a result, the proposed pothole detection showed robust performance for missing.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1551-1558
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• 2001

Recently, a number of underground pipelines have been drastically increased. The integrity of these buried pipelines, especially gas transmitting pipelines, is of importance due to an explosive characteristic of natural gas. The third party damage is known as one of the most critical factor which causes fatal accidents. For this reason, a number of systems detecting third party damage are under development. The major concern in the development of third party damage detection system is to transmit vibration signals out of accelerometer to signal conditioner and data acquisition system without any interference caused by noise. The objective of this paper is to develope a fiber optic accelerometer applicable to third party damage detection system. A fiber optic accelerometer was developed by use of combining principles of one degree of freedom vibration model and an extrinsic Fabry-Perot interferometer. The developed fiber optic accelerometer was designed to perform with a sensitivity of 0.06mVg, a frequency range of less than 6kHz and an amplitude range of -200g to 200g. The developed, accelerometer was compared with a piezoelectric accelerometer and calibrated. In order to verify the developed accelerometer, the field experiment was performed. From the field experiment, vibration signals and the location of impact were successfully detected. The developed accelerometer is expected to be used for the third party damage detection system which requires long distance transmission of signals.

• The KIPS Transactions:PartC
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• v.11C no.1
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• pp.39-46
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• 2004

It has been happening to transfer not only the general information but also the valuable information through the universal Internet. So security accidents as the expose of secret data and document increase. But we don't have stable structure for transmitting important data. Accordingly, in this paper we intend to use network based Intrusion Detection System modules and detect the extrusion of important data through the network, and propose and design the method for investigating concealment data to protect important data and investigate the secret document against the terrorism. We analyze the method for investigating concealment data, especially we use existing steganalysis techniques, so we propose and design the module emphasizing on the method for investigating stego-data in E-mail of attach files or Web-data of JPG, WAVE etc. Besides, we analyze the outcome through the experiment of the proposed stego-data detection system.