• 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 Korean Society of societal Security
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• v.2 no.2
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• pp.49-54
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• 2009

High incident of accidents in construction jobsite became a social problem. According to the International Labour Organization (ILO), more than 60,000 fatal accidents occur each year in construction workplace worldwide. This number of accidents accounts for about 17 percent of all fatal workplace accidents. Especially, accidents from struck-by and falls comprise of over 60 percent of construction fatalities. This paper introduces a prototype of a received signal strength index (RSSI)-based safety monitoring to mitigate the potential accidents caused by falls and struck-by. Correlation between signal strength and noise index is examined to create the distance profile between a transmitter and a receiver. Throughout the distributed sensor nodes attached on potential hazardous objects, the proposed prototype envisions that construction workers with a tracker-tag can identify and monitor their current working environment in construction workplace, and early warning system can reduce the incidents of fatal accident in construction job site.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.180-185
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• 2011

The TPMS(Tire Pressure Monitoring System) is an electronic system designed to display the air pressure inside the pneumatic tires and report real-time tire-pressure information to the driver of the vehicle, either via a gange, a pictogram display, or a simple low-pressure warning light. Although the data measured by TPMS sensor is transmitted to internal signal processer in a vehicle through wireless communication, the receiver may suffers from various interferences such as amateur radio station, RFID(Radio-Frequency IDentification) for controlling container, RKE(Remote Keyless Entry) signal, and so on. In this paper, we consider beamforming technology to suppress various high-power interference signals for the TPMS wireless communications. Also, we propose the proper data structure and antenna arrangement for the beamformer inside the vehicle. The performance for the interference suppression is illustrated by computer simulation example.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.947-950
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• 2015

In the cause of car accidents in Korea, drowsy driving has shown that it is larger fctors than drunk driving. Therefore, in order to prevent drowsy driving accidents, drowsiness detection and warning system for drivers has recently become a very important issue. Furthermore, Many researches have been published that measuring alpha wave of EEG signals is the effective way in order to be aware of drowsiness of drivers. In this study, we have developed EEG measuring device that applies a signal processing algorithm using the LabView program for detecting drowsiness. According to results of drowsiness inducement experiments for small test subjects, it was able to detect the pattern of EEG, which means drowsy state based on the changing of power spectrum, counterpart of alpha wave. After all, Comparing to the results of drowsiness pattern between commercial equipments and developed device, we could confirm acquiring similar pattern to drowsiness pattern. With this results, the driver's drowsiness prevention system expect that it will be able to contribute to lowering the death rate caused by drowsy driving accidents.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. 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. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. 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, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2891-2898
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• 2013

CCSDS Standard is widely used in international space telecommunication area. In this standard, Encryption is realized using a unique hierarchical encryption protocol and satisfied security requirements of communication channels. For synchronization, encryption sync is attached in the beginning of encrypted data. But exceptional case(timing jittering, abnormal system shutdown, etc.) is occurred, receiving equipment cannot decrypt received data. In this paper, we propose a SES Alarmed link encryption synchronization method for sending warning signal to the transmitter when some problems have been occurred during the transmission and we also suggest optimum conditions for SES Alarm signal through performance analysis.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.119-126
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• 2024

This study aims to evaluate the feasibility of an early warning system for college dropout by machine learning the main patterns that affect college student dropout and to suggest ways to implement a system that can actively prevent it. For this purpose, a performance comparison experiment was conducted using five types of machine learning-based algorithms using data from the Korean Educational Longitudinal Study, 2005, conducted by the Korea Educational Development Institute. As a result of the experiment, the identification accuracy rate of students with the intention to drop out was up to 94.0% when using Random Forest, and the recall rate of students with the intention of dropping out was up to 77.0% when using Logistic Regression. It was measured. Lastly, based on the highest prediction model, we will provide counseling and management to students who are likely to drop out, and in particular, we will apply factors showing high importance by characteristic to the counseling method model. This study seeks to implement a model using IT technology to solve the career problems faced by college students, as dropout causes great costs to universities and individuals.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.1
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• pp.32-38
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• 2011

Collisions of nearby ships are reported frequently because of bad weathers. A lot of efforts, using radar warning or other navigation security devices, were given to reduce the collisions, but the number of accidents could not be reduced. The main cause is that the ship personel are not watching carefully. In the paper, we propose a novel technique estimating the locations of nearby ships from their whistle blast and delivering the location information using mobile phones. We realized the technique using LabVIEW and showed its usefulness.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.330-335
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• 2017

Cardiac arrest is owing to the failure of the heart that makes the blood circulation stop. Arrested blood circulation prevents the supply of the oxygen and the glucose and it results the loss of consciousness and, finally, brain death. Many public institution installed the AED for emergency treatment, but, it is not efficient when the patient is alone. In this paper, we made multiplexed wearable device for cardiac arrest detection. With this device, we measure the individual's electrocardiography, heart sound and motion. If the cardiac arrest is detected, the device make a warning horn and transmit the signal for defibrillation. We obtain 98.33% of ECG data, 94.5% of PCG data and 98.38% of IMU data accuracy for each evaluation and 93.33% accuracy for integrated evaluation.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.2
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• pp.164-170
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• 2013

The latest vehicle yields a superior safety and reduction of driving burden by monitoring the driving state of vehicle and its environment with various sensors. To detect other vehicles and objects of the rear left and right-side blind spot area of driver, provide the information about a existence of objects inside the blind spot, and give a signal to avoid collision, this study proposes the intelligent outside rear-view mirror system. This study proposes SILS system with PreScan and Matlab/Simulink to verify practical applicability of developed BSDS. PreScan yields realistic driving environments and road conditions and vehicle model dynamics and collision warning is controlled by Matlab/Simulink.