• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1997.11a
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• pp.117-122
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• 1997

This paper presents a study on the fuzzy based approach for the safety assessment of human body under ELF electric and magnetic(EM) field considering power system states. The analysis of ELF EM field based on quasi-static method is introduced. UP to the present, the analysis of ELF EM field has been conducted with the consideration of one transmission line, or a power line model only In this paper, however, the power system is included to model the expected and/or unexpected uncertainty caused by the load fluctuation and parameter changes and the states are classified into two types, normal state resulting from normal operation and emergency state from outages. In order to analyze the uncertainty in the normal state, the Monte Carlo Simulation, a statistic approach was introduced and line current and bus voltage distribution are calculated by a contingency analysis method, in the emergency state. To access the safety of human body, the approach based on fuzzy linguistic variable is adopted to overcome the shortcomings of the assessment by a crisp set concept. In order to validate the usefulness of the approach suggested herein, the case study using a sample system with 765(kV) was done. The results are presented and discussed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.10
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• pp.1017-1026
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• 2013

Four countries are participating in exposure assessment for Mobi-Kids Study. Each country evaluates RF and ELF exposure from mobile phones considering telecommunication system and environment in the corresponding area. The results will be used to determine accumulation exposure level of each subject in case and control groups to assess the potential carcinogenic effects of childhood and adolescent exposure to radio frequency(RF) and extremely low frequency( ELF) from mobile phones on the central nervous system. This paper analyzes exposure types of mobile phones which have been used in Korea for about 10 years, based on Mobi-Kids protocol. It includes investigation of mobile phone models released in the domestic market, design of numerical phone models to represent each exposure type, and measurement of ELF radiation from mobile phones.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.1
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• pp.84-91
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• 2001

In in vitro cell experiments for the biological assessment of electromagnetic (EM) field, exposure system (ES) must be analyzed in terms of current density (J) and induced electric field intensity (E). Although in uniform B field, E and J in the sample medium are not distributed uniformly because of conductivity in sample dish. Thus, the precise estimation for E and J induced by uniform ELF within sample media is very important keys for successful in in vitro experiments. In this paper, we designed in vitro ELF ES with electromagnetic analysis using MATLAB simulator. Then we calculated from the measured B field to verify induced E & J distribution for random locations of cells within media in two cases of samples existence or not. ES with B field ranging from 0 to 20G consists of Helmholz coil and current generator based on the microprocessor. Also, we developed ELFES for each B field generation as uniform and non-uniform modes.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.124-131
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• 2014

In this paper, we propose the non-contact gesture recognition algorithm using 4- channel electrometer sensor array. ELF(Extremely Low Frequency) EMI and PLN are minimized because ambient electromagnetic noise around sensors has a significant impact on entire data in indoor environments. In this study, we transform AC-type data into DC-type data by applying a 10Hz LPF as well as a maximum buffer value extracting algorithm considering H/W sampling rate. In addition, we minimize the noise with the Kalman filter and extract 2-dimensional movement information by taking difference value between two cross-diagonal deployed sensors. We implemented the DTW gesture recognition algorithm using extracted data and the time delayed information of peak values. Our experiment results show that average correct classification rate is over 95% on five-gesture scenario.