• Bulletin of Korea Environmental Preservation Association
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• v.14 no.8
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• pp.26-31
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• 1992

• Environmental engineer
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• s.12
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• pp.38-39
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• 1987

• Environmental engineer
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• s.85
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• pp.26-30
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• 1993

• Environmental engineer
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• s.87
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• pp.22-27
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• 1993

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.69-75
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• 2001

Intra-speaker variation, noise environments, and mismatch between training and test conditions are the major reasons for the speaker verification system unable to use it practically. In this study, we propose robust end-point detection algorithm, noise cancelling with the microphone property compensation technique, and inter-speaker discriminate technique by weighting cepstrum for robust speaker verification system. Simulation results show that the average speaker verification rate is improved in the rate of 17.65% with proposed end-point detection algorithm using LPC residue and is improved in the rate of 36.93% with proposed noise cancelling and microphone property compensation algorithm. The proposed weighting function for discriminating inter-speaker variations also improves the average speaker verification rate in the rate of 6.515%.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.60-66
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• 2008

Laboratory soil washing experiments with HCl or EDTA were conducted to remediate lead-contaminated soil in a firing range. After lead bullets were removed by standard sieve #18 (1.0 mM), Pb concentrations were measured by EPA Method 3050B (9,443 mg/kg) and Korea Standard Test (4,803.5 mg/kg). The results of the batch test showed that the removal efficiency curve was logarithmic and approximately 90% of lead in soil was removed, when HCl was used. In case of EDTA, the removal efficiency increased proportionally to the concentration of EDTA, up to 98% lead removal with 0.1M EDTA. High mixing strength resulted in increase of removal efficiency and kinetics showed that the most lead was extracted in 10 min.

• Journal of Soil and Groundwater Environment
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• v.12 no.2
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• pp.65-71
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• 2007

This study has been focused on an application of microwave pre-treatment of soil contaminated with volatile organic chemicals, and BTEX(benzene, toluene, ethylene, xylene). Microwave experiments were carried out under different power conditions (2 kW, 4 kW) using different moisture contents and BTEX concentration. According to these results of this study, the most BTEX removal efficiency was with 20% moisture contents regardless of electric power. The result show that 2kW was determined to the optimum electric power at $10{\sim}30%$ moisture contents, but the optimum power was 4 kW at 50% of moisture content.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.31-31
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• 2003

최근 각종 산업 기술의 발달과 함께 다량의 악취, 오염 물질이 심각한 사회문제로 대두되고 있다. 특히 산업 현장의 경우, 악취로 인해 작업 환경이 매우 열악하여 환경 설비의 개발이 절실히 요구되고 있다. 본 연구에서는 기존의 스크러버 집진 장치의 친수성 가스를 제거할 수 있는 장점을 유지하면서, 소수성 기체의 제거율이 낮은 단점을 보완할 수 있는 TiO$_2$나노 입자의 광분해 특성을 이용한 스크러버 집진 장치를 제작하였다. TiO$_2$ 광촉매 나노 입자는 실험실에서 합성한 QT를 비롯하여, 국내외에서 생산된 P-25, NT-20, ST-01로 특성 분석을 실시하였다. 광촉매 나노 입자의 특성 분석에는 X선 회절 분석법, 적외선 분광기, 제타 전위 분석기, 동적 광산란 분광기를 사용하였으며, 그 결과 ST-01이 본 실험에 가장 적합한 광촉매로 선정되었다. 악취 및 대기 오염 물질 선정은 염색 가공 공장에서 정련제, 산화 환원제등으로 많이 사용하고 있는 암모니아, 아세트산, 옥탄올, 휘발성 유기물질인 톨루엔, 벤젠으로 하여, 실험실 자체 제작한 batch type과 산업 현장에서 사용할 수 있는 continuous type의 스크러버 이용하여 실험하였다. 이들 물질의 제거율은 가스 크로마토그래피, 자외선/가시광선 분광기를 이용하여 조사하였으며, 친수성 기체인 암모니아와 아세트산의 경우는 기존의 스크러버 효율의 약 70%인 반면에, 광촉매를 이용한 스크러버의 경우 90%이상의 우수한 제거 효과를 보였다. 또한, 기존의 스크러버가 거의 분해를 할 수 없었던 소수성 기체 옥탄올, 톨루엔, 벤젠의 경우는 기존의 스크러버가 약 10%내외를 제거하는 것에 비해, 광촉매를 이용한 스크러버의 경우 약 80% 이상의 제거 효율을 나타내었다. 결과적으로 광촉매를 이용한 스크러버의 제거효율이 기존의 스크러버에 비해, 친수성 가스의 경우 20% 이상, 소수성 가스의 경우70% 이상 향상되었음을 알 수 있었으며, 본 연구를 통해 광분해를 이용한 스크러버가 기존설비의 장.단점을 충분히 보완 가능한 환경 설비임을 확인할 수 있었다.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.845-853
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• 2020

In this paper, we propose a method for classifying environmental sound for selective noise cancellation in industrial sites. Noise in industrial sites causes hearing loss in workers, and researches on noise cancellation have been widely conducted. However, the conventional methods have a problem of blocking all sounds and cannot provide the optimal operation per noise type because of common cancellation method for all types of noise. In order to perform selective noise cancellation, therefore, we propose a method for environmental sound classification based on deep learning. The proposed method uses new sets of acoustic features consisting of temporal and statistical properties of Mel-spectrogram, which can overcome the limitation of Mel-spectrogram features, and uses convolutional neural network as a classifier. We apply the proposed method to five-class sound classification with three noise classes and two non-noise classes. We confirm that the proposed method provides improved classification accuracy by 6.6% point, compared with that using conventional Mel-spectrogram features.

• Applied Chemistry for Engineering
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• v.22 no.4
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• pp.433-438
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• 2011

In this study, effects of temperature, NSR, and oxygen concentration on the $NO_x$ removal efficiency of an SNCR process were investigated experimentally as well as numerically using CHEMKIN-II program. The NO removal efficiency increased with the reactor temperature under oxygen-free condition, whereas when the oxygen concentration was 4%, the NO removal efficiency showed a maximum value at $900{\sim}950^{\circ}C$. The pressure of oxygen was shown to enhance the NO removal at low temperature. Regardless of the oxygen concentration, the NO removal efficiency increased with NSR. The temperature and NSR-dependencies of the NO removal efficiency predicted by CHEMKIN-II simulations were similar to that of the experimental results.