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Glufosinate ammonium 중독 후에 발생한 해마 손상에 의한 선행성 기억상실 1례
설승환,박현수,안정환,박희영,이필휴,김기운,Seol Seung-Hwan,Park Hyeon-Soo,Ahn Jung-Hwan,Park Hee-Young,Lee Phil-Hyu,Kim Gi-Woon 대한임상독성학회 2006 대한임상독성학회지 Vol.4 No.1
Glufosinate ammonium (GLA), a phosphinic acid derivate of glutamate, is a broad-spectrum contact herbicide. It structurally resembles glutamate, a typical excitatory amino acid in the central nervous system. In korea, the ingestion of GLA for suicidal attempt or accidental event has increased. The neurological complication of GLA intoxication are characterized by loss of consciousness, convulsion, or memory impairment. But, the exact mechanism of GLA toxicity is yet unknown. This report is about a patient with GLA intoxication who showed anterograde amnesia with selective bilateral hip-pocampal lesions supported GLA intoxication with literature reviews supported.
기두부와 단 분리 시 조각의 식별을 위한 합성곱 신경망 구조 설계
설승환(Seung-Hwan Seol),최인식(In-Sik Choi),신진우(Jin-Woo Shin),정명수(Myung-Soo Chung) 한국정보기술학회 2018 한국정보기술학회논문지 Vol.16 No.6
In this paper, we designed CNN(Convolutional Neural Network) structure to identify warhead and debris in boosting part separation phase. Through simulation, we determined variables of each layer constituting the CNN and designed CNN structure. Simulation were performed to classify four types of warhead with coning motion and six types of debris with tumbling motion through the CNN designed by the proposed method. Then we compared the performance of CNN with the well-known VGGNet. Simulation results show that the CNN structure optimized by the convolution filter, pooling method, and pooling size determined using the proposed method has equal classification performance or better classification performance than VGGNet for all SNR. In addition, the training time was improved approximately 22 times.
CFAR와 합성곱 신경망을 이용한 기두부와 단 분리 시 조각 구분
설승환(Seung-Hwan Seol),최인식(In-Sik Choi) 한국정보기술학회 2019 한국정보기술학회논문지 Vol.17 No.6
Warhead and debris show the different micro-Doppler frequency shape in the spectrogram because of the different micro motion. So we can classify them using the micro-Doppler features. In this paper, we classified warhead and debris in the separation phase using CNN(Convolutional Neural Networks). For the input image of CNN, we used micro-Doppler spectrogram. In addition, to improve classification performance of warhead and debris, we applied the preprocessing using CA-CFAR to the micro-Doppler spectrogram. As a result, when the preprocessing of micro-Doppler spectrogram was used, classification performance is improved in all signal-to-noise ratio(SNR).
다중 채널 융합 기법을 이용한 DTV 기반 수동형 레이다의 표적 인식 방법
설승환(Seung-Hwan Seol),최영재(Young-Jae Choi),최인식(In-Sik Choi) 한국전자파학회 2017 한국전자파학회논문지 Vol.28 No.10
본 논문에서는 DTV(Digital Television) 기반의 수동형 레이다와 다중 채널 융합 기법을 이용한 항공기 표적 인식 방법을 제안하였다. DTV에서 송신되는 다수의 채널을 융합하여 표적인식에 필요한 해상도의 HRRP(High Resolution Range Profile)를 획득하였다. HRRP는 AR(Auto Regressive) 기법 또는 제로 패딩 기법을 이용하여 획득하였다. 획득한 HRRP로부터, 경사하강법을 이용한 CLEAN 기법을 통해 산란점을 추출한 후 특성벡터를 생성하였으며, 이를 신경망 구분기에 학습시켜 표적 인식을 수행하였다. 제안된 방법의 성능을 검증하기 위하여 실제 국내에서 운용되고 있는 3개의 송신소(관악산, 용문산, 견월악)의 주파수 대역을 가정하고, 4종의 항공기 실스케일 3D 캐드 모델을 이용하여 제안된 방법과 각 송신소의 단일 채널 주파수를 이용하였을 때의 표적인식 성능을 비교하였다. 시뮬레이션 결과, 제안된 방법이 3개의 송신소 모두에서 각 송신소의 단일 채널 주파수를 이용하였을 때보다 높은 표적 인식 성능을 보였다. In this paper, we proposed airborne target recognition using multi-channel combining method in DTV-based passive radar. By combining multi-channel signals, we obtained the HRRP with sufficient range resolution. HRRP was obtained by AR method or zeropadding. From the obtained HRRP, we extracted scattering centers by CLEAN algorithm using the gradient descent. We extracted feature vectors and performed target recognition after training neural network using the extracted feature vectors. To verify performance of proposed methods, we assumed frequency bands of three broadcasting transmitters operated in Korea(Mt. Gwan-ak, Mt. Yong-moon, Kyeon-wol-ak) and used full scale 3D CAD model of four targets. Also we compared the target recognition performance of the proposed method with that of using only single-channel of three broadcasting transmitters. As a result, proposed methods showed better performance than using only single-channel at three broadcasting transmitters.
생체모방 은밀 수중통신을 위한 머신러닝 기반 수중 채널 추정 방법
설승환(Seol Seung Hwan),이호준(Lee Ho Jun),김용철(Kim Yong Cheol),김완진(Kim Wan Jin),정재학(Chung Jae hak) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
본 논문에서는 생체모방 은밀 수중통신을 위한 머신러닝 기반의 수중 채널 추정 방법을 제안하였다. 돌고래 휘슬 모방 신호가 낮은 주파수 대역을 가지는 경우 수중 채널을 알 수 있다면 통신 성능을 높일 수 있다. 이를 위해 수중 채널을 추정하는 방법으로 머신러닝을 통해 수중에서의 수직 음속구조를 이용하여 수중 채널을 추정한다. 실제 해상 실험에서 얻은 수중 채널과 추정한 수중 채널이 유사함을 전산 모의실험을 통해 보였다.
개방성 치조골 골절의 고정법: 응급실에서 간편하게 적용할 수 있는 방법
설승환 ( Seung Hwan Seol ),차수현 ( Soo Hyun Cha ),최상천 ( Sang Cheon Choi ),안정환 ( Jung Hwan Ahn ),김기운 ( Gi Woon Kim ),최혜경 ( Hea Kyung Choi ),조준필 ( Joon Pil Cho ),정윤석 ( Yoon Seok Jung ) 대한외상학회 2007 大韓外傷學會誌 Vol.20 No.2
Purpose: The purpose of this clinical trial was to evaluate the fixation method for treating alveolar fractures in an emergency department. Methods: The efficiency of using the fixation method was judged on the basis of clinical criteria. Stability, occlusion state, bleeding amount after fixation, operation time, and difficulties during procedural operation were recorded. Results: Eight patients were enrolled in this study. In all instances, the fixation method was effective in bleeding control. Each patient had a noticeable decrement in bleeding. A wire was used for four of the eight patients, and nylon strings was used for the others. The average operation time was 6.3 minutes for the wire patients and 2.8 minutes for the Nylon string patients. No specific problem was identified during the procedural operation. However, the difference in the fixation material influenced the effectiveness of the procedure, the operation time, and the satisfaction of the doctor. Conclusion: In the emergency department, the fixation method using wire or nylon string in the treatment of alveolar fractures is effective in bleeding control (J Korean Soc Traumatol 2007;20:72-76)
다양한 레이더 구조를 이용한 HF 대역에서의 상관 기반 표적 인식 성능 비교
박치환(Chi-Hwan Park),설승환(Seung-Hwan Seol),최인식(In-Sik Choi) 한국정보기술학회 2017 한국정보기술학회논문지 Vol.15 No.5
Correlative target recognition is one of target recognition techniques which is used in the environment that we can’t extract the scattering centers because of narrow bandwidth, and it is very simple and robust to noise. In this paper, we performed the correlative target recognition for the various radar structures such as monostatic case, bistatic 30˚, bistatic 90˚ and bistatic 150˚. For the experiment, we used 6 targets, which are classified as fighter, stealth fighter and stealth bomber. We used both the target discrimination ratio and probability of target recognition as the performance measure for the various SNR. The result showed that the probability of target recognition is higher than 90% when the SNR is higher than 5dB. Furthermore, we can see that the bistartic 90˚ radar structure is most adequate for the correlative target recognition.