• 지적과 국토정보
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• 제44권1호
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• pp.97-111
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• 2014

도시화 및 산업화가 발달하면서 한정된 토지를 입체적으로 이용하고자 하는 필요성이 점점 대두되고 있다. 특히 수도권의 교통난을 해소하기 위하여 경기도가 GTX(Great Train Express, 광역급행철도) 건설계획을 발표하면서 이에 따른 지하공간 활용논의가 활발해지고 있다. 현행 지하공간 보상은 "도시철도 건설을 위한 지하부분 토지의 사용에 따른 보상기준에 관한 조례"에 의한 보상기준에 의거하여 산정하고 있으나, 입체적 권리에 대하여 완전한 권리보호가 어려운 점이 있다. 이에 본 연구에서는 토지소유권이 미치는 지하공간의 효력범위를 설정하는 구분지상권 및 사용재결 이용 현황을 검토하고 지하공간 활용에 따른 보상기준에 관한 쟁점을 분석하여 지하공간 활용의 권리보완을 위한 개선방안을 제시하였다.

• 재활복지공학회논문지
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• 제6권1호
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• pp.67-73
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• 2012

본 논문에서는 표면 근전도 신호를 사용하여 손목 움직임의 동작을 분류하기 위해 인공 신경 회로망(ANN : Artificial Neural Network)기반의 동작 분류 알고리즘을 제안한다. 손목 움직임에 무리가 없는 20~30대 성인 26명을 대상으로 척측 수근 굴근과 척측 수근 신근에 부착한 2채널의 전극으로부터 표면 근전도 신호를 취득하고, 취득한 근전도로부터 손목의 굴곡, 신전, 내전, 외전, 휴식 다섯 동작을 인식한다. 빠른 처리 속도를 위해 획득한 신호로부터 시간 영역에서의 특징점을 추출하고 ANN을 이용한 동작 분류에 사용된다. 특징점으로 DAMV, DASDV, MAV, RMS를 사용하였으며, ANN 기반의 동작 분류의 인식율은 DAMV는 98.03%, DASDV는 97.97%, MAV는 96.95%, 그리고 RMS는 96.82%의 정확도를 나타낸다.

• 대한의용생체공학회:의공학회지
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• 제33권2호
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• 식물분류학회지
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• 제41권3호
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• pp.175-181
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• 2011

Traditionally, the Oleaceae has been divided into subfamilies Oleoideae and Jasminoideae. In the present paper, the taxonomical results so far made on the family were reviewed on the basis of palynology. The subfamilial classification is not well supported palynoligically, because both Myxopyrum of Jasminoideae and Comoranthus of Oleoideae having foveolate surface are well distinguished from the rest of the family having reticulate surface. The recent subfamily Nyctanthoideae (Takhtajan, 1977) including the monotypic Nyctanthus, was suggested to be included within the Jasminoideae although its closest relative on the palynological basis is different from that on the molecular basis. Tribal classification systems of the Jasminoideae are not well supported palynologically on the basis of surface character: presence or absence of bands on the mural ridge surface of the reticulum. On the basis of palynology, tribe Forsythiae including Abeliophyllum, Fontanesia, and Forsythia is monophyletic, and Fontanesia is well distinguished from the rest two. Korean species of Forsythia is divided into two: Forsythia koreana group and F. ovata-nakaii-saxatilis group. Recent discovery of F. saxatilis at a locality of F. ovata raised a question if the distinction between the two species on the basis of hairiness would be right. In the recent molecular studies, F. saxatilis var. lanceolata seems to be identified as F. saxatilis. Molecular studies showed that F. saxatilis (seemingly var. lanceolata or var. pilosa) is close to F. koreana. The fact in which the molecular result showed a close relationship between F. saxatilis varieties and F. koreana, is controversial to the result by floral and vegetative morphology. An intensive taxonomic study of these taxa would be needed.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1995년도 춘계학술대회
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• pp.160-163
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• 1995

An optimal EEG and artifact classifier is proposed using neural network operating characteristics. The neural network operating characteristics are two dimensional parametric representations of the right and false identification probabilities of the network classifier. Since the EEG and EP signals acquired from multi -channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG), the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. Using the neural-network based classification, human expert's efforts and time can be substantially reduced. From experiments, the neural-network based classification performs as good as human experts: variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• 대한의용생체공학회:의공학회지
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• 제40권6호
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2004년도 추계학술발표대회 개요집
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• pp.275-277
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• 2004

Fictitious notch radius approach is based on the Neuter's microstructural support hypothesis which assumes that fatigue crack is governed by highly stressed volume of the material right on the weld toe area rather than the surface stress at a pin point of weld toe area. Variety of successes have been achieved in applying this methodology to the fatigue of welded joint, hence, it became one of recommended design procedure in IIW's recommendation as well as many ship classification societies. 1mm fictitious notch radius approach was applied to the various fatigue problems of welded joints in this study covering the effect of weld size, notch stress calculation for 3D geometry and low cycle fatigue problem. It was found that fictitious notch radius approach fumed out to be very effective and accurate in dealing with fatigue strength of welded joint.

• 동굴
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• 제68호
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• pp.99-120
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• 2005

It is a wrong decision to use only topographic and geological maps for the study of pediment morphology in Korea. For the study of pediment morphology it is necessary to survey the earth structure by field techniques. In Korea, pediments are mostly found in granite areas with hardrock cover. But, pediments also developed in gneiss areas and what is worse in limestone areas. So, all areas in Korea developed pediment morphology. Only in South Korea pediments show a direction from south to north or from west to east. They developed only in right angles to each other, either parallel or in right angles to the strike, depending on the bedrock structure. Pediments are found in two levels. The upper level pediments are correlated with the lower level erosion surface. Besides this pediments are found in Hoenggye-ri of the Taegwolryong area in a third level 800m above sea level. The pediments developed in basins, at the lower margins of steep slopes dividing three levels of erosion surfaces and around the residual mountains on the erosion surfaces. The first belong to the early stage of pedimentation, the second to the middle stage and the third to the last stage. Also, in Korea monadnock and residual mountain have developed the pediments are correlated the slope of the hinter mountains. Akagki states that the only pedimentation times have been times of arid climate and that they are dissected by gulley erosion with climatic change, but writer's study proves that pedimentation takes place with eustatic movement, reckless defore-station and convectional rain. These facts indicate that the landforms, geological character and process of erosional cycle of the pediments in Korea resemble much those in the Chugoku Mountains of south wertern Japan, but they are larger in scale than those in the Chugoku Mountains. In conclusion, while Akagki emphasizes the geological character and climatic change in pedimentation, the writer studies prove that eustatic movements, especially the sea level rise after the Wurm age are important factors for pedimentation. Besides this the author's studies allow a classification of gentle slopes.

• Clinical and Experimental Pediatrics
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• 제46권12호
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• pp.1266-1270
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• 2003

저자들은 비강내 종괴로 내원한 14세 환아에서 종괴 생검 및 면역 표현형 검사로 비강 T/NK 세포형 림프종으로 진단하고 치료 중인 1례를 보고하는 바이다.

• 한국HCI학회논문지
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• 제10권2호
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• pp.21-26
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• 2015

NUI(Natural User Interface)는 사용자의 자연스러운 동작이나 동작 시 발생하는 생체 신호를 해석하여 기계에 명령을 내리는 것을 말한다. 물리적인 변화가 있어야 사용이 가능한 가속도 센서나 영상 기반의 NUI와는 달리 특정 동작과 관련된 근육의 표면 근전도(surface Electromyogram, sEMG)를 측정하면 실제 움직임이 발생하지 않아도(isometric contraction) 동작 의도를 예측할 수 있다. 본 연구에서는 근전도 기반으로 손목 동작 의도를 분류할 때 전극 개수에 따른 정확도를 확인하고, 키보드 등에 적용 가능한 인터페이스 기술을 제안한다. 손목의 동작 중 신전(extension, up), 굴곡(flexion, down), 외전(abduction, right), 내전(adduction, left)의 네 가지 동작 의도를 분류하는 실험을 진행하였다. 50ms 간격으로 계산된 제곱평균제곱근(Root Mean Square, RMS)을 특징으로 사용하였고, 동작 의도 인식을 위해 역전파 알고리즘으로 학습한 다층 퍼셉트론 분류기를 사용하였다. 전극 쌍의 개수를 네 개(91.9%), 세 개(87.0%), 두 개(78.9%)로 줄여가며 정확도를 확인했다. 전극 쌍의 개수가 네 개에서 두 개로 줄었을 때 정확도는 약 13% 감소하였다. 두 쌍의 전극만 사용하는 경우의 분류 정확도를 높이기 위하여 직전의 RMS를 특징에 추가하였다. 150 ms 이전까지의 정보를 사용하였을 때, 분류 정확도가 78.9%에서 83.6%로 4.6% 증가하였다. 전극 쌍의 개수가 감소함에 따라 정확도는 감소하였지만, 이전 데이터를 함께 사용한 경우 부분적으로 증가 시킬 수 있음을 확인하였다.