• 대한원격탐사학회지
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• 제20권3호
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• pp.197-205
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• 2004

Wetlands are lands with a mixture of water, herbaceous or woody vegetation and wet soil. And linear spectral mixture analysis (LSMA) is one of the most often used methods in handling the spectral mixture problem. This study aims to test LSMA is an enhanced routine for classification of wetland land-covers in Paldang reservoir and vicinity (paldang Reservoir) using Landsat TM and ETM+ imagery. In the LSMA process, reference endmembers were driven from scatter-plots of Landsat bands 3, 4 and 5, and a series of endmember models were developed based on green vegetation (GV), soil and water endmembers which are the main indicators of wetlands. To consider phenological characteristics of Paldang Reservoir, a soil endmember was subdivided into bright and dark soil endmembers in spring and a green vegetation (GV) endmember was subdivided into GV tree and GV herbaceous endmembers in fall. We found that LSMA fractions improved the classification accuracy of the wetland land-cover. Four endmember models provided better GV and soil discrimination and the root mean squared (RMS) errors were 0.011 and 0.0039, in spring and fall respectively. Phenologically, a fall image is more appropriate to classify wetland land-cover than spring's. The classification result using 4 endmember fractions of a fall image reached 85.2 and 74.2 percent of the producer's and user's accuracy respectively. This study shows that this routine will be an useful tool for identifying and monitoring the status of wetlands in Paldang Reservoir.

• ETRI Journal
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• 제45권1호
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.2412-2420
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• 2024

The International Atomic Energy Agency (IAEA) mandates safeguards to ensure non-proliferation of nuclear materials. Among inspection techniques used to detect partial defects within spent nuclear fuel (SNF), gamma emission tomography (GET) has been reported to be reliable for detection of partial defects on a pin-by-pin level. Conventional GET, however, is limited by low detection efficiency due to the high density of nuclear fuel rods and self-absorption. This paper proposes a new type of GET named Spent Fuel Internal Tomography (SFIT), which can acquire sinograms at the guide tube. The proposed device consists of the housing, shielding, C-shaped collimator, reflector, and gadolinium aluminum gallium garnet (GAGG) scintillator. For accurate attenuation correction, the source-distinguishable range of the SFIT device was determined using MC simulation to the region away from the proposed device to the second layer. For enhanced inspection accuracy, a proposed specific source-discrimination algorithm was applied. With this, the SFIT device successfully distinguished all source locations. The comparison of images of the existing and proposed inspection methods showed that the proposed method, having successfully distinguished all sources, afforded a 150 % inspection accuracy improvement.

• 동의생리병리학회지
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• 제23권5호
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• pp.938-941
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• 2009

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. In the present study we tried to develop the statistical diagnostic tool discriminating the subtypes of oriental medicine diagnostic system, syndrome differentiation (SD). Discriminant analysis was carried out using clinical data collected from 1,478 stroke patients with the same subtypes diagnosed identically by two clinical experts with more than 3 year experiences. Numerical discriminant models were constructed using important 61 symptom and syndrome indices. Diagnostic accuracy and prediction rate of 5 SD subtypes: The overall diagnostic accuracy of 5 SD subtypes using 61 indices was 74.22%. According to subtypes, the diagnostic accuracy of "phlegm-dampness" was highest (82.84%), and followed by "qi-deficiency", "fire/heat", "static blood", and "yin-deficiency". On the other hand, the overall prediction rate was 67.12% and that of qi-deficiency was highest (73.75%). Diagnostic accuracy and prediction rate of 4 SD subtypes: The overall diagnostic accuracy and prediction rate of 4 SD subtypes except "static blood" were 75.06% and 71.63%, respectively. According to subtypes, the diagnostic accuracy and prediction rate was highest in the "phlegm-dampness" (82.84%) and qi-deficiency (81.69%), respectively. The statistical discriminant model of constructed using 4 SD subtypes, and 61 indices can be used in the field of oriental medicine contributing to the objectification of SD.

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.828-835
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• 2008

We present a novel method for eye location by means of a two-level classifier scheme. Locating the eye by machine-inspection of an image or video is an important problem for Computer Vision and is of particular value to applications in biomedical imaging. Our method aims to overcome the significant challenge of an eye-location that is able to maintain high accuracy by disregarding highly variable changes in the environment. A first level of computational analysis processes this image context. This is followed by object detection by means of a two-class discrimination classifier(second algorithmic level).We have tested our eye location system using FERET and BioID database. We compare the performance of two-level classifier with that of non-level classifier, and found it's better performance.

• 대한전기학회논문지:전력기술부문A
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• 제51권5호
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• pp.247-254
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• 2002

This paper describes a fault location algorithm in real combined transmission systems with underground power cable. The algorithm to calculate the fault location was developed using DWT wavelet transform and travelling wave occurred at fault point. And the proposed algorithm is also used the transient signal of one end in stead of the signal information of two ends. On the other hand, in this papers, the method to discriminate fault point between overhead line and cable section is also Proposed. Variety simulations were carried out to verify the accuracy and effectiveness of the proposed algorithm using EMTP/ATFDraw and Matlab. Simulation results show that the proposed method has the excellent ability for discrimination of fault section and fault location in combined transmission systems with power cables.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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• pp.109-112
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• 2000

The purpose of the present study is to evaluate the basic upper-limb involved in products manipulation. Upper-limb muscular deformations and electromyography (EMG) measurements are used as indexes for estimated motion: hand opening and closing, wrist extending and flexing, pronation and supination, grasping conditions. Measured values are analyzed by multivariate analysis and a regression equation is obtained for estimating the characteristics of upper-limb performance. Muscular deformation is defined as a change in shape, such as a pressure changes when the hand or wrist moves. hand opening and closing can be discriminated at a higher percentage of accuracy by muscular deformation data than by EMG data. Muscular deformation measurements using air-pack pressure sensors were verified to be effective in motion estimation applications.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.917-924
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• 2018

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.

• Journal of international Conference on Electrical Machines and Systems
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• 제3권3호
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• pp.325-330
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• 2014

Eddy current induction is used in a wide range of electronic devices, for example in detection sensors. Due to the advances in computer hardware and software, the need for 3D computation and system comprehension is a requirement to develop and optimize such devices nowadays. Pure theoretical models are mostly limited to special cases. On the other hand, the classical use of commercial Finite Element (FE) electromagnetic 3D models is not computationally efficient and lacks modeling flexibility or robustness. The proposed approach focuses on: (1) implementing theoretical formulations in 3D (FE) model of a detection device as well as (2) an automatic Volumetric Estimation Method (VEM) developed to selectively model the target finite elements. Due to these two approaches, this model is suitable for parametric studies and optimization of the number, location, shape, and size of PCB receivers in order to get the desired target discrimination information preserving high accuracy with tenfold reduction in computation time compared to commercial FE software.

• Journal of Information Processing Systems
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• 제12권3호
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• pp.502-510
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• 2016

Considering video copy transform diversity, a multi-feature video copy detection algorithm based on a Speeded-Up Robust Features (SURF) local descriptor is proposed in this paper. Video copy coarse detection is done by an ordinal measure (OM) algorithm after the video is preprocessed. If the matching result is greater than the specified threshold, the video copy fine detection is done based on a SURF descriptor and a box filter is used to extract integral video. In order to improve video copy detection speed, the Hessian matrix trace of the SURF descriptor is used to pre-match, and dimension reduction is done to the traditional SURF feature vector for video matching. Our experimental results indicate that video copy detection precision and recall are greatly improved compared with traditional algorithms, and that our proposed multiple features algorithm has good robustness and discrimination accuracy, as it demonstrated that video detection speed was also improved.