• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.211-221
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• 2024

Live streaming commerce has emerged as an innovative e-commerce model. This study, based on the Elaboration Likelihood Model (ELM), aims to explore the impact of Key Opinion Consumers' (KOCs) attributes in live streaming commerce on purchase intentions on short video platforms. A survey was conducted with 411 consumers, and data analysis and hypothesis testing were performed using SPSS 24.0 and AMOS 23.0 software. Research has found that differences in consumers' information processing abilities lead to different pathway selections. Central route factors such as recommendation consistency, product involvement, and professionalism, as well as peripheral route factors such as recommendation timeliness, all have significant positive effects on consumers' purchase intention. However, visual cues in the peripheral route do not have a significant impact. This study aims to provide theoretical support and practical guidance for the development of the live streaming commerce industry, and to help companies adjust their promotion strategies based on differences in consumer information processing, thereby improving purchase conversion rates.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Geotechnical Engineering
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• v.5 no.3
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• pp.39-50
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• 1989

A method is investigated to analyze the reliability of the gravity retaining wall which is designed to allow a limiting translational movement induces by the earthquake loading. Application of FOSM method to the Richards and Elms model yields a practical procedure for the analyses of the reliability and sensitivity of the retaining wall sujected to the earthquake. After examination of the practice (or the earthquake design of the retaining wall, the methods of the reliability analysis are considered. Finally, this study presents the step-by.step procedure for analyzing the reliability of the earth retaining structure for pratical convinience.

• The Korean Journal of Ecology
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• v.24 no.6
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• pp.365-370
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• 2001

The vegetation of the Khogno Khan Natural Reserve of the central Mongolia was studied in terms of the Zurich-Montpellier School's method. Twenty plant communities were identified from the three different landscape types such as mountain areas(63%), plains(32%), and wetlands(5%). Actual vegetation map using five vegetation domains was accomplished in order to understand the spatial distribution of regional vegetation. Steppe vegetation of 88% vegetation cover to the whole area is representative, which is composed of a matrix of landscape. The birch-aspen forests and the elm bush forests are relics as a patch distribution. It is recognized that the whole territory of protected area be under the effects of severe grazing from the phytosociological viewpoint.

• BMB Reports
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• v.43 no.10
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• pp.670-676
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• 2010

In this paper, a novel approach, ELM-PCA, is introduced for the first time to predict protein subcellular localization. Firstly, Protein Samples are represented by the pseudo amino acid composition (PseAAC). Secondly, the principal component analysis (PCA) is employed to extract essential features. Finally, the Elman Recurrent Neural Network (RNN) is used as a classifier to identify the protein sequences. The results demonstrate that the proposed approach is effective and practical.

• Toxicological Research
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• v.19 no.2
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• pp.147-152
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• 2003

We investigated antitumor activities of the ethanol extract from mushroom Phellinus linteus and Phellinus baumii on mulberry, oak and elm. in vitro test, the ethanol extract of mushroom cultivated on oak of Phellinus linteus showed highest activities about SK-OV-3, HCT15, XF498, SK-MEL-2 and A549. SK-OV-3 cell line showed 100% cytotoxicity in 100 $\mu\textrm{g}$/ml and HCT15 (98.39%), XF498 (89.62%), SK-MEL-2 (84.07%) and A549 (79.92%) cytotoxicity respectively. Also $IC_{50}$ showed 3.99 $\mu\textrm{g}$/ml to SK-OV-3 cell line and HCT15 (4.37 $\mu\textrm{g}$/ml), A549 (5.48 $\mu\textrm{g}$/ml), SK-MEL-2 (6.72 $\mu\textrm{g}$/ml), XF 498 (6.88 $\mu\textrm{g}$/ml). As those results, cultivated oak of Phellinus linteus showed a very low $IC_{50}$ value against SK-OV-3, HCT15, XF498, SK-MEL-2 and A549 cancer cell lines.

• Journal of the Korean housing association
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• v.13 no.1
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• pp.10-18
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• 2002

The purpose of this study is to discover the meaning of water and traditional water space by the literature review and field study of the water space in Korea. The water and water space were symbolized by mythology, elm-yang-oh-hang, feng-shui, taoism and confucian ideas, buddhism, shin-sun ideas, shijo and landscape panting. That is, the symbolic meanings of water and water space are melt in arts and thoughts. According to literature review and field study, the water and water space represent symbolic meanings, integration with nature, reflection of nature, territoriality, role of boundary, purgation, centralization and practical application.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1369-1370
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• 2015

본 논문에서는 얼굴 인식을 위하여 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 지능형 알고리즘인 퍼지 집합 이론을 이용하여 주변 노이즈에 매우 강한 특성을 보이며 학습 속도가 매우 빠른 새로운 패턴 분류기를 제안한다. 제안된 퍼지 패턴 분류기는 기존 신경회로망의 학습 속도에 비해 매우 빠른 학습 속도를 보이며, 패턴 분류기의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 특성을 퍼지 집합 이론과 결합하여 퍼지 패턴 분류기의 일반화 성능을 개선하였다. 제안된 퍼지 패턴 분류기는 얼굴 인식 데이터를 이용하여 성능을 평가 하였다.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.411-411
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• 2011

KSTAR 토카막 플라즈마의 전자온도 측정을 위한 전자 사이클로트론 방출(ECE) 진단계가 완성되어 KSTAR 3차 운전기간 동안 전자온도를 측정하였다. ECE 진단계는 2단의 헤테로다인 수신기 2개와 75채널의 RF 검출기 그리고 비디오 증폭기로 이루어져있다. 2개의 헤테로다인 수신기의 주파수 범위는 각각 110 GHz~162 GHz, 164 GHz~196 GHz 이며 163 GHz multiplexer에 의해 ECE power를 나눠갖는다. 각 채널 사이의 주파수 간격은 1 GHz이며 토로이달 자장을 2.5T로 운전한다면 플라즈마 반경방향의 모든 위치에서 측정이 가능하다. 또한 시간분해능도 100 kHz로 우수하여 반경방향의 전자온도분포의 시간 변화를 측정할 수 있다. 이 포스터에서는 2010년 KSTAR 실험동안 반경위치에 대한 전자온도를 측정과 sawtooth, ELM 등 MHD 현상 관측 결과에 대해 발표하였다. 그리고 중성빔(NB) 가열을 하는 동안 나타난 H-mode 때 전자온도의 변화도 살펴보았다.