• Journal of the Korean Society of Environmental Restoration Technology
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• v.10 no.6
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• pp.120-129
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• 2007

Rapid urbanization has transformed the spatial pattern of urban land use or cover. This paper concentrates that changed characteristics of landscape structure in the Tancheon Watershed, from 1995 to 2003 were investigated using land cover map. We used FRAGSTATS software to calculate landscape indices to characterize the landscape structure. We found that built up area has been increased rapidly during the study period, while cultivated area and forest area have been decreased rapidly in the same period. From 1995 to 2003, built up area was increased from 19.73% to 39.62% and cultivated area and forest area was decreased 17.60% to 5.97% and 58.31% to 49.41%. Number of patches, mean euclidean nearest-neighbor distance, contagion index, Shannon's diversity index increased considerably from 1995 to 2003, also suggesting the landscape in the study area became more fragmented and heterogeneous. but because of continuously fragmentation, landscape became homogeneity. The study demonstrates that landscape metrics can be a useful indicator in landscape monitoring and landscape assessment.

• IE interfaces
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• v.25 no.2
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• pp.170-177
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• 2012

Process control is essential to operate the semiconductor process efficiently. This paper consider fault classification of semiconductor based cyclic signal for process control. In general, process signal usually take the different pattern depending on some different cause of fault. If faults can be classified by cause of faults, it could improve the process control through a definite and rapid diagnosis. One of the most important thing is a finding definite diagnosis in fault classification, even-though it is classified several times. This paper proposes the method that one-class classifier classify fault causes as each classes. Hotelling T2 chart, kNNDD(k-Nearest Neighbor Data Description), Distance based Novelty Detection are used to perform the one-class classifier. PCA(Principal Component Analysis) is also used to reduce the data dimension because the length of process signal is too long generally. In experiment, it generates the data based real signal patterns from semiconductor process. The objective of this experiment is to compare between the proposed method and SVM(Support Vector Machine). Most of the experiments' results show that proposed method using Distance based Novelty Detection has a good performance in classification and diagnosis problems.

• Bulletin of the Korean Chemical Society
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• v.30 no.7
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• pp.1603-1606
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• 2009

A new 1D oxalato bridged compound Ni(en)(ox)-2$H_2$O, (ox = oxalate; en = ethylenediamine) has been hydrothermally synthesized and characterized by single crystal X-ray diffraction, IR spectrum, TG analysis, and magnetic measurements. In the structure the Ni atoms are coordinated with four oxygen atoms in two oxalate ions and two nitrogen atoms in one ethylenediamine molecule. The oxalate anion acts as a bis-bidentate ligand bridging Ni atoms in cis-configuration. This completes the infinite zigzag neutral chain, [Ni(en)(ox)]. The interchain space is filled by water molecules that link the chains through a network of hydrogen bonds. Thermal variance of the magnetic susceptibility shows a broad maximum around 50 K characteristic of one-dimensional antiferromagnetic coupling. The theoretical fit of the data for T > 20 K led to the nearest neighbor spin interaction J = -43 K and g = 2.25. The rapid decrease in susceptibility below 20 K indicate this compound to be a likely Haldane gap candidate material with S = 1.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1A
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• pp.7-16
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• 2003

Handover is very important to support the mobility of user because wireless ATM networks have smaller cell size such as micro/pico cell for broadband mobile multimedia service In this paper, we propose dynamic path reservation handover scheme for fast inter-MSC (Mobile Switching Center) handovers To reduce the handover delay for connection re-routing, the proposed scheme reserves virtual channels from nearest common node to neighbor MSC in advance Especially, our handover scheme predicts the number of inter-MSC handover calls at each period by the prediction algorithm and reserve virtual channels The simulation and analysis results show that our scheme reduce handover complexity and has higher reservation channel utilization, compared with DVCT scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.200-205
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• 2006

Abstract This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition forthe images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

• Quantitative Bio-Science
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• v.36 no.1
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• pp.23-31
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• 2017

Datasets from DNA microarray experiments, which are in the form of large matrices of expression levels of genes, often have missing values. However, the existing statistical methods including the principle components analysis (PCA) and Hotelling's t-test are not directly applicable for the datasets having missing values due to the fact that they assume the observed dataset is complete in general. Many methods have been proposed in previous literature to impute the missing in the observed data. Troyanskaya et al. [1] study the k-nearest neighbor (kNN) imputation, Kim et al. [2] propose the local least squares (LLS) method and Rubin [3] propose the multiple imputation (MI) for missing values. To identify differentially expressed genes, we propose a new testing procedure when the missing exists in the observed data. The proposed procedure uses the Stouffer's z-scores and combines the test results of individual imputed samples, which are dependent to each other. We numerically show that the proposed test procedure based on MI performs better than the existing test procedures based on single imputation (SI) by comparing their ROC curves. We apply the proposed method to analyzing a public microarray data.

• ETRI Journal
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• v.45 no.1
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.89-89
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• 2021

부영양화된 호수나 유속이 느린 하천에서 발생하는 녹조의 과도한 발생은 하천 생태계 훼손, 동식물의 건강, 담수의 오염 등 환경 사회 경제적으로 큰 피해를 준다. 현재 수질 측정망은 정해진 지점에서 Chlorophyll-a(Chl-a), Phycocyanin(PC)을 대표농도로 산정하고 조류경보에 활용하고 있으나, 일주일에 한번씩 샘플링을 통해 Chl-a 및 PC를 측정하여 시공간적인 신뢰성의 문제가 제기될 수 있다. 본 연구에서는 기존 점단위 조류 모니터링의 한계점을 개선하기 위해 초분광영상 자료를 머신러닝 기법에 적용하여 Chl-a 및 PC 산정 알고리즘을 개발하였다. 이를 위해 Chl-a와 PC의 최대 흡수, 반사 파장대, 주요 물 흡수 파장대 자료를 조합하여 9개의 파장비를 구축하였으며, 기존 연구에서 활용한 머신러닝 기법인 Partial Least Square, Random Forest, Gradient Boosting, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Network를 검토하여 최적 모델을 선정하였다. 학습된 머신러닝의 성능을 R², NSE, RMSE 목적함수를 이용해 평가하였으며, 그 결과 ANN이 각각 PC 0.801, 0.755, 11.774 mg/m³, Chl-a 0.733, 0.622, 8.736 mg/m³로 가장 우수한 성능을 보였다. 최적화 된 ANN 모델을 백제보 상류 2016-2017년 항공 초분광영상에 적용하여 시공간에 따른 조류 분포변화를 평가하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.207-209
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• 2022

In the IoT environment, it is possible to collect life pattern data by recognizing human physical activity from smart devices. In this paper, the proposed model consists of a prediction stage and a recommendation stage. The prediction stage predicts the scale of anxiety and depression by using logistic regression and k-nearest neighbor algorithm through machine learning on the dataset collected from life pattern data. In the recommendation step, if the symptoms of anxiety and depression are classified, the principal component analysis algorithm is applied to recommend food and light exercise that can improve them. It is expected that the proposed anxiety/depression prediction and food/exercise recommendations will have a ripple effect on improving the quality of life of individuals.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.