• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.4
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• pp.57-67
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• 2014

The vigor condition of trees is an important indicator for the management of urban forested area. But difficulties in how to assess the tree vigor condition still remain. Previous efforts were limited in the 1) measurement of single indicator rather than using multiple indices, 2) purpose-oriented measurement such as for air-pollution effect or specific pathological symptom, and 3) ordinal-scale evaluations by field crews 4) despite human errors based on his/her experiences or prior knowledge. Therefore, this study attempted to develop a quantitative and objective methodology for assessing tree vigor condition, by measuring multiple modules and building the profile inventory. Furthermore, the possibility and limitations were discussed in terms of schematic frames describing tree vigor condition. The vigor condition of 56 flowering cherry plants in urban park were assessed by in-situ measurements of following eight items; growth of crown(Gc), growth of shoots, individual tree volume(Vol), plant area index, woody area index, leaf area index, leaf chlorophyll content(Lc) and leaf water content(Lw). For validation, these measurements were compared with the ranks of holistic tree vigor condition, which were visually assessed using a 4-point grading scale based on the expert's knowledge. As a result, the measures of each evaluation item successfully highlighted a variety of aspects in tree vigor condition, including the states of both photosynthetic and non-photosynthetic parts. The variation in the results depending on evaluated parts was shown within an individual tree, even though the broad agreement among the results was found. The result of correlation analysis between the tested measurements and 4-point visual assessment, demonstrated that the state of water-stressed foliage of the season (Lw) or the development of plant materials since sapling phase (Vol) could be better viewed from the outer appearance of trees than other symptoms. But only based on the visual assessment, it may be difficult to detect the quality of photosynthesis (Lc) or the recent trend in growth of trees (Gc). To make this methodology simplified for the broad-scale application, the tested eight measurements could be integrated into two components by principal component analysis, which was labelled with 'the amount of plant materials' and 'vigor trend', respectively. In addition, the use of these quantitative and multi-scale indicators underlies the importance of assessing various aspects of tree vigor condition, taking into account the response(s) on different time and spatial scale of pressure(s) shown in each evaluated module. Future study should be advanced for various species at diverse developing stages and environment, and the application to wide areas at a periodic manner.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.82-89
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• 2004

This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary WLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.539-544
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• 2003

This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (Principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary MLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.118-121
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• 2003

본 논문은 동영상 압축 부호화에 대한 표준안인 MPEG기반의 압축 비디오 stream에서［1, 2］, 질의 영상에 대한 효율적인 검색 기법을 제안한다. 비디오 검색은 높은 차원의 색인 정보를 이용하는데, 높은 차원의 data set을 색인 정보로 하여 효율적인 검색 능력을 보여주는 KD-Trees(K Dimensional-Trees)알고리즘［3］을 비디오 검색기법에 적용하고자 한다. 먼저, key frame에 PCA (Principal Component Analysis) 알고리즘［4］을 이용하여 색인 정보를 추출한 다음, 추출된 색인 정보를 KD-Trees에 적용하여 효율적인 검색을 가능하게 한다. 실험 결과, 기존의 검색 기법보다 상당한 양의 처리 시간과 메모리 공간을 줄일 수 있음을 보였다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.114-117
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• 2003

본 논문에서는 video stream내의 움직이는 객체 정보를 추정하고 동적 GTM(genetic tree-map) 알고리즘을 사용하여 얼굴 영역 검출 기법을 제안한다. 기존의 일반적인 객체 추정 기법은 클러스터(cluster)과정을 통하여 영상 정보를 분할하고 그 중 움직이는 객체 부분을 복원함으로서 추정하였다. 제안하는 기법은 BMA(block matching algorithm)［1］ 알고리즘을 사용하여 video stream 에서 움직이는 객체 정보를 얻고 클러스터 알고리즘으로 PCA(principal component analysis)를 사용한다. PCA 기법은 입력 데이터에 관해 통계적 특성을 이용하여 주성분을 찾는다. 주축과 영역분할 알고리즘을 사용하여 데이터를 분할하고, 분할된 객체 정보를 사용하여 특정 객체만을 추정하는 것이 가능하다. 이렇게 추정된 객체를 얼굴영역의 feature에 대하여 신경망 학습인 동적 GTM 알고리즘을 사용하여 생성된 동적 GTM 맵의 정보에 따라 객체의 얼굴영역만을 추출해 낼 수 있다［2-6］.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• Steel and Composite Structures
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• v.52 no.6
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• pp.695-711
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• 2024

Suspension bridges are critical to urban transportation, but those in earthquake-prone areas face unique challenges. In the event of a moderate or strong earthquake, conventional linear theory-based approaches for detecting bridge damage become inadequate. This study presents an efficient method for identifying damage in suspension bridges using time history nonlinear inelastic analysis. A practical advanced analysis program is employed to model cable-supported bridges with low computational cost, generating a dataset for four hybrid models: PSO-DT, PSO-RF, PSO-XGB, and PSO-CGB. These models combine decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with particle swarm optimization (PSO) to capture nonlinear correlations between displacement response and damage. Principal component analysis reduces dataset dimensions, and PSO selects the optimal model. A numerical case study of a suspension bridge under simulated earthquake conditions identifies PSO-XGB as the best model for predicting stiffness reduction. The results demonstrate the method's robustness for nonlinear damage detection in suspension bridges under earthquake excitation.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Current Research on Agriculture and Life Sciences
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• v.13
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• pp.45-53
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• 1995

This research was conducted to investigate morphological and genetic variation of Allium victorialis val. platyphyllum which growed wild in Mt. Hambaek, Mt. Odae, and Ullungdo. The tree layer of Allium victorialis var. platyphyllum community in Mt. Hambaek and Mt. Odae was dominated by Quercus mongolica. The tree layer of Ullungdo generally consist of Fagus crenata var. multinervis, Acer triflorum, Sorbus commixta, and Tilia insularis. In the herb layer, Rumohra standishii, Trillium tschonoskii, and Lilium hansonii are common at Allium victorialis var. platyphyllum community in Ullungdo. The vegetation in Ullungdo was widely different from those in Mt. Hambaek and Mt. Odae by species composition. The result of Principal Component Analysis(PCA) and Canonical Discriminent Analysis of by the 8 characters showed that Allium victorialis var. platyphyllum could be classified into 2 groups: (one ; Mt. Hambaek and Mt. Odae, the other ; Ullungdo). In PCA, the major factors in the first principal component group was angle of leaf apex. Variation of band by isozyme GOT(glautamate oxaloaccetate transaminase) is similar between Mt. Hambaek and Mt. Odae. However, Ullungdo differed from Mt. Hambaek and Mt. Odae in variation of bands.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.834-841
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• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.