• 인간식물환경학회지
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• 제24권2호
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• pp.219-227
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• 2021

Background and objective: Urban street trees play an important role in carbon reduction in cities where greenspace is scarce. There are ongoing studies on carbon reduction by street trees. However, information on the carbon reduction capacity of street trees based on field surveys is still limited. This study aimed to quantify carbon uptake and storage by urban street trees and suggest a method to improve planting of trees in order to increase their carbon reduction capacity. Methods: The cities selected were Sejong, Chungju, and Jeonju among cities without research on carbon reduction, considering the regional distribution in Korea. In the cities, 155 sample sites were selected using systematic sampling to conduct a field survey on street environments and planting structures. The surveyed data included tree species, diameter at breast height (DBH), diameter at root collar (DRC), height, crown width, and vertical structures. The carbon uptake and storage per tree were calculated using the quantification models developed for the urban trees of each species. Results: The average carbon uptake and storage of street trees were approximately 7.2 ± 0.6 kg/tree/yr and 87.1 ± 10.2 kg/tree, respectively. The key factors determining carbon uptake and storage were tree size, vertical structure, the composition of tree species, and growth conditions. The annual total carbon uptake and storage were approximately 1,135.8 tons and 22,737.8 tons, respectively. The total carbon uptake was about the same amount as carbon emitted by 2,272 vehicles a year. Conclusion: This study has significance in providing the basic unit to quantify carbon uptake and storage of street trees based on field surveys. To improve the carbon reduction capacity of street trees, it is necessary to consider planning strategies such as securing and extending available grounds and spaces for high-density street trees with a multi-layered structure.

• 한국철도학회논문집
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• 제11권3호
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• pp.216-224
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• 2008

본 연구는 합리적인 역세권 범위를 설정하고 이에 미치는 요인을 규명하기 위해 CART(Categorical Analysis Regression Tree) 분석을 이용하여 4대 신도시(분당, 평촌, 일산, 산본)에 대해 SIA 모형을 개발하였으며, 그 결과를 요약하면 다음과 같다. 첫째, 지하철 역세권에 영향을 마치는 변수를 중심으로 상관관계를 분석한 결과, 역세권 지가에 영향을 미치는 주요요인이 도보거리로 나타났으며, 두 관계를 이용하여 SIA 모형을 개발하였다. 둘째, SIA모형식(선형식, 다항식)을 비교분석한 결과, 신도시별 역세권의 범위는 철도역사로부터 도보거리기준으로 분당 신도시가 856m, 일산 산본 신도시가 508m, 평촌신도시가 495m로 각각 다르게 나타났다. 셋째, SIA 모형간 계수를 비교분석한 결과, 철도역사로부터 도보거리가 가까울수록 지가에 대한 영향이 커지는 것으로 나타났다. 또한 신도시별로는, 분당 평촌신도시가 일산 산본 선도시 보다 도보거리에 대한 지가의 영향이 크고 지가 또한 높은 것으로 나타났다. 따라서 현행 도시철도법상 역세권 범위인 반경 500m 이내로 획일적으로 정한 역세권 범위기준은 불합리하며 신도시지역의 토지이용 및 보행접근성(도보거리) 특성을 반영하여 재설정하는 것이 바람직하다고 판단된다.

• 스마트미디어저널
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• 제12권11호
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• pp.36-47
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• 2023

주요 산업현장에서 설비를 제어하는 산업제어시스템(ICS, Industrial Control System)이 네트워크로 다른 시스템과 연결되는 사례가 증가하고 있다. 또한, 이러한 통합과 함께 한 번의 외부 침입이 전체 시스템 마비로 이루어질 수 있는 지능화된 공격의 발달로, 산업제어시스템에 대한 보안에 대한 위험성과 파급력이 증가하고 있어, 사이버 공격에 대한 보호 및 탐지 방안의 연구가 활발하게 진행되고 있으며, 비지도학습 형태의 딥러닝 모델이 많은 성과를 보여 딥러닝을 기반으로 한 이상(Anomaly) 탐지 기술이 많이 도입되고 있다. 어어, 본 연구에서는 딥러닝 모델에 전처리 방법론을 적용하여 시계열 데이터의 이상 탐지성능을 향상시키는 것에 중점을 두어, 그 결과 웨이블릿 변환(WT, Wavelet Transform) 기반 노이즈 제거 방법론이 딥러닝 기반 이상 탐지의 전처리 방법론으로 효과적임을 알 수 있었으며, 특히 센서에 대한 군집화(Clustering)를 통해 센서의 특성을 반영하여 Dual-Tree Complex 웨이블릿 변환을 차등적으로 적용하였을 때 사이버 공격의 탐지성능을 높이는 것에 가장 효과적임을 확인하였다.

• 융합신호처리학회논문지
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• 제4권4호
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• pp.30-39
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• 2003

본 논문에서는 한국어 음성 데이터를 대상으로 HM-Net(Hidden Markov Network) 음성인식 시스템의 성능평가를 수행하였다. 음향모델 작성은 음성인식에서 널리 사용되고 있는 통계적인 모델링 방법인 HMM(Hidden Markov Model)을 개량한 HM-Net을 도입하였다. HM-Net은 기존의 SSS(Successive State Splitting) 알고리즘을 개량한 PDT(Phonetic Decision Tree)-SSS 알고리즘에 의해 문맥방향과 시간방향의 상태분할을 수행하여 생성되는데, 특히 문맥방향 상태분할의 경우 학습 음성데이터에 출현하지 않는 문맥정보를 효과적으로 표현하기 위해 음소결정트리를 채용하고 있으며, 시간방향 상태분할의 경우 학습 음성데이터에서 각 음소별 지속시간 정보를 효과적으로 표현하기 위한 상태분할을 수행하며, 마지막으로 파라미터의 공유를 통해 triphone 형태의 최적인 모델 네트워크를 작성하게 된다. 인식에 사용된 알고리즘은 음소 및 단어인식의 경우에는 One-Pass Viterbi 빔 탐색을 사용하며 트리 구조 형태의 사전과 phone/word-pair 문법을 채용하고 있다. 연속음성인식의 경우에는 단어 bigram과 단어 trigram 언어모델과 목구조 형태의 사전을 채용한 Multi-Pass 빔 탐색을 사용하고 있다. 전체적으로 본 논문에서는 다양한 조건에서 HM-Net 음성인식 시스템의 성능평가를 수행하였으며, 지금까지 소개된 음성인식 시스템과 비교하여 매우 우수한 인식성능을 보임을 실험을 통해 확인할 수 있었다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.122-129
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• 2006

The context-awareness has become the one of core technologies and the indispensable function. for application services in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a music recommendation system. Our proposed system consists of such components as Intention Module, Mood Module and Recommendation Module. Among these modules, the Intention Module infers whether a user wants to listen to the music or not from the environmental context information. We built the Intention Module using data mining techniques such as decision tree, support vector machine and case-based reasoning. The results showed that the case-based reasoning model outperformed the other models and its accuracy was 84.1%.

• Communications for Statistical Applications and Methods
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• 제31권6호
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• pp.627-644
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• 2024

Quantile regression models have become popular due to their benefits in obtaining robust estimates. Some machine learning (ML) models can estimate conditional quantiles. However, current ML methods mainly focus on just adapting quantile regression. In this paper, we propose a local quantile ensemble based on ML methods, which averages multiple estimated quantiles near the target quantile. It is designed to enhance the stability and accuracy of the quantile fits. This approach extends the composite quantile regression algorithm that typically considers the central tendency under a linear model. The proposed methods can be applied to various types of data having nonlinear and heterogeneous trend. We provide an empirical rule for choosing quantiles around the target quantile. The bias-variance tradeoff inherent in this method offers performance benefits. Through empirical studies using Monte Carlo simulations and real data sets, we demonstrate that the proposed method can significantly improve quantile estimation accuracy and stabilize the quantile fits.

• 산업공학
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• 제22권1호
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• pp.44-55
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• 2009

We propose a method for selection of input-output factors in DEA. It is designed to select better combinations of input-output factors that are well suited for evaluating substantial performance of DMUs. Several selected DEA models with different input-output factors combinations are evaluated, and the relationship between the computed efficiency scores and a single performance criterion of DMUs is investigated using decision tree. Based on the results of decision tree analysis, a relatively better DEA model can be chosen, which is expected to well represent the true performance of DMUs. We illustrate the effectiveness of the proposed method by applying it to the efficiency evaluation of 101 listed companies in steel and metal industry.

• The Journal of the Acoustical Society of Korea
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• 제17권4E호
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• pp.11-16
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• 1998

For large vocabulary speech recognition using HMMs, context-dependent subword units have been often employed. However, when context-dependent phone models are used, they result in a system which has too may parameters to train. The problem of too many parameters and too little training data is absolutely crucial in the design of a statistical speech recognizer. Furthermore, when building large vocabulary speech recognition systems, unseen triphone problem is unavoidable. In this paper, we propose the modified phonetic decision tree algorithm for the automatic prediction of unseen triphones which has advantages solving these problems through following two experiments in Japanese contexts. The baseline experimental results show that the modified tree based clustering algorithm is effective for clustering and reducing the number of states without any degradation in performance. The task experimental results show that our proposed algorithm also has the advantage of providing a automatic prediction of unseen triphones.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.324-330
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• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• Geomechanics and Engineering
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• 제35권1호
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• pp.67-80
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• 2023

Due to the nature of the conjunctive Cone Penetration Test(CPT), which does not verify the actual sample directly, geotechnical engineers commonly classify the underground geomaterials using CPT results with the classification diagrams proposed by various researchers. However, such classification diagrams may fail to reflect local geotechnical characteristics, potentially resulting in misclassification that does not align with the actual stratification in regions with strong local features. To address this, this paper presents an objective method for more accurate local CPT soil classification criteria, which utilizes C4.5 decision tree models trained with the CPT results from the clay-dominant southern coast of Korea and the sand-dominant region in South Carolina, USA. The results and analyses demonstrate that the C4.5 algorithm, in conjunction with oversampling, outlier removal, and pruning methods, can enhance and optimize the decision tree-based CPT soil classification model.