• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.383-392
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• 2005

Vegetation classes, especially grass and tree classes, are often confused in classification when conventional spectral pattern recognition techniques are used to classify urban areas. This paper reports on a study to improve the classification results by using an automated process of considering height information in separating urban vegetation classes, specifically tree and grass, using three-band, high-spatial resolution, digital aerial imagery. Height information was derived photogrammetrically from stereo pair imagery using cross correlation image matching to estimate differential parallax for vegetation pixels. A threshold value of differential parallax was used to assess whether the original class was correct. The average increase in overall accuracy for three test stereo pairs was $7.8\%$, and detailed examination showed that pixels reclassified as grass improved the overall accuracy more than pixels reclassified as tree. Visual examination and statistical accuracy assessment of four test areas showed improvement in vegetation classification with the increase in accuracy ranging from $3.7\%\;to\;18.1\%$. Vegetation classification can, in fact, be improved by adding height information to the classification procedure.

• Journal of Engineering Education Research
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• v.21 no.4
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• pp.46-52
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• 2018

In general, lecture evaluation has been used in most universities as an important criterion to evaluate quality of education. This study is exploratory research on the predictors that determine lecture evaluation in college of engineering to give practical implications for improvement of engineering education. For the exploration of predictors of lecture evaluation, the data of lecture evaluation in A College of Engineering located in the metropolitan area was used, and Decision Tree Analysis was utilized as an analysis method. As a result, the characteristics of students turned out to be the most distinct predictor comparing with those of course and instructor at lecture evaluation in college of engineering. That is, as various elements other than teaching competency influence lecture evaluation in college of engineering, it is necessary to be more careful in evaluating quality of lecture or teaching competence. Thus, a follow-up study should be conducted to adjust the influence by the predictors that instructors can hardly control.

• Structural Engineering and Mechanics
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• v.89 no.3
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• pp.283-300
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• 2024

Machine learning (ML) models based on artificial neural network (ANN) and decision tree (DT) were developed for estimation of axial capacity of concrete columns reinforced with fiber reinforced polymer (FRP) bars. Between the design codes, the Canadian code provides better formulation compared to the Australian or American code. For empirical models based on elastic modulus of FRP, Hadhood et al. (2017) model performed best. Whereas for empirical models based on tensile strength of FRP, as well as all empirical models, Raza et al. (2021) was adjudged superior. However, compared to the empirical models, all ML models exhibited superior performance according to all five performance metrics considered. The performance of ANN and DT models were comparable in general. Under the present setup, inclusion of the transverse reinforcement information did not improve the accuracy of estimation with either ANN or DT. With selective use of inputs, and a much simpler ANN architecture (4-3-1) compared to that reported in literature (Raza et al. 2020: 6-11-11-1), marginal improvement in correlation could be achieved. The metrics for the best model from the study was a correlation of 0.94, absolute errors between 420 kN to 530 kN, and the range being 0.39 to 0.51 for relative errors. Though much superior performance could be obtained using ANN/DT models over empirical models, further work towards improving accuracy of the estimation is indicated before design of FRP reinforced concrete columns using ML may be considered for design codes.

• The Journal of Information Systems
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• v.20 no.3
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• pp.1-24
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• 2011

Recently, six sigma has been widely adopted in a variety of industries as a disciplined, data-driven problem solving approach or methodology supported by a handful of powerful statistical tools in order to reduce variation through continuous process improvement. Also, data mining has been widely used to discover unknown knowledge from a large volume of data using various modeling techniques such as neural network, decision tree, regression analysis, etc. This paper proposes a six sigma methodology based on data mining for effectively and efficiently processing massive data in driving six sigma projects. The proposed methodology is applied in the hot stove system which is a major energy-consuming process in a "P" steel company for improvement of heat efficiency through reduction of energy consumption. The results show optimal operation conditions and reduction of the hot stove energy cost by 15%.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1139-1149
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• 2017

Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

• Journal of Korean Society of Rural Planning
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• v.17 no.2
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• pp.23-32
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• 2011

The purpose of this study is to produce basic planning criteria required in ecological restoration and improvement works of streams in rural area through the application of stream assessment methods (water quality, soil environment, and ecological function assessment) at 6 study sites of Han River basin. The investigation results were as followings; 1) There were the evaluation items like a manure use, salt degree, river peripheral tree, which did not fitted to apply to domestic streams, in the SVAP (Stream Visual Assessment Protocol) and NRCS Riparian Assessment that were evaluation models developed in USDA. The area inhabitants with a little knowledge and education personally seems to utilize the evaluation methods through improvement partly with an aspect that evaluation is slightly easy. 2) From the stream assessment results, the construction of diverse pools, large woody debris and isolated backwater pool are needed to improve a few of problems observed at the mostly study sites. The result of NRCS Riparian Assessment showed that the improvement of stream bank vegetative communities is needed by planting tree with deep-binding root masses, and managing of noxious weeds and exotic undesirable plants. 3) Summing up, the assessment results showed that the assessment scores were higher at upstream than downstream, the stream with totally maintenance than that with partly maintenance, the stream with slope bank than that with vertical bank, and the stream with a flood plain than that without a flood plain. So, the direction of stream maintenance projects must be set by consideration of those results.

• The Journal of Bigdata
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• v.3 no.1
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• pp.95-103
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• 2018

In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.249-252
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• 2007

센서 네트워크에서의 에너지 효율적인 통신을 위한 infrastructure를 제공하고자 가상 백본(Virtu Backbone) 개념이 등장했다. 최근에는 트리 구조를 이용하여 가장 백본을 구성한 EVBT (Energy-aware Virtual Backbone Tree)가 제안되었다. 본 논문에서는 EVBT의 문제점을 개선한 m-EVBT(modified-EVBT) 생성 알고리즘에 대해 다룬다. EVBT와 달리 m-EVBT 생성 알고리즘은 백본 트리에 속하지 않은 센서 노드들의 업스트림 링크의 선정에 물리적 거리가 아닌 에너지 소모량 청보를 이용한다. 이 정보는 백본 트리를 만들 때 이용되는 ECR(EVBT Construction Request) 패킷에 포함되어 전송된다. 시뮬레이션을 통해 m-EVBT는 EVBT에 비해 데이터 전송시 에너지를 절약하고, 백본 트리 구축에 드는 추가적인 비용도 작다는 것을 확인할 수 있었다.

• Korean Journal of Organic Agriculture
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• v.21 no.4
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• pp.589-600
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• 2013

The Purpose of this paper is to analysis on consumer's purchasing behavior and intention of local food. To analysis consumer's purchasing behavior, a series of homemaker surveys were conducted. The sample size of the survey is 416 respectively. As a survey result, consumer's purchasing behavior shows that purchasing ratio of local food and buying place is various type. By decision tree model analysis showed that consumer's purchasing intention is enough to establishing local food system in region. Therefore, strategies for regional consumption are needed expression of the place city and county of origin, diversification of purchasing item and buying area, and sustainable improvement for safety and trust on local food.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.1
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• pp.150-153
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• 2015

An energy-efficient 64-bit prefix adder is proposed for micro-server processors based on both semidynamic and bypassing structures. Prefix adders consist of three main stages i.e. propagate-generate (PG) stage, carry merge (CM) tree, and sum generators. In this architecture, the PG and CM stages consume most of the power because these are based on domino circuits. This letter proposes a semidynamic PG stage for its energy-efficiency. In addition, we adopt the bypassing structure on the CM tree to reduce its switching activity. Experimental results show 19.1% improvement of energy efficiency from prior art.