• Journal of Korean Society of Forest Science
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• v.101 no.3
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• pp.365-373
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• 2012

This study purposed to extract and assess the accuracy of assessment for deforestation area in Wonju city using medium resolution satellite image. The total size of deforestation area during the last nine years (2000-2008) was about 467 ha, and it was occurred annually about 52 ha. The most frequent form of deforestation was settlements (72%). Ninety percent of the size of deforestation was less than 2 ha in size. In addition, 79 percent of deforestation area was found within 500 m from the road network and within 100 m of the Forest/Non-forest boundary. This study compared the deforestation based on the administrative information (GIS deforestationI) with the deforestation (RS deforestation) extracted from the satellite imagery by vegetation indices (NDVI, NBR, NDWI). Extraction accuracy, mean-standard deviation${\times}1.5$ applied 3 by 3 filtering, showed reliable accuracy 35.47% k-value 0.20. However, error could be occurred because of the difference of land-use change and land-cover change. The actual rate of land-cover change deforestation area was 32% on administrative information. The 7.52% of forest management activities area was misjudged as deforestation by RS deforestation. Finally, the comparison of land-cover change deforestation (GIS deforestationII) with the RS deforestation accuracy, as a result NDVI mean-standard deviation${\times}2$ applied 3 by 3 filtering, showed improved accuracy 61.23%, k-value 0.23.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.4
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• pp.383-390
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• 2013

In order to improve the integrating accuracy and to elevate the serviceability of the KM(Korean Medicine) ontology constructed by the Korea Institute of Oriental Medicine, this research simplified the many-to-many corresponding relationship between groups of methods of treatment and groups of accompanied symptoms from disease ontology and categorized systematically the relationship. We first extracted the combinations of methods of treatment and accompanied symptoms from the KM ontology, then categorized the attributes of combinations that their frequencies were over 10 times by analyzing KM terms definition and the basic KM theory. We constructed the classification hierarchy having 14 kinds of classification in 4 steps and extracted 450 meaningful combinations. This research improved the integrating accuracy and elevated the serviceability of KM information by the classification system.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.57-67
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• 2017

This study examines methods for creating terrain models of reservoirs and techniques for verifying the accuracy. Such methods and techniques use unmanned aerial vehicles which are capable of capturing high-resolution images repetitively, are highly economic, and capable of surveying wide areas. In addition, this study suggests methods of acquiring data for reservoir safety management, the methods which also employ the unmanned aerial vehicles. Therefore, this study helps solving problems that can arise when National Disaster Management System rebuilds a reservoir management database, such as a shortage of local government manpower. This study also contributes to providing element technology necessary for advancing the database.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.361-370
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• 2020

Automatic building material recognition has been a popular research interest over the past decade because it is useful for construction management and facility management. Currently, the extensively used methods for automatic material recognition are mainly based on 2D images. A terrestrial laser scanner (TLS) with a built-in camera can generate a set of coloured laser scan data that contains not only the visual features of building materials but also other attributes such as material reflectance and surface roughness. With more characteristics provided, laser scan data have the potential to improve the accuracy of building material recognition. Therefore, this research aims to develop a TLS-based building material recognition method by combining machine learning techniques. The developed method uses material reflectance, HSV colour values, and surface roughness as the features for material recognition. A database containing the laser scan data of common building materials was created and used for model training and validation with machine learning techniques. Different machine learning algorithms were compared, and the best algorithm showed an average recognition accuracy of 96.5%, which demonstrated the feasibility of the developed method.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.53-60
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• 2018

In order to manage energy efficiency by analyzing the amount of energy, it would determine the nature of the factors involved in the energy utilization. Therefore, accurate measurement of the energy consumption data is an important factor in the energy management. In this study, we are aware of the importance of the data measurement, and proposes the accuracy assessment of electric energy monitoring system. According to conventional statistical methods it is proceeded as follows; i)the measurement error value would be determined by a random variable, ii) setting the confidence interval to consider the distribution of the statistic and determines the confidence level of the measurement accuracy. And using the t-distribution CDF is used to facilitate even small sample data.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.20 no.4
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• pp.14-20
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• 2012

A properly determined separation minima applied in Air Traffic Management(ATM) is critical for safe and efficient aircraft operations. The separation minima is primarily determined by the accuracy performance of surveillance system, and, due to the stringent aviation safety standard, the position accuracy of the surveillance system must be estimated with a high level of reliability. This study proposed a method for estimating the position accuracy of surveillance system with a relatively small amount of data by finding upper confidence limit instead of maximum likelihood values of unknown parameters. Through the proposed method, it is possible to determine a required separation minima with a more reliability in the face of data scarcity which often occurs when we implement a new surveillance system such as Automatic Dependent Surveillance-Broadcast (ADS-B).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.94-100
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• 2013

This study is to analyze the accuracy of position determination in cadastral surveying using VRS GNSS(GPS/GLONASS), and is to suggest a possibility to improvement of accuracy in decision making of cadastral surveying result based on this result. As a result of this study, the position accurate of this study, which decides position combining with GPS/GLONASS satellite data is about 3cm more accurate than using only GPS satellite data. Therefore, if GNSS integrated receiving method is to be applied on cadastration, it can be expected to improve to estimate the position accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.

• Journal of Veterinary Clinics
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• v.37 no.6
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• pp.305-310
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• 2020

To confirm the accuracy of the prediction of parturition day, the actual parturition days were compared with each day of the prediction of parturition day (n = 80). The accuracy of the prediction of parturition day was 80.0% (64/80) with a precision of ± 2 days from the first day of estrus after the first vaginal discharge, 97.5% (78/80) from the day when plasma progesterone concentrations increase above 4.0 ng/ml, and 72.5% (58/80) from the first day of diestrus, respectively. The accuracy of the prediction of parturition day by plasma progesterone concentration was higher than that by the first day of estrus and diestrus after the first vaginal discharge. These results indicated that the determination of plasma progesterone concentrations at estrus were a useful method for estimating of parturition day and for the reproductive management in pregnant bitches.