• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.714-720
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• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.993-998
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• 2020

The technology of interiors is currently developing around the world. According to various studies, the use of plants to create an environment in the home interior is increasing. However, households using furniture are designed as environment-friendly environment interiors, and in Korea and abroad, plants are used for home interiors. Unexpected accidents are occurring. As a result, there were books and broadcasts about the dangers of specific plants, but until now, accidents continue to occur because they do not properly recognize the dangers of specific plants. Therefore, in this paper, we propose a toxic plant identification system based on a multiplicative neural network model that identifies common toxic plants commonly found in Korea. We propose a high efficiency model. Through this, toxic plants can be identified with higher accuracy and safety accidents caused by toxic plants.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.107-115
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• 2023

High turbidity in source water can have adverse effects on water treatment plant operations and aquatic ecosystems, necessitating turbidity management. Consequently, research aimed at predicting river turbidity continues. This study developed a multi-class classification model for prediction of turbidity using LightGBM (Light Gradient Boosting Machine), a representative ensemble machine learning algorithm. The model utilized data that was classified into four classes ranging from 1 to 4 based on turbidity, from low to high. The number of input data points used for analysis varied among classes, with 945, 763, 95, and 25 data points for classes 1 to 4, respectively. The developed model exhibited precisions of 0.85, 0.71, 0.26, and 0.30, as well as recalls of 0.82, 0.76, 0.19, and 0.60 for classes 1 to 4, respectively. The model tended to perform less effectively in the minority classes due to the limited data available for these classes. To address data imbalance, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm was applied, resulting in improved model performance. For classes 1 to 4, the Precision and Recall of the improved model were 0.88, 0.71, 0.26, 0.25 and 0.79, 0.76, 0.38, 0.60, respectively. This demonstrated that alleviating data imbalance led to a significant enhancement in Recall of the model. Furthermore, to analyze the impact of differences in input data composition addressing the input data imbalance, input data was constructed with various ratios for each class, and the model performances were compared. The results indicate that an appropriate composition ratio for model input data improves the performance of the machine learning model.

• Korean Journal of Construction Engineering and Management
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• v.22 no.2
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• pp.63-71
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• 2021

This paper looks into how to enhance construction project management, focusing on the change order, which is often considered one of the major causes for construction delays, disputes, and claims in the middle east construction. First, this paper categorizes the major causes of change orders. It suggests a detailed classification standard for affecting factors resulting from change orders based on a case study result of an on-going construction project in the Middle East. In particular, this paper presents a method to apply a machine learning-based feature selection to quantify the importance of change order triggers and affecting factors. As a result, the case study identifies six major change order triggers and eight affecting factors. Also, a meaningful relationship between change order triggers and affecting factors by each category is presented. This paper will contribute to setting a clear guideline for change order management for the international plant construction field while helping prevent construction delays and cost run-ups by reducing the time required for change order resolution between project owners and contractors.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.155-164
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• 2021

Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Nevertheless, barks show a complex shape with a large variation depending on the environment, and there are insufficient materials that can be utilized to train algorithms. Here, in addition to the previously published bark image dataset, BarkNet v.1.0, images of barks were collected, and a dataset consisting of 53 tree species that can be easily observed in Korea was presented. A convolutional neural network (CNN) was trained and tested on the dataset, and the factors that interfere with the model's performance were identified. For CNN architecture, VGG-16 and 19 were utilized. As a result, VGG-16 achieved 90.41% and VGG-19 achieved 92.62% accuracy. When tested on new tree images that do not exist in the original dataset but belong to the same genus or family, it was confirmed that more than 80% of cases were successfully identified as the same genus or family. Meanwhile, it was found that the model tended to misclassify when there were distracting features in the image, including leaves, mosses, and knots. In these cases, we propose that random cropping and classification by majority votes are valid for improving possible errors in training and inferences.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.863-874
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• 2018

The purpose of this study is to evaluate the future climate change impact on stream aquatic ecology health of Han River watershed ($34,148km^2$) using SWAT (Soil and Water Assessment Tool) and random forest. The 8 years (2008~2015) spring (April to June) Aquatic ecology Health Indices (AHI) such as Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI) and Fish Assessment Index (FAI) scored (0~100) and graded (A~E) by NIER (National Institute of Environmental Research) were used. The 8 years NIER indices with the water quality (T-N, $NH_4$, $NO_3$, T-P, $PO_4$) showed that the deviation of AHI score is large when the concentration of water quality is low, and AHI score had negative correlation when the concentration is high. By using random forest, one of the Machine Learning techniques for classification analysis, the classification results for the 3 indices grade showed that all of precision, recall, and f1-score were above 0.81. The future SWAT hydrology and water quality results under HadGEM3-RA RCP 4.5 and 8.5 scenarios of Korea Meteorological Administration (KMA) showed that the future nitrogen-related water quality in watershed average increased up to 43.2% by the baseflow increase effect and the phosphorus-related water quality decreased up to 18.9% by the surface runoff decrease effect. The future FAI and BMI showed a little better Index grade while the future TDI showed a little worse index grade. We can infer that the future TDI is more sensitive to nitrogen-related water quality and the future FAI and BMI are responded to phosphorus-related water quality.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.86-96
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• 2020

Based on the use cases of image recognition technology, this study looked at how artificial intelligence plays a role in image recognition technology. Through image recognition technology, satellite images can be analyzed with artificial intelligence to reveal the calculation of oil storage tanks in certain countries. And image recognition technology makes it possible for searching images or products similar to images taken or downloaded by users, as well as arranging fruit yields, or detecting plant diseases. Based on deep learning and neural network algorithms, we can recognize people's age, gender, and mood, confirming that image recognition technology is being applied in various industries. In this study, we can look at the use cases of domestic and overseas image recognition technology, as well as see which methods are being applied to the industry. In addition, through this study, the direction of future research was presented, focusing on various successful cases in which image recognition technology was implemented and applied in various industries. At the conclusion, it can be considered that the direction in which domestic image recognition technology should move forward in the future.

• The Journal of Korean Medical History
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• v.24 no.2
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• pp.25-55
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• 2011

The currently existing "Bonchojeonghwa (本草精華)" is a manuscript without the preface and the epilogue, composed of 2 books in 2 volumes. This book is a quintessence of knowledge on science of medicinal ingredients (medicinal phytology I herbal science) as well as an trial of new development in Chosun medical science. I.e. this book includes surprising change representing medical science in Chosun dynasty as a single publication on science of medicinal ingredients. It holds a value essential to clinician as a specialized book in medicinal ingredients, and Includes richer content on medicinal ingredients than any other books published before. In addition, it is away from boring list-up of superfluous knowledge as seen in "Bonchokangmok(本草綱目)" published in China, and well summarizes essential knowledge which can be used within a range of medicines available in Korea. This book has an outstanding structure that can be even used in today's textbook on science of medicinal ingredients, as it has clear theory, system and classification. Because it handles essential learning points prior to prescription to disease, it is possible to configure new prescription and adjustment of medicinal materials. Moreover, this book can play a good role for linguistic study at the time of publication, because it describes many drugs in Hangul in many parts of the book. "Bonchojeonghwa" includes a variety of animals, plants and mineral resources in Korea, like "Bonchokangmok" which was recently listed in UNESCO. As such, it has a significance in natural history as well as pharmacy in Korean Medicine. It has various academic relationships all in biologic & abiologic aspects. It has importance in sharing future biological resources, building up international potential, setting up the standard for biologic species under IMF system, and becoming a base for resource diplomacy. We should not only see it as a book on medicinal ingredients in terms of Oriental Medicine, but also make an prudent approach to it in terms of study strengthening Korea's national competitiveness. After bibliographical reviewing on the features & characteristics of the only existing copy of "Bonchojeonghwa" housed in Kyujanggak(奎章閣) of Seoul National University, the followings are noted. First, "Bonchojeonghwa" is a specialized book on medicinal ingredients voluntarily made by private hands to distribute knowledge on drugs in the desolate situation after Imjinoeran (Japanese Invasion in 1592), without waiting for governmental help. Second, it raised accessibility and practicality by new editing. Third, it classified 990 different kinds of drugs into plant, animal, and mineral at large, and dassified more in detail into 15 'Bu' and 48 'Ryu' at 258 pages. Fourth, the publication of this book is estimated to be around 1625~1633, at the time of Injo's reign in 17th century. Fifth, it contains the existing & up-to-date knowledge at the time of publication, and it is possible to see the supply-demand situation by Hangul descriptions in 149 places in the book. By the fact that there are many linguistic evidences of 17th century, explains well when the book was published.