• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.453-456
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• 2022

The role of the power grid is to ensure stable power supply. It is necessary to take various measures to prepare for unstable situations without notice. After identifying the relationship between features through exploratory data analysis using weather data, a machine learning based energy production prediction model is modeled. In this study, the prediction reliability was increased by extracting the features that affect energy production prediction using principal component analysis and then applying it to the machine learning model. By using the proposed model to predict the production energy for a specific period and compare it with the actual production value at that time, the performance of the energy production prediction applying the principal component analysis was confirmed.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Smart Media Journal
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• v.11 no.7
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• pp.61-69
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• 2022

In the learned machine learning, the performance of machine learning degrades at the same time as drift occurs in terms of learning models and learning data over time. As a solution to this problem, I would like to propose the concept and evaluation method of ML drift to determine the re-learning period of machine learning. An XAI test and an XAI test of an apple image were performed according to strawberry and clarity. In the case of strawberries, the change in the XAI analysis of ML models according to the clarity value was insignificant, and in the case of XAI of apple image, apples normally classified objects and heat map areas, but in the case of apple flowers and buds, the results were insignificant compared to strawberries and apples. This is expected to be caused by the lack of learning images of apple flowers and buds, and more apple flowers and buds will be studied and tested in the future.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.105-111
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• 2023

Currently, most of content restoration using artificial intelligence learning is 2D learning. However, 3D form of artificial intelligence learning is in an incomplete state due to the disadvantage of requiring a lot of computation and learning speed from the existing 2 axes (X, Y) to 3 axes (X, Y, Z). The purpose of this paper is to secure a data-set for artificial intelligence learning by analyzing and 3D modeling the stone pagodas of ourinari by era based on the two-dimensional information (image) of cultural assets. In addition, we analyzed the differences and characteristics of towers in each era in Korea, and proposed a feature modeling method suitable for artificial intelligence learning. Restoration of cultural properties relies on a variety of materials, expert techniques and historical archives. By recording and managing the information necessary for the restoration of cultural properties through this study, it is expected that it will be used as an important documentary heritage for restoring and maintaining Korean traditional pagodas in the future.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.31-39
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• 2021

In Korea, more than half of work-related fatalities have occurred on construction sites. To reduce such occupational accidents, safety inspection by government agencies is essential in construction sites that present a high risk of serious accidents. To address this issue, this study developed risk prediction models of serious accidents in construction sites using five machine learning methods: support vector machine, random forest, XGBoost, LightGBM, and AutoML. To this end, 15 proactive information (e.g., number of stories and period of construction) that are usually available prior to construction were considered and two over-sampling techniques (SMOTE and ADASYN) were used to address the problem of class-imbalanced data. The results showed that all machine learning methods achieved 0.876~0.941 in the F1-score with the adoption of over-sampling techniques. LightGBM with ADASYN yielded the best prediction performance in both the F1-score (0.941) and the area under the ROC curve (0.941). The prediction models revealed four major features: number of stories, period of construction, excavation depth, and height. The prediction models developed in this study can be useful both for government agencies in prioritizing construction sites for safety inspection and for construction companies in establishing pre-construction preventive measures.

• Journal of Korean Medical classics
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• v.31 no.2
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• pp.49-69
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• 2018

Objectives: Following the Jin Yuan Dynasty, the Ming and Song Dynasties witnessed a great development of Yunqi xue. A study into this development has a vast significance in studying the history of the development of traditional Chinese medicine. Methods: The contents relating to Yunqi within the Comprehensive Medical Books, published during the Ming period, and medical texts separately published specifically dealing with Yunqi were used in order to review the unique characters of the study of Yunqi during this period. Results: There were many cases in the comprehensive medical books during the Ming period that dealt with Yunqi. Some of the examples are: Yunqilu in Yixueliuyao, YunQiZongLun in Yixuerumen, and Yunqilu in Yixueliuyao. A number of books that followed suit from the previous generation's study were published, the examples of which are Wangji's Yunqiyilan, and ZhangJiebin's LeiJingtuyi. WangJi, in his book, opposed the mechanic utilization of YunQi theory, and advocated the flexible application of the theory at the doctor's discretion. Liwei, in his YunQiZongLun, wrote a great deal of knowledge which he gained based on the previous-generation medical masters' achievements. Conclusions: Yunqi became widely accepted during the Ming period which led to some doctors advocating the flexible application of the YunQi theory, and some doctors even completely denouncing Yunqi.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Journal of the Daesoon Academy of Sciences
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• v.34
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• pp.141-169
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• 2020

This study focuses on the interpretation and transformation of "The Great Learning" within the Korean new religion, Daesoon Jinrihoe. Joseon Dynasty Korea was a member of the Chinese Character Cultural Sphere in East Asia. The examination and recruitment system of the Yuan Dynasty influenced the Joseon Dynasty for a long historical period. Zhu Xi's (朱熹) version of The Four Books were accepted and applied in imperial examinations during the Joseon Dynasty. The 18th century Confucian thinker, Jeong Yak-Yong (丁若鏞), overturned and rebuilt his own system for studying and interpreting The Four Books (四書學). Zhu Xi and Jeong Yak-Yong's systems of thought influenced Confucianism knowledge in that era. The historical figure deified as the Supreme God by Daesoon Jinrihoe, Kang Jeungsan (姜甑山), was trained in the study of The Four Books within that cultural and philosophical context, and this is especially evident in his interpretation and transmission of "The Great Learning." Kang Jeungsan regarding The Great Learning as deeply important. That text combined Confucian discourse on Principle, Mind, and Practice. In his interpretation, The Great Learning was also a medical and religious book that had holy and mysterious powers. In Mugeuk-do and Taegeuk-do (direct predecessors to Daesoon Jinrihoe), Jo Jeongsan interpreted the concept of Sincerity and Regularizing the Mind and incorporated them into doctrine as 'Sincerity, Respectfulness, and Faithfulness' and 'Guarding against Self-deception.' Park Wudang practiced and spread those doctrines to Korea, and Daesoon Jinrihoe devotees continue to follow those doctrines in present times.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.367-373
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• 2021

In this study, deep learning model was set up to predict the wave heights inside a harbour. Various machine learning techniques were applied to the model in consideration of the transformation characteristics of offshore waves while propagating into the harbour. Pohang New Port was selected for model application, which had a serious problem of unloading due to swell and has lots of available wave data. Wave height, wave period, and wave direction at offshore sites and wave heights inside the harbour were used for the model input and output, respectively, and then the model was trained using deep learning method. By considering the correlation between the time series wave data of offshore and inside the harbour, the data set was separated into prevailing wave directions as a pre-processing method. As a result, It was confirmed that accuracy and stability of the model prediction are considerably increased.

• Journal of the Korean Society of School Health
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• v.25 no.1
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• pp.122-132
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• 2012

Purpose: The purpose of this study is to determine the effectiveness of cooperative learner-centered methods of sex education. Methods: This study was carried out on 5th grade elementary school students in D-district. Nine classes were divided into 3 groups using each different teaching methods: group A (a cooperative learning), group B (a lecture) and group C (a control group for a comparison). The study period was from Oct. 17 to Dec. 2 in 2011. Both groups A and B received sex education lessons for 40 minutes for 4 weeks and were tested their sex knowledge and attitude to compare the differences. Results: The scores of sex knowledge for all three groups were increased and their sex attitude was increased as well. The points of sex knowledge between pre and post test in group A are greater than the group B's. Thus, the cooperative learning approach with Group A was more effective to improve student's sex knowledge. But the difference between the sex attitude scores was not statistically significant. Group A and B showed a positive improvement in both their sex knowledge and attitudes compared with the control group. Conclusion: This experiment shows that an active teaching methods is more effective to improve student sex knowledge than a passive approach. Thus, a cooperative learning method results in increases of both student's sex knowledge and interests in learning sex education. It needs to develop more diverse teaching methods and programs on sex education that are more systematic and tailored.