• Journal of the Korean Society for Precision Engineering
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• v.23 no.10
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• pp.113-120
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• 2006

A cam profile CNC grinding machine is developed for manufacture of high precision contoured cams. The developed machine is composed of the high precision spindle using boll bearings, the high stiffness box layer type bed and the three axis CNC controller with the high resolution AC servo motor. In this paper, structural and modal analysis for the developed machine is carried out to check the design criteria of the machine. The analysis is carried out by FEM simulation using the commercial software, CATIA V5. The machine is modeled by placing proper shell and solid finite elements. And also, this paper presents the measurement system and experimental investigation on the modal analysis of a grinding machine. The weak part of the machine is found by the experimental evaluation. The results provide structure modification data for good dynamic behaviors. And safety of the machine was confirmed by the modal analysis of modified machine design. Finally, the cam profile grinding machine was successfully developed.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.269-276
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• 2015

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.57-63
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• 2022

The purpose of this study is to provide basic data on policy development using big data analysis and machine learning algorithms as part of preparing measures to prevent child abuse. In order to analyze big data for developing machine learning algorithms to prevent child abuse, frequency analysis, related word analysis, and emotional analysis were performed after defining academic databases and social network service data as big data. related words, and emotional analysis were conducted. As a result of the study, a preventive child abuse algorithm can be developed by preparing a data collection and sharing network system to prevent child abuse from the perspective of children affected by child abuse, perpetrators, and government authorities. Although it will be possible by institutionalizing infant self-esteem, depression, and anxiety tests with clues that depression and anxiety appear due to a decrease in self-concept in the characteristics of children affected by child abuse. We suggest that continuous progress of big data collection and analysis and algorithm development research to prevent child abuse, and expects that effective policies to prevent child abuse will be realized to eradicate child abuse crimes.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.571-573
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• 2018

The loss of data collected from the sensor node in the wireless sensor network environment is caused by the hidden node of the sensor node and power shortage. In order to solve these problems, researches have been actively carried out to maintain the network effectively, but there is no study on the situation where the maintenance of the network is impossible. Therefore, research is needed to infer lost data in situations where network maintenance is impossible. In this paper, use particulate matter data of specific cities to deduce lost data. Analyze the accumulated data through machine learning and identify the possibility of inferring lost data.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.72-74
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• 2022

Several types of data are collected from buoy due to the development of hardware technology.. However, the collected data are difficult to use due to errors including missing values and outliers depending on mechanical faults and meteorological environment. Therefore, in this study, linear interpolation is performed by adding the missing time data to enable machine learning to the insufficient meteorological data. After the linear interpolation, XGBoost and KNN-regressor, are used to forecast error data and suggested model is evaluated by using real-world data of a buoy.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.4
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• pp.281-290
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• 2013

The IT asset is a core part that supports the management objective of an organization, and the fast settlement of the IT asset fault is very important. In this study, a fault recovery prediction technique is proposed, which uses the existing fault data to address the IT asset fault. The proposed fault recovery prediction technique is as follows. First, the existing fault recovery data were pre-processed and classified by fault recovery type; second, a rule was established for the keyword mapping of the classified fault recovery types and reported data; and third, a machine learning process that allows the prediction of the fault recovery method based on the established rule was presented. To verify the effectiveness of the proposed machine learning process, company A's 33,000 computer fault data for the duration of six months were tested. The hit rate for fault recovery prediction was approximately 72%, and it increased to 81% via continuous machine learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.171-177
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• 2021

Through the rapid developments in machine learning, there have been diverse utilization approaches not only in industrial fields but also in daily life. Implementations of machine learning on financial data, also have been of interest. Herein, we employ machine learning algorithms to store sales data and present future applications for fintech enterprises. We utilize diverse missing data processing methods to handle missing data and apply gradient boosting machine learning algorithms; XGBoost, LightGBM, CatBoost to predict the future revenue of individual stores. As a result, we found that using median imputation onto missing data with the appliance of the xgboost algorithm has the best accuracy. By employing the proposed method, fintech enterprises and customers can attain benefits. Stores can benefit by receiving financial assistance beforehand from fintech companies, while these corporations can benefit by offering financial support to these stores with low risk.