• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.358-358
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• 2000

An iterative learning control technique based on a linear quadratic optimal criterion is proposed for temperature uniformity control of a silicon wafer in rapid thermal processing.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.414-422
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• 2002

In this paper, we propose a new method of adaptive neuro-fuzzy control using CFCM(Conditional Fuzzy c-means) clustering and fuzzy equalization method to deal with adaptive control problem. First, in the off-line design, CFCM clustering performs structure identification of adaptive neuro-fuzzy control with the homogeneous properties of the given input and output data. The parameter identification are established by hybrid learning using back-propagation algorithm and RLSE(Recursive Least Square Estimate). In the on-line design, the premise and consequent parameters are tuned to RLSE with forgetting factor due to a characteristic of time variant. Finally, we applied the proposed method to the water temperature control system and obtained better results than previous works such as fuzzy control.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.197-202
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• 2021

In order to prevent the spread of COVID 19, the body temperature is measured when entering the building. In this paper, we try to certify the entry of the building through real-time face recognition based on the face learning data of visitors. The number of learning images are designed to be automatically labeled to increase facial recognition. Also, it designates the forehead region from the face region as the region of interest for accurate temperature measurements. In the future, we plan to establish a database that stores the temperature, access time, and information of visitors.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.17-23
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• 2019

In this paper, quality control (QC) is applied to each meteorological element of weather data collected from seven IoT sensors such as temperature. In addition, we propose a method for estimating the data regarded as error by means of machine learning. The collected meteorological data was linearly interpolated based on the basic QC results, and then machine learning-based QC was performed. Support vector regression, decision table, and multilayer perceptron were used as machine learning techniques. We confirmed that the mean absolute error (MAE) of the machine learning models through the basic QC is 21% lower than that of models without basic QC. In addition, when the support vector regression model was compared with other machine learning methods, it was found that the MAE is 24% lower than that of the multilayer neural network and 58% lower than that of the decision table on average.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.412-417
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• 2013

Analytical modeling of photovoltaic power systems has been receiving significant attentions in recent years in that it is easy to apply for prediction of its dynamics and fault detection and diagnosis in advanced engineering technologies. This paper presents a novel probabilistic modeling approach for such power systems with a big data sequence. Firstly, we express input/output function of photovoltaic power systems in which solar irradiation and ambient temperature are regarded as input variable and electric power is output variable respectively. Based on this functional relationship, conditional probability for these three random variables(such as irradiation, temperature, and electric power) is mathematically defined and its estimation is accomplished from ratio of numbers of all sample data to numbers of cases related to two input variables, which is efficient in particular for a big data sequence of photovoltaic powers systems. Lastly, we predict the output values from a probabilistic model of photovoltaic power systems by using the expectation theory. Two case studies are carried out for testing reliability of the proposed modeling methodology in this paper.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.5
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• pp.413-420
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• 2021

This study is about a machine learning model for predicting insufficient parameters through other parameters when one of the collected parameters is insufficient. A regression model was created to predict time, temperature, humidity, CO2, and light quantity data through the machine learning regression analysis function in Matlab. In addition, the three models with the lowest RMSE values for each parameter were selected and verified. For verification, the predicted values were obtained by applying the test data to the prediction model derived from each parameter, and the correlation coefficient and error average between the measured values and the obtained predicted values were obtained and then compared.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2933-2940
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• 2018

It is known that fruit is more affected by the weather than other crops. Therefore, in order to create high value for farmers, it is necessary to develop a wholesale price model considering the weather. Peaches produced under relatively limited conditions were chosen as subjects of study. The data were collected from 2015 to 2017 provided by okdab 4.0. The meteorological data used for the analysis were generated by weighting the cultivation area and the variables with high correlation among the weather data were selected from the day before to 7 days before. Randomforest, gradient boosting machine, and XGboost were used for the analysis. As a result of analysis, XGboost showed the best performance in the sense of RMSE and correlation, and price prediction was comparatively well predicted, but the accuracy of the trading volume prediction was not so good enough. The top three weather variables affecting to the peach were minimum temperature, average maximum temperature, and precipitation.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.201-207
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• 2017

Failure of the power apparatus causes many inconveniences and problems due to power outage in all places using power such as industry and home. The main causes of faults in the Power Apparatus are aging, natural disasters such as typhoons and earthquakes, and animals. At present, the long high temperature status is monitored only by the assumption that a fault occurs when the temperature of the power apparatus becomes higher. Therefore, it is difficult to cope with the failure of the power apparatus at the right time. In this paper, we propose a power apparatus monitoring system as an efficient countermeasure against general faults except for faults caused by sudden natural disasters. The proposed monitoring system monitors the power apparatus in real time by attaching a thermal sensor, collects the monitored data, and predicts the failure using the accumulated information through learning using the artificial neural network. Through the learning and experimentation of artificial neural network, it is shown that the proposed method is efficient.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1491-1495
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• 2023

This study utilized automated machine learning (AutoML) to calculate Arctic ice surface temperature (IST). AutoML-derived IST exhibited a strong correlation coefficient (R) of 0.97 and a root mean squared error (RMSE) of 2.51K. Comparative analysis with deep neural network (DNN) models revealed that AutoML IST demonstrated good accuracy, particularly when compared to Moderate Resolution Imaging Spectroradiometer (MODIS) IST and ice mass balance (IMB) buoy IST. These findings underscore the effectiveness of AutoML in enhancing IST estimation accuracy under challenging polar conditions.