• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.130-136
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• 2017

The three-wheel pitching machine is a device that throws balls automatically instead of a pitcher and is used chiefly to train baseball players. The machine is abundantly used by people in indoor baseball grounds for baseball games. However, in Korea, foreign products are more popular because the efficiency of domestic products is poor as compared to that of the foreign ones. Therefore, a miniature pitching machine was manufactured to analyze and solve the problems of the existing machine. We added a feeder device to insert the balls in the machine and developed a smart phone application. The machine is easily controlled by a smart phone with bluetooth. While manufacturing the miniature, the existing problems were mitigated and the machine was redesigned for mass production. This study attempted to render the pitching machine more convenient and safer as a substitute for foreign pitching machines.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.585-590
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• 2011

In the past few years, M2M (Machine-to-Machine) applications have become a hot topic in the wireless industry. While M2M applications can be used for many purposes (smart homes, smart metering/electricity meter reading, fleet management, mobile workforce, automobile insurance, vending machines, etc), and in many sectors (healthcare, agriculture, commercial, industrial, retail, utility, etc.), smart metering applications or smart grids present the biggest growth potential in the M2M market today. M2M platform is the future ubiquitous network technologies which provide the integrated service with the networks and devices. The promising technologies to tackle these problems are the Semantic technologies, for interoperability, and the Agent technologies for management of complex systems. In this paper the information communication technique based on the disaster prevention system's for the M2M, concepts and its requirement technology and application are studied.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.797-802
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• 2020

Over the past few years, smart devices, including smartphones, have been continuously required by users based on portability. The performance is improving. Ubiquitous computing environment and sensor network are also improved. Due to various network connection technologies, mobile terminals are widely used. Smart terminals need technology to make energy monitoring more detailed for more stable operation during use. The smart terminal which is light in small size generates the power shortage problem due to the various multimedia task among the terminal operation. Various estimation hardwares have been developed to prevent such situation in advance and to operate stable terminals. However, the method and performance of estimating the remaining amount are not relatively good. In this paper, we propose a method for estimating the remaining amount of smart terminals. The Capacity Estimation of lithium ion cells for stable operation was estimated based on machine learning. Learning the characteristics of lithium ion cells in use, not the existing hardware estimation method, through a map learning algorithm using machine learning technique The optimized results are estimated and applied.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Journal of KIBIM
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• v.8 no.3
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• pp.12-19
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• 2018

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens' convenience by responding to urban conditions.

• Communications of the Korean Mathematical Society
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• v.13 no.2
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• pp.393-403
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• 1998

We consider $M_1,M_2/M/1$ retrial queues with two classes of customers in which the service rates depend on the total number or the customers served since the beginning of the current busy period. In the case that arriving customers are bloced due to the channel being busy, the class 1 customers are queued in the priority group and are served as soon as the channel is free, whereas the class 2 customers enter the retrical group in order to try service again after a random amount of time. For the first $N(N \geq 1)$ exceptional services model which is a special case of our model, we derive the joint generating function of the numbers of customers in the two groups. When N = 1 i.e., the first exceptional service model, we obtain the joint generating function explicitly and if the arrival rate of class 2 customers is 0, we show that the results for our model coincide with known results for the M/M/1 queues with smart machine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.130-133
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• 2020

Smart pig farm which is based on IoT has been widely adopted by many pig farmers. In order to achieve optimal control of smart pig farm, the relation between environmental conditions and performance metric should be characterized. In this study, the relation between multiple environmental conditions including temperature, humidity and various performance metrics, which are daily gain, feed intake, and MSY, is analyzed based on data obtained from 55 real pig farm. Especially, based on preprocessing of data, various regression based machine learning algorithms are considered. Through performance evaluation, we show that the performance can be predicted with high precision, which can improve the efficiency of management.

• Journal of electromagnetic engineering and science
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• v.17 no.4
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• pp.186-190
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• 2017

Visible-near-infrared (VIS-NIR) spectroscopy is a fast and non-destructive method for analyzing materials. However, most commercial VIS-NIR spectrometers are inappropriate for use in various locations such as in homes or offices because of their size and cost. In this paper, we classified eight food powders using a portable VIS-NIR spectrometer with a wavelength range of 450-1,000 nm. We developed three machine learning models using the spectral data for the eight food powders. The proposed three machine learning models (random forest, k-nearest neighbors, and support vector machine) achieved an accuracy of 87%, 98%, and 100%, respectively. Our experimental results showed that the support vector machine model is the most suitable for classifying non-linear spectral data. We demonstrated the potential of material analysis using a portable VIS-NIR spectrometer.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.25-31
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• 2004

This paper is research about the improvement of recognition rate of the biometrics user identification system using the data previously stored in the non contact Ic smart card. The proposed system identifies the user by analyzing the iris pattern his or her us. First, after extracting the area of the iris from the image of the iris of an eye which is taken by CCD camera, and then we save PCA Coefficient using GHA(Generalized Hebbian Algorithm) into the Smart Card. When we confirmed the users, we compared the imformation of the biometrics of users with that of smart card. In case two kinds of information was the same, we classified the data by using SVM(Support Vector Machine). The Experimental result showed that this system outperformed the previous developed system.