• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.216-224
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• 2023

Smart lighting systems have become increasingly popular in several public sectors because of trends toward urbanization and intelligent technologies. In this study, we designed and implemented a web application platform to explore and monitor data acquired from lighting devices at Thammasat University (Rangsit Campus, Thailand). The platform provides a convenient interface for administrative and operative staff to monitor, control, and collect data from sensors installed on campus in real time for creating geographically specific big data. Platform development focuses on both back- and front-end applications to allow a seamless process for recording and displaying data from interconnected devices. Responsible persons can interact with devices and acquire data effortlessly, minimizing workforce and human error. The collected data were analyzed using an exploratory data analysis process. Missing data behavior caused by system outages was also investigated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1013-1018
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• 2022

Game Industry sustained growth for some time, but the lifespan of a game is shortening. Various efforts to improve the quality of services for the game players which play a role in extending the lifespan of games. When a game is serviced, the server of the game starts to store log informations, and the stored data became important measures to predict game user's activities. As the game's data gathers, it becomes highly useful big data. By analyzing the data of the game stored in this way, a game service issue analysis procedure is proposed to improve the quality of the game service and to proceed with a better service, and based on the analysis in this way, it was applied to the balance test process and verified through expert to the balance test process. If the log analysis process is applied through this paper, it will be a basic data that can improve the quality of game services.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.2
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• pp.527-536
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• 1995

Analysis of pultrusion process for the thermosetting composites containing volatiles was performed. Degree of cure, amount of volatile evolved and pulling force were calculated for the processing variables such as die temperature and pulling speed. Cure kinetics was modeled from the data obtained by DSC(Differential Scanning Calorimeter). The volatile evolution kinetics was modeled from the data by DSC as well as TGA(Thermo Gravimetric Analyzer). The cure kinetics and volatile evolution kinetics models were incorporated into the energy equation. The resulting governing equation was solved using finite element method. Pulling force was calculated through the analysis of pressure developed inside the pultrusion die. Experiments were performed and the data were compared with the calculated results. Good agreements were observed.

• Journal of Aerospace System Engineering
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• v.4 no.4
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• pp.37-43
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• 2010

Need for agile satellites increases for performing various mission due to increase of satellite image applications and users. This paper performs attitude maneuver analysis by using Satellite Tool Kit(STK) made by AGI. In order to automate the STK analysis process, a MATLAB program is developed to generate STK input data and to process STK output data. Five attitude maneuver modes are analyzed and attitude angle variation and required torques are calculated.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.91-100
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• 2022

Major developed countries are seriously considering smart factories to increase their manufacturing competitiveness. Smart factory is a customized factory that incorporates ICT in the entire process from product planning to design, distribution and sales. This can reduce production costs and respond flexibly to the consumer market. The smart factory converts physical signals into digital signals, connects machines, parts, factories, manufacturing processes, people, and supply chain partners in the factory to each other, and uses the collected data to enable the smart factory platform to operate intelligently. Enhancing personalized value is the key. Therefore, it can be said that the success or failure of a smart factory depends on whether big data is secured and utilized. Standardized communication and collaboration are required to smoothly acquire big data inside and outside the factory in the smart factory, and the use of big data can be maximized through big data analysis. This study examines big data analysis and standardization in smart factory. Manufacturing innovation by country, smart factory construction framework, smart factory implementation key elements, big data analysis and visualization, etc. will be reviewed first. Through this, we propose services such as big data infrastructure construction process, big data platform components, big data modeling, big data quality management components, big data standardization, and big data implementation consulting that can be suggested when building big data infrastructure in smart factories. It is expected that this proposal can be a guide for building big data infrastructure for companies that want to introduce a smart factory.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• Convergence Security Journal
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• v.20 no.4
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• pp.3-12
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• 2020

Although the related regulations or guidelines are not specified in the defense weapon system R&D process, R&D authorities frequently encounter problems that require rational decision-making. If the rational process is not applied in the matter of alternative choice, the project could be disrupted, which can result in longer project periods or more resource provision. In particular, a variety of decision-making methods are needed for test&evaluation of missile R&D. The issue of selecting a test site is one of the representative decision-making problems. If it is needed to determine the priority of multiple sites, Delphi Method and Analytic Hierarchy Process(AHP) will be applied. However, if the input of cost is to be considered, Data Envelopment Analysis(DEA) is more valuable to solve the problem. This paper proposes a solution to handle quantitatively various decision-making problems that can occur in missile flight test, and shows how DEA is applied through a simulated case study of selecting a foreign test site.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.289-301
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• 2003

In this paper, we propose Shewhart control chart and EWMA control chart using the autocorrelated data which are common in chemical and process industries and lead to increase the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and simulation is conducted to investigate the performances of the proposed control charts.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.