• 한국안전학회지
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• 제23권6호
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• pp.1-6
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• 2008

Machine operator and quality are affected by chip during cutting process to product machine parts. This paper presents a study of the influence of cutting conditions on the surface roughness obtained by turning using Taguchi method for safety of turning operator. In the machining of titanium alloy, high cutting temperature and strong chemical affinity between the tool and the work material are generated because of its low thermal conductivity and chemical reactivity. Therefore titanium alloys are known as difficult-to materials. An orthogonal array, the signal-to-noise ratio, the analysis of variance are employed to investigate the cutting characteristics of implant material bars using tungsten carbide cutting tools of throwaway type. Also Experimental results by orthogonal array are compared with optimal condition to evaluate advanced reliability. Required simulations and experiments are performed, and the results are investigated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.73.1-73
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• 2001

To utilize the uranium resources contained in the spent nuclear fuel generated from the nuclear power plants, the remote handling and dismantling technology is required. The dismantling process of the sport fuel is the most common process involved in the spent fuel recycling, the rod consolidation and the disposal processes. Since the machine used in the dismantling process are located and operated in isolated space, so called a hot cell, the reliability of machines is very important. To enhance the reliability of the process, in this research, the graphical monitoring system is developed for the fuel dismantling process. The graphic model of each machine is composed of many parts and every parts of the graphic model are given their own kinematics. Using the kinematics and simulating the graphic model in the virtual environment, the validity of the conceptual design can be verified before ...

• KEPCO Journal on Electric Power and Energy
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• 제7권2호
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• pp.301-307
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• 2021

After the spread of the data collection devices such as smart meters, energy data is increasingly collected in a variety of ways, and its importance continues to grow. However, due to technical or practical limitations, errors such as missing or outliers in the data occur during data collection process. Especially in the case of customer-related data, billing problems may occur, so energy companies are conducting various research to process such data. In addition, efforts are being made to create added value from data, which makes it difficult to provide such services unless reliability of data is guaranteed. In order to solve these challenges, this research analyzes prior research related to bad data processing specifically in the energy field, and propose new missing value processing methods to improve the reliability and field utilization of energy data.

• Geomechanics and Engineering
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• 제37권3호
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• 한국IT서비스학회지
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• 제17권2호
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• pp.111-128
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• 2018

About 3.9 million apps and 24 primary categories can be approved on Apple iTunes Store. Making accurate categorization can potentially receive many benefits for developers, app stores, and users, such as improving discoverability and receiving long-term revenue. However, current categorization problems may cause usage inefficiency and confusion, especially for cross-attribution, etc. This study focused on evaluating the reliability of app categorization on Apple iTunes Store by using several rounds of inter-rater reliability statistics, locating categorization problems based on Machine Learning, and making more accurate suggestions about representative functionality stems for each primary category. A mixed methods research was performed and total 4905 popular apps were observed. The original categorization was proved to be substantial reliable but need further improvement. The representative functionality stems for each category were identified. This paper may provide some fusion research experience and methodological suggestions in categorization research field and improve app store's categorization in discoverability.

• Nuclear Engineering and Technology
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• 제50권4호
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• pp.570-581
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• 2018

Recently, a software program has been used in nuclear power plants (NPPs) to digitalize many instrumentation and control systems. To guarantee NPP safety, the reliability of the software used in safetycritical instrumentation and control systems must be quantified and verified with proper test cases and test environment. In this study, a software testing method using a simulation-based software test bed is proposed. The test bed is developed by emulating the microprocessor architecture of the programmable logic controller used in NPP safety-critical applications and capturing its behavior at each machine instruction. The effectiveness of the proposed method is demonstrated via a case study. To represent the possible states of software input and the internal variables that contribute to generating a dedicated safety signal, the software test cases are developed in consideration of the digital characteristics of the target system and the plant dynamics. The method provides a practical way to conduct exhaustive software testing, which can prove the software to be error free and minimize the uncertainty in software reliability quantification. Compared with existing testing methods, it can effectively reduce the software testing effort by emulating the programmable logic controller behavior at the machine level.

• 대한안전경영과학회지
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• 제14권2호
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• pp.147-158
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• 2012

This paper aims to develop a new chain metrics for obtaining lean Overall Equipment Effectiveness(OEE) and present implementation strategy which considers the properties for Total Productive Maintenance(TPM) to reduce machine losses, Performance Analysis and Control(PAC) to reduce labor losses, Lean Production System(LPS) to reduce floor wastes, and Theory of Constraints(TOC) to minimize the problem of Capacity Constrained Resource(CCR). The study reviews the related literatures and reformulates the structure of machine losses, labor losses and field wastes. The research also develops the integrated productivity metrics according to time, units, reliability and maintainability. It is found that the study develops the actual productivity measure in terms of efficiency, effectiveness and standard productivity. In addition to that, it outlines and develops by using the integrated LPS and TPM, lean OEE measures such as Time Based Productivity(TBP), Unit Based Productivity(UBP), and Reliability & Maintainability Based Availability(RMBA). Implication examples are proposed to make it easier and available for practioners to understand the implementation strategies about TPM OEE, lean OEE and TOC OEE. Futhermore related to other studies, the research contributes to create a new chain productivity measures to clear the interrelationship concepts of productivity, efficiency and effectiveness. Moreover the paper develops the enhanced OEE measures by integration of TPM, PAC, LPS and TOC with the perspective of schedule, throughput, reliability, maintainability and availability.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.90-95
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• 2005

To perform the finish outside-diameter grinding process of ferrules which are widely used as fiber optic connectors, a high-precision centerless grinding machine is necessary. In this study, the thermal characteristics of the high-precision centerless grinding machine such as the temperature distribution, temperature rise and thermal deformation, are estimated based on the virtual prototype of the grinding machine and the heat generation rates of heat sources related to the machine operation conditions. The reliability of the predicted results is demonstrated by the temperature characteristics measured from the physical prototype. Especially, the predicted and measured results show the fact that the high-precision centerless grinding machine consisted of the hydrostatic GW and RW spindle systems, hydrostatic RW feeding mechanism, RW swivel mechanism, on-machine GW and RW dressers, and concrete-filled steel bed, has very stable thermal characteristics.

• 전기학회논문지
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• 제63권8호
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• pp.1098-1103
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• 2014

This study deals with the development of an electrofusion welding machine that is capable of joining plastic pipes using a recently developed electrofusion fitting. This fitting has built-in conductive plastics that are used to weld the joint together as a heating element. In order to explain the mechanism of the new machine, 1) the resistance characteristics of the heating element were explained, 2) the method of electric welding that uses the electrofusion fitting was described, and 3) the method of power supply based on controlling the firing angle was explained. A control system for an intelligent electrofusion welding machine was proposed. This system has the ability to recognize the diameter of an electrofusion fitting using a lookup-table based on the difference of resistance curves according to fitting types, and it is able to weld the fittings regardless of the ambient temperature. A new algorithm was developed to control the power of electric welding through the recognition of feature points from the resistance curve of the heating element. In order to evaluate the performance of the developed welding machine, tests involving the welding of 16 mm- and 20 mm-type fittings were carried out. Examining the welding results, we concluded that the proposed welding machine will offer high productivity and reliability in the field of electrofusion welding.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.