• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.328-331
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• 2013

As digital ecosystem has been rapidly transforming into the mobile based platform, several mobile operating system, which is in charge of user interface with mobile device has been appeared. This research suggest critical factors affecting success and failure of several commercial mobile operating systems from Palm OS appearing in 1996 to main mobile OSs appearing in 2013. For this, we analyse several mobile operating OS cases, elicit factors affecting success and failure of mobile OS, and conduct ID3 based inductive learning analyses based on elicted factors and values in case dataset. Through this, we draw rules in success and failure of mobile OS and suggest strategic implications for the commercial success of mobile OS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.258-264
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• 2015

This research suggests critical factors affecting success and failure of several commercial mobile operating systems from Palm OS appearing in to main mobile OSs appearing in 2013. For this, we analyses several mobile operating cases, elicit factors affecting success and failure of mobile OS, and conduct ID3 based inductive learning analyses based on elicited factors and values in case dataset. Through this, we draw rules in success and failure of mobile OS and suggest strategic implications for the commercial success of mobile OS in perspective of innovation and globalization.

• Computers and Concrete
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• v.31 no.4
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• pp.307-314
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• 2023

In this paper we back-analyze a failure event of a 9 m high concrete cantilever wall subjected to earth loading. Granular soil was deposited into the space between the wall and a nearby rock slope. The wall segments were not designed to carry lateral earth loading and collapsed due to excessive bending. As many geotechnical programs rely on the Mohr-Coulomb (MC) criterion for elastoplastic analysis, it is useful to apply this failure criterion to the concrete material. Accordingly, the back-analysis is aimed to search for the suitable MC parameters of the concrete. For this study, we propose a methodology for accelerating the back-analysis task by automating the numerical modeling procedure and applying a machine-learning (ML) analysis on FE model results. Through this analysis it is found that the residual cohesion and friction angle have a highly significant impact on model results. Compared to traditional back-analysis studies where good agreement between model and reality are deemed successful based on a limited number of models, the current ML analysis demonstrate that a range of possible combinations of parameters can yield similar results. The proposed methodology can be modified for similar calibration and back-analysis tasks.

• Journal of Korean Elementary Science Education
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• v.28 no.1
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• pp.24-34
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• 2009

The purpose of this study is to examine the effects of background knowledge on problem solving. To achieve this aim, I proposed the model which shows problem solving process centering around background knowledge, conducted the lessons concerning the concept 'weightlessness' on pre-service elementary teachers, and then classified the pre-service elementary teachers into several groups by the difference of the results presented in the process of solving the problems on weightlessness. And I examined qualitatively the effects of background knowledge on problem solving through the interview with 11 volunteers. On the cause of the failing the problem solving, the failure of acquiring or activating the background knowledge related to the learning concept was most frequently, secondly the use of the background knowledge unrelated to the learning concept, and thirdly the failure of understanding the teaming concept. To acquire or activate the background knowledge related to the teaming concept was more difficult than to understand the new teaming concept, and the cases that use the background knowledge unrelated to the learning concept failed to solve problem. The result of interview, all interviewee understood the learning concept correctly, but all of them who fail to acquire or activate the background knowledge related to the learning concept, or use the background knowledge unrelated to the learning concept, could not solve the problem.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.25-34
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• 2012

In this study, in the process of testing before the release of the software products designed, software testing manager in advance should be aware of the testing-information. Therefore, the effective learning effects perspective has been studied using the NHPP software. The finite failure nonhomogeneous Poisson process models presented and applied property of learning effect based on truncated time and delayed S-shaped software reliability. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model can be confirmed. This paper, a failure data analysis was performed, using time between failures, according to the small sample and large sample sizes. The parameter estimation was carried out using maximum likelihood estimation method. Model selection was performed using the mean square error and coefficient of determination, after the data efficiency from the data through trend analysis was performed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.83-92
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• 2022

In this paper, we designed an algorithm that can control the state of PM by learning the chain failure pattern of PHM based air conditioning facility. It is an inevitable spread of PM due to the downtime caused by the failure of the air conditioning facility. The algorithm developed by us is to establish a PM management system through PHM, and it is an algorithm that maintains a constant stabilization state through learning the stop/operation pattern of the air conditioner and manages PM based on this. As a result of the simulating at a subway station for the performance qualification of the algorithm, it was verified that the concentration of PM reduces by 30% on average. In the case of stations with many passengers using the subway, the concentration of PM exceeded the Ministry of Environment Standards(100 ㎍/m³), but it was verified that the concentration of PM was improved at all stations where the simulation was conducted. In the future research is to expand the system to comprehensively manage not only PM but also pollutants such as CO₂, CO, and NO₂ in subway stations.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.249-260
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• 1997

The manufacturing environment is rife with nonlinear processes. In this context, an intelligent production controller should be able to predict the dynamic behavior of various subsystems as they react to transient environmental conditions, the varying internal condition of the manufacturing plant, and the changing demands of the production schedule. This level of adaptive capability may be achieved through a coherent methodology for a learning coordinator to predict nonlinear and stochastic processes. The system is to serve as a real time, online supervisor for routine activities as well as exceptional conditions such as damage, failure, or other anomalies. The complexity inherent in a learning coordinator can be managed by a modular architecture incorporating case based reasoning. In the interest of concreteness, the concepts are presented through a case study involving a knowledge based robotic system.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.1-8
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• 2022

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users' safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

• Tribology and Lubricants
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• v.11 no.5
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• pp.15-19
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• 1995

Learning from the failure of mechanical systems is a necessity, given that it is the understanding of how and why things fail that generates effective redesign. This subsequently enables the technology that surrounds us to become more reliable, safer, and more economical by extending component life and minimizing the wasteful decisions made to replace systems that am either sound for continued operation could be easily repaired. Considerations for cost-effective decision making, so as to promote healthy machinery, equipment, and structures, are discussed in terms of learning from failure analysis, improving via reliability engineering, and achieving longevity through integrated diagnostics.