• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.27-39
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• 2006

Many real world cases in material failure analysis do not follow perfectly the normal distribution. Forcing of the normality assumption may lead to inaccurate predictions and poor product quality. We examine the failure process of the internal bond (IB or tensile strength) of medium density fiberboard (MDF). We propose a forced censoring technique that closer fits the lower tails of strength distributions and better estimates extremely smaller percentiles, which may be valuable to continuous quality improvement initiatives. Further analyses are performed to build an accelerated common-shaped Weibull model for different product types using the $JMP^{(R)}$ Survival and Reliability platform. In this paper, a forced censoring technique is implemented for the first time as a software module, using $JMP^{(R)}$ Scripting Language (JSL) to expedite data processing, which is crucial for real-time manufacturing settings. Also, we use JSL to automate the task of fitting an accelerated Weibull model and testing model homogeneity in the shape parameter. Finally, a package script is written to readily provide field engineers customized reporting for model visualization, parameter estimation, and percentile forecasting. Our approach may be more accurate for product conformance evaluation, plus help reduce the cost of destructive testing and data management due to reduced frequency of testing. It may also be valuable for preventing field failure and improved product safety even when destructive testing is not reduced by yielding higher precision intervals at the same confidence level.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.15-31
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• 2016

Sales Force Automation (SFA) is a strategic information system and one of the components of operational CRM system. SFA supports salespeople's activities such as selection of potential customers, creative value proposition, after-sales services, etc. SFA is increasingly used in many companies because it has the advantages to raise the salespeople's productivity by developing forecasting ability, value proposition ability, after sales service ability etc. Many researches have shown that implementation of SFA leads to the increase of salepeople performance, organizational performance, and quality of customer relationship. However, Some prior studies have discussed on the SFA implementation failure and pointed out that one of important causes of this failure is salespeople's resistance to SFA. Although many researches explain SFA acceptance phenomenon using Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB), these researches didn't deeply investigate the salespeople's resistance to SFA. Therefore, this study focuses on the factors influencing salespeople's resistance to SFA and the relationships among these factors. This study identified three factors (salespeople's perceived loss of power, perceived loss of autonomy, and perceived time and effort waste) influencing salespeople's resistance to SFA. The hypotheses testing results showed that salespeople's perceived loss of power and perceived time and effort waste significantly increased salespeople's resistance to SFA. And salespeople's perceived loss of power plays a mediating role between perceived loss of autonomy/perceived time and effort waste and salespeople's resistance to SFA. At the end of the paper, theoretical and managerial implications of this study and the limitations and future research directions are discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.6
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• pp.483-490
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• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do likelihood inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The infinite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of Musa-Okumo and Power law type property.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.61-64
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• 2001

The electric power load during the peak time in summer is strongly affected by cooling load. which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data approached to the actual data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.8
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• pp.626-633
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system etc. are used to settle this problem. In this study, the method of estimating temperature and humidity to forecast the cooling load of ice storage system is suggested. And also the method of forecasting the cooling load using neural network is suggested. For the simulation, the cooling load is calculated using actual temperature and humidity, The forecast of the temperature, humidity and cooling load are simulated. As a result of the simulation, the forecasted data is approached to the actual data.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.948-953
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system are possible solutions to settle this problem. In this study. the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested, then the method of forecasting the cooling load using fuzzy logic is suggested by simulating that the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated, and it is shown that the forecasted data approach to the actual data. Operating the ice-storage system by the forecast of cooling load with night electric power will improve the ice-storage system efficiency and reduce the peak electric power load during the summer season as a result.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.11
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• pp.1495-1504
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• 2013

Renewable energy integration and increased system complexities make system operator maintain supply and demand balance harder than before. To keep the grid frequency in a stable range, an appropriate spinning reserve margin should be procured with consideration of ever-changing system situation, such as demand, wind power output and generator failure. This paper propose a novel concept of dynamic reserve, which arrange different spinning reserve margin depending on time. To investigate the effectiveness of the proposed dynamic reserve, we developed a new short-term reliability criterion that estimates the probability of a spinning reserve shortage events, thus indicating grid frequency stability. Uncertainties of demand forecast error, wind generation forecast error and generator failure have been modeled in probabilistic terms, and the proposed spinning reserve has been applied to generation scheduling. This approach has been tested on the modified IEEE 118-bus system with a wind farm. The results show that the required spinning reserve margin changes depending on the system situation of demand, wind generation and generator failure. Moreover the proposed approach could be utilized even in case of system configuration change, such as wind generation extension.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.1
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• pp.1-13
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• 2022

The Yongin Light Rail train was manufactured by Bombardier Transportation in Canada in 2008 and is a privately invested railway line that has been operating in Yongin-si, Gyeonggi-do, since 2013. When the frequency of train failure increases due to aging, and there is a delay in the delivery period of imported parts used in the Bombardier manufactured trains, timely vehicle maintenance may not be performed due to lack of parts. To solve this problem, it is necessary to build a 'vehicle parts maintenance demand forecasting system' that analyzes the accurate and actual maintenance demand annual based on the condition of vehicle parts. The full scope of analysis in this paper analyzes failure data from various angles after opening of Yongin light rail vehicle to analyze failure patterns for each part and identify replacement cycles according to possible failures and consumption of parts. Based on this study, it is expected that Yongin Light Rail's maintenance system will change from the existing time-based replacement (TBM) concept to the condition-based maintenance (CBM) concept. It is expected that this study will improve the efficiency of the Yongin Light Rail maintenance system and increase vehicle availability. This paper is a fundamental for establishing of a system for predicting the replacement timing of vehicle parts for Yongin Light Rail. It reports the results of data analysis on some vehicle parts.

• Korean System Dynamics Review
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• v.12 no.2
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• pp.37-67
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• 2011

This study first attempted to point out fundamental reasons behind the failure to have WiBro services off the saddle, investigating the previous studies, and second to screen the WiBro promotion policies after reviewing the major steps conducted thus far by the government. In order to put the screened policies in order by the importance, an AHP analysis was conducted targeting WiBro professionals in various fields such as government offices, universities, research institutes, and industry. One step further, causal loop diagramming on the system dynamics methodology was conducted to examine the dynamic structure of the WiBro market in holistic standpoint of view. Finally, a simulation model was developed based on the causal loop diagrams and the results of the AHP analysis in order to examine how various policy scenarios influence the growth of the WiBro market over time, and to compare the mix of policy options. Finally, the study attempted to draw some implications for WiBro vitalization.

• The Journal of Engineering Geology
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• v.24 no.2
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• pp.191-199
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• 2014

We describe a method for detecting slope instability in flume tests using pore pressure and water content data in conjunction with a statistical control chart analysis. Specifically, we conducted univariate statistical analysis on x-MR control chart data (pore pressure and water content) collected at several points along the flume slope, which we separated into three parts: upper, middle, and lower. To assess our results in the context of landslide forecasting and warning systems, we applied control limit lines at $1{\sigma}$, $2{\sigma}$, and $3{\sigma}$ levels of uncertainty. In doing so, we observed that dispersion time varies depending on the control limit line used. Moreover, the detection of instabilities is highly dependent on the position and type of sensor. Our findings indicate that different characteristics of the data on various factors predict slope failure differently and these characteristics can be identified by univariate statistical analysis. Therefore, we suggest that a univariate statistical approach is an effective method for the early detection of slope instability.