• 대한산업공학회지
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• 제41권4호
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• pp.408-413
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• 2015

The quality prediction of the semiconductor industry has been widely recognized as important and critical for quality improvement and productivity enhancement. The main objective of this paper is to predict the final quality of semiconductor chips based on fail bit count information obtained from probe tests. Our proposed method consists of solving the data imbalance problem, non-parametric variable selection, and adjusting the parameters of the model. We demonstrate the usefulness and applicability of the proposed procedure using a real data from a semiconductor manufacturing.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.79-84
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• 2023

Social studies show that the number of separated families have lately increased due to different reasons. Despite the causes for family rift, many problems are resulted which affected the children physically and psychologically. This effect may cause them fail in their life especially at school. This paper focuses on the negative reaction of the parents' separation with other factors from the computer science prospective. Since the artificial intelligent field is the most common widespread in computer science, a predictive model is built to predict if a specific child whose parents separated, may complete the school successfully or fail to continue his education. This will be done using Decision Tree that have proved their effectiveness on the predication applications. As an experiment, a sample of individuals is randomly chosen and applied on our prediction model. As a result, this model shows that the separation may cause the child success at school if other factors are satisfied; the intelligent of the guardian, the relation between the parents after the separation, his age at the separation time, etc.

• Structural Engineering and Mechanics
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• 제9권6호
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• pp.557-568
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• 2000

As time-variant reliability approaches become increasingly used for service life prediction of the aging infrastructure, the demand for computer solution methods continues to increase. Effcient computer techniques have become well established for the reliability analysis of structural systems. Thus far, however, this is largely limited to time-invariant reliability problems. Therefore, the requirements for time-variant reliability prediction of deteriorating structural systems under time-variant loads have remained incomplete. This study presents a computer program for $\underline{REL}$iability of $\underline{T}$ime-Variant $\underline{SYS}$tems, RELTSYS. This program uses a combined technique of adaptive importance sampling, numerical integration, and fault tree analysis to compute time-variant reliabilities of individual components and systems. Time-invariant quantities are generated using Monte Carlo simulation, whereas time-variant quantities are evaluated using numerical integration. Load distribution and post-failure redistribution are considered using fault tree analysis. The strengths and limitations of RELTSYS are presented via a numerical example.

• 벤처창업연구
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• 제9권1호
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• pp.119-132
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• 2014

본 연구는 코스닥 기업의 횡령 배임 및 최대주주변경을 고려한 부실기업 예측 모형을 연구하였다. 모형개발을 위해 2009년부터 2012년까지 코스닥시장에서 상장폐지된 제조기업 83개사를 부실기업표본으로 선정하였고. 정상기업 표본은 같은 기간 코스닥시장에 상장되어 정상적인 영업활동을 하고 있으며 부실기업과 동일아이템 혹은 동종업종에 속한 83개사를 선정하여 총 166개사를 쌍대표본 추출법으로 구성하였다. 본 연구는 상기 표본기업의 상장폐지 직전 5년간 재무비율 80개를 선정하여 T-test를 실시하여 유의미한 변수 중에서 5년 연속 출현한 19개를 도출하였고 전진선택법을 이용하여 로지스틱 회귀분석 모형식을 추정하였다. 기존 연구에서는 상장폐지 직전 3년간 자료만을 분석하였으나 본 연구는 직전 5년간 자료를 분석하여 기업이 부실화되는 초기과정부터 어떤 유의미한 재무적 특성이 시차를 두고 부실화에 영향을 미치는 지를 연구했다는 점과 선행 연구에서 시도되지 않은 횡령 배임과 최대 주주변경이라는 비재무적인 특성을 더미변수로써 고려된 부실기업예측모형을 구축하여 그 정보의 유용함을 실증적으로 분석한 점이 기존 선행연구들과 차별화 된다. 연구결과, 더미변수를 추가한 모형의 판별력은 T-1년에 95.2%, T-2년에 88.0%, T-3년에 81.3%, T-4년에 79.5%, T-5년에 74.7%로 나타났으며, 상장폐지 년도에 가까워지면서 판별력도 점차 올라갔으며 기존 선행연구의 결과보다도 대체로 높은 판별력을 보였다. 본 연구가 사전에 부실화될 가능성이 높은 기업을 찾아냄으로써 해당기업은 물론 투자자, 금융기관 및 기타 이해관계자들의 피해를 조금이나마 줄여 줄 수 있을 것이라고 기대된다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
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• pp.425-428
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• 1999

This paper presents the evaluation of behavior and the prediction of tensile capacity of anchors that fail concrete, as the design basis for anchorage. Tests of cast-in-place headed anchors, domestically manufactured and installed in uncracked, unreinforced concrete are performed to investigate the behavior of single anchors and multiple anchors with the consideration of various embedment lengths and edge distances.

• 대한산업공학회지
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• 제34권1호
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• pp.57-65
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• 2008

In the vehicle design, the research on driving posture has stood out as one of the important issues. Recently, the research on 3D human modeling focused on more exact implementation of real driving posture. However, prediction of driving posture through the 3D human modeling fail to reflect on the model the phenomenon called sagging, which refers to the retraction or shrinking of the torso while driving. 30 male subjects participated in the experiment where total subjects were divided into four groups according to height percentile(under 50%ile, 51%ile to 75%ile, 76%ile to 95%ile, over 95%ile). The independent variables were seat back angle(4 levels) and seat pan angle(2 levels). The dependent variable was capacity or the degree of retraction of the torso. First this study measured the sagging capacity by using a paired T-test between erect and retracted posture. Secondly it was tried to find out significant anthropometric variables that were statistically correlated by the analysis of correlation. Finally, a prediction model was derived which explains the capacity of sagging.

• Computers and Concrete
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• 제11권4호
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• International Journal of Computer Science & Network Security
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• 제21권3호
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• pp.229-234
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• 2021

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

• Geomechanics and Engineering
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• 제35권6호
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• pp.647-663
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• 2023

The investigation of the disc cutter wear prediction has an important guiding role in TBM equipment selection, project planning, and cost forecasting, especially when tunneling in a long-distance rock formations with high strength and high abrasivity. In this study, a comprehensive database of disc cutter wear data, geological properties, and tunneling parameters is obtained from a 1326 m excavated metro tunnel project in leptynite in Shenzhen, China. The failure forms and wear consumption of disc cutters on site are analyzed with emphasis. The results showed that 81% of disc cutters fail due to uniform wear, and other cutters are replaced owing to abnormal wear, especially flat wear of the cutter rings. In addition, it is found that there is a reasonable direct proportional relationship between the uniform wear rate (WR) and the installation radius (R), and the coefficient depends on geological characteristics and tunneling parameters. Thus, a preliminary prediction formula of the uniform wear rate, based on the installation radius of the cutterhead, was established. The correlation between some important geological properties (K_V and UCS) along with some tunneling parameters (F_n and p) and wear rate was discussed using regression analysis methods, and several prediction models for uniform wear rate were developed. Compared with a single variable, the multivariable model shows better prediction ability, and 89% of WR can be accurately estimated. The prediction model has reliability and provides a practical tool for wear prediction of disc cutter under similar hard rock projects with similar geological conditions.

• 대한산업공학회지
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• 제41권3호
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• pp.267-274
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• 2015

In the semiconductor manufacturing post-FAB process, predicting a package test result accurately in the wafer testing phase is a key element to ensure the competitiveness of companies. The prediction of package test can reduce unnecessary inspection time and expense. However, an analysing method is not sufficient to analyze data collected at wafer testing phase. Therefore, many companies have been using a summary information such as a mean, weighted sum and variance, and the summarized data reduces a prediction accuracy. In the paper, we propose an analysis method for Wafer Map Image collected at wafer testing process and conduct an experiment using real data.