The semiconductor manufacturing industry is managed by a number of parameters from the FAB which is the initial step of production to package test which is the final step of production. Various methods for prediction for the quality and yield are required to reduce the production costs caused by a complicated manufacturing process. In order to increase the accuracy of quality prediction, we have to extract the significant features from the large amount of data. In this study, we propose the method for extracting feature from the cell level data of probe test process using OPTICS which is one of the density-based clustering to improve the prediction accuracy of the quality of the assembled chips that will be placed in a package test. Two features extracted by using OPTICS are used as input variables of quality prediction model because of having position information of the cell defect. The package test progress for chips classified to the correct quality grade by performing the improved prediction method is expected to bring the effect of reducing production costs.

The purpose of this study is to identify a relationship between the time taken and the characteristics of touch key for touch-screen-based in-vehicle information system (IVIS) and to suggest a new Fitts' law formula that is added a driving speed parameter. Many studies already have shown that Fitts' law is well fitted in various devices for primary tasks, but there is no study of Fitts' law for secondary task in dual-task situation. Fitts' law may not be applied to the secondary task as it is, because the secondary task performance can be affected by the amount of attention for the primary task. To verify this, we carried out an experiment that showed whether pointing task to touch-screen-based IVIS during driving is affected by driving speeds or not. In the experiment, 30 people were volunteered for participants and the participants carried out driving task and pointing task on the screen of IVIS simultaneously. We measured the time to point a touch key on IVIS for every condition (3 driving speeds${\times}5$ touch key sizes${\times}7$ distances between steering wheel and touch key). As a result, there was an effect of driving speed on the pointing time. As we extended the index of difficulty of the conventional Fitts' law formula by incorporating driving speed, we established an extended Fitts' law formula for pointing on IVIS, which showed better accordance with dual task situation. This study can be evidence that secondary task performance is affected by degree of concentration on primary task, and the extended Fitts' law formula can be useful to design interfaces of IVIS.

One of the most widely used methods for dimensionality reduction is principal component analysis (PCA). However, the reduced dimensions from PCA do not provide a clear interpretation with respect to the original features because they are linear combinations of a large number of original features. This interpretation problem can be overcome by feature selection approaches that identifying the best subset of given features. In this study, we propose an unsupervised feature selection method based on the geometrical information of PCA loading vectors. Experimental results from a simulation study demonstrated the efficiency and usefulness of the proposed method.

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Demand forecasting plays a key role in overall business activities such as production planning, distribution management, and inventory management. Especially, for a fast-changing environment of the clothing industry, logical forecasting techniques are required. In this study, we propose a procedure to predict product life cycle using data mining algorithms. The proposed procedure involves three steps : extracting key variables from profiles, clustering, and classification. The effectiveness and applicability of the proposed procedure were demonstrated through a real data from a leading clothing company in Korea.

Objective : The present study was intended to develop a design ideation and evaluation process for the development of ecology-, ergonomics-, and economy-friendly ($E^3$-friendly) products. Background : Due to increasing social and legal requirements on global sustainability, manufacturing companies have made more efforts ever than before for the development of eco-friendly products. However, most eco-friendly products are often criticized due to lacking ergonomic and/or economic considerations. Method : An $E^3$-friendly product development process consisting of (1) survey of eco-friendly products, (2) characterization of eco-friendly products, (3) design ideation for $E^3$-friendly product, and (4) design idea evaluation for $E^3$-friendliness was established and applied to the development of a novel product which supports drinking of daily recommended amount of water. Results : Fifty-five design characteristics were identified by a survey of forty eco-friendly products and incorporated into the proposed ideation and evaluation process. New ideas and design changes were developed effectively using the proposed development process for a novel $E^3$-friendly bottle for support of water drinking. Conclusion : The proposed process was found effective for the development of eco-friendly design ideas and improvements. Application : The proposed system would be of use to develop better design ideas having market competitiveness.

This research deals with a problem that minimizes makespan in a non-identical parallel machine system with sequence and machine dependent setup times and machine dependent processing times. We first present a new mixed integer programming formulation for the problem, and using this formulation, one can easily find optimal solutions for small problems. However, since the problem is NP-hard and the size of a real problem is large, we propose four heuristic algorithms including genetic algorithm based heuristics to solve the practical big-size problems in a reasonable computational time. To assess the performance of the algorithms, we conduct a computational experiment, from which we found the heuristic algorithms show different performances as the problem characteristics are changed and the simple heuristics show better performances than genetic algorithm based heuristics for the case when the numbers of jobs and/or machines are large.

We consider the problem to find economic inventory quantity of a single commodity under stochastic demands and order cancellation. In contrast to the traditional economic production quantity (EPQ) model, we assume that once the amount of inventory reaches to a predetermined level of quantity then the production is not halted but its production speed decreases until the inventory level drops to zero. We establish two probabilistic models representing the behaviors of both the high-production period and low-production period, respectively, and derive the relationship between the level of inventory and costs of production, cancellation, and holding, from which the quantity of economic inventory is obtained.

Kwon et al. (2013) studied the optimal monitoring interval of systems with finite life cycle. It is assumed that there are several failure modes from several failure causes and the occurrence of causes follows a homogeneous Poisson process. The total expected cost is used as an optimization criterion. In this article, we derive newly the total expected cost under the same assumptions and consider some extended models.

High cognitive workload decreases a driver's ability of judgement and response in traffic situation and could result in a traffic accident. Electrocardiography (ECG) has been used for evaluation of drivers' cognitive workload; however, individual differences in ECG response corresponding to cognitive workload have not been fully considered. The present study developed an evaluation method of individual driver's cognitive workload based on ECG data, and evaluated its usefulness through an experiment in a driving simulator. The evaluation method developed by the present study determined the optimal ECG evaluation condition for individual participant by analysis of area under the receiver operating characteristic curve (AUC) for various conditions (total number of conditions = 144) in terms of four aspects (ECG measure, window span, update rate, and workload level). AUC analysis on the various conditions showed that the optimal ECG evaluation condition for each participant was significantly different. In addition, the optimal ECG evaluation condition could accurately detect changes in cognitive workload for 47% of the total participants (n = 15). The evaluation method proposed in the present study can be utilized in the evaluation of individual driver's cognitive workload for an intelligent vehicle.

The purpose of this study is to forecast the penetration rate of renewable energy and a reasonable scale for the R&D investment plan in Korea based on the relationship between the diffusion and R&D investments drawn by analogy from empirical cases of advanced countries. Among numerous candidate developed countries, the German market was chosen based on the similarity of the diffusion patterns to those of the Korean plan. We then figured out how the investment triggers the growth of technology from the selected benchmark, and applied the technology S-curve relation formula to derive the desirable investment plan for Korea. The present paper is a pioneering attempt to forecast the diffusion process of renewable energy technology in Korea using the comparative analogy from cases of advanced countries.

The present study analyzes the relationship between research and development (R&D) inputs and performance of a national technology innovation R&D program using successive binary Logistic regression models based on a typical R&D logic model. In particular, this study focuses on to answer the following three main questions; (1) "To what extent, do the R&D inputs have an effect on the performance creation?"; (2) "Is an obvious relationship verified between the immediate predecessor and its successor performance?"; and (3) "Is there a difference in the performance creation between R&D government subsidy recipient types and between R&D collaboration types?" Methodologically, binary Logistic regression models are established successively considering the "Success-Failure" binary data characteristic regarding the performance creation. An empirical analysis is presented analyzing the sample n = 2,178 R&D projects completed. This study's major findings are as follows. First, the R&D inputs have a statistically significant relationship only with the short-term, technical output, "Patent Registration." Second, strong dependencies are identified between the immediate predecessor and its successor performance. Third, the success probability of the performance creation is statistically significantly different between the R&D types aforementioned. Specifically, compared with "Large Company", "Small and Medium-Sized Enterprise (SMS)" shows a greater success probability of "Sales" and "New Employment." Meanwhile, "R&D Collaboration" achieves a larger success probability of "Patent Registration" and "Sales."