The learning curve model is a mathematical form which represents the relationship between the manufacturing experience and its effectiveness. The semiconductor manufacturing is widely known as an appropriate example for the learning effect due to its complicated manufacturing processes. In this paper, I propose a new compound learning curve model for semiconductor products in which the general learning curve model and the growth curve are composed. The dependent variable and the effective independent variables of the model were abstracted from the existing learning curve models and selected according to multiple regression processes. The simulation results using the historical DRAM data show that the proposed compound learning curve model is one of adequate models for describing learning effect of semiconductor products.

As the market becomes increasingly globalized and competition among companies increases in severity, various specialized organizations are participating across the process plant lifecycle, including the stages of design, construction, operation and maintenance, and dismantlement, in order to ensure efficiency and elevate competitiveness. In this regard, it is an important technical issue to develop services or information systems for sharing process plant data among participating organizations. ISO 15926 is an international standard for integration of lifecycle data for process plants including oil and gas facilities. ISO 15926 Part 7, a part of the ISO 15926 standard, specifies an implementation method called a facade that uses Web Services and ontology technologies for constructing plant data repositories and related services, with the aim of sharing lifecycle data of process plants. This paper discusses the ISO 15926-based prototype facade implemented for storing equipment data of nuclear power plants and servicing the data to interested organizations.

With the development of science and technology, modern submarines are equipped with high technology devices and multi-functioned precise armaments, consequently, acquisition cost as well as maintenance cost of the submarines are getting higher and higher. However, tight defense budget forces navy to significantly reduce military operating and maintenance costs. In this study, the maintenance and operating costs of submarine Jangbogo-II are estimated through M&S (Modeling and simulation) methodologies in order to reasonably and consistently work out the requirement verification system of Jangbogo-II. The maintenance and operating costs of Jangbogo-II along the next 25 years are estimated as 312.65 billion won via engineering analysis methods while 312.69 billion won from PRICE Model, which shows only 0.04 billion won differences as a whole. This study is expected to be able to provide meaningful decision making data for not only short and/or mid term operating planning but military budgeting.

Virtual metrology (VM), a critical component of semiconductor manufacturing, is an efficient way of assessing the quality of wafers not actually measured. This is done based on a model between equipment sensor data (obtained for all wafers) and the quality characteristics of wafers actually measured. This paper considers principal component regression (PCR), partial least squares regression (PLSR), kernel PCR (KPCR), and kernel PLSR (KPLSR) as VM models. For each regression model, two cases are considered. One utilizes all explanatory variables in developing a model, and the other selects significant variables using the genetic algorithm (GA). The prediction performances of 8 regression models are compared for the short- and long-term etch process data. It is found among others that the GA-KPLSR model performs best for both types of data. Especially, its prediction ability is within the requirement for the short-term data implying that it can be used to implement VM for real etch processes.

This paper considers a localization problem of a moving tag on RFID(Radio Frequency Identification) systems, where a positioning engine collects TDOA(Time-difference of Arrival) signal from a target tag to estimate the position of the tag. To localize the tag in the RFID system, we develop two heuristic algorithms and evaluate their performance in the estimation error and computational time by using randomly generated numerical examples. Based upon the performance evaluation, we can conclude our algorithms are valuable for localization the moving target.

As wireless and mobile technologies have advanced significantly, lots of large sized healthcare organizations have implemented so called mobile hospital (m-Hospital) which provides a location independent and point of care (POC) clinical environment. Implementation of m-Hospital enhances quality of care because health professionals such as physicians and nurses can use hospital information systems at the very place where patients are located without any delay. This paper presents a real-time patient monitoring system based on wireless network technologies. A general framework for the patient monitoring process is introduced and the architecture and components of the proposed monitoring system is described. The system collects and analyzes biometric signals of in-patients who suffer from cancer. Specifically, it continuously monitors oxygen saturation of patients in bed and alarms health professionals instantly when an abnormal status of the patient is detected. The monitoring system has been used and clinically verified in a university hospital.

Taguchi defined a good quality as 'A correspondence of product characteristic's expected value to the objective value satisfying the minimum variance condition.' For his good quality, he suggested Taguchi Method which is called Robust design which is irrelevant to the effect of these noise factors. Taguchi Method which has many success examples and which is used by many manufacturing industry. But Optimal solution of Taguchi Method is one among the experiments which is not optimal area of experiment point. On the other hand, Response Surface Method (RSM) which has advantage to find optimal solution area experiments points by approximate polynomial regression. But Optimal of RSM is depended on initial point and RSM can not use many factors because of a great many experiment. In this paper, we combine the Taguchi Method and the Response Surface Method with each advantage which is called Taguchi-RSM. Taguchi-RSM has two step, first step to find first solution by Taguchi Method, second step to find optimal solution by RSM with initial point as first step solution. We give example using catapults.