• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.2
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• pp.132-139
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• 2009

Yield is a very important measure that can expresses simply for productivity and performance of company. So, yield is used widely in many industries nowadays. With the development of the information technology and online based real-time process monitoring technology, many industries operate the production lines that are developed into automation system. In these production lines, the product structures are very complexity and variety. So, there are many multi-variate processes that need to be monitored with many quality characteristics and associated process variables at the same time. These situations have made it possible to obtain super-large manufacturing process data sets. However, there are many difficulties with finding the cause of process variation or useful information in the high capacity database. In order to solve this problem, neural networks technique is a favorite technique that predicts the yield of process for process control. This paper uses a neural networks technique for improvement and maintenance of yield in manufacturing process. The purpose of this paper is to model the prediction of a sub process that has much effect to improve yields in total manufacturing process and the prediction of adjustment values of this sub process. These informations feedback into the process and the process is adjusted. Also, we show that the proposed model is useful to the manufacturing process through the case study.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.353-361
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• 2011

A precise embedded ultrasonic localization system is developed for autonomous mobile robots in indoor environments, which is essential for autonomous navigation of mobile robots with various tasks. Although ultrasonic sensors are more cost-effective than other sensors such as LRF (Laser Range Finder) and vision, they suffer inaccuracy and directional ambiguity. First, we apply the matched filter to measure the distance precisely. For resolving the computational complexity of the matched filter for embedded systems, we propose a new matched filter algorithm with fast computation in three points of view. Second, we propose an accurate ultrasonic localization system which consists of three ultrasonic receivers on the mobile robot and two or more transmitters on the ceiling. Last, we add an extended Kalman filter to estimate position and orientation. Various simulations and experimental results show the effectiveness of the proposed system.

• Journal of the Korean Electrochemical Society
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• v.7 no.4
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• pp.211-219
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• 2004

A classical Lippmann equation valid for liquid electrodes can not describe the interfacial properties of solid electrodes due to the elastic surface strain on solid electrodes. Although there have been many attempts to derive the thermodynamic equations for solid electrodes Outing the past few decades, their validity has been still questioned by many researchers. In practice, although there are various experimental techniques to measure surface energy of solid electrodes, the results obtained by each technique are rather inconsistent due to the complexity of the surface strain on solid electrodes. This article covers these controversial issues in surface energy of solid electrodes. After giving brief summaries of the definition of the important thermodynamic parameters and the derivation of the thermodynamic equations for solid electrodes, the several experimental methods were introduced for the measurement of surface energy of solid electrodes. And then we discussed in detail the inconsistent results in the measurement of the potential of zero charge (pac) and the potential of electrocapillary maximum (ecm).

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.73-81
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• 2015

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.8-18
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• 2008

In this paper, we propose an efficient algorithm using wavelet transform for still image database retrieval. Especially, it uses only the lowest frequency sub-band in multi-level wavelet transform so that a retrieval system uses a smaller quantity of memory and takes a faster processing time. We extract different textured features, statistical information such as mean, variance and histogram, from low frequency sub-band. Then we measure the distances between the query image and the images in a database in terms of these features. To obtain good retrieval performance, we use the first feature (mean and variance of wavelet coefficients) to filter out most of the unlikely images. The rest of the images are considered to be candidate images. Then we apply the second feature (histogram of wavelet coefficient) to rank all the candidate images. To evaluate the algorithm, we create various distorted image databases using MIT VisTex texture images and PICS natural images. Through simulations, we demonstrate that our method can achieve performance satisfactorily in terms of the retrieval accuracy as well as the both memory requirement and computational complexity. Therefore it is expected to provide good retrieval solution for JPEG-2000 using wavelet transform.

• Journal of IKEEE
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• v.17 no.1
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• pp.29-35
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• 2013

In this paper, we suggest an improved Convex Hull algorithm considering sort in plane point set. This algorithm has low computational complexity since processing data are reduced by characteristic of extreme points. Also it obtains a complete convex set with just one processing using an convex vertex discrimination criterion. Initially it requires sorting of point set. However we can't quickly sort because of its heavy operations. This problem was solved by replacing value and index. We measure the execution time of algorithms by generating a random set of points. The results of the experiment show that it is about 2 times faster than the existing algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.137-145
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• 2020

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.1
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• pp.36.1-36.1
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• 2011

It is generally believed that eruptive phenomena in the solar atmosphere such as solar flares and coronal mass ejections (CMEs) occur in the solar active regions with complex magnetic structures. Magnetic helicity has been recognized as a useful parameter to measure the complexity such as twists, kinks, and inter-linkages of magnetic field lines. The objective of this study is to understand a long-term (a few days) variation of magnetic helicity in active regions and its relationship with the energy buildup and instability leading to flares and CMEs. Statistical studies of flare productivity and magnetic helicity injection in about 400 active regions were carried out. The temporal variation of magnetic helicity injected through the photosphere of active regions was also examined related to 46 CMEs. The main findings in this study are as follows: (1) the study of magnetic helicity for active regions producing major flares and CMEs indicates that there is always a significant helicity injection through the active-region photosphere over a long period of 0.5 - a few days before the flares and CMEs; (2) for the 30 CMEs under investigation, it is found that there is a fairly good correlation (linear correlation coefficient of 0.71) between the average helicity injection in the CME-productive active regions and the CME speed. Beside the scientific contribution, a major impact of this study is the observational discovery of a characteristic variation pattern of magnetic helicity injection in flare/CME-productive active regions which can be used for the improvement of solar eruption forecasting.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.537-552
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• 2010

Software Debugging is the most time consuming and costly process in the software development process. Many techniques have been proposed to isolate different faults in a program thereby creating separate sets of failing program statements. Debugging in parallel is a technique which proposes distribution of a single faulty program segment into many fault focused program slices to be debugged simultaneously by multiple debuggers. In this paper we propose a new technique called Faulty Slice Distribution (FSD) to make parallel debugging more efficient by measuring the time and labor associated with a slice. Using this measure we then distribute these faulty slices evenly among debuggers. For this we propose an algorithm that estimates an optimized group of faulty slices using as a parameter the priority assigned to each slice as computed by value of their complexity. This helps in the efficient merging of two or more slices for distribution among debuggers so that debugging can be performed in parallel. To validate the effectiveness of this proposed technique we explain the process using example.

• The Journal of the Korea Contents Association
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• v.8 no.2
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• pp.27-32
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• 2008

In this paper, we propose an orientation tracking method and a digestion path model based on angular displacement. The proposed method expresses a capsule's orientation as 3-dimension vectors and its rotation angle. Errors in roll, pitch, and yaw representing capsule's orientation information is down to $1.6^{\circ}$. Using the proposed method we can measure a roll which is not Possible to be measured using the magnetic field method. We reduce algorithm complexity lower than a previous methods based on Euler angle.