• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.6
• /
• pp.535-545
• /
• 2013

The goal of stock investment is earning high rate or return with stability. To accomplish this goal, using a portfolio that distributes stocks with high rate of return with less variability and a stock price prediction model with high accuracy is required. In this paper, three methods are suggested to require these conditions. First of all, in portfolio re-balance part, Max-Return and Min-Risk (MRMR) model is suggested to earn the largest rate of return with stability. Secondly, Entering/Leaving Rule (E/L) is suggested to upgrade portfolio when particular stock's rate of return is low. Finally, to use outstanding stock price prediction model, a model based on Semi-Supervised Learning (SSL) which was suggested in last research was applied. The suggested methods were validated and applied on stocks which are listed in KOSPI200 from January 2007 to August 2008.

• Proceedings of the KSME Conference
• /
• 2004.11a
• /
• pp.172-177
• /
• 2004

Fatigue life prediction is based on fracture mechanics and database which is established from experimental method. Rubber material also uses the same way for fatigue life prediction. But the absence of standardization of rubber material, various way of composition by each rubber company and uncertainty of fracture criterion makes the design of fatigue life by experimental method almost impossible. Tearing energy which has its origin in energy release rate is evaluated as fracture criterion of rubber material and the applicability of fatigue life prediction method are considered. The system of measuring tearing energy using the principal of virtual crack extension method and fatigue life prediction by the minimum number of experiments are proposed.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2006.05a
• /
• pp.351-352
• /
• 2006

There are many methodologies fur doing analysis of system's reliability in early design stage. Among the methods, PRISM is, as compared to MIL-HDBK-217, a newly developed technology but not easy to use. Because PRISM provides models that predict a part failure rate and field database, called EPRD and NPRD that can be combined with prediction models. This paper presents some capabilities of the prediction models in PRISM and usability of EPRD and NPRD database in system level reliability prediction.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.10C
• /
• pp.778-786
• /
• 2008

The H.264/AVC video coding standard shows superior coding efficiency by adopting many new techniques. However, the encoding complexity increases greatly to achieve higher coding efficiency. Especially, the rate distortion optimization technique, which is used to decide the intra-prediction mode, increases the encoding complexity. In this paper, we propose an efficient intra-prediction mode decision method. By using the variance of pixel values and the edge direction, the computational complexity of the intra-prediction mode decision is greatly reduced.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.1
• /
• pp.49-54
• /
• 2005

Recently, the reliability are applied for many industrial products, and many products are required to guarantee in quality and in performance. The purpose of this paper is to present some of reliability prediction methodologies using failure rate database for machinery parts that are applicable to machine tools. VDI Turret, which is core component of the NC Lathe, was chosen as the target of the reliability prediction. The results of reliability prediction has shown the failure rate, MTBF(Mean Time Between Failure), and reliability of the VDI Turret. It is expected that proposed methodologies will be applicable to prediction of reliability for other components of machine tools.

• Journal of Applied Reliability
• /
• v.11 no.3
• /
• pp.305-318
• /
• 2011

In the statement of logistics reliability prediction methodology, all components should be managed as the analysis objectives. However, in some reliability prediction of weapon systems, fastening parts, e.g., screws, bolts and nuts, have been frequently ignored because some organizations related to weapon systems have emphasized that those parts are not significant in their failures rate and functions. In this paper, failure rates, modes, and distributions were presented to prove that fastening parts should be included in reliability prediction objectives. Also, failure rate prediction methods of fastening parts are presented and compared.

• Journal of Korean Society for Quality Management
• /
• v.22 no.3
• /
• pp.179-190
• /
• 1994

Financial variables have been used in bankruptcy prediction. Despite of possible errors in prediction, most existing approaches do not consider the causal time sequence of prediction activity and bankruptcy phenomena. This paper proposes a prediction method using Neyman-Pearson Theorem and Bayes' rule. The proposed method uses posterior probability concept and determines a prediction policy with appropriate error rate.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.1
• /
• pp.33-46
• /
• 1996

Inthis paper, we investigate the compatible coding technique, which receives much interest ever since the introduction of HDTV. First, attempts have been made to analyze the theoretical transform coding gains for various hierarchical decomposition techniques, namely subband, pyramid and DCT-based decomposition techniques. It is shown that the spatical domain techniques proide higher transform coding gains than the DCT-based coding technique. Secondly, we compare the performance of these spatial domain techniques, in terms of the PSNR versus various rate allocations to each layer. Based on these analyses, it is believed that the pyramid decomposition is more appropriate for the compatible coding. Also in this paper, we propose a hybrid prediction pyramid coding technique, by combining the spatio-temporal prediction in MPEG-2[3] and the adaptive MC(Motion Compensation)[1]. In the proposed coding technigue, we also employ an adaptive DCT coefficient scanning technique to exploit the direction information of the 2nd-layer signal. Through computer simulations, the proposed hybrid prediction with adaptive scanning technuque shows the PSNR improvement, by about 0.46-1.78dB at low 1st-layer rate(about 0.1bpp) over the adaptive MC[1], and by about 0.33-0.63dB at high 1st-layer rate (about 0.32-0.43bpp) over the spatio-temporal prediction[3].

• Journal of Applied Reliability
• /
• v.17 no.2
• /
• pp.92-102
• /
• 2017

Purpose: Reliability prediction standards consider environmental conditions, such as temperature, humidity and vibration in order to predict the reliability of the electronics components. There are many types of standards, and each standard has a different failure rate prediction model, and requires different environmental conditions. The purpose of this study is to make a sensitivity analysis by changing the temperature which is one of the environmental conditions. By observing the relation between the temperature and the failure rate, we perform the sensitivity analysis for standards including MIL-HDBK-217F, RiAC-HDBK-217Plus and FIDES. Methods: we establish environmental conditions in accordance with maneuver weapon systems's OMS/MP and mission scenarios then predict the reliability using MIL-HDBK-217F, RiAC-HDBK-217Plus and FIDES through the case of DC-DC Converter. Conclusion: Reliability prediction standards show different sensitivities of their failure rates with respect to the changing temperatures.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.7
• /
• pp.963-971
• /
• 2022

Water pipeline network in local and metropolitan area is buried underground, by which it is hard to know the degree of pipe aging and leakage. In this study, assuming various sensor combinations installed in the water pipeline network, the optimal algorithm was derived by predicting the water flow rate and pressure through artificial intelligence algorithms such as linear regression and neuro fuzzy analysis to examine the possibility of detecting pipe leakage according to the data combination. In the case of leakage detection through water supply pressure prediction, Neuro fuzzy algorithm was superior to linear regression analysis. In case of leakage detection through water supply flow prediction, flow rate prediction using neuro fuzzy algorithm should be considered first. If flow meter for prediction don't exists, linear regression algorithm should be considered instead for pressure estimation.