• Journal of the military operations research society of Korea
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• 제27권1호
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• pp.73-88
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• 2001

Designing high quality products and processes at a low cost is central technological and economic challenge to the engineer. The combination of engineering concepts and statistical implementations offered by Taguchi\`s off-line design technique has proven t be invaluable. By examining some deficiencies in designs from the Taguchi\`s highly fractional, orthogonal main effect plan based on orthogonal arrays, alternative method is proposed. The maximum resolution or the minimum aberration criterion is commonly used for selecting 2$^{n-m}$ fractional designs. We present new high resolution (low aberration) linear graphs to simplify the complexity of selecting designs with desirable statistical properties. The new linear graphs approach shows a substantial improvement over Taguchi\`s linear graphs and other related graphical methods for planning experiment. The new set of linear graphs will allow the experimenter to maintain the simple approach suggested by Taguchi while obtaining the best statistical properties of the resulting design such as minimum aberration as a by-product without dependency on complicated computational algorithm or additional statistical training.g.

• Journal of Power Electronics
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• 제16권1호
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• pp.121-132
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• 2016

In this paper, high-efficiency design methodology of a zero-voltage-switching full-bridge (ZVS-FB) pulse width modulation (PWM) converter for server-computer power supply is discussed based on self-driven synchronous rectifier (SR) performance. The design approach focuses on rectifier conduction loss on the secondary side because of high output current application. Various-number parallel-connected SRs are evaluated to reduce high conduction loss. For this approach, the reliability of gate control signals produced from a self-driver is analyzed in detail to determine whether the converter achieves high efficiency. A laboratory prototype that operates at 80 kHz and rated 1 kW/12 V is built for various-number parallel combination of SRs to verify the proposed theoretical analysis and evaluations. Measurement results show that the best efficiency of the converter is 95.16%.

• The Korean Journal of Financial Studies
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• 제3권2호
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• pp.267-279
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• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.

• Journal of Energy Engineering
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• 제8권4호
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• pp.583-591
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• 1999

this paper proposes a new approach for calculating the reliability indices of generation system, such as interruption frequency and duration, by using the moment matching technique Two separate expressions are derived, one for the loss of load expectation(LOLE) and the other for the loss of load frequency (LOLF). These expressions are combination of exponentials and are therefore easily integrable and can be readily evaluated. In this paper, the parameters of the distribution functions of the LOLE and LOLF are evaluated by using Genetic Algorithm and Complex Method, and the proposed approach is quite comparable with the other methods at the aspect of accuracy and efficiency.

• Journal of Information Processing Systems
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• 제13권6호
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.210-218
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• 2023

The author presents a simple data-driven intraday technical indicator trading approach based on Genetic Programming (GP) for return forecasting in the Bitcoin market. We use five trend-following technical indicators as input to GP for developing trading rules. Using data on daily Bitcoin historical prices from January 2017 to February 2020, our principal results show that the combination of technical analysis indicators and Artificial Intelligence (AI) techniques, primarily GP, is a potential forecasting tool for Bitcoin prices, even outperforming the buy-and-hold strategy. Sensitivity analysis is employed to adjust the number and values of variables, activation functions, and fitness functions of the GP-based system to verify our approach's robustness.

• Journal of Yeungnam Medical Science
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• 제40권4호
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• pp.352-363
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• 2023

Sarcopenia is a condition in which muscle mass and strength are decreased and muscle function is impaired. It is an indicator of frailty and loss of independence in older adults. It is also associated with increased physical disability, which increases the risk of falls. As a multifactorial disease, sarcopenia is caused by a combination of factors including aging, hormonal changes, nutritional deficiencies, and physical inactivity. Understanding the underlying pathophysiology of sarcopenia and identifying its different causes is critical to developing effective prevention and treatment strategies. This review summarizes the pathophysiology, consequences, diagnostic methods, and multidisciplinary approaches to sarcopenia.

• Journal of Korean Society of Water and Wastewater
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• 제38권2호
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• pp.83-93
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• 2024

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

• KIPS Transactions on Software and Data Engineering
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• 제2권3호
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• pp.209-218
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• 2013

Early researches in human action recognition have focused on tracking and classifying articulated body motions. Such methods required accurate segmentation of body parts, which is a sticky task, particularly under realistic imaging conditions. Recent trends of work have become popular towards the use of more and low-level appearance features such as spatio-temporal interest points. Given the great progress in pose estimation over the past few years, redefined views about pose-based approach are needed. This paper addresses the issues of whether it is sufficient to train a classifier only on low-level appearance features in appearance approach and proposes effective pose-based approach with pose estimation for emotional action recognition. In order for these questions to be solved, we compare the performance of pose-based, appearance-based and its combination-based features respectively with respect to scenario of various emotional action recognition. The experiment results show that pose-based features outperform low-level appearance-based approach of features, even when heavily spoiled by noise, suggesting that pose-based approach with pose estimation is beneficial for the emotional action recognition.

• Journal of the Korean Data Analysis Society
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• 제20권6호
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• pp.3049-3067
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• 2018

Although there is a growing body of research on organizational commitment profiles based on a person-centered approach, it is not widely applied to the commitment research conducted by Korean organizational scholars yet. Therefore, in this paper, we introduced the concept and analytical methods, such as cluster analysis and latent profile analysis (LPA), of the person-centered approach. In addition, we also performed both cluster analysis and LPA to identify types of organizational commitment profiles of Korean employees based on the combination of affective, continuance and normative commitment on the sample from a range of different fields in South Korea (n = 349). Both analyses extracted two comparable sets of 6 commitment profiles. These six profiles were then contrasted with employee turnover intention. Finally, implications for commitment theory, practices and future research issues were discussed.