• Geomechanics and Engineering
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• v.36 no.2
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.427-436
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• 2000

The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.246-259
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• 2021

IMO stability regulations include various stability parameters such as GM values. To assess the stability of the ships, we should check all stability parameters of the IMO requirements. However, since this process is complex, a more convenient way to evaluate stability performance is required. In this research, the index for marine ship intact stability assessment (IMSISA) model was developed to solve these problems. The IMSISA model consists of a stability index calculation module and a stability assessment module. In the stability index calculation module, ten stability parameters, including GM, were used to develop the stability index, which has the advantage of being able to quantify the ship stability. The stability assessment module uses the stability index value to determine the stability status of the ship and provides the captain with stability management guidelines. To verify the proposed model, the basic stability calculations were performed for two model ships in 32 loading situations. The proposed model was found to provide better performance in the stability assessment than the previous study. By applying the IMSISA model to the ships, the captain can assess the ship stability more quantitatively and efficiently.

• Journal of Korean Society for Quality Management
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• v.44 no.2
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• pp.297-308
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• 2016

Purpose: This paper treats a defense-supply chain quality management model for analyzing the Korea defense industry. Methods: Various literature, the quality collaboration index for supply chain management model proposed by Korean Standards Association and shared growth index presented by Korea Commission for Corporate Partnership are reviewed to develop the defense supply chain quality management index and model based on the method presented by Hafeez et al.(2006). In addition, based on the proposed model, we survey the supply chain quality management efficiency including focused group interviews in the defense industry. Results: We propose a defense-supply chain quality management index and model for analyzing the quality level between the parent companies and its partners. In addition, the results of applying the model are proposed to improve the quality of military items. Conclusion: A model is developed for improving the quality of military items. This proposed model will be adopted to enhance the quality of military items.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.305-312
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• 2015

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.

• Journal of the Korean Society of Safety
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• v.22 no.6
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• pp.63-68
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• 2007

The purpose of this study is to verify the relation between the risk index using AHP(Analytic Hierarchy Process) and the risk index using Computing Model. For doing the objective, this research classified 22 work types in architectural construction work from the analysis Korean architectural standard specification and Korea occupational safety & health agency code. Based on the classified 22 work types in architectural construction work, the risk index of each work type was calculated by AHP and Computing Model. For verifying the correlation of risk index between AHP and Computing Model methods, SAS version 8.0 System, which is one of the statistics programs, was used.

• ETRI Journal
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• v.27 no.4
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• pp.377-384
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• 2005

We investigate near-infrared light (NIR) propagation in a model of an adult head using an extensive Monte Carlo (MC) simulation. The adult head model is a four-layered slab which consists of a surface layer, a cerebrospinal fluid layer, a gray-matter layer, and a white-matter layer. We study the effects of a refractive index mismatch on the model, calculating the intensity of detected light, mean flight time, and partial mean flight time of each layer for various refractive indices of the cerebrospinal fluid layer as functions of source-detector spacing. The Monte Carlo simulation shows that the refractive index mismatch presents very rich results including rapidly decaying intensity of detected light and a peak and cross-over in the partial mean flight time with source-detector spacing. We also investigate spatial sensitivity profiles at various source-detector spacings, discussing the index mismatch effect on the model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.489-493
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• 2024

Happiness index is a measurement system for understanding collective happiness. As values change, studies have been proposed to add the value of behavior to the happiness index. However, there is a lack of studies analyze the relationship using individual emotions. Using a deep learning model, we predicted happiness index using social variables and individual emotional index. First, we collected social and emotional variables from January 2005 to December 2020. Second, we preprocessed the data and identified significant variables. Finally, we trained deep learning-based regression model. Our proposed model was evaluated using 5-fold cross validation. The proposed model showed 90.86% accuracy on test sets. Our model will be expected to analyze the significant factors of country-specific happiness index.

• Korean Management Science Review
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• v.30 no.3
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• pp.1-16
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• 2013

In this paper, we deal with an enhanced index fund strategy by implementing the exchange trade funds (ETFs) within the context of the Black-Litterman approach. The KOSPI200 index ETF is used to build risk-controlled portfolio that tracks the benchmark index, while the proposed Black-Litterman model mitigates estimation errors in incorporating both active investment views and equilibrium views. First, we construct a Black-Litterman model portfolio with the active market perspective based on the momentum strategy. Then, we update the portfolio with the KOSPI200 index ETF by using the equilibrium return ratio and weighted averages, while devising optimization modeling for improving the information ratio (IR) of the portfolio. Finally, we demonstrate the empirical viability of the proposed enhanced index strategies with KOSPI 200 data.

• Journal of Korean Library and Information Science Society
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• v.37 no.4
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• pp.247-270
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• 2006

The purpose of this study is to measure customer satisfaction index of NDSL. we propose new model of the user satisfaction index for NDSL and verify the new model through the analysis of structural equation model, LISREL. We also build new measurement method for customer satisfaction index by different dimension and measure diverse customer satisfaction index according to different calculation method.