• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.621-637
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• 2006

Although the approaches implementing strut-tie models are the valuable tools for designing discontinuity regions of structural concrete, the approaches of the current design codes have to be improved for the design of structural concrete subjected to complex loading and geometrical conditions because of the uncertainties in the selection of strut-tie model, in the use of an indeterminate strut-tie model, and in the effective strengths of struts and nodal zones. To improve the uncertainties, a grid struttie model approach is proposed in this study. The proposed approach, allowing to perform a consistent and effective design of structural concrete, employs an initial grid strut-tie model in which various load combinations can be considered. In addition, the approach performs an automatic selection of an optimal strut-tie model by evaluating the capacities of struts and ties using a simple optimization algorithm. The validity and effectiveness of the proposed approach is verified by conducting the analysis of the four reinforced concrete deep beams tested to failure and the design of shearwalls with two openings.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.298-309
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• 1999

In this study, we attempt to solve the loading and scheduling problems of an FMS in an integrated manner. We propose an integrated approach and its solution methodologies which can fully exploit the flexibility of an FMS effectively and make decisions about tool allocation, machine selection, and job sequencing simultaneously. The proposed approach consists of two main modules : 'schedule generating module' which makes partial schedules and 'tool-allocation checking module' which investigates the feasibility of tool-allocation for unscheduled tasks. Utilizing two interacting modules, we can finally settle the loading and scheduling problems. Experimental results show that in most cases the proposed integrated approach outperforms existing hierarchical approaches in the scheduling performance and the computational time required. In addition to that, the difference between the two approaches tends to increase when the number of part types and the number of alternative machines increase and the tool constraints become tight. To conclude, the experimental results show that the proposed approach is a viable one for solving practical problems.

• ETRI Journal
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• v.33 no.1
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• pp.99-109
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• 2011

This paper concerns a robust real-time voice activity detection (VAD) approach which is easy to understand and implement. The proposed approach employs several short-term speech/nonspeech discriminating features in a voting paradigm to achieve a reliable performance in different environments. This paper mainly focuses on the performance improvement of a recently proposed approach which uses spectral peak valley difference (SPVD) as a feature for silence detection. The main issue of this paper is to apply a set of features with SPVD to improve the VAD robustness. The proposed approach uses a weighted voting scheme in order to take the discriminative power of the employed feature set into account. The experiments show that the proposed approach is more robust than the baseline approach from different points of view, including channel distortion and threshold selection. The proposed approach is also compared with some other VAD techniques for better confirmation of its achievements. Using the proposed weighted voting approach, the average VAD performance is increased to 89.29% for 5 different noise types and 8 SNR levels. The resulting performance is 13.79% higher than the approach based only on SPVD and even 2.25% higher than the not-weighted voting scheme.

• Journal of Microbiology
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• v.45 no.6
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• pp.572-577
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• 2007

To enhance therapeutic potential of murine monoclonal antibody, humanization by CDR grafting is usually used to reduce immunogenic mouse residues. Most humanized antibodies still have mouse residues critical for antigen binding, but the mouse residues may evoke immune responses in humans. Previously, we constructed a new humanized version (AKA) of mouse CC49 antibody specific for tumor-associated glycoprotein, TAG-72. In this study, to select a completely human antibody light chain against TAG-72, guided selection strategy using phage display was used. The heavy chain variable region (VH) of AKA was used to guide the selection of a human TAG-72-specific light chain variable region (VL) from a human VL repertoire constructed from human PBL. Most of the selected VLs were identified to be originated from the members of the human germline VK1 family, whereas the VL of AKA is more homologous to the VK4 family. Competition binding assay of the selected Fabs with mouse CC49 suggested that the epitopes of the Fabs overlap with that of CC49. In addition, they showed better antigen-binding affinity compared to parental AKA. The selected human VLs may be used to guide the selection of human VHs to get completely human anti-TAG72 antibody.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.3
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• pp.393-403
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• 2014

Objectives: Korean Occupational Safety and Health Agency(KOSHA) intended to provide guidance to respirator program administrators on respirator selection. The purpose of this project is to suggest a preliminary respirator selection guide(PRSG) for the final guidance certified by KOSHA for the correct selection of respirators in the workplace. Materials: This PRSG was made on the based of current status of respirator usage including respirators available in Korea, foreign documents search and so on. PRSG was partially modified NIOSH pocket guide to chemical hazards, 3M Respirator Protection Resource Guide(RPRG) and OSHA Assigned Protection Factor(APF) rulemaking. Results: Respirators for chemicals having Korean Occupational Exposure Limits(KOELs) can be recommended in PRSG. For the others chemicals that have no KOELs, PRSG tells you what respirators you can select, using a simple step-by-step approach after health hazard assessment like HSE procedure. Conclusions: PRSG helps you to decide the right level of protection for a given hazardous substance and how to select the right respirators. PRSG is thus expected to reduce significant misuses of respirators for the work environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.574-592
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• 2016

Range-free localization algorithm computes a normal node's position by estimating the distance to anchors which know their actual position. In recent years, reliable anchor selection research has been gained a lot of attention because this approach improves localization accuracy by selecting the only subset of anchors called reliable anchor. The distance estimation accuracy and the geometric shape formed by anchors are the two important factors which need to be considered when selecting the reliable anchors. In this paper, we study the relationship between a relative position of three anchors and localization error. From this study, under ideal condition, which is with zero localization error, we find two conditions for anchor selection, thereby proposing a novel anchor selection algorithm that selects three anchors matched most closely to the two conditions, and the validities of the conditions are proved using two theorems. By further employing the conditions, we finally propose a novel range-free localization algorithm. Simulation results show that the proposed algorithm shows considerably improved performance as compared to other existing works.

• Genomics & Informatics
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• v.5 no.3
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• pp.95-101
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• 2007

In this paper, we consider the variable selection methods in the Cox model when a large number of gene expression levels are involved with survival time. Deciding which genes are associated with survival time has been a challenging problem because of the large number of genes and relatively small sample size (n<

• Journal of the Korea Safety Management & Science
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• v.13 no.4
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• pp.117-124
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• 2011

Supplier selection is one of the most important activities of a company. This importance is increased even more by new strategies in a supply chain, because of the key role suppliers perform in terms of quality, costs and services which affect the outcome in the buyer's company. In addition, green production has become an important issue for almost every manufacturer and will determine the sustainability of a manufacturer. Therefore a performance evaluation system for supplier and green suppliers is necessary to determine the suitability of suppliers to cooperate with the company. Supplier and green supplier selection is a multiple criteria decision making problem in which the objectives are not equally important. In practice, vagueness and imprecision of the goals, constraints and parameters in these problems make the decision making complicated. The objective of this study is to construct a decision-making process using fuzzy analytic hierarchy process (FAHP) and balanced scorecard (BSC) for evaluating supplier and green suppliers in the manufacturing industry. The BSC concept is applied to define the hierarchy with four major perspectives and performance indicators are selected for each perspective. FAHP is then proposed in order to tolerate vagueness and ambiguity of information. Finally, FAHP is applied to facilitate the solving process. With the proposed approach, manufacturers can have a better understanding of the capabilities that supplier and green supplier must possess and can evaluate and select the most suitable supplier for cooperation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.59-67
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• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• Genomics & Informatics
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• v.21 no.3
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• pp.28.1-28.13
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• 2023

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer's disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer's Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.