• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• The Journal of Economics, Marketing and Management
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• v.12 no.2
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• pp.1-10
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• 2024

Purpose: This study conducted an empirical study to estimate the loss aversion rate of individual investors in the Seoul condominium market. Research design, data and methodology: A survey was conducted with Seoul residents ranging from 30's to 60's with various backgrounds. Descriptive statistical analysis and a paired sample t-test were conducted using SPSS 27.0 statistical package. Results: The results of the t-test showed that Seoul residents are indeed more sensitive to loss than gains, as pointed out in various researches related to behavioral economics. Also, the loss aversion rate associated with KRW 50 million risk was found to be 2.14. Finally, the same question was asked with KRW 100 million risk, doubled associated risk of previous question, using the same scenario, and it's been verified that the loss aversion rate increases as the associated risk or stake increases. The loss aversion rate with double risk is 2.26 which is about 5% higher than the one with KRW 50 million risk. Conclusions: This study can help many groups of people in society who need to establish rewards and punishment policies within any organization. In particular, incorporating human cognitive biases, such as loss aversion can help the South Korean government shape more effective reward and punishment policies when building rewards and punishments using taxes.

• Management & Information Systems Review
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• v.30 no.1
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• pp.39-69
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• 2011

This study is conducted to clarify when a person faces an ethical dilemma in business situation, what affects on individual ethical decision-making(from ethical judgments to behavioral intention, nonethical behaviors) in the organizational context. especially, the present study examined the direct and indirect effects of love of money, individuals' perceptions of work climate, perceptions of organizational justice on their ethical judgments and behavioral intentions regarding an ethical dilemma. The subjects of the research were Pusan National University business school enrolled students(n=160). The research was substantiated through statistical analysis of credibility and validity, correlation, variance, and moderated regression analysis. The results indicated that, although perceived ethical climate dimensions did not have a direct effect on behavioral intentions, but had a direct effect on nonethical behaviors. Climates perceived as egoistic were significant moderating effects. and love of money had a direct effect on behavioral intention. Perceptions of organizational justice had a direct effect on behavioral intentions. Especially, interactional justice had a strong influence on ethical behavioral intentions than distributive justice and procedural justice. In depth discussion implications, limitations, and future research directions were supplied.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.54-61
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• 2000

A method and results of classification of 4 types metallic wear debris were presented by using their color features. The color image of wear debris was used (or the initial data, and the color properties of the debris were specified by HSI color model. Particle was characterized by a set of statistical features derived from the distribution of HSI color model components. The initial feature set was optimized by a principal component analysis, and multidimensional scaling procedure was used for the definition of classification plane. It was found that five features, which include mean values of H and S, median S, skewness of distribution of S and I, allow to distinguish copper based alloys, red and dark iron oxides and steel particles. In this work, a method of probabilistic decision-making of class label assignment was proposed, which was based on the analysis of debris-coordinates distribution in the classification plane. The obtained results demonstrated a good availability for the automated wear particle analysis.

• International Journal of Advanced Culture Technology
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• v.2 no.2
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• pp.15-18
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• 2014

This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.245-259
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• 2001

AHP는 원래 단일 의사결정자의 분석도구로 개발된 다속성 의사결정기법이지만 복잡한 시스템에 대해서는 여러 전문가가 참여하는 집단의사결정에 많이 쓰인다. AHP를 집단의사결정에 이용하기 위해서는 첫째, 전문가들의 의견을 종합하는 방법론의 문제와 둘째, 신속하고 정확한 의견수립의 도구문제가 해결되어야한다. 이 논문에서는 전문가들이 제시한 비교행렬들로부터 하나의 중요도(combined priority)를 구하는 베이지안 방법을 정리하고, 이를 바탕으로 웹 기반에서 AHP의사결정에 대한 설문지를 작성하여 의견을 수립하고 분석할 수 있는 의사결정 시스템을 제시한다. 개발된 시스템의 구성 및 특성을 설명하고 가상의 의사결정 문제를 통하여 시스템의 기능과 사용방법 및 화면설계 등을 예시한다.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1673-1679
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• 2010

A photovoltaic (PV) generator is significantly regarded as one important alternative of renewable energy systems recently. Fault detection and diagnosis of engineering dynamic systems is a fundamental issue to timely prevent unexpected damages in industry fields. This paper presents an intelligent monitoring approach and fault detection technique for PV generator systems by means of artificial neural network and statistical signal detection theory. We devise a multi-Fourier neural network model for representing dynamics of PV systems and apply a general likelihood ratio test (GLRT) approach for investigating our decision making algorithm in fault detection and diagnosis. We make use of a test-bed of ubiquitous sensor network (USN) based PV monitoring systems for testing our proposed fault detection methodology. Lastly, a real-time experiment is accomplished for demonstrating its reliability and practicability.

• Computers and Concrete
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• v.12 no.2
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• pp.109-129
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• 2013

This work presents an approach to model concrete shrinkage. The goal is to permit the concrete industry's experts to develop independent prediction models based on a reduced number of experimental data. The proposed approach combines fuzzy logic and genetic algorithm to optimize the fuzzy decision-making, thereby reducing data collection time. Such an approach was implemented for an experimental data set related to self-compacting concrete. The obtained prediction model was compared against published experimental data (not used in model development) and well-known shrinkage prediction models. The predicted results were verified by statistical analysis, which confirmed the reliability of the developed model. Although the range of application of the developed model is limited, the genetic-fuzzy approach introduced in this work proved suitable for adjusting the prediction model once additional training data are provided. This can be highly inviting for the concrete industry's experts, since they would be able to fine-tune their models depending on the boundary conditions of their production processes.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.66-72
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• 2006

The process capability indices are widely used to measure the capability of the process to manufacture items within the specified tolerance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. The index $C_p$ has been widely used in various industries to assess process performance. In this paper, we propose new testing procedure on assessing $C_p$ index for practitioners to use in determining whether a given process is capable. The provided approach is easy to use and the decision making is more reliable. Whether a process is clearly normal or nonnormal, our bootstrap testing procedure could be applied effectively without the complexity of calculation. A numerical result based on the proposed approach is illustrated.