• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.6 no.4
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• pp.137-152
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• 2011

The purpose of this study is to estimate efficiency of agricultural business corporations using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generate efficiency estimates through Monte Carlo simulation re-sampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of the corporations is 0.749 0.790, 0.948 respectively. Among the 692 agricultural business corporations, the number of Increasing Returns to Scale (IRS)-type corporations was analyzed to be 539(77.9%). The number of Constant Returns to Scale (CRS)-type corporations was 108(15.6%), and that of Decreasing Returns to Scale (DRS)-type corporations was 45(6.5%). Since an increase in input is lower than an increase in output in IRS, an increase in input factors such as new investments is required. The Tobit model suggests that the type of corporation, capital level, and period of operation affect the efficiency score more than others. The positive coefficient of capital level and period of operation variable indicates that efficiency score increases as capital level and period of operation increases.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.133-141
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• 2009

The main objective of water distribution system is to supply enough water to users with proper pressure. Hydraulic analysis of water distribution system can be divided into Demand Driven Analysis (DDA) and Pressure Driven Analysis (PDA). Demand-driven analysis can give unrealistic results such as negative pressures in nodes due to the assumption that nodal demands are always satisfied. Pressure-driven analysis which is often used as an alternative requires a Head-Outflow Relationship (HOR) to estimate the amount of possible water supply at a certain level of pressure. However, the lack of data causes difficulty to develop the relationship. In this study, effective supply, which is the possible amount of supply while meeting the pressure requirement in nodes, is proposed to estimate the serviceability and user's convenience of the network. The effective supply is used to calculate Subsystem Importance Index (SII) which indicates the effect of isolating a subsystem on the entire network. Harmony Search, a stochastic search algorithm, is linked with EPANET to maximize the effective supply. The proposed approach is applied in example networks to evaluate the capability of the network when a subsystem is isolated, which can also be utilized to prioritize the rehabilitation order or evaluate reliability of the network.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.3 s.49
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• pp.101-111
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• 2006

This paper proposes a method for modeling new fault ground motion due to moderate size earthquakes in Stable Continental Regions (SCRs) for the first time. The near fault ground motion is characterized by a single long period velocity pulse of large amplitude. In order to model the velocity pulse, its period and peak amplitude need be determined in terms of earthquake magnitude and distance from the causative fault. Because there have been observed very few new fault ground motions, it is difficult to derive the model directly from the recorded data in SCRs. Instead an indirect approach is adopted in this work. The two parameters, the period and peak amplitude of the velocity pulse, are known to be functions of the rise time and the slip velocity. For Western United States (WUS) that belongs active tectonic regions, there art empirical formulas for these functions. The relations of rise time and slip velocity on the magnitude in SCRs are derived by comparing related data between Western United States and Central-Eastern United States that belongs to SCRs. From these relations, the functions of these pulse parameters for NFGM in SCRs can be expressed in terms of earthquake magnitude and distance. A time history of near fault ground motion of moderate magnitude earthquake in stable continental regions is synthesized by superposing the velocity pulse on the for field ground motion that is generated by stochastic method. As an demonstrative application, the response of a single degree of freedom elasto-plastic system is studied.

• Journal of Korea Water Resources Association
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• v.39 no.12 s.173
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• pp.997-1012
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• 2006

We have presented a nonparametric stochastic approach for the SOI(Southern Oscillation Index) series that used nonlinear methodology called Nonlinear AutoRegressive(NAR) based on conditional kernel density function and CAFPE(Corrected Asymptotic Final Prediction Error) lag selection. The fitted linear AR model represents heteroscedasticity, and besides, a BDS(Brock - Dechert - Sheinkman) statistics is rejected. Hence, we applied NAR model to the SOI series. We can identify the lags 1, 2 and 4 are appropriate one, and estimated conditional mean function. There is no autocorrelation of residuals in the Portmanteau Test. However, the null hypothesis of normality and no heteroscedasticity is rejected in the Jarque-Bera Test and ARCH-LM Test, respectively. Moreover, the lag selection for conditional standard deviation function with CAFPE provides lags 3, 8 and 9. As the results of conditional standard deviation analysis, all I.I.D assumptions of the residuals are accepted. Particularly, the BDS statistics is accepted at the 95% and 99% significance level. Finally, we split the SOI set into a sample for estimating themodel and a sample for out-of-sample prediction, that is, we conduct the one-step ahead forecasts for the last 97 values (15%). The NAR model shows a MSEP of 0.5464 that is 7% lower than those of the linear model. Hence, the relevance of the NAR model may be proved in these results, and the nonparametric NAR model is encouraging rather than a linear one to reflect the nonlinearity of SOI series.

• Journal of Korea Port Economic Association
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• v.40 no.1
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• pp.87-118
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• 2024

Coastal ports play an essential role in developing a country and a city. Port efficiency is an important factor affecting port trade, and the importance of port efficiency for port performance has been recognized in previous literature. DEA (Data Envelopment Analysis) and SFA (Stochastic Frontier Analysis) are widely used in this field of research. However, these two methods are limited in selecting input and output variables. In addition, the literature studies on Chinese coastal ports mainly focus on the study of port clusters in local areas, which lacks a holistic approach and generally lacks up-to-date data. Therefore, to fill the gap in this area of research, this paper introduces a model combining principal component analysis and data envelopment analysis to analyze the operational efficiency of the top 17 coastal ports in China in terms of throughput based on the most recent data available in 2021. This paper identifies container throughput as the output variable, and 13 second indicators are selected as input variables from four primary indicators: land, capital, labor, and infrastructure. Four principal components were selected from 13 second indicators using PCA.After that, DEA (BBC) and DEA (CCR) were used to analyze the 17 ports, among which five were Shanghai, Ningbo-Zhoushan, Guangzhou, Xiamen, and Dongguan, respectively, DEA efficient, and the remaining 12 ports were non-DEA efficient. Finally, improvement directions for each port are derived, and brief suggestions are made. This paper provides some reference value for developing and constructing coastal ports in China.

• Advances in concrete construction
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• v.17 no.2
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• pp.111-126
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• 2024

During the clinkering stages of cement production, the chemical composition of fine raw materials such as limestone and clay, which include iron oxide (Fe₂O₃), silicon dioxide (SiO₂) and aluminum oxide (Al₂O₃), significantly influences the quality of the final product. Specifically, the chemical interaction of Fe₂O₃ with CaO, SiO₂ and Al₂O₃ during clinkerisation plays a key role in determining the chemical reactivity and overall quality of the final cement, shaping the properties of the concrete produced. As an extension, this study aims to investigate the physical effects of incorporating nanosized Fe₂O₃ particles as fillers in concrete matrices, and their impact on concrete structures, namely slabs. To accurately model the reinforced concrete (RC) slabs, a refined trigonometric shear deformation theory (RTSDT) is used. Additionally, the stochastic Eshelby's homogenization approach is employed to determine the thermoelastic properties of nano-Fe₂O₃ infused concrete slabs. To ensure comprehensive coverage in the study, the RC slabs undergo various mechanical loads and are exposed to temperature fields to assess their thermo-mechanical performance. Furthermore, the slabs are assumed to rest on a three-parameter viscoelastic foundation, comprising the Winkler elastic springs, Pasternak shear layer and a damping parameter. The equilibrium governing equations of the system are derived using the principle of virtual work and subsequently solved using Navier's technique. The findings indicate that while ferric oxide nanoparticles enhance the mechanical properties of concrete against mechanical loading, they have less favorable effects on its performance against thermal exposure. However, the viscoelastic foundation contributes to mitigating these effects, improving the concrete's overall performance in both scenarios. These results highlight the trade-offs between mechanical and thermal performance when using Fe₂O₃ nanoparticles in concrete and underscore the importance of optimizing nanoparticle content and loading conditions to improve the structural performance of concrete structures.