• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.445-451
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• 2013

We consider the estimators for the parameters of the exponential model with limited replacements under the type I censoring scheme. Also, we propose the desirable number of replacements to provide the similar effects in terms of the mean square errors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1614-1618
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• 2008

This paper propose a novel yield difference model according to layout modification. The difference of average number of faults by layout modification to increase or decrease spaces between geometries is formulated for short faults and open faults. Complex modification including wire bending with jogs is also modeled by dividing patterns into segments and redefining spaces and widths. This model can help to monitor the yield change and to generate a cost function of defect-limited yield quickly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11B
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• pp.1596-1606
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• 2010

In this paper, we present a new analytical model that can precisely estimate the blocking performance of wavelength-routed WDM networks with sparse-partial-limited wavelength conversion (SPLWC). The proposed model accounts for the two sources of call blocking in a wavelength converter: range blocking originated from the limited conversion range of a wavelength converter; and capacity blocking induced from the limited number of wavelength converters. Based on the proposed model, we also present a new converter placement algorithm that minimizes the amount of wavelength conversion capability, while satisfying the given constraint on the network-wide blocking probability. From the numerical results obtained from the EON, we demonstrate that the blocking probability of the analytical model closely matches with that of the simulation. We also show that, by efficiently combining the existing sparse, partial, and limited wavelength conversion, the SPL WC can achieve the required blocking performance with the least amount of wavelength conversion cost.

• ETRI Journal
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• v.41 no.5
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• pp.585-598
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• 2019

A cyber-physical system (CPS) is a new mechanism controlled or monitored by computer algorithms that intertwine physical and software components. Advanced persistent threats (APTs) represent stealthy, powerful, and well-funded attacks against CPSs; they integrate physical processes and have recently become an active research area. Existing offensive and defensive processes for APTs in CPSs are usually modeled by incomplete information game theory. However, honeypots, which are effective security vulnerability defense mechanisms, have not been widely adopted or modeled for defense against APT attacks in CPSs. In this study, a honeypot game-theoretical model considering both low- and high-interaction modes is used to investigate the offensive and defensive interactions, so that defensive strategies against APTs can be optimized. In this model, human analysis and honeypot allocation costs are introduced as limited resources. We prove the existence of Bayesian Nash equilibrium strategies and obtain the optimal defensive strategy under limited resources. Finally, numerical simulations demonstrate that the proposed method is effective in obtaining the optimal defensive effect.

• Journal of KIBIM
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• v.7 no.3
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• pp.40-48
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• 2017

It only takes one failed project to wipe out an entire year's profit, when the projects are not managed efficiently. Additionally, escalating costs of materials and a competitive local construction market make subcontractors a challenge. Subcontractors have finite resources that should be allocated simultaneously across many projects in a dynamic manner. Significant scheduling problems are posed by concurrent multi-projects with limited resources. The objective of this thesis is to identify the effect of productivity changes on the total cost resulting from shifting crews across projects using a descriptive model. To effectively achieve the objective, this study has developed a descriptive cost model for a subcontractor with multi-resources and multi-projects. The model was designed for a subcontractor to use as a decision-making tool for resources allocation and scheduling. The model identified several factors affecting productivity. Moreover, when the model was tested using hypothetical data, it produced some effective combinations of resource allocation with associated total costs. Furthermore, a subcontractor minimizes total costs by balancing overtime costs, tardiness penalties, and incentive bonus, while satisfying available processing time constraints.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.165-165
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• 2023

Streamflow prediction is a critical task in water resources management and essential for planning and decision-making purposes. However, the streamflow prediction is challenging due to the complexity and non-linear nature of hydrological processes. The transfer learning is a powerful technique that enables a model to transfer knowledge from a source domain to a target domain, improving model performance with limited data in the target domain. In this study, we apply the transfer learning using the Informer model, which is a state-of-the-art deep learning model for streamflow prediction. The model was trained on a large-scale hydrological dataset in the source basin and then fine-tuned using a smaller dataset available in the target basin to predict the streamflow in the target basin. The results demonstrate that transfer learning using the Informer model significantly outperforms the traditional machine learning models and even other deep learning models for streamflow prediction, especially when the target domain has limited data. Moreover, the results indicate the effectiveness of streamflow prediction when knowledge transfer is used to improve the generalizability of hydrologic models in data-sparse regions.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.479-482
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• 2015

The objective of this study is to develop adaptive neuro-fuzzy inference system (ANFIS) model for estimating daily solar radiation using limited weather variables at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using ANFIS model. From the performance evaluation and scatter diagrams of ANFIS model, ANFIS 3 (three input) model produces the best results for both stations. Results obtained indicate that ANFIS model can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois. These results testify the generation capability of ANFIS model and its ability to produce accurate estimates in Illinois.

• Asia Marketing Journal
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• v.22 no.2
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• pp.1-18
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• 2020

We estimate a choice-based aggregate demand model accounting for consumers' consideration sets, and study its implications on market structure. In contrast to past research, we model and estimate consumer demand using aggregate-level consumer browsing data in addition to aggregate-level choice data. The use of consumer browsing data allows us to study consumer demand in a realistic setting in which consumers choose from a subset of products. We calibrate the proposed model on both data sets, avoid biases in parameter estimates, and compute the price elasticity measures. As an empirical application, we estimate consumer demand in the camcorder category and study its implications on market structure. The proposed model predicts a limited consumer price response and offers a more discriminating competitive landscape from the one assuming universal consideration set.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.346-354
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• 2024

Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4B
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• pp.394-402
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• 2009

In this paper, we present a new analytical model for estimating the blocking performance of multi-fiber WDM networt:s with sparse and limited wavelength conversion (SLWC). The proposed model is a reduced-load approximation model that can obtain accurate estimates of blocking probability of such networks. Our model employs three new recurrence formulae to obtain the free wavelength distribution on a multi-fiber link, the free wavelength distribution after limited-range wavelength conversion and the end-to-end blocking probability of a multi-hop path, respectively. From the numerical results on the NSFNET, we demonstrate that the blocking performance of two-fiber NSFNET with three wavelength-convertible nodes, each of which translates an input wavelength to its adjacent output wavelengths, closely approximates the blocking performance of full wavelength conversion.