• Smart Media Journal
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• v.6 no.3
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• pp.49-56
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• 2017

Images and Videos that include the human face contain a lot of information. Therefore, accurately extracting human face is a very important issue in the field of computer vision. However, in real life, human faces have various shapes and textures. To adapt to these variations, A model-based approach is one of the best ways in which unknown data can be represented by the model in which it is built. However, the model-based approach has its weaknesses when the motion between two frames is big, it can be either a sudden change of pose or moving with fast speed. In this paper, we propose an enhanced human face-tracking model. This approach included human face detection and motion estimation using Cascaded Convolutional Neural Networks, and continuous human face tracking and modeling correction steps using the Active Appearance Model. A proposed system detects human face in the first input frame and initializes the models. On later frames, Cascaded CNN face detection is used to estimate the target motion such as location or pose before applying the old model and fit new target.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1997.11a
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• pp.117-122
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• 1997

This paper presents a study on the fuzzy based approach for the safety assessment of human body under ELF electric and magnetic(EM) field considering power system states. The analysis of ELF EM field based on quasi-static method is introduced. UP to the present, the analysis of ELF EM field has been conducted with the consideration of one transmission line, or a power line model only In this paper, however, the power system is included to model the expected and/or unexpected uncertainty caused by the load fluctuation and parameter changes and the states are classified into two types, normal state resulting from normal operation and emergency state from outages. In order to analyze the uncertainty in the normal state, the Monte Carlo Simulation, a statistic approach was introduced and line current and bus voltage distribution are calculated by a contingency analysis method, in the emergency state. To access the safety of human body, the approach based on fuzzy linguistic variable is adopted to overcome the shortcomings of the assessment by a crisp set concept. In order to validate the usefulness of the approach suggested herein, the case study using a sample system with 765(kV) was done. The results are presented and discussed.

• Journal of Information Technology Services
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• v.4 no.1
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• pp.129-139
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• 2005

This paper proposes a framework of Operational Risk-based Business Continuity System(ORBCS), and develops protection system for operational risk through operational risk assessment and loss distribution approach based on risk management guideline announced in the basel II. In order to find out financial operational risk, business processes of domestic bank are assorted by seven event factors and eight business activities so that we can construct the system. After we find out KRI(Key Risk Indicator) index, tasks and risks, we calculated risk possibility and expected cost by analyzing quantitative data, questionnaire and qualitative approach for AHP model from the past events. Furthermore, we can assume unexpected cost loss by using loss distribution approach presented in the basel II. Each bank can also assume expected loss distributions of operational risk by seven event factors and eight business activities. In this research, we choose loss distribution approach so that we can calculate operational risk. In order to explain number of case happened, we choose poisson distribution, log-normal distribution for loss cost, and estimate model for Monte-Carlo simulation. Through this process which is measured by operational risk. of ABC bank, we find out that loss distribution approach explains closer unexpected cost directly compared than internal measurement approach, and makes less unexpected cost loss.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.285-299
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• 2013

To enable hard real-time systems to take advantage of multicore processors, it is crucial to obtain the worst-case execution time (WCET) for programs running on multicore processors. However, this is challenging and complicated due to the inter-thread interferences from the shared resources in a multicore processor. Recent research used the combined cache conflict graph (CCCG) to model and compute the worst-case inter-thread interferences on a shared L2 cache in a multicore processor, which is called the CCCG-based approach in this paper. Although it can compute the WCET safely and accurately, its computational complexity is exponential and prohibitive for a large number of cores. In this paper, we propose three counter-based approaches to significantly reduce the complexity of the multicore WCET analysis, while achieving absolute safety with tightness close to the CCCG-based approach. The basic counter-based approach simply counts the worst-case number of cache line blocks mapped to a cache set of a shared L2 cache from all the concurrent threads, and compares it with the associativity of the cache set to compute the worst-case cache behavior. The enhanced counter-based approach uses techniques to enhance the accuracy of calculating the counters. The hybrid counter-based approach combines the enhanced counter-based approach and the CCCG-based approach to further improve the tightness of analysis without significantly increasing the complexity. Our experiments on a 4-core processor indicate that the enhanced counter-based approach overestimates the WCET by 14% on average compared to the CCCG-based approach, while its averaged running time is less than 1/380 that of the CCCG-based approach. The hybrid approach reduces the overestimation to only 2.65%, while its running time is less than 1/150 that of the CCCG-based approach on average.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.95-117
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• 2005

This research Investigates the determinants of customer Intention to participate In Internet auction. Based on technology acceptance and trust related studies, our research proposes a theoretical model consisting of factors such as perceived usefulness, perceived ease of use, institution based trust, beliefs on sellers, trusting beliefs, and participation Intention. For examining the relationships implied by the research model, a field study using a survey methodology for data collection was conducted. The data were analyzed using AMOS based on the structural equation modeling, a second-generation multivariate technique, which has gained distinct advantages over other technique. After some model modification according to model generating strategy approach, this study shows that trusting beliefs have significant effects on the participating intention in Internet auction site. In conclusion, Implications are discussed along with limitations and further research direction.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.382-387
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• 2012

A virtual prototype (VP) model of the rotor-to-stator rub of the rotor system is established to study the nonlinear vibration characteristics. The non-linear bearing stiffness is considered to approximate to an actual system in the model. In order to validate the effectiveness of the proposed approach, a special structure of stator is designed to simulate different kinds of rub condition. The results of experiment are well consistent with the results of simulation by VP. The vibration characteristics of rub-impact are well observed by VP model under different conditions. Based on the validated model, the torsional vibration of rub-impact is discussed. The contribution of this paper is to provide one new approach to study rub-impact problem. Based on the validated VP model, the more research can be done for incident fault identification.

• KIISE Transactions on Computing Practices
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• v.22 no.8
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• pp.393-398
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• 2016

Collective Adaptive System is an adaptive multi-agent system which accomplishes its goal by collaborating various agents. Because the collective property of the Collective Adaptive System is accomplished by the goal of the system being based on collaboration, testing the goal accomplishment and their interactions among heterogeneous agents is important. This paper presents a feasibility study of applying a model-based testing approach using task-based test model to a Collective Adaptive System. This paper describes additional information to be applied for Collective Adaptive System for future studies. To analyze our approach, we applied the proposed approach to a smart home system as a case study; our results indicated that we can systematically derive test cases to check whether the Collective Adaptive System successfully achieved its goals by modifying and extending the existing task model.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.573-588
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• 2023

Purpose: This study presents a research approach that utilizes deep reinforcement learning to construct optimal portfolios based on the business cycle for stocks and other assets. The objective is to develop effective investment strategies that adapt to the varying returns of assets in accordance with the business cycle. Methods: In this study, a diverse set of time series data, including stocks, is collected and utilized to train a deep reinforcement learning model. The proposed approach optimizes asset allocation based on the business cycle, particularly by gathering data for different states such as prosperity, recession, depression, and recovery and constructing portfolios optimized for each phase. Results: Experimental results confirm the effectiveness of the proposed deep reinforcement learning-based approach in constructing optimal portfolios tailored to the business cycle. The utility of optimizing portfolio investment strategies for each phase of the business cycle is demonstrated. Conclusion: This paper contributes to the construction of optimal portfolios based on the business cycle using a deep reinforcement learning approach, providing investors with effective investment strategies that simultaneously seek stability and profitability. As a result, investors can adopt stable and profitable investment strategies that adapt to business cycle volatility.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.3
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• pp.9-19
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• 2015

Internal air temperature of greenhouse is an important variable that can be influenced by the complex interaction between outside weather and greenhouse inside climate. This paper focuses on a data-based model approach to predict internal air temperature of the greenhouse. External air temperature, solar radiation, wind speed and wind direction were measured next to an experimental greenhouse supported by the Electronics and Telecommunications Research Institute and used as input variables for the model. Internal air temperature was measured at the center of three sections of the greenhouse and used as an output variable. The proposed model consisted of a transfer function including the four input variables and tested the prediction accuracy according to the sampling interval of the input variables, the orders of model polynomials and the time delay variable. As a result, a second-order model was suitable to predict the internal air temperature having the predictable time of 20-30 minutes and average errors of less than ${\pm}1K$. Afterwards mechanistic interpretation was conducted based on the energy balance equation, and it was found that the resulting model was considered physically acceptable and satisfied the physical reality of the heat transfer phenomena in a greenhouse. The proposed data-based model approach is applicable to any input variables and is expected to be useful for predicting complex greenhouse microclimate involving environmental control systems.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.