• International conference on construction engineering and project management
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• 2013.01a
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• pp.467-474
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• 2013

Construction contactors must continuously seek to improve their cash flows, which reside at the heart of their financial success. They require careful planning, analysis, and optimization to avoid the risk of bankruptcy, remain profitable, and secure long-term growth. Sources of cash include bank loans and retained earnings, which are conceptually similar in that they both incur a cost of capital. Financial management therefore requires accurate yet customizable modeling capabilities that can quantify all expenses, including said cost of capital. However, currently existing cash flow models in construction engineering and management have strongly simplified the manner in which interest is assessed, which may even lead to overstating it at a disadvantage to contractors. The variable nature of cash balances, especially in the early phases of construction projects, contribute to this challenging issue. This research therefore extends a new cash flow model with an accurate interest calculation. It utilizes singularity functions, so called because of their ability to flexibly model changes across any number of different ranges. The interest function is continuous for activity costs of any duration and allows the realistic case that activities may begin between integer time periods, which are often calendar months. Such fractional interest calculation has hitherto been lacking from the literature. It also provides insights into the self-referential behavior of compound interest for variable cash balances. The contribution of this study is twofold; augmenting the corpus of financial analysis theory with a new interest formula, whose strengths include its generic nature and that it can be evaluated at any fractional value of time, and providing construction managers with a tool to help improve and fine-tune the financial performance of their projects.

• Korean Journal of Construction Engineering and Management
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• v.21 no.2
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• pp.79-87
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• 2020

The Korean domestic construction management at risk (CMAR) market is in the process of completing the pilot project execution under the leadership of the Ministry of Land, Infrastructure and Transport as of December 2019. The government starts practicing CMAR an alternative delivery method widely in order to diversify delivery methods and enhance construction technology. The CMAR market is thus expected to grow. This study was conducted to improve CMAR firms' capability by developing self-assessment tools for them to evaluate current capability more effectively. As a result of defining standard core capability and additional elements categorized by project execution phase and management area, and performing evaluation from the CMAR project participants, it was found that the general project management capability in the pre-design and procurement phase and quality management area was lower compared to the construction phase and other areas. In addition, the capability of cost management area was lower in spite of its high importance. Communication and coordination, process optimization, and target values achievement were at the initial level of capability and continuous improvement was required.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.10
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• pp.535-542
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• 2016

As one of the most cost-effective methods for surface emission measurements, flux chamber method has been used worldwide. It can be classified into two types: SFC (with slope method) and DFC (with steady-state method). SFC (static flux chamber) type needs only simple equipment and is easy to handle. However, the value of flux might vary with SFC method, because it assumes that the change of concentration in chamber is linear with time. Although more specific equipments are required for DFC (dynamic flux chamber) method, it can lead to a constant result without any ambiguity. We made a self-designed DFC using a small and compact kit, which recorded good sample homogeneity (RSD < 5%) and recovery ( > 90%). Relative expanded measurement uncertainty of this improved DFC method was 7.37%, which mainly came from uncontrolled sweep air. The study shows that the improved DFC method can be used to collect highly reliable emission data from large landfill area.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.688-695
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• 2017

This paper deals with characteristics analysis of a permanent magnet (PM) linear generator using analytical methods for wave energy harvesting. The wave energy is carried out from the movement of a yo-yo system. A linear generator using permanent magnets to generate a magnetic force itself does not require a separate power supply and has the advantage of simple maintenance. In addition to the use of a rare earth, a permanent magnet having a high-energy density can be miniaturized and lightweight, and can obtain high energy-conversion efficiency. We derived magnetic field solutions produced by the permanent magnet and armature reaction based on 2D polar coordinates and magnetic vector potential. Induced voltage is obtained via arbitrary sinusoidal input. In addition, electrical parameters are obtained, such as back-EMF constant, resistance, and self- and mutual-winding inductances. The space harmonic method used in this paper is confirmed by comparing it with finite element method (FEM) results. These facilitate the characterization of the PM-type linear generator and provide a basis for comparative studies, design optimization, and machine dynamic modeling.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.3
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• pp.13-21
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• 1987

The object of this study was to evaluate the management alternatives with respect to the attainable water quality and total cost(construction+O & M cost) in order to devise a reasonable water quality management system. Joong Ryang-cheon stream located Seoul, Korea was taken as the study area, and dissolved oxygen concentration as the water quality index. Water quality simulation model QUAL2E and linear programming optimization technique were used for scientific and rational analyses. It was assumed that the improvement of water quality could be obtained by the treatment of major point sources where imaginary treatment plants were constructed. And by this, the relationship between total cost of the treatment plants and the stepwise improvement of water quality was studied. The result showed that 3.5mg/l of DO(=dissolved oxygen) level at best could be attained in Joong Ryang-cheon stream during summer. When the DO standard was set 3.0mg/l in the severely polluted regions, more than 5.0mg/l of DO level can be achieved by the construction of 4 treatment plants. Also, the cost comparisons showed that the uniform treatment method is economically inefficient(\$24.8{\times}10^8$) in comparison with the least cost method(\$22.9{\times}10^8$), and there is little difference between the least cost method and the the zoned treatment method(\$23.0{\times}10^8$) that is regarded as more equitable, which shows the characteristics of this basin.

• Korean Journal of Cognitive Science
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• v.27 no.3
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• pp.407-420
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• 2016

We live under the consequences of countless decisions, among which significant number of decisions is made by representatives acting on behalf of us. However, individuals often make disparate decisions depending on which identity they are assigned as an agent or with which opponent they are interplaying. In the current research, behavioral discontinuity depending upon actor identity and social relationship was investigated using the ultimatum game. Participants behaved in a more economically rational way when they acted as a group representative compared with when they made decisions as a private individual. However, the direction of the individual-representative discontinuity was reversed when rivalry came into play. Furthermore, more fairness was requested to accept the offers in the interaction with the rival compared with the neutral countergroup. Especially when interacting with the rival group, participants showed contrasting level of decision bias - measured by rejection rate toward unfair offers - according to the degree of mind attribution to the opponent. Specifically, the greater participants attributed a mind to the rival group, the more they rejected the unfair offers from it. The present research is important in that it provides insight into individuals' decision-making in a group context, which sometimes forgoes the financial gain of the entire group and ultimately leads to the sub-optimization of social welfare.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.337-352
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• 2023

In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.