• Steel and Composite Structures
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• v.42 no.1
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• pp.1-22
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• 2022

In cold-formed steel (CFS) structures, such as trusses, transmission towers and portal frames, the use of back-to-back built-up CFS unequal angle sections are becoming increasingly popular. In such an arrangement, intermediate welds or screw fasteners are required at discrete points along the length, preventing the angle sections from buckling independently. Limited research is available in the literature on axial strength of back-to-back built-up CFS unequal angle sections. The issue is addressed herein. This paper presents an experimental investigation reported by the authors on back-to-back built-up CFS unequal angle sections with intermediate stiffeners under axial compression. The load-axial shortening behaviour along with the deformed shapes at failure are reported. A nonlinear finite element (FE) model was then developed, which includes material non-linearity, geometric imperfections and modelling of intermediate fasteners. The FE model was validated against the experimental test results, which showed good agreement, both in terms of failure loads and deformed shapes at failure. The validated finite element model was then used for the purpose of a parametric study comprising 96 models to investigate the effect of longer to shorter leg ratios, stiffener provided in the longer leg, thicknesses and lengths on axial strength of back-to-back built-up CFS unequal angle sections. Four different thicknesses and seven different lengths (stub to slender columns) with three overall widths to the overall depth (B/D) ratios were investigated in the parametric study. Axial strengths obtained from the experimental tests and FE analyses were used to assess the performance of the current design guidelines as per the Direct Strength Method (DSM); obtained comparisons show that the current DSM is conservative by only 7% and 5% on average, while predicting the axial strengths of back-to-back built-up CFS unequal angle sections with and without the stiffener, respectively.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.4
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• pp.657-671
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• 2022

This study aims to develop three dimensional (3D) yoga knee protectors that provide excellent wearing comfort. Three types of pads were modeled using 3D human data: two types of 3.0-cm-wide pads separated into top and bottom with thicknesses of 0.1 cm (TPU-1: A) and 0.2 cm (TPU-2: B); and one type with three 0.2-cm-thick separated panels (TPU-S: C). Based on these models, five knee protectors were developed using 3D patterning and 3D printing. Types A, B, and C were integrated with 0.6-cm neoprene pads. Type D was fabricated with a donut-shaped 0.6-cm neoprene pad inserted, while Type E consisted of two discrete 0.6-cm neoprene pads embedded in the protector's upper and lower sides. Wearing comfort was evaluated in terms of fit, pressure, and cushioning while in a standing and kneeling position and while in motion. The findings suggest that the fabricated knee protectors were evaluated as comfortable to the individuals with knee pain, rather than those without knee pain. The individuals with knee pain preferred the soft pads made of neoprene positioned around the knee (NEO-S: E), while those without knee pain favored the cushioned pads with a pattern structure maintained by thin 3D-printed pads (TPU-1: A).

• Korean Journal of Social Welfare Studies
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• v.42 no.4
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• pp.365-388
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• 2011

By analyzing 1998~2008 Korean Labor and Income Panel Study(KLIPS), this study examines socio-economic characteristics of people who become poor. The study also explores the reason why they are in the state of poverty. To find determinants affecting poverty entrance, discrete-time hazard models are applied. Major findings are as follows. The socio-economic characteristics driving people into poverty are in the middle way of the long-term poor and the non-poor, combining the characteristics of both groups. This implies that many cases of the newly poor tend to enter and exit from poverty repeatedly. Poverty entry rate was at a high level right after the economic crises, then was a downturn and remained fairly stable since 2000. However, the young, the high-educated, and even the professional are on the rise as a new poverty group. The major reason people become poor is temporary job loss. This factor is confirmed again by multi-variate analyses. In building anti-poverty policies, it is important to distinguish the long-term poor from the short-term poor. For the long-term poor, virtually the only affective policy will be income support. On the other hand, a labor-market strategy for jos security will be more effective for the short-term poor. The characteristics and determinants of poverty entry may affect poverty duration and exit in the future. Future research will be needed to investigate the relationship among these factors.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.67-75
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• 2023

This paper deals with the operation of air taxis, which is one of the latest innovative technologies aimed at solving the issue of traffic congestion in cities. A key challenge for the successful introduction of the technology and efficient operation is a vertiport location problem. This paper employs a discrete choice model to calculate choice probabilities of transportation modes for each route, taking into account factors such as cost and travel time associated with different modes. Based on this probability, a mathematical formulation to maximize the utilization rate for air taxi is proposed. However, the proposed model is NP-hard, effective and efficient solution methodology is required. Compared to previous studies that simply proposed the optimization models, this study presents a solution methodology using the cross-entropy algorithm and confirms the effectiveness and efficiency of the algorith through numerical experiments. In addition to the academic excellence of the algorithm, it suggests that decision-making that considers actual data and air taxi utilization plans can increase the practial usability.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1730-1738
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• 2011

Personal Rapid Transit (PRT) has emerged as a promising alternative transportation mode for transit-oriented sustainable communities by creating compact and walkable environments with competitive construction and operational costs. This study seeks to capture the changes in travel mode choice behavior in response to the introduction of PRT to travelers who have no previous experience of using it. A critical issue in modeling the PRT mode choice is how to capture travelers' perception and evaluation of the unexperienced travel mode. The data used come from questionnaire surveys, in which RP (Revealed Preference) and SP (Stated Preference) data were collected in relation to travel mode choices with and without PRT systems. The questionnaire was designed especially for mitigating the potential bias in favor of or against choosing PRT. In addition, an efficient approach was proposed to reduce the number of SP questions by avoiding the complex fractional factorial design which tends to make it difficult for respondents to keep their attention throughout the survey. The analysis results show that the proposed approach is able to realistically capture the effects of explanatory variables on the travel mode choice. Discrete choice models are developed to predict travelers' mode choices under different choice scenarios by varying PRT system specifications and operational characteristics. PRT patronages are projected for two different test sites using the developed PRT mode choice models.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.39-47
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• 2013

Simulation based Battle Experimentation is to examine the readiness for a battle using simulation technology. It heavily relies on the weapon systems modeling and simulation. To analyze the characteristics and complexity of the weapon systems in the experiment, the modeling & simulation environment has to be able to break down the system of systems into components and make the use of high fidelity components such as real hardware in simulation. In that sense, the modular and hierarchical structure of DEVS (Discrete EVent System Specification) framework provides potentials to meet the requirements of the battle experimentation environment. This paper describes the development of the DEVS integrated development environment for Simulation based Battle Experimentation. With the design principles of easy, flexible, and fast battle simulation, the newly developed battle experimentation tool mainly consists of 3 parts - model based graphical design tool for making DEVS models and linking them with external simulators easily through diagrams, the experiment plan tool for speeding up a statistic analysis, the standard components model libraries for lego-like building up a weapon system. This noble simulation environment is to provide a means to analyze complex simulation based experiments with different levels of models mixed in a simpler and more efficient way.

• 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 Transportation
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• v.22 no.4 s.75
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• pp.147-158
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• 2004

A class of SWP(Stochastic Wane Propagation) models microscopically mimics individual vehicles' stochastic behavior and traffic jam propagation with simplified car-following models based on CA(Cellular Automata) theory and macroscopically captures dynamic traffic flow relationships based on statistical physics. SWP model, a program-oriented model using both discrete time-space and integer data structure, can simulate a huge road network with high-speed computing time. However, the model has shortcomings to both the capturing of low speed within a jam microscopically and that of the density and back propagation speed of traffic congestion macroscopically because of the generation of spontaneous jam through unrealistic collision avoidance. In this paper, two additional rules are integrated into the NaSch model. The one is SMR(Stopping Maneuver Rule) to mimic vehicles' stopping process more realistically in the tail of traffic jams. the other is LAR(Low Acceleration Rule) for the explanation of low speed characteristics within traffic jams. Therefore, the CA car-following model with the two rules prevents the lockup condition within a heavily traffic density capturing both the stopping maneuver behavior in the tail of traffic jam and the low acceleration behavior within jam microscopically, and generates more various macroscopic traffic flow mechanism than NaSch model's with the explanation of propagation speed and density of traffic jam.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.3
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• pp.356-364
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• 2009

The current ruminant feeding models require parameterization of the digestion kinetics of carbohydrate fractions in feed ingredients to estimate the supply of nutrients from a ration. Using an automated gas production technique, statistically welldefined digestion rate of carbohydrate, including soluble carbohydrate, can be estimated in a relatively easy way. In this study, the gas production during in vitro fermentation was measured and recorded by an automated gas production system to investigate degradation kinetics of carbohydrate fractions of a wide range of ruminant feeds: corn silage, rice straw, corn, soybean hull, soybean meal, and cell mass from lysine production (CMLP). The gas production from un-fractionated, ethanol insoluble residue and neutral detergent insoluble residue of the feed samples were obtained. The gas profiles of carbohydrate fractions on the basis of the carbohydrate scheme of the Cornell Net Carbohydrate and Protein System (A, B1, B2, B3 and C) were generated using a subtraction approach. After the gas profiles were plotted with time, a curve was fitted with a single-pool exponential equation with a discrete lag to obtain kinetic parameters that can be used as inputs for modern nutritional models. The fractional degradation rate constants (Kd) of corn silage were 11.6, 25.7, 14.8 and 0.8%/h for un-fractioned, A, B1 and B2 fractions, respectively. The values were statistically well estimated, assessed by high t-value (>12.9). The Kd of carbohydrate fractions in rice straw were 4.8, 21.1, 5.7 and 0.5%/h for un-fractioned, A, B1 and B2 fractions, respectively. Although the Kd of B2 fraction was poorly defined with a t-value of 4.4, the Kd of the other fractions showed tvalues higher than 21.9. The un-fractioned corn showed the highest Kd (18.2%/h) among the feeds tested, and the Kd of A plus B1 fraction was 18.7%/h. Soybean hull had a Kd of 6.0, 29.0, 3.8 and 13.8%/h for un-fractioned, A, B1 and B2, respectively. The large Kd of fraction B2 indicated that NDF in soybean hull was easily degradable. The t-values were higher than 20 except for the B1 fraction (5.7). The estimated Kd of soybean meal was 9.6, 24.3, 5.0%/h for un-fractioned, A and B1 fractions, respectively. A small amount of gas (5.6 ml at 48 ho of incubation) was produced from fermentation of CMLP which contained little carbohydrate. In summary, the automated gas production system was satisfactory for the estimation of well defined (t-value >12) kinetic parameters and Kd of soluble carbohydrate fractions of various feedstuffs that supply mainly carbohydrate. The subtraction approach, however, should be applied with caution for some concentrates, especially those which contain a high level of crude protein since nitrogen-containing compounds can interfere with gas production.