• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.171-178
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• 2021

In this paper, the author proposes a method for detecting modification of transmitted messages in C/C++ based Discrete Event System Specification (DEVS) simulation. When a message generated by a model instance is delivered to other model instances, it may be modified by some of the recipients. Such modifications may corrupt simulation results, which may lead to wrong decision making. In the proposed method, every model instance stores a copy of every transmitted message. Before the deletion of the transmitted message, the instance compares them. Once a modification has been detected, the method interrupt the current simulation run. The procedure is automatically performed by a simulator instance. Thus, the method does not require programmers to follow secure coding or to add specific codes in their models. The performance of the method is compared with a DEVS simulator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1121-1129
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• 2022

Object recognition is carried out using RGB images in various object recognition studies. However, RGB images in dark illumination environments or environments where target objects are occluded other objects cause poor object recognition performance. On the other hand, IR images provide strong object recognition performance in these environments because it detects infrared waves rather than visible illumination. In this paper, we propose an RGB-IR fusion model, high-frequency interchange network (HINet), which improves object recognition performance by combining only the strengths of RGB-IR image pairs. HINet connected two object detection models using a mutual high-frequency transfer (MHT) to interchange advantages between RGB-IR images. MHT converts each pair of RGB-IR images into a discrete cosine transform (DCT) spectrum domain to extract high-frequency information. The extracted high-frequency information is transmitted to each other's networks and utilized to improve object recognition performance. Experimental results show the superiority of the proposed network and present performance improvement of the multispectral object recognition task.

• Journal of Microbiology and Biotechnology
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• v.17 no.3
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• pp.496-510
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• 2007

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-a-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an integrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 $(2^3)$ fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.785-791
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• 2011

Two-lane and two-way traffic flow shows various dynamic relationships according to the behaviors of low-speed vehicle and overtaking. And it is essential to develop a vehicle model which simultaneously explains the behaviors of low-speed vehicle and overtaking using opposite lane in order to microscopically analyze various two-lane and two-way traffic flows by traffic flow simulation. In Korea, some studies for car-following and lane-changing models for freeway or signalized road have been reported, but few researches for the development of vehicle model for two-lane and two-way highway have been done. Hence, a microscopic two-lane and two-way vehicle model was, in this study, developed with the consideration of overtaking process and is based on CA (Cellular Automata) which is one of discrete time-space models. The developed model is parallel combined with an adjusted CA car-following model and an overtaking model. The results of experimental simulation showed that the car-following model explained the various macroscopic relationships of traffic flow and overtaking model reasonably generated the various behaviors of macroscopic traffic flows under the conditions of both opposite traffic flow and stochastic parameter to consider overtaking. The vehicle model presented in this study is expected to be used for the simulation of more various two-lane, two-way traffic flows.

• Explosives and Blasting
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• v.41 no.3
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• pp.62-72
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• 2023

The aging of plant structures due to industrialization in the 1970s has increased the demand for blast demolition. While blasting can reduce exposure to environmental pollution by shortening the demolition period, improper blasting design and construction plans pose significant safety risks. Thus, it is vital to consider optimal blasting demolition conditions and other factors through collapse behavior simulation. This study utilizes a 3-D combined finite-discrete element method (FDEM) code-based 3-D DFPA to simulate the collapse of a chimney structure in a thermal power plant in Seocheon, South Korea. The collapse behavior from the numerical simulation is compared to the actual structure collapse, and the numerical simulation result presents good agreement with the actual building demolition. Additionally, various numerical simulations have been conducted on the chimney models to analyze the impact of the duct size in the pre-weakening area. The no-duct, duct, and double-area duct models were compared in terms of crack pattern and history of Z-axis displacement. The findings show that the elapse-time for demolition decreases as the area of the duct increases, causing collapse to occur quickly by increasing the load-bearing area.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.83-92
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• 2002

Freight mode choice models are essential to the analysis of many areas of transport research. However, observations of actual market choices have only been made in a limited number of situations. Therefore, stated preference(SP) techniques have emerged as an alternative source of actual market choices to be used for estimating freight mode choice models. Considerable confidence exists about SP data, but little consideration has been given to the potential for estimation bias. This paper has been motivated by the theoretical side of estimating SP discrete choice models, focusing on a case study of freight mode choice. Recently developed simulation methods are used to construct inherent random heterogeneity legit models, which consider individual heterogeneity, its inheritance to the next choices and overcome the independence from irrelevant alternatives (IIA) property. This Paper contributes to the development of models dealing with heterogeneity and its inheritance, and sheds light on the heterogeneity of freight transport.

• The Journal of Engineering Geology
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• v.7 no.2
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• pp.91-99
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• 1997

In general, there are various approaches available in literature to model discontinuous rock masses and engineers are often confused which one to use for designing structures in rock masses. Modelling rock masses can be classified mainly into two approaches. One is discrete modelling of intact rock and discontinuities and the other is the equivalent continuum modelling. In this study five models are selected ;(1) Crack tensor model, (2) Equivalent volume defect model, (3) Damage model, (4) Micro - structure model (Parallel model and Series model), and (5) Homogenization model. Most of these models are mainly concerned with how to define additional strain due to discontinuities over the representative elementary volume (REV) and how to relate the stress field of discontinuities to that acting on the REV. The characteristics of these models are clarified by comparing with results of some laboratory tests.

• Wind and Structures
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• v.31 no.3
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2829-2840
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• 2018

The random variable with an arbitrary value or more is called semi-continuous variable or zero-inflated one in case that its boundary value is more frequently observed than expected. This means the boundary value is likely to be practically observed more than it should be theoretically under certain probability distribution. When the distribution considered is continuous, the variable is defined as semi-continuous and when one of discrete distribution is assumed for the variable, we regard it as zero-inflated. In this study, we introduce the two-part model, which consists of one part for modelling the binary response and the other part for modelling the variable greater than the boundary value. Especially, the zero-inflated regression models are explained by using Poisson distribution and negative binomial distribution. In real data analysis, we employ the zero-inflated regression models to estimate the number of days under extreme heat-wave circumstances during the last 10 years in South Korea. Based on the estimation results, we create prediction maps for the estimated number of days under heat-wave advisory and heat-wave warning by using the universal kriging, which is one of the spatial prediction methods.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.