• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.87-93
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• 2009

In this paper, we propose and evaluate the availability of single sensor node using a hierarchial modeling approach. We divides a sensor node into a software and hardware and analyze failures of each component. We construct Markov chains to represent the components of a sensor node, and then we construct a hierarchical model which use fault tree in upper level and Markov chains in lower level. We evaluate the availability and down of single sensor node.

• Computers and Concrete
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• v.21 no.5
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• pp.593-605
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• 2018

A nonlinear finite element modeling approach is developed to assess the behavior of a dowel bar embedded on a single concrete block substrate, subjected to monotonic loading. In this approach, a discrete representation of the steel reinforcing bar is considered, using beam finite elements with nonlinear material behavior. The bar is connected to the concrete embedment through nonlinear Winkler spring elements. This modeling approach can only be used if a new constitutive model is developed for the spring elements, to simulate the deformability and strength of the concrete substrate. To define this constitutive model, an extensive literature review was conducted, as well as 3 experimental tests, in order to select the experimental data which can be used in the calibration of the model. Based on this data, an empirical model was established to predict the global dowel response, for a wide range of bar diameters and concrete strengths. This empirical model provided the information needed for calibration of the nonlinear Winkler spring model, valid for dowel displacements up to 4 mm. This new constitutive model is composed by 5 stages, in order to reproduce the concrete substrate response.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1596-1599
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• 2010

Group method of data handling neural networks model (GMDH-NNM) is used to estimate daily pan evaporation (PE) using limited climatic variables such as max temperature ($T_{max}$), min temperature ($T_{min}$), mean wind speed ($W_{mean}$), mean relative humidity ($RH_{mean}$) and sunshine duration (SD). And, for the performances of GMDH-NNM, it is composed of training and test performances, respectively. The training and test performances are carried out using daily time series data, respectively. From this research, we evaluate the impact of GMDH-NNM for the modeling of the nonlinear time series data. We should, thus, construct the credible data of the daily PE data using GMDH-NNM, and can suggest the methodology for the irrigation and drainage networks system. Furthermore, this research represents that the strong nonlinear relationship such as pan evaporation modeling can be generalized using GMDH-NNM.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.11a
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• pp.19-25
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• 2003

Since container throughput is continually increasing, the main issues facing decision-makers at port container terminals are how to expand the existing container terminals and construct new container terminals. Simulations that support user needs require modeling tools that are both easy to use and sufficiently to reflect real world system. The object-oriented approach provides for both reusability and modularity that best fits these requirements. This paper present the design procedure a simulator for port container terminal that was based on the object-oriented approach. The simulator in order to model and simulate the TC-based container terminals is developed.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.777-782
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• 2011

Neural network technique is widely employed in the fields of signal processing, control systems, pattern recognition, etc. Learning of neural networks is an important procedure to accomplish dynamic system modeling. This paper presents a novel learning approach for differential neural network models based on the Kalman-Bucy filter theory. We construct an augmented state vector including original neural state and parameter vectors and derive a state estimation rule avoiding gradient function terms which involve to the conventional neural learning methods such as a back-propagation approach. We carry out numerical simulation to evaluate the proposed learning approach in nonlinear system modeling. By comparing to the well-known back-propagation approach and Kalman-Bucy filtering, its superiority is additionally proved under stochastic system environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1583-1598
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• 2019

Recently, software has increased in complexity and been applied in various industrial fields. As a result, the presence of software bugs cannot be avoided. Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved. In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence). First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA). Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report. Next, we extract the bug reports belonging to the selected topics and input them to a Naïve Bayes Multinomial (NBM) algorithm. Finally, we predict the bug severity in the new bug report. In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naïve Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach. Our approach exhibits a better performance than the compared methods.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.279-291
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• 2020

This study aims to develop the concept of innovation models with the marketing channel construct approach, marketing innovation, product segmentation, and customer insight; as well as improvements to the theory of resource-based combined with the method of service-dominant logic. This study approach is based on quantitative descriptive conducted with three stages of testing scenarios. The first test is the mapping of the innovation model construct through testing the validity and reliability with the moderation of customer orientation variables. The second scenario examines the relationship of influence between the independent variables on the dependent variable of 29 hypothetical analysis equation modeling. The unit of analysis was conducted on 497 SMEs involved in the food and beverage sectors, with the criteria being SMEs must have a rating of 4-5 points on the Go-Food applications software. The results shown that: 1) the construct used to develop an innovative model both directly and via moderation is positive and significant; 2) Through a complicated relationship that involves all components of the variable, it outlines a positive and significant effect except for the path of analysis (μ5). The theoretical and managerial implications state that the service-dominant logic approach and resource-based view theory have extreme reliability and interrelations.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.277-286
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• 2002

Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to original time series to get the first mathematical model. Numerical values of the model are subtracted from the original time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.2001-2009
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• 1989

This study suggests the use of fuzzy model in the semiconductor devices modeling as a black box approach. When membership functions of fuzzy sets used in a fuzzy model are simple piecewise-linear functions, the fuzzy model can be reresented in a simple equation. To show that the fuzzy model can be very realistic and simple when used in semiconductor devices modeling, we construct fuzzy models for bipolar transistor, MOSFET and GaAs FET, and compare those with canonical piecewise-linear models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.436-443
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• 1992

Presented in the paper is a systematic approach to constructing a Petri net model of FAS (flexible assembly system). Petri net is widely used in modeling automated manufacturing systems. But, it found to be very difficult for an FA engineer to build a correct model of an FAS with Petri net symbols (ie, place, transition, and token) from the beginning. An automated manufacturing system in general is built from a set of "standard" hardware components. An FAS in particular is usually composed of assembly robots, work tables, conveyor lines, buffer storages, part feeders, etc. In the proposed modeling scheme, each type of standard resources is represented as a standard "module" which is a sub Petri net. Then, the model of a FAS can be conveniently constructed using the predefined modules the same way the FAS itself is built from the standard components. The network representation of a FAS is termed a JR-net (job resource relation net) which is easy to construct. This JR net is then mechanically converted to a formal Petri net (to simulate the behavior of the FAS). The proposed modeling scheme may easily be extended to the modeling of other types of automated manufacturing systems such as FMS and AS/RS.ch as FMS and AS/RS.