• Journal of the Korean Society of Safety
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• v.38 no.1
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.11a
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• pp.171-179
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• 2005

Computer virus modeling and simulation research has been conducted with focus on the network vulnerability analysis. However, computer virus generally shows the biological virus characters such as proliferation, reproduction and evolution. Therefore it is necessary to research the computer virus modeling and simulation using Artificial Life. The approach of computer modeling and simulation using the Artificial Life technology Provides the efficient analysis method for the effects on the network by computer virus and the behavioral mechanism of the computer virus. Hence this paper proposes the methodology of computer virus modeling and simulation using Artificial Life, which may be contribute the research on the computer virus vaccine.

• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.375-386
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• 2008

Conventional static reliability analysis methods are inadequate for modeling dynamic interactions between components of a system. Various techniques such as dynamic fault tree, dynamic Bayesian networks, and dynamic reliability block diagrams have been proposed for modeling dynamic systems based on improvement of the conventional modeling methods. In this paper, we review these methods briefly and introduce dynamic nodes to the existing reliability graph with general gates (RGGG) as an intuitive modeling method to model dynamic systems. For a quantitative analysis, we use a discrete-time method to convert an RGGG to an equivalent Bayesian network and develop a software tool for generation of probability tables.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.391-392
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• 1999

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.221-225
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• 2004

This paper shows the application of artificial intelligence technique such as polynomial neural network in modeling and identification of sequencing batch reactor (SBR). A wastewater treatment process for nitrogen removal in the SBR is presented. Simulation results have shown that the nonlinear process can be modeled reasonably well by the Present scheme which is simple but efficient.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.371-376
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• 1994

This paper presents an on-line modeling method approach for the machine condition. the machine condition is continuously monitored with a sensor such as, a vibration, a current, an acoustic emission (AE) sensor. In this study, neural network modeling by radial basis function is designed for analysis a prediction error. An on-line learning algorithm is designed using the RLS(recursive least square) estimation and the existing clustering method of Kohonen neural network. Experimental results show that the proposed RBNN modeling is suitable for predicting simulated data.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.291-303
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• 2024

In this paper, we consider a feedforward network of overloaded multiclass processor sharing queues and we give a fluid model solution under the condition that the system is initially empty. The main theorem of the paper provides sufficient conditions for a fluid model solution to be linear with time. The results are illustrated through examples.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.84-96
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• 2021

Certainly, the success of the Unified Modeling Language (UML) as the de facto standard for modeling software systems does not imply closing the door on scientific exploration or experimentation with modeling in the field. Continuing studies in this area can produce theoretical results that strengthen UML as the leading modeling language. Recently, a new modeling technique has been proposed called thinging machine (TM) modeling. This paper utilizes TM to further understand UML, with two objectives: (a) Fine issues in UML are studied, including theoretical notions such as events, objects, actions, activities, etc. Specifically, TM can be used to solve problems related to internal cross-diagram integration. (b) TM applies a different method of conceptualization, including building a model on one-category ontology in contrast to the object-oriented paradigm. The long-term objective of this study is to explore the possibility of TM complementing certain aspects in the UML methodology to develop and design software systems. Accordingly, we alternate between UML and TM modeling. A sample UML model is redesigned in TM, and then UML diagrams are extracted from TM. The results clarify many notions in both models. Particularly, the TM behavioral specification seems to be applicable in UML.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.134-141
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• 2003

A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.