• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Smart Structures and Systems
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• v.22 no.5
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• pp.575-586
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• 2018

Computational Building Information Modeling (BIM) is an intelligent 3D model-based process that provides architecture, engineering, and construction professionals the insight to plan, design, construct, and manage buildings and infrastructure more efficiently. This paper aims at using BIM in Hospitals configurations protection. Infrastructure projects are classified as huge structural projects taking advantage of many resources such as finance, materials, human labor, facilities and time. Immense expenses in infrastructure programs should be allocated to estimating the expected results of these arrangements in domestic economy. Hence, the significance of feasibility studies is inevitable in project construction, in this way the necessity in promoting the strategies and using global contemporary technologies in the process of construction maintenance cannot be neglected. This paper aims at using the building information modeling in covering Imam Khomeini Hospital's equipment. First, the relationship between hospital constructions maintenance and repairing, using the building information modeling, is demonstrated. Then, using library studies, the effective factors of constructions' repairing and maintenance were collected. Finally, the possibilities of adding these factors in Revit software, as one of the most applicable software within BIM is investigated and have been identified in some items, where either this software can enter or the software for supporting the repairing and maintenance phase lacks them. The results clearly indicated that the required graphical factors in construction information modeling can be identified and applied successfully.

• CDE review
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• v.22 no.1
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• pp.20-24
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• 2016

• CDE review
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• v.3 no.3
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• pp.46-50
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• 1997

• Journal of computational fluids engineering
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• v.7 no.3
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• pp.17-26
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• 2002

Computational fluid dynamic(CFD) analysis has been frequently applied to the waste incinerators to understand the flow performance for various design and operating parameters. Since the computational modeling inevitably requires many simplifications and complicated sub-models, validity of the results should be carefully evaluated. In this study, major computational modeling and procedure of usual simulation methods for the grate-type waste incinerators were assessed. Usual simulation method does not explicitly incorporate the waste combustion, simply by assuming the combustion gas properties from the waste bed which is treated as an inlet plane. However, effect of this arbitrary assumption on the overall flow pattern is not significant, since the flow pattern is dominated by strong pattern of jet flows of the secondary air. Thus, this method is valid in understanding the effect of flow-related parameters. In analyzing the results, deriving conclusive information directly from temperature and chemical species concentration should be avoided, since the model prediction for the gaseous reaction and the radiation reveals significant discrepancies against the actual phenomena. Use of quantitative measures such as residence time is very efficient in evaluating the flow performance.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.2
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• pp.127-153
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• 2012

This paper presents an advanced computational method for the prediction of the responses in the frequency domain of general linear dissipative structural-acoustic and fluid-structure systems, in the low-and medium-frequency domains and this includes uncertainty quantification. The system under consideration is constituted of a deformable dissipative structure that is coupled with an internal dissipative acoustic fluid. This includes wall acoustic impedances and it is surrounded by an infinite acoustic fluid. The system is submitted to given internal and external acoustic sources and to the prescribed mechanical forces. An efficient reduced-order computational model is constructed by using a finite element discretization for the structure and an internal acoustic fluid. The external acoustic fluid is treated by using an appropriate boundary element method in the frequency domain. All the required modeling aspects for the analysis of the medium-frequency domain have been introduced namely, a viscoelastic behavior for the structure, an appropriate dissipative model for the internal acoustic fluid that includes wall acoustic impedance and a model of uncertainty in particular for the modeling errors. This advanced computational formulation, corresponding to new extensions and complements with respect to the state-of-the-art are well adapted for the development of a new generation of software, in particular for parallel computers.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.321-331
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• 2003

Experimental software datasets describing software projects in terms of their complexity and development time have been the subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such approaches as neural networks, fuzzy, and fuzzy neural network models. In this study, we introduce the concept of the Rule-based fuzzy polynomial neural networks (RFPNN) as a hybrid modeling architecture and discuss its comprehensive design methodology. The development of the RFPNN dwells on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The architecture of the RFPNN results from a synergistic usage of RFNN and PNN. RFNN contribute to the formation of the premise part of the rule-based structure of the RFPNN. The consequence part of the RFPNN is designed using PNN. We discuss two kinds of RFPNN architectures and propose a comprehensive learning algorithm. In particular, it is shown that this network exhibits a dynamic structure. The experimental results include well-known software data such as the NASA dataset concerning software cost estimation and the one describing software modules of the Medical Imaging System (MIS).

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.408-417
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• 2005

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput 'omics' data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.

• Korean Journal of Computational Design and Engineering
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• v.5 no.3
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• pp.242-254
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• 2000

This paper describes an efficient solid modeling method for thin-walled plastic or sheet metal parts, based on the non-manifold offsetting operations. Since the previous methods for modeling and converting a sheet into a solid have adopted the boundary representations for solid object as their topological framework, it is difficult to represent the exact adjacency relationship between topological entities of a sheet model and a mixture of wireframe and sheet models that can appear in the meantime of modeling procedure, and it is hard to implement topological operations for sheet modeling and transformation of a sheet into a solid. To solve these problems, we introduce a non-manifold B-rep and propose a sheet conversion method based on a non-manifold offset algorithm. Because the non-manifold offset aigorithm based on mathematical definitions results in an offset solid with tubular and spherical thickness-faces we modify it to generate the ruled or planar thickness-faces that are mostly shown in actual plastic or sheet metal parts. In addition, in order to accelerate the Boolean operations used the offset algorithm, we also develope an efficient face-face intersection algorithm using topological adjacency information.

• Korean Journal of Computational Design and Engineering
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• v.21 no.3
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• pp.341-352
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• 2016

Manual 3D pipeline modeling from LiDAR scanned point cloud data is laborious and time-consuming process. This paper presents a method to extract the pipe, elbow and branch information which is essential to the automatic modeling of the pipeline connection. The pipe geometry is estimated from the point cloud data through the Hough transform and the elbow position is calculated by the medial axis intersection for assembling the nearest pair of pipes. The branch is also created for a pair of pipe segments by estimating the virtual points on one pipe segment and checking for any feasible intersection with the other pipe's endpoint within the pre-defined range of distance. As a result of the automatic modeling, a complete 3D pipeline model is generated by connecting the extracted information of pipes, elbows and branches.