• Korean Journal of Computational Design and Engineering
• /
• v.22 no.2
• /
• pp.202-214
• /
• 2017

The augmented visualization of analysis results can play an import role as a post-processing tool for the modeling & simulation (M&S) technology. In particular, it is essential to develop such an M&S tool which can run on various multi-devices. This paper presents an augmented reality (AR) approach to visualizing and interacting with M&S post-processing results through mobile devices. The proposed approach imports M&S data, extracts analysis information, and converts the extracted information into the one used for AR-based visualization. Finally, the result can be displayed on the mobile device through an AR marker tracking and a shader-based realistic rendering. In particular, the proposed method can superimpose AR-based realistic scenes onto physical objects such as 3D printing-based physical prototypes in a seamless manner, which can provide more immersive visualization and natural interaction of M&S results than conventional VR or AR-based approaches. A user study has been performed to analyze the qualitative usability. Implementation results will also be given to show the advantage and effectiveness of the proposed approach.

• Earthquakes and Structures
• /
• v.23 no.6
• /
• pp.549-580
• /
• 2022

This paper assesses the influences of modeling assumptions and uncertainties on the performance of the non-linear finite element (FE) model updating procedure and model clustering method. The results of a shaking table test on a four-story steel moment-resisting frame are employed for both calibrations and clustering of the FE models. In the first part, simple to detailed non-linear FE models of the test frame is calibrated to minimize the difference between the various data features of the models and the structure. To investigate the effect of the specified data feature, four of which include the acceleration, displacement, hysteretic energy, and instantaneous features of responses, have been considered. In the last part of the work, a model-based clustering approach to group models of a four-story frame with similar behavior is introduced to detect abnormal ones. The approach is a composition of property derivation, outlier removal based on k-Nearest neighbors, and a K-means clustering approach using specified data features. The clustering results showed correlations among similar models. Moreover, it also helped to detect the best strategy for modeling different structural components.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11c
• /
• pp.623-631
• /
• 2000

Advanced Life Support Systems (ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses have been delivered through the Internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. They include object-oriented approach for top-level models, procedural approach for process-level models, and application of commercially available modeling tools such as MATLAB$\^$(R)//Simulink$\^$(R)/. Every paradigm has its particular applicability for the purpose of modeling work. An overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Agricultural and Biosystems Engineering
• /
• v.2 no.2
• /
• pp.41-49
• /
• 2001

Advanced Life Support Systems(ALSS) are being studied to support human life during long-duration space missions. ALSS can be categorized into four subsystems: Crew, Biomass Production, Food Processing and Nutrition, Waste Processing and Resource Recovery. The System Studies and Modeling (SSM) team of New Jersey-NASA Specialized Center of Research and Training (NJ-NSCORT) has facilitated and conducted analyses of ALSS to address systems level issues. The underlying concept of the SSM work is to enable the effective utilization of information to aid in planning, analysis, design, management, and operation of ALSS and their components. Analytical tools and computer models for ALSS analyses have been developed and implemented for value-added information processing. The results of analyses heave been delivered through the internet for effective communication within the advanced life support (ALS) community. Several modeling paradigms have been explored by developing tools for use in systems analysis. they include objected-oriented approach for top-level models, procedureal approach for process-level models, and application of commercially available modeling tools such as $MATLAB^{R}$/$Simulink^{R}$. Every paradigm has its particular applicability for the purpose of modeling work. an overview is presented of the systems studies and modeling work conducted by the NJ-NSCORT SSM team in its efforts to provide systems analysis capabilities to the ALS community. The experience gained and the analytical tools developed from this work can be extended to solving problems encountered in general agriculture.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.5
• /
• pp.361-366
• /
• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• 한국방재학회:학술대회논문집
• /
• 2010.02a
• /
• pp.51.2-51.2
• /
• 2010

A novel approach of Poisson cluster stochastic rainfall generator was validated in its ability to reproduce important rainfall and watershed response characteristics at 104 locations of the United States. The suggested novel approach - The Hybrid Model(THM), as compared to the traditional ones, has an additional function to account for the year-to-year variability of rainfall statistics. The two-sample Kolmogorov-Smirnov test was used to see how well THM and traditional approach of Poisson cluster rainfall model reproduce the distribution of the following hydrologic variables: monthly maximum rainfall depths with 1, 3, 6, 12, and 24 hour duration, monthly maximum flow peaks at the virtual watersheds with Curve Number of 50, 60, 70, 80 and 90; and monthly runoff depths at the same virtual watersheds. In all of the testing variables, THM significantly outperformed the traditional approach. This result indicates that the year-to-year variability of rainfall statistics contains important information about the characteristics of rainfall processes that were not considered by the conventional approach of Poisson cluster rainfall modeling and that further considering it in rainfall simulation will enhance the performance of the rainfall models.

• Interaction and multiscale mechanics
• /
• v.1 no.2
• /
• pp.191-209
• /
• 2008

In this paper, we model grain boundary evolution based on a multiple level set method. Grain boundary migration under a curvature-induced driving force is considered and the level set method is employed to deal with the resulting topological changes of grain structures. The complexity of using a level set method for modeling grain structure evolution is due to its N-phase nature and the associated geometry compatibility constraint. We employ a multiple level set method with a predictor-multicorrectors approach to reduce the gaps in the triple junctions down to the grid resolution level. A ghost cell approach for imposing periodic boundary conditions is introduced without solving a constrained problem with a Lagrange multiplier method or a penalty method. Numerical results for both uniform and random grain structures evolution are presented and the results are compared with the solutions based on a front tracking approach (Chen and Kotta et al. 2004b).

• Knowledge Management Research
• /
• v.7 no.2
• /
• pp.53-68
• /
• 2006

Knowledge has become an important resource for organization. The manufacturing industry is usually operated on the basis of business processes, and most workers are familiar with their own processes. The process-based approach, therefore, can provide an efficient way to capture and navigate knowledge. In this study, we focus on knowledge which may be missed during knowledge transfer among workers. For this, we propose a method for analyzing knowledge flow, which is passed among business processes. We propose a process-based knowledge management framework for analyzing knowledge flow, which employs a two-phase analysis: process analysis and knowledge flow analysis. A knowledge flow model, represented by Knowledge Flow Diagram, is proposed as a tool for representing knowledge. We formulate several semantics for knowledge flow modeling. We build the three-level schema: conceptual, logical, and physical in order to automate the knowledge model adaptive to knowledge management systems. The proposed approach is verified and illustrated through a case study on the manufacturing process of A Company.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.453-458
• /
• 1998

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. So as to handle a variety of nonlinearity and reflect the degree of confidence in the informations about system, we combine multiple model method with hierarchical prioritized structure. The mountain clustering technique is used in partition of system, and TSK rule structure is adopted to form the fuzzy rules. Back propagation algorithm is used for learning parameters in the rules. Computer simulations are performed to verify the effectiveness of the proposed method. It is useful for the treatment fo the nonlinear system of which the quantitative math-approach is difficult.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.3 no.2
• /
• pp.75-90
• /
• 1978

The objective of this paper is to demonstrate how System Dynamics Approach may be used to develop new ways of analyzing and projecting manpower requirements and resources. For this purpose, a System Dynamics Model is presented as an example. An examination of the model will show that a System Dynamics modeling approach is an innovative and useful tool for manpower policy analysis and planning. Second, with minor modifications, the model may be used for manpower policy analysis and planning for any skilled personnel in Korea. For example, a similar model nay be built for engineers to analyze the effects of alternative policies about engineering education, sur as the number of available places in the various institutions of training, scholarships and loans, and the duration of training. An engineer's model may also be used to make the projections of the supply and requirements of engineers in the future according to various alternative assumptions where each assumption represents a policy option.