• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.294-299
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• 2012

This paper suggests a modeling technique for shape and volume prediction of fishes to cut them with identical weights for group meals. The measurement and prediction of frozen fishes for group meals are very difficult because they have a bending deformation occurring at frozen stage and a hollow by eliminating the internals. Besides there exist twinkles problem of surface caused by freeze and variable weights by moisture conditions. Therefore a complex estimation algorithm is necessary to predict the shape and volume prediction of fishes exactly. Hollow prediction, pattern classification and modeling for tails using neural network, integration based volume prediction algorithm are suggested and combined to solve those problems. In order to validate the proposed method, the experiments of 3-dimensional measurement, volume prediction and fish cutting for spanish mackerel, saury, and mackerel are executed. The cutting experiments for real fish are executed.

• Korean Journal of Remote Sensing
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• v.18 no.1
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• pp.1-12
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• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.5
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• pp.625-638
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• 1999

Air quality modeling for coastal urban region has been composed of a complex system including meteorological, chemical and physical processes and emission characteristics in complex terrain. In this study, we studied about an effect prediction for deposition phenomenon affecting on air quality in Pusan metopolitan metropolitan city. In air quality modeling including ship sources, a situation considered deposition process habe better result than not considered when compared with observed value. Air pollutants emitted into urban air during the daytime nearly removed through urban atmosphere polluted. Also these phenomena correlated concentration variation connent with sea/land breezes and terrain effect. Therefore we conclude that the concentration was low at daytime when deposition flux is high, and deposition effect on industrial complex and Dongrae region is considerable in particular.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.179-182
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• 2002

Rapid Prototyping (RP) technologies provide the ability to fabricate initial prototypes from various model materials. Stratasys' Fused Deposition Modeling (FDM) is a typical RP process that can fabricate prototypes out of plastic materials, and the parts made from FDM were often used as load-carrying elements. Because FDM deposits materials in about $300\mutextrm{m}$ thin filament with designated orientation, parts made from FDM show anisotropic material properties. This paper proposes an analytic model to predict the tensile strength of FDM parts. Applying the Classical Lamination Theory, which was developed for laminated composite materials, a computer code was implemented. Tsai-Wu failure criterion was added to the code to predict the failure of the FDM parts. The tensile strengths predicted by the analytic model were compared with experimental data. The data and prediction agreed reasonably well to prove the validity of the model. In addition, a web-based advisory service was developed to provide to strength prediction and design rules for FDM parts.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.597-598
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• 2012

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.655-660
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• 2009

The context prediction algorithms are not suitable to provide real-time personalized service for users in context-awareness environment. The algorithms have problems like time delay in training data processing and the difficulties of implementation in real-time environment. In this paper, we propose a prediction algorithm with user modeling to shorten of processing time and to improve the prediction accuracy in the context prediction algorithm. The algorithm uses moving path of user contexts for context prediction and generates user model by time-series analysis of user's moving path. And that predicts the user context with the user model by sequence matching method. We compared our algorithms with the prediction algorithms by processing time and prediction accuracy. As the result, the prediction accuracy of our algorithm is similar to the prediction algorithms, and processing time is reduced by 40% in real time service environment.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1631-1638
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• 2007

In this paper, a modeling for the dynamic behavior of a train vehicle is suggested for the prediction of the braking characteristics. In the dynamic modeling, effects of the primary and secondary suspension elements are considered and interactions between two vehicles are also estimated. This study can offer some fundamental results for a further research to enhance the braking performance using active braking control.

• Convergence Security Journal
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• v.16 no.2
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• pp.63-70
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• 2016

With the development of information and communication technologies like internet, the environment where people are able to access internet at any time and at any place has been established. As a result, cyber threats have been tried through various routes. Of cyber threats, DDoS is on the constant rise. For DDoS prediction modeling, this study drew a DDoS security index prediction formula on the basis of event data by using a statistical technique, and quantified the drawn security index. It is expected that by using the proposed security index and coming up with a countermeasure against DDoS threats, it is possible to minimize damage and thereby the prediction model will become objective and efficient.