• Computers and Concrete
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• v.34 no.2
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• pp.151-168
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• 2024

This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R² and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R² and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.

• Journal of electromagnetic engineering and science
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• v.13 no.3
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• pp.186-188
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• 2013

A triple 2-of-3 pulse repetition frequency (PRF) scheme is presented for medium PRF pulsed-Doppler airborne radars, and a real-time method is developed that searches for optimal or sub-optimal PRF sets according to momentary battle situations. The effectiveness of the real-time search method of PRF sets is demonstrated by the experimental results obtained using simulated data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1257-1265
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• 2004

In this paper, We describe a change set that is to manage historical information and to maintain ontologies during long term. We propose several storage policies of ontology using a change set. A change set includes a change operation set and temporal dimensions. A change operation set is composed with schema change operations and data change operations. We propose three storage policies that are storing all the change sets and storing the change sets and versions periodically, storing the change sets and the versions using sum of change operations. Also, we present the experimental results for evaluating the performance of different storage policies.

• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.265-271
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• 2016

Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.205-208
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• 2007

Measuring the similarity between fuzzy sets plays a vital role in several fields. However, none of all well-known similarity measure methods is all-powerful, and all have the localization of its usage. This paper defines some operations on the similarity measures of fuzzy sets such as summation and multiplication of two similarity measures. Also, these operations will be generalized to any number of similarity measures. These operations will be very useful especially in the field of computer vision, and data retrieval because these fields need to combine and find some relations between similarity measures.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.433-436
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• 2013

This study suggests IFC-based bridge information modeling methods and its application model in BIM environment. Data model extension for bridge structure was achieved using user-defined property sets based on IFC framework. First, identification information was added. Bridge members are identified through physical and spatial semantic information added as property sets. Instances for semantic information were assigned according to standardized rules. Second, CO2 related factors were added for application information model. It can play a role to calculate and manage the quantity of CO2 emission. Third, properties for temporary structure to estimate and manage the construction cost were added. Finally, we investigated proposed methods through implementing the application information model of bridges.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2784-2799
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• 2012

Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, "unmeaningful" DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.243-251
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• 2000

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause the inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.47 no.3
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• pp.222-233
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• 2011

This study is for the multi-dimensional analysis of diverse data sets for artificial reefs off the coast of Shimonoseki, Yamaguchi prefecture, Japan. Various data sets recorded in artificial reefs ground were integrated in new GIS software: to reveal the relationships between water temperature and fish schools; to visualize the quantitative connection between the reefs and the fish schools; and to compare the seabed types derived from two different data sources. The results obtained suggest that the application of GIS in analyzing multi-dimensional data is a better way to understand the characteristics of fish schools and environmental information around artificial reefs and particularly in the evaluation of the effectiveness of artificial reefs.

• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.11-18
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• 1997

Scientific volume visualization addresses the representation, manipulation, and rendering of volumetric data sets, providing mechanisms for looking closely into structures and understanding their complexity and dynamics. In the past several years, a tremendous amount of research and development has been directed toward algorithms and data modeling methods for a scientific data visualization. But there has been very little work on developing a mathematical volume model that feeds this visualization. Especially, in flow visualization, the volume model has long been required as a guidance to display the very large amounts of data resulting from numerical simulations. In this paper, we focus on the mathematical representation of volumetric data sets and the method of extracting meaningful information from the derived volume model. For this purpose, a B-spline volume is extended to a high dimensional trivariate model which is called as a flow visualization model in this paper. Two three-dimensional examples are presented to demonstrate the capabilities of this model.