• Advances in Computational Design
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• v.9 no.1
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• pp.1-23
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• 2024

Present paper deals with the cost effective design of reinforced concrete building frame employing unified particle swarm optimization (UPSO). A building frame with G+8 stories have been adopted to demonstrate the effectiveness of the present algorithm. Effect of seismic loads and wind load have been considered as per Indian Standard (IS) 1893 (Part-I) and IS 875 (Part-III) respectively. Analysis of the frame has been carried out in STAAD Pro software.The design loads for all the beams and columns obtained from STAAD Pro have been given as input of the optimization algorithm. Next, cost optimization of all beams and columns have been carried out in MATLAB environment using UPSO, considering the safety and serviceability criteria mentioned in IS 456. Cost of formwork, concrete and reinforcement have been considered to calculate the total cost. Reinforcement of beams and columns has been calculated with consideration for curtailment and feasibility of laying the reinforcement bars during actual construction. The numerical analysis ensures the accuracy of the developed algorithm in providing the cost optimized design of RC building frame considering safety, serviceability and constructional feasibilities. Further, Monte Carlo simulations performed on the numerical results, proved the consistency and robustness of the developed algorithm. Thus, the present algorithm is capable of giving a cost effective design of RC building frame, which can be adopted directly in construction site without making any changes.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.434-437
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• 1999

The primary aim of this paper is to introduce and illustrate a radial basis function (RBF) modeling approach fur software module characterization, as an alternative to current techniques. The RBF model has been known to provide a rich analytical framework fur a broad class of so-called pattern recognition problems. Especially, it features both nonlinearity and linearity which in general are treated separately by its learning algorithm, leading to offer conceptual and computational advantages. Furthermore, our new modeling methodology fer determining model parameters has a sound mathematical basis and showed very interesting results in terms of model consistency as well as performance.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.151-156
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• 2003

Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.

• Journal of Internet Computing and Services
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• v.3 no.1
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• pp.51-59
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• 2002

The conventional procedure to verify rule bases are corresponding to the propositional logic-level knowledge representation. Building knowledge bases, in real applications, we utilize the predicate logic-level rules. In this paper, we present a verification algorithm of rule bases using Pr/T nets which represent the predicate logic-level rules naturally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.11
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• pp.928-937
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• 2013

This paper presents a novel contrast enhancement method based on singular value decomposition and image pyramid. The proposed method consists mainly of four steps. The proposed algorithm firstly decomposes image into band-pass images, including basis image and detail images, to improve both the global contrast and the local detail. In the global contrast process, singular value decomposition is used for contrast enhancement; the local detail scheme uses weighting factors. In the final image composition process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. Experimental results show that the proposed algorithm improves contrast performance and enhances detail compared to conventional methods.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.65-78
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• 2008

This study compares the relative accuracy and consistency of four split-window land surface temperature (LST) algorithms (Becker and Li, Kerr et ai., Price, Ulivieri et al.) using 24 sets of Terra (Aqua)/Moderate Resolution Imaging Spectroradiometer (MODIS) data, observed ground grass temperature and air temperature over South Korea. The effective spectral emissivities of two thermal infrared bands have been retrieved by vegetation coverage method using the normalized difference vegetation index. The intercomparison results among the four LST algorithms show that the three algorithms (Becker-Li, Price, and Ulivieri et al.) show very similar performances. The LST estimated by the Becker and Li's algorithm is the highest, whereas that by the Kerr et al.'s algorithm is the lowest without regard to the geographic locations and seasons. The performance of four LST algorithms is significantly better during cold season (night) than warm season (day). And the LST derived from Terra/MODIS is closer to the observed LST than that of Aqua/MODIS. In general, the performances of Becker-Li and Ulivieri et al algorithms are systematically better than the others without regard to the day/night, seasons, and satellites. And the root mean square error and bias of Ulivieri et al. algorithm are consistently less than that of Becker-Li for the four seasons.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.799-821
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• 2020

The optimum design of truss structures is one of the significant categories in structural optimization that has widely been applied by researchers. In the present study, new mathematical programming called Consistent Approximation (CONAP) method is utilized for the simultaneous optimization of the size and shape of truss structures. The CONAP algorithm has already been introduced to optimize some structures and functions. In the CONAP algorithm, some important parameters are designed by employing design sensitivities to enhance the capability of the method and its consistency in various optimum design problems, especially structural optimization. The cross-sectional area of the bar elements and the nodal coordinates of the truss are assumed to be the size and shape design variables, respectively. The displacement, allowable stress and the Euler buckling stress are taken as the design constraints for the problem. In the proposed method, the primary optimization problem is replaced with a sequence of explicit sub-problems. Each sub-problem is efficiently solved using the sequential quadratic programming (SQP) algorithm. Several truss structures are designed by employing the CONAP method to illustrate the efficiency of the algorithm for simultaneous shape and size optimization. The optimal solutions are compared with some of the mathematical programming algorithms, the approximation methods and metaheuristic algorithms those reported in the literature. Results demonstrate that the accuracy of the optimization is improved and the convergence rate speeds up.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1070-1075
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• 2009

In this paper, we propose new depth estimation and intermediate view synthesis algorithms for three-dimensional video generation. In order to improve temporal consistency of the depth map sequence, we add a temporal weighting function to the conventional matching function when we compute the matching cost for estimating the depth information. In addition, we propose a boundary noise removal method in the view synthesis operation. after finding boundary noise areas using the depth map, we replace them with corresponding texture information from the other reference image. Experimental results showed that the proposed algorithm improved temporal consistency of the depth sequence and reduced flickering artifacts in the virtual view. It also improved visual quality of the synthesized virtual views by removing the boundary noise.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.93-100
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• 2005

We propose an optimal thresholding method for the voxel coloring in the reconstruction of a 3D shape. Our purposed method is a new approach to resolve the trade-off error of the threshold value on determining the photo-consistency in the conventional method. Optimal thresholding value is decided to compare the photo-consistency of a surface with inside voxel on the optic ray of the center camera. As iterating the process of the vokels, the threshold is approached to the optimal value for the individual surface voxel. And also, graph cut method is reduced to the surface noise on eliminating neighboring voxel. To verify the proposed algorithm, we simulated in the virtual and real environment. It is advantaged to speed up and accuracy of a 3D face reconstruction by applying the methods of optimal threshold and graph as compare with conventional algorithms.