• International conference on construction engineering and project management
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• 2022.06a
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• pp.608-615
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• 2022

Construction projects are large-scale projects that require extensive construction costs and resources. Especially, scheduling is considered as one of the essential issues for project success. However, the schedule and resource management are challenging to conduct in high-tech construction projects including complex design of MEP and architectural finishing which has to be constructed within a limited workspace and duration. In order to deal with such a problem, this study suggests resource and sequence optimization using constraint programming in construction projects. The optimization model consists of two modules. The first module is the data structure of the schedule model, which consists of parameters for optimization such as labor, task, workspace, and the work interference rate. The second module is the optimization module, which is for optimizing resources and sequences based on Constraint Programming (CP) methodology. For model validation, actual data of plumbing works were collected from a construction project using a five-minute rate (FMR) method. By comparing actual data and optimized results, this study shows the possibility of reducing the duration of plumbing works in construction projects. This study shows decreased overall project duration by eliminating work interference by optimizing resources and sequences within limited workspaces.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.3
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• pp.233-238
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• 2008

In this paper, a shape design optimization method for linearly elastic problems is developed using isogeometric approach. In many design optimization problems for practical engineering models, initial raw data usually come from a CAD modeler. Then, designers should convert the CAD data into finite element mesh data since most of conventional design optimization tools are based on finite element analysis. During this conversion, there are some numerical errors due to geometric approximation, which causes accuracy problems in response as well as design sensitivity analyses. As a remedy for this phenomenon, the isogeometric analysis method can be one of the promising approaches for the shape design optimization. The main idea of isogeometric approach is that the basis functions used in analysis is exactly the same as the ones representing the geometry. This geometrically exact model can be used in the shape sensitivity analysis and design optimization as well. Therefore the shape design sensitivity with high accuracy can be obtained, which is very essential for a gradient-based optimization. Through numerical examples, it is verified that the shape design optimization based on an isogeometic approach works well.

• Journal of the Korean Operations Research and Management Science Society
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• v.42 no.3
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• pp.25-34
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• 2017

K-means is a popular and efficient data clustering method that only uses intra-cluster distance to establish a valid index with a previously fixed number of clusters. K-means is useless without a suitable number of clusters for unsupervised data. This paper aimsto propose the Group Search Optimization (GSO) using Silhouette to find the optimal data clustering solution with a number of clusters for unsupervised data. Silhouette can be used as valid index to decide the number of clusters and optimal solution by simultaneously considering intra- and inter-cluster distances. The performance of GSO using Silhouette is validated through several experiment and analysis of data sets.

• Genomics & Informatics
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• v.9 no.2
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• pp.89-91
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• 2011

DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip. And the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties such as cancer. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. The Particle Swarm Optimization algorithm which was recently proposed is a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed is efficient in terms of execution time for clustering DNA chip data, and thus be used to extract valuable information such as cancer related genes from DNA chip data with high cluster accuracy and in a timely manner.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.59-65
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• 2016

Performance optimization of applications with massive stream data processing has been performed by considering I/O data coherency problem where a memory is shared between processors and hardware accelerators. A formula for performance analyses is derived based on profiling results of system-level simulations. Our experimental results show that overall performance was improved by 1.40 times on average for various image sizes. Also, further optimization has been performed based on the parameters appeared in the derived formula. The final performance gain was 3.88 times comparing to the original design and we can find that the performance of the design with cacheable shared memory is not always.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1300-1305
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• 2014

In this paper, we suggest an optimization method which can save about 5[%] of the cost though the optimizing of configuration and capacity for the facility. To achieve this goal, we compared the design data of the power, motor and drive system with the actual operation data of the plate mill plant in K-Steelworks. Therefore we measured the actual loading data by facilities considering the operating conditions of the plate mill plant in K-Steelworks, after that analyzed these data. In addition, we review the optimal capacity for transformer, switchgear and drive, and also reconfigured the electrical room and power single line diagram through the validation of motor data by equipment and the confirmation of process data considering the load characteristics. Consequently, the optimization method of capacity for the facilities shall have effectiveness in building new plate mill plant to further reduce costs at future.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.231-242
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• 2003

Multidisciplinary Design Optimization (MDO) is an optimization technique considering simultaneously multiple disciplines such as dynamics, mechanics, structural analysis, thermal and fluid analysis and electromagnetic analysis. A software system enabling multidisciplinary design optimization is called MDO framework. An MDO framework provides an integrated and automated design environment that increases product quality and reliability, and decreases design cycle time and cost. The MDO framework also works as a common collaborative workspace for design experts on multiple disciplines. In this paper, we present the architecture for an MDO framework along with the requirement analysis for the framework. The requirement analysis has been performed through interviews of design experts in industry and thus we claim that it reflects the real needs in industry. The requirements include integrated design environment, friendly user interface, highly extensible open architecture, distributed design environment, application program interface, and efficient data management to handle massive design data. The resultant MDO framework is datasever-oriented and designed around a centralized data server for extensible and effective data exchange in a distributed design environment among multiple design tools and software.

• Industrial Engineering and Management Systems
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• v.13 no.2
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• pp.210-220
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• 2014

This article describes a 2-in-1 methodology utilizing simulation optimization technique and Data Envelopment Analysis in measuring an accurate efficiency score. Given the high level of stochastic data in real environment, a novel methodology known as Data Collection Budget Allocation-Data Envelopment Analysis (DCBA-DEA) is developed. An example of the method application is shown in banking institutions. In addition to the novel approach presented, this article provides a new insight to the application domain of efficiency measurement as well as the way one conducts efficiency study.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1166-1169
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• 2012

본 논문은 사용자 행동인식을 위해 기존 PSO (Particle Swarm Optimization) 알고리즘의 경계선을 통한 데이터 분류에서 데이터의 수집환경에 의해 발생하는 문제를 벡터의 길이비교를 이용한 보정을 통해 보완한 알고리즘을 제안한다. 기존의 PSO 알고리즘은 데이터 분류를 위해서 데이터의 최소, 최대값을 이용하여 경계를 생성하고, 이를 이용하여 데이터를 분류하였다. 그러나 PSO를 이용하여 행동인식을 할 때 행동이 수집되는 환경에 따라서 경계에 포함되지 못해 행동이 분류되지 못하는 문제가 있다. 이러한 분류의 문제를 보완하기 위해 경계를 벗어난 데이터와 각 행동을 대표하는 데이터의 벡터 길이를 계산하고 최소길이를 비교하여 분류한다. 실험결과, 기존 PSO 방법에 비해 개선된 방법이 평균적으로 앉기 1%, 걷기 7%, 서기 7%의 개선된 결과를 얻었다.