• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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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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• 제90권2호
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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.

• 한국전산구조공학회논문집
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• 제21권3호
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• pp.233-238
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• 2008

본 논문에서는 등기하 해석법을 이용하여 선형 탄성문제에 대한 형상 최적설계 기법을 개발하였다. 실용적인 공학문제에 대한 많은 최적설계 문제에서는 초기의 데이터가 CAD 모델로부터 주어지는 경우가 많다. 그러나 대부분의 설계 최적화 도구들은 유한요소법에 기초하고 있기 때문에 설계자는 이에 앞서 CAD 데이터를 유한요소 데이터로 변환해야 한다. 이 변환과정에서 기하 모델의 근사화에 따른 수치적 오류가 발생하게 되고, 이는 응답 해석뿐만 아니라 설계민감도 해석에 있어서도 정확도 문제를 발생시킨다. 이러한 점에서 등기하 해석법은 형상 최적설계에 있어서 유망한 방법론 중 하나가 될 수 있다. 등기하 해석법의 핵심은 해석에 사용되는 기저 함수와 기하 모델을 구성하는 함수가 정확히 일치한다는 것이다. 이러한 기하학적으로 정확한 모델은 설계민감도 해석 및 형상 최적설계에 있어서도 사용된다. 이로 인해 높은 정확도의 설계민감도를 얻을 수 있으며, 이는 설계구배 기반의 최적화에 있어서 매우 중요하게 작용한다. 수치 예제를 통하여 본 논문에서 제시된 등기하 해석 기반의 형상 최적설계 방법론이 타당함을 확인하였다.

• 한국경영과학회지
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• 제42권3호
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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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• 제9권2호
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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.

• 전자공학회논문지
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• 제53권8호
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• pp.59-65
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• 2016

본 논문은 대량의 stream data를 처리하는 어플리케이션에서 하드웨어 가속기들이 접근하는 메모리가 non-cacheable에서 cacheable으로 변경됨에 따라 발생할 수 있는 데이터 일관성 문제를 고려하여 시스템 최적화를 진행하였다. 이를 위해 상위 수준 시뮬레이션을 통한 프로파일링 결과를 토대로 분석식을 만들어 활용하였다. 실험한 결과 여러 이미지 크기에서 메모리가 cacheable로 변경됨에 따라 평균 1.40배의 성능 향상을 보였다. 분석식의 주요 파라미터 최적화를 통해 최종적으로 3.88배의 성능 이득이 발생했으며, 항상 메모리가 cacheable인 경우의 성능이 항상 우월한 것은 아님을 확인할 수 있었다.

• 전기학회논문지
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• 제63권9호
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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.

• 한국CDE학회논문집
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• 제8권4호
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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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• 제13권2호
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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.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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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.