• Steel and Composite Structures
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• 제51권6호
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.119-127
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• 2022

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.

• 지능정보연구
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• 제14권4호
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• pp.147-160
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• 2008

업무프로세스 관리시스템(BPMS：business process management system)을 이용하면 새로운 프로세스를 정의하거나 기존의 프로세스를 갱신하는 일이 매우 용이하다. 대체로 표준화되고 일상적인 업무를 대상으로 프로세스를 관리하는 것은 이러한 소프트웨어를 사용하여 효율성을 높일 수 있겠으나, 비일상적인 예외상황에 대한 처리를 위해서는 별도의 전문가 참여나 특수한 의사결정 과정을 거쳐야 하는 경우가 많다. 본 논문은 다수의 예외처리용 업무 프로세스가 저장소에 축적된 상황을 전제로 예외처리 프로세스 선정의 자동화 방안을 제시한다. 예외처리에 가장 적합한 프로세스를 검색하는 것은 예외상황에 관한 충분한 이해가 필요하기 때문에 상황의 인지(context awareness)는 매우 중요한 과제이다. 예외상황의 이해를 원활히 하고, 예외처리 프로세스의 효율적인 선정을 위해 본 연구에서는 '상황변수'와 '의사결정변수' 자료구조를 도입하였다. 전자상거래의 배송과정의 예외사례를 사용하여 제시한 변수구조가 어떻게 검색트리 생성에 활용되는지를 예시하였다. C5.0 알고리즘은 최적검색트리를 생성해주며, 그것은 또한 문제의 상황에 최적인 예외처리 프로세스의 선정을 위한 검색경로를 설정한 것임을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권4호
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• pp.1911-1930
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• 2017

Overlay routing has emerged as a promising approach to improve reliability and efficiency of the Internet. The key to overlay routing is the placement and maintenance of the overlay infrastructure, especially, the selection and placement of key relay nodes. Spurred by the observation that a few relay nodes with high betweenness centrality can provide more optimal routes for a large number of node pairs, we propose a resilient routing overlay network construction method by introducing Super-Relay nodes. In detail, we present the K-Minimum Spanning Tree with Super-Relay nodes algorithm (SR-KMST), in which we focus on the selection and connection of Super-Relay nodes to optimize the routing quality in a resilient and scalable manner. For the simultaneous path failures between the default physical path and the overlay backup path, we also address the selection of recovery path. The objective is to select a proper one-hop recovery path with minimum cost in path probing and measurement. Simulations based on a real ISP network and a synthetic Internet topology show that our approach can provide high-quality overlay routing service, while achieving good robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• 한국지진공학회논문집
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• 제28권5호
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• pp.275-283
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• 2024

Many school buildings are vulnerable to earthquakes because they were built before mandatory seismic design was applied. This study uses machine learning to develop an algorithm that rapidly constructs an optimal reinforcement scheme with simple information for non-ductile reinforced concrete school buildings built according to standard design drawings in the 1980s. We utilize a decision tree (DT) model that can conservatively predict the failure type of reinforced concrete columns through machine learning that rapidly determines the failure type of reinforced concrete columns with simple information, and through this, a methodology is developed to construct an optimal reinforcement scheme for the confinement ratio (CR) for ductility enhancement and the stiffness ratio (SR) for stiffness enhancement. By examining the failure types of columns according to changes in confinement ratio and stiffness ratio, we propose a retrofit scheme for school buildings with masonry walls and present the maximum applicable stiffness ratio and the allowable range of stiffness ratio increase for the minimum and maximum values of confinement ratio. This retrofit scheme construction methodology allows for faster construction than existing analysis methods.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (D)
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• pp.40-42
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• 2006

무선 센서 네트워크는 제한된 배터리를 갖는 노드로 토폴로지를 구성한다. 이러한 이유 때문에 전체 네트워크의 수명을 극대화하고 라우팅에 에너지 효율성을 고려하여 토폴로지를 구성하는 것이 중요하다. 네트워크 토폴로지는 전송범위에 의해 결정되며 노드의 고정된 전송범위로 인한 에너지 비효율성 문제를 해결하는 방법으로 최적의 전송범위 혹은 MST(Minimum Spanning Tree)기반으로 토폴로지를 구성하는 대안이 있지만 최적의 솔루션은 아니다. 본 논문에서는 단계에 따라 전송범위를 차별화시켜 라우팅을 수행하는 DR(Differential Routing)을 제안하였다. DR은 전송범위에 기반하여 최적의 토폴로지를 구성한다. 그리고 무선 센서 네트워크의 특정한 통신 패턴에 맞게 트리 구성 단계와 데이터 수집 단계의 전송범위를 차별화시켜 전송파워를 조절함으로써 전체 네트워크의 수명 극대화, 파티션 방지, 그리고 에너지 효율성을 향상시킨 알고리즘이다. 실험을 통해 제안한 DR이 최적의 토폴로지를 구성하여 에너지 효율성 측면에서 좋은 성능을 보임을 확인할 수 있었다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2012년도 한국컴퓨터종합학술대회논문집 Vol.39 No.1(A)
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• pp.403-405
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• 2012

접미사 트리는 문자열 압축, 텍스트 처리, 생물정보학 등 다양한 응용 분야에서 사용되는 인덱스 자료구조이다. 최근 64bit 하드웨어와 멀티코어 CPU가 보급됨에 따라 메모리상에서 병렬로 접미사 트리를 생성하는 알고리즘이 활발히 연구되고 있다. 본 논문에서는 McCreight의 선형시간 알고리즘과 Chen의 병렬 알고리즘을 기반으로 메모리상에서 접미사 트리를 병렬로 생성하는 구현 방법을 보였으며, TBB, Cilk Plus와 같은 병렬 프로그래밍 라이브러리를 이용하여 병렬 알고리즘을 구현하였다. 알고리즘 실험 결과 병렬로 수행한 알고리즘이 직렬로 수행한 결과보다 최대 4배 가량 성능 향상을 얻을 수 있었으며, 병렬 라이브러리를 사용함으로써 가지는 오버헤드는 극히 적은 것으로 나타났다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2005년도 춘계학술발표대회
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• pp.1497-1500
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• 2005

멀티캐스트는 실시간 멀티미디어 전송 등에서 그 중요성이 매우 커지고 있다. 이러한 응용 기술들은 네트워크의 QoS(Quality of Service)보장을 위해 많은 자원을 필요로 한다. 네트워크의 자원은 한정되어 있기 때문에, 효율적인 자원의 사용을 위해서는 효율적인 멀티캐스트 라우팅 경로를 설정하는 것이 결정적 수단이다. 최소비용 멀티캐스트 라우팅 문제는 다양한 트리 최적화 문제를 해결하기 위한 기본적인 문제이며 다양한 연구가 있어왔다. 제안하는 알고리즘은 최소비용멀티캐스트 트리를 생성하는 휴리스틱 알고리즘으로 잘 알려진 TM 알고리즘과 가중치를 사용하여, 멀티캐스팅의 다양한 트리 최적화 문제에 적용되어 QoS에 따른 네트워크 자원의 사용효율을 극대화 하는데 기여할 것이다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.929-930
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• 2006

In this paper, we propose a planning algorithm which automatically generates robust behavior plans for service robots in the dynamically changing environments. The proposed method searches for paths to perform the given tasks in the physical space and the configuration space where tasks are described. And then, the characteristics of paths for successfully performed task are abstracted and generalized to build an ordered-tree structure. The resulting robust behavior plans guarantee that the given tasks are successfully performed. The validity of our method is tested by simulation work for a pushing-box task.