• Steel and Composite Structures
• /
• 제46권2호
• /
• pp.153-173
• /
• 2023

Rectangular concrete-filled steel tubular (RCFST) column, a type of concrete-filled steel tubular (CFST), is widely used in compression members of structures because of its advantages. This paper proposes a robust machine learning-based framework for predicting the ultimate compressive strength of RCFST columns under both concentric and eccentric loading. The gradient boosting neural network (GBNN), an efficient and up-to-date ML algorithm, is utilized for developing a predictive model in the proposed framework. A total of 890 experimental data of RCFST columns, which is categorized into two datasets of concentric and eccentric compression, is carefully collected to serve as training and testing purposes. The accuracy of the proposed model is demonstrated by comparing its performance with seven state-of-the-art machine learning methods including decision tree (DT), random forest (RF), support vector machines (SVM), deep learning (DL), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and categorical gradient boosting (CatBoost). Four available design codes, including the European (EC4), American concrete institute (ACI), American institute of steel construction (AISC), and Australian/New Zealand (AS/NZS) are refereed in another comparison. The results demonstrate that the proposed GBNN method is a robust and powerful approach to obtain the ultimate strength of RCFST columns.

• International Journal of Advanced Culture Technology
• /
• 제4권2호
• /
• pp.38-42
• /
• 2016

In the growing data saturated world, the question of "whether data can be used" has shifted to "can it be utilized effectively?" More data is being generated and utilized than ever before. As the collection of data increases, data mining techniques also must become more and more accurate. Thus, to ensure this data is effectively utilized, the analysis of the data must be efficient. Interpretation of results from the analysis of the data set presented, have their own on the basis it is possible to obtain the desired data. In the data mining method a decision tree, clustering, there is such a relationship has not yet been fully developed algorithm actually still impact of various factors. In this experiment, the classification method of data mining techniques is used with easy decision tree. Also, it is used special technology of one R and J48 classification technique in the decision tree. After selecting a rule that a small error in the "one rule" in one R classification, to create one of the rules of the prediction data, it is simple and accurate classification algorithm. To create a rule for the prediction, we make up a frequency table of each prediction of the goal. This is then displayed by creating rules with one R, state-of-the-art, classification algorithm while creating a simple rule to be interpreted by the researcher. While the following can be correctly classified the pattern specified in the classification J48, using the concept of a simple decision tree information theory for configuring information theory. To compare the one R algorithm, it can be analyzed error rate and accuracy. One R and J48 are generally frequently used two classifications${\ldots}$

• 한국컴퓨터정보학회논문지
• /
• 제17권10호
• /
• pp.155-165
• /
• 2012

본 논문은 지금까지 해결하지 못한 NP-Hard 문제들 중의 하나인 외판원 문제를 해결할 수 있는 알고리즘을 제안한다. 제안된 알고리즘은 간선교환 방법을 적용한 발견적 알고리즘이다. 초기해를 구하는 전형적인 방법은 첫 번째 노드부터 가장 인접한 노드를 방문하여 외판원의 경로를 결정하는 방법이다. 본 논문에서는 각 노드의 최소 간선을 선택하여 선택된 간선들 중 최소값을 가진 노드부터 출발하는 Min-Min 방법과 최대값을 가진 노드부터 출발하는 Min-Max 방법을 적용하고 두 방법 중 최소 경로길이를 가진 방법을 초기해로 결정하였다. 초기해로부터 최적해를 구하는 과정은 기존의 2-간선 교환 방법 (2-opt)을 기본적으로 적용하고, 추가로 확장된 3-opt와 4-opt를 제안하였다. 이와 같은 방법을 7개의 실제 데이터들에 적용한 결과 지금까지 알려진 최적해를 빠르고 정확히 구하는데 성공하였다.

• 정보과학회 논문지
• /
• 제43권2호
• /
• pp.229-236
• /
• 2016

세타조인은 데이터베이스에 있어서 가장 기본적이면서도 중요한 질의 중 하나이다. 최근 처리해야 하는 데이터의 양이 증가함에 따라, 맵리듀스와 같은 분산 병렬 처리 프레임워크를 사용한 데이터베이스의 질의처리가 많이 연구되고 있다. 대표적인 연구로 M-Bucket-I 휴리스틱을 이용한 세타조인이 있으나, 이 알고리즘은 수행시간이 입력 데이터의 크기를 n이라 할 때, 각 레코드를 $r_{max}$개의 리듀서 중 어느 리듀서로 보낼지 정하는 리듀서맵핑을 구하는데 O(n)의 시간이 걸려 쉽게 사용할 수 없다는 문제가 있다. 본 논문에서는 기존의 M-Bucket-I 휴리스틱을 개선하여, 같은 리듀서 매핑 결과를 내놓더라도 수행시간이 $O(r_{max}log\;n)$으로 보다 짧은 새로운 알고리즘을 제시한다. 다양한 실험을 통하여 기존의 맵리듀스를 이용한 세타조인보다 성능을 10% 정도 향상시킬 수 있음을 보였다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
• /
• pp.2775-2778
• /
• 2003

Neural network-based fault diagnosis algorithm to detect and isolate faults in the nonlinear systems is proposed. In the proposed method, the fault is detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the system outputs are transferred to the fault classifier by ART2 NN (adaptive resonance theory 2 neural network) for fault isolation. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• 대한기계학회논문집
• /
• 제18권8호
• /
• pp.1939-1951
• /
• 1994

This study introduces a new tool breakage detecting technology comprised of an unsupervised neural network combined with adaptive time series autoregressive(AR) model where parameters are estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(Recursive Least Square). Experiment indicates that AR parameters are good features for tool breakage, therefore it can be detected by tracking the evolution of the AR parameters during milling process. an ART 2(Adaptive Resonance Theory 2) neural network is used for clustering of tool states using these parameters and the network is capable of self organizing without supervised learning. This system operates successfully under the wide range of cutting conditions without a priori knowledge of the process, with fast monitoring time.

• Journal of information and communication convergence engineering
• /
• 제14권2호
• /
• pp.129-135
• /
• 2016

In this paper, we present a novel algorithm for image-based maritime obstacle detection using global sparsity potentials (GSPs), in which "global" refers to the entire sea area. The horizon line is detected first to segment the sea area as the region of interest (ROI). Considering the geometric relationship between the camera and the sea surface, variable-size image windows are adopted to sample patches in the ROI. Then, each patch is represented by its texture feature, and its average distance to all the other patches is taken as the value of its GSP. Thereafter, patches with a smaller GSP are clustered as the sea surface, and patches with a higher GSP are taken as the obstacle candidates. Finally, the candidates far from the mean feature of the sea surface are selected and aggregated as the obstacles. Experimental results verify that the proposed approach is highly accurate as compared to other methods, such as the traditional feature space reclustering method and a state-of-the-art saliency detection method.

• Interaction and multiscale mechanics
• /
• 제6권2호
• /
• pp.257-270
• /
• 2013

The use of lightweight materials has been steadily increasing in the automotive industry, and presents new challenges to material joining. Among many joining processes, self-piercing riveting (SPR) is particularly promising for joining lightweight materials (such as aluminum alloys) and dissimilar materials (such as steel to Al, and metal to polymer). However, to establish a process window for optimal joint performance, it often requires a long trial-and-error testing of the SPR process. This is because current state of the art in numerical analysis still cannot effectively resolve the problems of severe material distortion and separation in the SPR simulation. This paper presents a coupled meshfree/finite element with a moving boundary algorithm to overcome these numerical difficulties. The simulation results are compared with physical measurements to demonstrate the effectiveness of the present method.

• Ocean Systems Engineering
• /
• 제7권2호
• /
• pp.75-87
• /
• 2017

A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables.

• Advances in robotics research
• /
• 제2권3호
• /
• pp.219-236
• /
• 2018

This research focuses on controlling robots and their formations using rough mereology as a means for spatial reasoning. The authors present the state of the art theory behind path planning, robot cooperation domains and ways of creating robot formations. Furthermore, the theory behind Rough Mereology as a way of implementing mereological potential field based path creation and navigation for single and multiple robots is described. An implementation of the algorithm is shown in simulation using RoboSim simulator. Five formations are tested (Line, Rhomboid, Snake, Circle, Cross) along with three decision systems (First In, Leader First, Horde Mode) as compared to other methods.