• Computers and Concrete
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• 제33권6호
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• IEIE Transactions on Smart Processing and Computing
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• 제6권4호
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• pp.281-291
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• 2017

Nowadays, as traffic jams are a daily elementary problem in both developed and developing countries, systems to monitor, predict, and detect traffic conditions are playing an important role in research fields. Comparing them, researchers have been trying to solve problems by applying many kinds of technologies, especially roadside sensors, which still have some issues, and for that reason, any one particular method by itself could not generate sufficient traffic prediction results. However, these sensors have some issues that are not useful for research. Therefore, it may not be best to use them as stand-alone methods for a traffic prediction system. On that note, this paper mainly focuses on predicting traffic conditions based on a hybrid prediction approach, which stands on accuracy comparison of three prediction models: multinomial logistic regression, decision trees, and support vector machine (SVM) classifiers. This is aimed at selecting the most suitable approach by means of integrating proficiencies from these approaches. It was also experimentally confirmed, with test cases and simulations that showed the performance of this hybrid method is more effective than individual methods.

• 한국암반공학회:학술대회논문집
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• 한국암반공학회 2000년도 암반공학문제의 수치해석(Numerical Analysis in Rock Engineering Problems)
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• pp.77-91
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• 2000

암반 절리 조사는 크게 절리 방향 측정과 트레이스 정보 등을 얻기 위한 매핑으로 구분할 수 있다. 본 연구에서는 절리 방향 측정 및 트레이스 매핑을 위한 기존의 수작업을 대체하기 위하여 입체사진측량학과 화상처리 기법 등을 이용한 암반 절리 조사시스템을 개발하였다. 절리 방향 측정을 위해 입체사진측량학을 적용하였으며, 트레이스 길이 및 빈도등의 정보를 얻기 위해 화상 처리 기법을 이용한 두 가지 혼합 접근법(hybrid approach)을 고안하였다. 입체사진측량학을 이용한 절리 방향 측정 기법 및 화상 처리 기법은 접근이 어려운 지역에서의 원거리 측정 및 신속하고 객관적인 매핑이 가능하다는 장점을 가진다.

• 터널과지하공간
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• 제10권3호
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• pp.329-343
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• 2000

암반 절리 조사는 크게 절리 방향 측정과 트레이스 정보 등을 얻기 위한 매핑으로 구분할 수 있다. 본 연구에서는 절리 방향 측정 및 트레이스 매핑을 위한 기존의 수작업을 대체하기 위하여 입체사진측량학과 화상처리 기법 동을 이용한 암반 절리 조사 시스템을 개발하였다. 절리 방향 측정을 위해 입체사진측량학을 적용하였으며, 트레이스 길이 및 빈도 등의 정보를 얻기 위해 화상 처리 기법을 이용한 두 가지 혼합 접근법 (hybrid approach) 을 고안하였다. 입체사진측량학을 이용한 절리 방향 측정 기법 및 화상 처리 기법은 접근이 어려운 지역에서의 원거리 측정 및 신속하고 객관적인 매핑이 가능하다는 장점을 가진다.

• Industrial Engineering and Management Systems
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• 제15권3호
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• pp.268-277
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• 2016

Load balancing is a significant technique to prolong a network's lifetime in sensor network. This paper introduces a hybrid approach named as Load Distributing Hybrid Routing Protocol (LDHRP) composed with a border node routing protocol (BDRP) and greedy forwarding (GF) strategy which will make the routing effective, especially in mobility scenarios. In an existing solution, because of the high network complexity, the data delivery latency increases. To overcome this limitation, a new approach is proposed in which the source node transmits the data to its respective destination via border nodes or greedily until the complete data is transmitted. In this way, the whole load of a network is evenly distributed among the participating nodes. However, border node is mainly responsible in aggregating data from the source and further forwards it to mobile sink; so there will be fewer chances of energy expenditure in the network. In addition to this, number of hop counts while transmitting the data will be reduced as compared to the existing solutions HRLBP and ZRP. From the simulation results, we conclude that proposed approach outperforms well than existing solutions in terms including end-to-end delay, packet loss rate and so on and thus guarantees enhancement in lifetime.

• Industrial Engineering and Management Systems
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• 제13권2호
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• pp.163-171
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• 2014

Under amazing increase in markets and certain demand on qualified service in the delivery system, global logistic optimization is becoming a keen interest to provide an essential infrastructure coping with modern competitive prospects. As a key technology for such deployment, we have been engaged in the practical studies on vehicle routing problem (VRP) in terms of Weber model, and developed a hybrid approach of meta-heuristic methods and the graph algorithm of minimum cost flow problem. This paper extends such idea to multi-depot VRP so that we can give a more general framework available for various real world applications including those in green or low carbon logistics. We show the developed procedure can handle various types of problem, i.e., delivery, direct pickup, and drop by pickup problems in a common framework. Numerical experiments have been carried out to validate the effectiveness of the proposed method. Moreover, to enhance usability of the method, Google Maps API is applied to retrieve real distance data and visualize the numerical result on the map.

• 유통과학연구
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• 제19권12호
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• pp.45-56
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• 2021

Purpose: This study aims to introduce a hybrid MCDM model to support the selection of retail store location. Research design, data, and methodology: The hybrid approach of ANP and TOPSIS was used to address the location selection problem. The ANP technique was employed to compute the weights of the selection criteria, whilst the TOPSIS was used to rank alternatives. The proposed approach was then applied into a fashion company in Vietnam to select the best alternatives to be the retail store. Results: The results showed that Candidate 1 - Hai Ba Trung street is the most appropriate selection for locating retail stores. Conclusions: The proposed approach provides the decision makers with more useful methods than traditional ones. Therefore, the model can be applied to the location selection in all industries. In terms of academic contribution, the selection criteria proposed in the research can devote to the literature in the selection of location along with the concept of distribution channels. Additionally, the research also provides insight and guidelines for firms in making decision on retail store location based on limited resources to avoid the waste of funds. However, the results only answer to the context of Vietnam - a developing country. Thus, future research may be extended to developed countries where have better conditions.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2854-2863
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• 2023

Objective: To present a hybrid approach that incorporates a constrained beam-hardening estimator (CBHE) and deep learning (DL)-based post-refinement for metal artifact reduction in dental cone-beam computed tomography (CBCT). Methods: Constrained beam-hardening estimator (CBHE) is derived from a polychromatic X-ray attenuation model with respect to X-ray transmission length, which calculates associated parameters numerically. Deep-learning-based post-refinement with an artifact disentanglement network (ADN) is performed to mitigate the remaining dark shading regions around a metal. Artifact disentanglement network (ADN) supports an unsupervised learning approach, in which no paired CBCT images are required. The network consists of an encoder that separates artifacts and content and a decoder for the content. Additionally, ADN with data normalization replaces metal regions with values from bone or soft tissue regions. Finally, the metal regions obtained from the CBHE are blended into reconstructed images. The proposed approach is systematically assessed using a dental phantom with two types of metal objects for qualitative and quantitative comparisons. Results: The proposed hybrid scheme provides improved image quality in areas surrounding the metal while preserving native structures. Conclusion: This study may significantly improve the detection of areas of interest in many dentomaxillofacial applications.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.231-236
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• 1997

We propose the hybrid control for the path control of wheeled mobile robot system. To develop the hybrid control of mobile robot, the continuous dynamics of mobile robot are modeled by the switched systems. The abstract model and digital automata for the path control are developed. This hybrid control system has the 3-layered hierachical structure : digital automata as the higher process, mobile robot system as the lower process, and the interface as the interaction process between the continuous dynamics and the discrete dynamics. The control of following the desired-paths with edges are investigated as the applications by the computer simulation.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.381-384
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• 2006

Self Organizing Map(SOM) is a powerful neural network model for unsupervised loaming. In many clustering works with exploratory data analysis, it has been popularly used. But it has a weakness which is the poorly theoretical base. A lot more researches for settling the problem have been published. Also, our paper proposes a method to overcome the drawback of SOM. As compared with the presented researches, our method has a different approach to solve the problem. So, a hybrid SOM is proposed in this paper. Using Monte Carlo computing, a hybrid SOM improves the performance of clustering. We verify the improved performance of a hybrid SOM according to the experimental results using UCI machine loaming repository. In addition to, the number of clusters is determined by our hybrid SOM.