Article
Engineering, Civil
Hongtai Yang, Zhaolin Zhang, Wenbo Fan, Feng Xiao
Summary: Demand Responsive Connector (DRC) operates by picking up passengers based on requests and delivering them to a common destination, with demand influenced by fare, travel time, and time reliability. This paper introduces an elastic demand function and time deviation penalty model to formulate one-vehicle and two-vehicle DRC models, optimizing social welfare through decision variables such as fare, service area, and operating cycle time. Simulation and sensitivity analysis reveal key parameters affecting the models' validity and the threshold values for the applicability of one-vehicle and two-vehicle schemes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Marine
Mengmeng Wang, Jiaxuan Leng, Shizhe Feng, Zhixiong Li, Atilla Incecik
Summary: This paper proposes a Markov Chain Monte Carlo (MCMC)-based Bayesian updating method to build a high-fidelity, highly-precise virtual model for offshore platforms. The method utilizes the natural frequencies and mode shapes of the platform to construct the likelihood function and generate the most probable values of the uncertain parameters through Markov Chains for updating the finite element model.
Article
Computer Science, Interdisciplinary Applications
Shuang Zhou, Jianguo Zhang, Qingyuan Zhang, Meilin Wen
Summary: This paper proposes a methodology of hybrid reliability analysis and optimization based on chance theory to control aleatory and epistemic uncertainties in the preliminary design phase of engineering structures. It uses random variables to describe aleatory uncertainty and uncertain variables to quantify epistemic uncertainty. The chance measure and chance reliability indicator (CRI) are introduced to model structural reliability in the presence of hybrid uncertainty. Two CRI estimation methods and two solving strategies are developed for mixed reliability assessment and design optimization. The performance and feasibility of the proposed analysis model and solution technique are verified through four engineering applications.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Engineering, Civil
Yun Yang, Jichun Wu, Qiankun Luo, Jianfeng Wu
Summary: A new multi-objective optimization algorithm and a decision-making strategy have been developed to enhance the reliability and effectiveness of groundwater remediation systems. Results indicate that uncertain parameters significantly affect the final optimization results.
JOURNAL OF HYDROLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Xuzhen He, Fang Wang, Wengui Li, Daichao Sheng
Summary: This paper presents an efficient reliability analysis framework using trained artificial neural networks as surrogate models for geotechnical problems with uncertain random field parameters. By generating representative outcomes through experimental design, the framework demonstrates high efficiency and accuracy in predicting bearing capacity.
COMPUTERS AND GEOTECHNICS
(2021)
Article
Chemistry, Multidisciplinary
Yufeng Lyu, Zhenyu Liu, Xiang Peng, Jianrong Tan, Chan Qiu
Summary: In this study, a unified reliability measure method is proposed to consider uncertainties of input variables and their distribution parameters simultaneously, using evidence theory and Gaussian interpolation algorithm to construct probability density functions of uncertain distribution parameters of epistemic uncertainties, and representing epistemic uncertainties using a weighted sum. The effectiveness of this method has been demonstrated in engineering examples through comparison with the Monte Carlo method.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Iraj Faraji Davoudkhani, Abdolmajid Dejamkhooy, Saber Arabi Nowdeh
Summary: This paper proposes a new optimal framework for designing a hybrid photovoltaic-wind system integrated with battery storage, taking into account cloud-based uncertainty modeling and battery degradation. The framework uses an optimization algorithm called opposition-based learning and Gradientbased optimizer (OBLGBO) to determine the optimal values for decision variables such as the number of PVs, WTs, batteries, inverter power, and PV installation angle. The cloud theory method is applied to model energy resources and load demand uncertainties. Simulation results show that considering battery degradation costs increases the overall cost, while incorporating uncertainties based on the cloud theory increases the design cost and weakens system reliability. The OBLGBO algorithm proves to achieve lower design cost and better reliability indices compared to traditional optimization algorithms.
Article
Engineering, Industrial
Ling Chunyan, Lei Jingzhe, Kuo Way
Summary: This paper proposes an effective method to mitigate computational burden in reliability-based design optimization of modular systems. The method tackles coupling effects of modules using the individual module feasible approach and builds an alternative model for the probabilistic constraint function using Bayesian-inference-based support vector machine. The optimal decision scheme is obtained by solving the formulated conventional RBDO using the alternative model.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Mohamed Zaki El-Sharafy, Shivam Saxena, Hany E. Farag
Summary: This article introduces an optimal zone clustering algorithm for islanded microgrids (IMG) based on supply adequacy, utilizing distributed particle filter (DPF) technique for state estimation and optimizing the virtual boundaries of zones. The proposed algorithm considers various events and scenarios to come up with the best configuration, showing efficacy in Monte Carlo simulations even under severely corrupted measurements. The DPF performs similarly to its centralized implementation with computational savings based on the number of zones.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Tae-Yang Nam, Dong-Il Cho, Won-Sik Moon, Jae-Chul Kim, Joong-Woo Shin
Summary: A method for assessing the tradeoff between system availability and cost in modular multilevel converters (MMCs) is proposed, considering reliability and operational costs in redundancy design.
Article
Computer Science, Interdisciplinary Applications
Jin-gyun Park, Heonjun Yoon, Byeng D. Youn
Summary: This paper presents a probabilistic framework for the optimal design of gearboxes used in general-purpose industrial robots considering random use conditions. The start and end positions of a single motion profile are modeled as uniform distribution based on the assumption of no information about the robot use pattern. The proposed framework calculates the system-level lifetime by combining component scale factors and demonstrates its effectiveness through a case study.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Energy & Fuels
Khaled Hosny Ibrahim, Eslam Mohamed Ahmed, Saber Mohamed Saleh
Summary: The optimal sizing of a PV-wind hybrid system can be challenging due to the large number of structure settings and the irregular nature of solar radiation and wind power sources. This paper minimizes generation costs by adjusting the PV-tilt angle and wind turbine hub height to increase system capacity factor and get closer to the generation peak. By considering these design parameters, diversity between generation patterns increases, resulting in lower costs.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2021)
Article
Green & Sustainable Science & Technology
Abbas Khanahmadi, Reza Ghaffarpour
Summary: This paper focuses on hybrid renewable energy systems as an alternative for conventional energy systems, introduces a hybrid algorithm based on Harmony Search and Ring Theory-based Evolutionary Algorithm, and shows its potential in reducing emissions and costs simultaneously.
Article
Computer Science, Interdisciplinary Applications
Xiang Xu, Feiran Wang, Yuyue Chen, Bainan Yang, Song Zhang, Xiaokang Song, Liang Shen
Summary: The global coronavirus pandemic has led to a significant increase in urban medical waste, posing risks to disease transmission and public health. This study proposes an optimization model considering loading reliability to minimize the overall cost of medical waste recycling. The proposed MACO-GKA algorithm shows better performance compared to the CPLEX solver.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Automation & Control Systems
Kaiye Gao, Xiangbin Yan, Rui Peng, Liudong Xing
Summary: LMCCSs, linear multistate consecutively connected systems, are widely used in telecommunications. Most studies focus on the uncertainty in connection ranges of CEs, with none considering signal loss during transmission. Proposed model evaluates signal fraction receivable by the sink node subject to signal loss and solves optimal design policy problem to minimize system cost while meeting reliability and signal fraction constraints. Three examples illustrate the model.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Geological
Nak-Youl Ko, Sung-Hoon Ji, Yong-Kwon Koh, Jong-Won Choi
ENGINEERING GEOLOGY
(2015)
Article
Engineering, Geological
Nak-Youl Ko, Sung-Hoon Ji, Yong-Kwon Koh, Jong-Won Choi
ENGINEERING GEOLOGY
(2012)
Article
Geosciences, Multidisciplinary
Nak-Youl Ko, Kang-Kun Lee
GEOSCIENCES JOURNAL
(2009)
Article
Engineering, Environmental
Nak-Youl Ko, Kang-Kun Lee
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2009)
Article
Engineering, Environmental
Nak-Youl Ko, Kang-Kun Lee
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2010)
Article
Geosciences, Multidisciplinary
Dong Kyu Park, Nak-Youl Ko, Kang-Kun Lee
GEOSCIENCES JOURNAL
(2007)
Article
Geosciences, Multidisciplinary
NY Ko, KK Lee, Y Hyun
GEOSCIENCES JOURNAL
(2005)
