Article
Operations Research & Management Science
Bakhtiar Ostadi, Ramtin Hamedankhah
Summary: The study introduces a two-stage reliability optimization method for redundancy allocation considering sale of worn-out parts, aiming to address the series-parallel redundancy allocation problem. Part of the budget is allocated to maximize system reliability upon launch, while the rest is used for replacement of parts.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Computer Science, Information Systems
Qinghua Gu, Siping Huang, Qian Wang, Xuexian Li, Di Liu
Summary: This article proposes a Chaotic Differential Evolution and Symmetric Direction Sampling (CDE-SDS) method for large-scale multiobjective optimization. The CDE-SDS method utilizes chaotic differential evolution strategy to accelerate convergence and employs symmetric direction sampling strategy to explore the high-dimensional decision space. Experimental results show that CDE-SDS outperforms seven compared algorithms in terms of diversity and convergence under limited function evaluations.
INFORMATION SCIENCES
(2023)
Article
Engineering, Industrial
Hadi Gholinezhad
Summary: This paper proposes a new Reliability Redundancy Allocation Problem (RRAP) where non-identical components can be allocated to each subsystem and the redundancy strategy is variable. An improved genetic algorithm is presented to solve the proposed problem. The results demonstrate that the proposed RRAP outperforms the previous models in terms of reliability improvement.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Automation & Control Systems
Shangce Gao, Yang Yu, Yirui Wang, Jiahai Wang, Jiujun Cheng, MengChu Zhou
Summary: The article introduces a novel variant of the JADE algorithm that improves its performance by incorporating chaotic local search mechanisms. Experimental and statistical analyses demonstrate the superior performance of this variant compared to traditional JADE and other state-of-the-art optimization algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Engineering, Industrial
Abdossaber Peiravi, Mustapha Nourelfath, Masoumeh Kazemi Zanjani
Summary: This paper proposes a new universal redundancy strategy that allows for changing system structure at any time for improved system reliability. The strategy offers a wide range of possibilities by selecting the optimal reconfiguration instant and number of components.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Zixuan Zhang, Lin Yang, Youwei Xu, Ran Zhu, Yining Cao
Summary: This paper proposes a novel approach for the Reliability Redundancy Allocation Problem (RRAP) in complex systems. The complex system structure is modeled with a graph, and the system reliability is calculated using a functionality multi-graph. The proposed method is validated through comparative experiments and a case study of security system design.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Aerospace
Thanakorn Khamvilai, John B. Mains, Louis Sutter, Aqib Syed, Philippe Baufreton, Francois Neumann, Eric Feron
Summary: This article proposes an optimization-based framework for guaranteeing the desired reliability of any graph-based networked system, focusing on using minimally redundant components in the integrated modular avionics architecture. The framework consists of two steps, computing the minimum number of components in the architecture and determining the minimal number of connections between these components.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Tsung-Jung Hsieh
Summary: A new strategic model called RRAP-CM-MCCS is proposed in the study, utilizing simplified swarm optimization (SSO) to improve system reliability through a multi-role resource sharing strategy and save resources.
ENGINEERING OPTIMIZATION
(2022)
Article
Energy & Fuels
Abdulaziz Almalaq, Tawfik Guesmi, Saleh Albadran
Summary: This study proposes a new multiobjective optimization technique combining the differential evolution algorithm and chaos theory to solve the nonconvex and nonsmooth economic emission dispatch problem. The technique extracts an accurate Pareto front and overcomes the limitations of local optima and the conventional DE algorithm. A slack TGU is defined to handle the power balance constraint, and the proposed technique optimizes the thermal units to achieve the optimization objectives.
Article
Computer Science, Artificial Intelligence
Raktim Biswas, Deepak Sharma
Summary: Reliability-based design optimization (RBDO) is an efficient tool for generating reliable and optimal solutions under uncertainty in design variables. This paper proposes a single-loop RBDO formulation that addresses the challenges of generating optimal reliable solutions and higher computational costs. The formulation incorporates a shifting vector approach and utilizes target and trial vectors of differential evolution (DE) to guide the algorithm. The proposed RBDO method is tested on mathematical and engineering examples, and its reliability is verified using Monte Carlo simulations.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Hafiz Tayyab Rauf, Jiechao Gao, Ahmad Almadhor, Ali Haider, Yu-Dong Zhang, Fadi Al-Turjman
Summary: A novel variant of differential evolution called MPC-DE is proposed to solve multi-model and multi-objective optimization problems. It utilizes multiple selection strategies and chaotic mapping methods for population initialization and mutation. The performance of MPC-DE is evaluated on benchmark problems and compared with recent DE variants, showing superior results for multi-objective optimization problems and the economic load dispatch problem.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Peng Wang, Bing Xue, Jing Liang, Mengjie Zhang
Summary: Feature selection aims to reduce data dimensionality and classification error rate through a multiobjective approach. This article proposes a niching-based method that minimizes the number of selected features and the classification error rate simultaneously. The proposed method can generate a diverse set of feature subsets with good convergence and distribution, and with almost the same lowest classification error rate for the same number of features.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Hui Chen, Xiaobo Li, Shaolang Li, Yuxin Zhao, Junwei Dong
Summary: The Slime Mould Algorithm (SMA) is a meta-heuristics algorithm inspired by the behaviors of slime mould. Despite its effective performance, SMA tends to fall into local optima and lacks population diversity. This paper proposes an improved SMA algorithm named CHDESMA, which uses chaotic maps for better diversity and incorporates differential evolution for enhanced searching ability. Experimental results and statistical analysis show that CHDESMA performs competitively compared to advanced algorithms.
Article
Engineering, Chemical
Jiawen Wei, Qi Zhang, Zhihong Yuan
Summary: The study aims to address key issues in sensor network design for chemical plants, proposing a unified approach and emphasizing the importance of a multiscenario approach in achieving optimality.
Article
Construction & Building Technology
Eslam Mohammed Abdelkader, Osama Moselhi, Mohamed Marzouk, Tarek Zayed
Summary: The study presents an automated model for optimizing bridge maintenance, consisting of three components: identifying bridge inventory characteristics, designing a multi-objective optimization model for optimal maintenance plans, and selecting the best plans based on different criteria. Results show that the model outperforms traditional algorithms in performance evaluation.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Engineering, Biomedical
Phornpot Chainok, Karla de Jesus, Leandro Coelho, Helon Vicente Hultmann Ayala, Mateus Gheorghe de Castro Ribeiro, Ricardo J. Fernandes, Joao Paulo Vilas-Boas
Summary: The purpose of this study was to predict the performance determinant factors of 15m backstroke-to-breaststroke turning using machine-learning models and comparing linear and tree-based models. The collected data revealed that the best models showed similar performance in different turning techniques, with balanced contributions between turn-in and turn-out variables.
SPORTS BIOMECHANICS
(2023)
Review
Computer Science, Artificial Intelligence
Luiza Scapinello Aquino da Silva, Yan Lieven Souza Lucio, Leandro dos Santos Coelho, Viviana Cocco Mariani, Ravipudi Venkata Rao
Summary: The Jaya Algorithm, a population-based optimization method, has become a valuable tool in swarm intelligence. This paper provides a comprehensive review and bibliometric study of the algorithm's applicability and variants, emphasizing its versatility. The study aims to inspire new researchers to utilize this simple and efficient algorithm for problem-solving. Evaluation: 8/10.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Mathematics, Interdisciplinary Applications
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Gabriel Trierweiler Ribeiro, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Efficient models for short-term load forecasting in electricity distribution and generation systems are crucial for companies' energetic planning. In this study, an ensemble learning model based on dual decomposition approach, machine learning models and hyperparameters optimization is proposed. The model successfully decomposes the time series and handles the non-linearities, and achieves accurate load forecasting results with reduced errors.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Energy & Fuels
Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The cost of electricity and gas has a direct impact on people's everyday routines, but the value of electricity is closely related to spot market prices, which can increase in winter due to higher energy demand. Existing models for forecasting energy costs are not robust enough due to competition, seasonal changes, and other variables. This study proposes combining seasonal and trend decomposition using LOESS and Facebook Prophet methodologies to improve the accuracy of analyzing time series data on Italian electricity spot prices.
Article
Computer Science, Artificial Intelligence
Allan Christian Krainski Ferrari, Gideon Villar Leandro, Leandro dos Santos Coelho, Myriam Regattieri De Biase Silva Delgado
Summary: This work proposes a fuzzy mechanism to improve the convergence of the rat swarm optimizer algorithm. The proposed fuzzy model uses the normalized fitness and population diversity as input. The results show that the fuzzy mechanism improves convergence and is competitive with other metaheuristics.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Energy & Fuels
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The energy price has a significant impact on investment and economic development. Forecasting future energy prices can support industrial planning and help avoid economic recession.
Article
Chemistry, Analytical
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Insulators installed outdoors are prone to accumulation of contaminants, causing increased conductivity and leakage current, eventually leading to flashover. To enhance power system reliability, it is possible to predict fault development and potential shutdown by evaluating the increase in leakage current. This paper proposes a method, optimized EWT-Seq2Seq-LSTM with attention, which combines empirical wavelet transform (EWT) to reduce non-representative variations and the attention mechanism with LSTM recurrent network for prediction. The model achieved a 10.17% lower mean square error (MSE) compared to standard LSTM and a 5.36% lower MSE compared to the model without optimization, demonstrating the effectiveness of the attention mechanism and hyperparameter optimization.
Article
Chemistry, Analytical
Andressa Borre, Laio Oriel Seman, Eduardo Camponogara, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The dataset is used to train a hybrid CNN-LSTM architecture, which employs quantile regression to manage uncertainties in the data. The results show that this approach outperforms traditional reference models, making it beneficial for companies to optimize maintenance schedules and improve the performance of their electric machines.
Article
Thermodynamics
Stefano Frizzo Stefenon, Laio Oriel Seman, Luiza Scapinello Aquino, Leandro dos Santos Coelho
Summary: This paper proposes a Seq2Seq LSTM neural network model with an attention mechanism and wavelet transform for reservoir level prediction. The proposed approach outperforms other models and provides accurate and timely predictions of water levels, allowing for better decision-making in dam management under emergency conditions.
Article
Computer Science, Artificial Intelligence
Alan Naoto Tabata, Alessandro Zimmer, Leandro dos Santos Coelho, Viviana Cocco Mariani
Summary: This study used synthetic datasets from the CARLA simulator and real-world dataset from WAYMO Open to train and evaluate computer vision algorithms. An efficient automated method for pedestrian and vehicle identification and counting was developed, which can quickly identify target features among many images and output formatted results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper evaluates a time series of leakage current from a high-voltage laboratory experiment using porcelain pin-type insulators. Time series forecasting is performed with ensemble learning approaches, and the results show that applying these approaches enhances the performance of the machine learning models in predicting breakdowns in the electrical power system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Chemistry, Analytical
Guilherme Augusto Silva Surek, Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper aims to evaluate and map the current scenario of human actions in red, green, and blue videos using deep learning models. A semi-supervised learning approach is employed to evaluate a residual network (ResNet) and a vision transformer architecture (ViT). The results obtained using a bi-dimensional ViT structure demonstrated great performance in human action recognition, achieving an accuracy of 96.7% on the HMDB51 dataset.
Article
Chemistry, Analytical
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Jose Henrique Kleinubing Larcher, Andre Mendes, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper proposes a new hybrid framework combining STACK ensemble learning and a JADE algorithm for nonlinear system identification. The model performs well in decoding EEG signals, achieving an average explanation of 94.50% and 67.50% of data variability, and outperforms other methods in terms of accuracy.
Article
Chemistry, Analytical
Stefano Frizzo Stefenon, Laio Oriel Seman, Nemesio Fava Sopelsa Neto, Luiz Henrique Meyer, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper presents a novel hybrid method for fault prediction based on the time series of leakage current of contaminated insulators. The proposed CFRW-GMDH method, with a root-mean-squared error of 3.44x10(-12), outperformed other models in fault prediction. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.
Article
Thermodynamics
Andrey Barbosa Colaco, Viviana Cocco Mariani, Mohamed Reda Salem, Leandro dos Santos Coelho
Summary: The thermal performance index (TPI), known as the efficiency parameter, evaluates the heat transfer highlight with the same pumping power requirement. Using multi-objective optimization techniques, the design of a double pipe heat exchanger with perforated baffles in the annulus side was improved, resulting in significantly enhanced heat transfer performance compared to a plain double pipe heat exchanger. The Nusselt number increased by approximately 8 times, and the friction factor increased by approximately 7-10 times.
APPLIED THERMAL ENGINEERING
(2022)
Article
Mathematics, Interdisciplinary Applications
Bo Li, Tian Huang
Summary: This paper proposes an approximate optimal strategy based on a piecewise parameterization and optimization (PPAO) method for solving optimization problems in stochastic control systems. The method obtains a piecewise parameter control by solving first-order differential equations, which simplifies the control form and ensures a small model error.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Guram Mikaberidze, Sayantan Nag Chowdhury, Alan Hastings, Raissa M. D'Souza
Summary: This study explores the collective behavior of interacting entities, focusing on the co-evolution of diverse mobile agents in a heterogeneous environment network. Increasing agent density, introducing heterogeneity, and designing the network structure intelligently can promote agent cohesion.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Gengxiang Wang, Yang Liu, Caishan Liu
Summary: This investigation studies the impact behavior of a contact body in a fluidic environment. A dissipated coefficient is introduced to describe the energy dissipation caused by hydrodynamic forces. A new fluid damping factor is derived to depict the coupling between liquid and solid, as well as the coupling between solid and solid. A new coefficient of restitution (CoR) is proposed to determine the actual physical impact. A new contact force model with a fluid damping factor tailored for immersed collision events is proposed.
CHAOS SOLITONS & FRACTALS
(2024)
