Article
Energy & Fuels
Radharani Panigrahi, Nita R. Patne, Sumanth Pemmada, Ashwini D. Manchalwar
Summary: This study focuses on predicting hourly electricity demand of an electrical grid using a machine learning model. By handling categorical features and optimizing the estimation of leaf values, the proposed model achieves better prediction accuracy. It is compared and evaluated against five other models.
Article
Computer Science, Information Systems
Nan Xia, Jun Deng, Tianyue Zheng, Haijing Zhang, Jianbo Wang, Shutao Peng, Lin Cheng
Summary: This article presents an effective approach to solve the network reconfiguration problem during power system restoration by using an adaptively tunable fuzzy logic controller. The method uniquely determines the restoration sequence of loads and generators through tailored membership functions. Results from testing on the IEEE 118-bus system suggest that the proposed strategy is effective and feasible.
IEEE SYSTEMS JOURNAL
(2022)
Article
Engineering, Marine
Abdusselam Altunkaynak, Anil Celik, Murat Baris Mandev
Summary: Based on literature review, numerous research attempts to forecast significant wave height with short prediction time horizons are incapable in yielding accurate predictions. To improve this, singular spectrum analysis (SSA) is proposed as a decomposition procedure to enhance prediction accuracy.
Article
Green & Sustainable Science & Technology
Arman Torkfar, Amir Arefian, Reza Hosseini-Abardeh, Mohsen Bahrami
Summary: This study evaluates active and passive control strategies for achieving more stable power generation in Solar Chimney Power Plant (SCPP) using Fuzzy Logic Control (FLC) and Thermal Energy Storage (TES) systems. The results show that using TES can reduce output power fluctuations and generate different electrical energy generation for different media such as sand, limestone soil, and water-filled bags. By integrating the FLC and TES systems, the goal of continuous power generation for up to 4 hours after sunset has been achieved.
Article
Multidisciplinary Sciences
Xingchun Wei, Zhiming Wang, Junfeng Guo
Summary: To accurately predict the reliable lifetime of transformer oil, a reliability assessment method based on accelerated life testing is proposed. By establishing an inverse power Weibull distribution model, the study provides a numerical procedure for estimating model parameters and reliability indices, and validates the feasibility and correctness of the method using real lifetime data. The results emphasize the need to consider both reliable lifetime and mean lifetime under reliability limit when repairing or replacing transformer insulating oil.
SCIENTIFIC REPORTS
(2022)
Article
Automation & Control Systems
Balasundar Chelladurai, Chinnayan Karuppaiyah Sundarabalan, Srinath Nangavaram Santhanam, Josep M. Guerrero
Summary: This article proposes a novel high-quality charging scheme for electric vehicles, utilizing an interval type-2 fuzzy logic controlled shunt converter. The system includes bidirectional converters, dc-dc converters, and a static compensator, with multistep constant current control for battery charging and discharging. The fuzzy logic controller and genetic algorithm optimization show better performance in V2G and G2V operations.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Multidisciplinary Sciences
Franck Aby, Louis-Etienne Lorenzo, Zoe Grivet, Rabia Bouali-Benazzouz, Hugo Martin, Stephane Valerio, Sara Whitestone, Dominique Isabel, Walid Idi, Otmane Bouchatta, Philippe De Deurwaerdere, Antoine G. Godin, Cyril Herry, Xavier Fioramonti, Marc Landry, Yves De Koninck, Pascal Fossat
Summary: Research has shown that serotonin (5-HT) neurons have a pain-relieving effect in mice who have not experienced pain before, but become pain-promoting in neuropathic pain models. An imbalance in spinal KCC2 function turns this pain relief into pain promotion, but KCC2 enhancers can restore the pain-relieving effect. Additionally, combining selective serotonin reuptake inhibitors (SSRIs) with KCC2 enhancers can effectively relieve pain hypersensitivity caused by nerve injury.
Article
Green & Sustainable Science & Technology
Hegazy Rezk, Mokhtar Aly, Rania M. Ghoniem
Summary: This study improves the design of fuzzy logic control (FLC) systems for proton exchange membrane fuel cells (PEMFCs) maximum power point tracking (MPPT) by using a gradient-based optimizer (GBO). The results of the proposed GBO-FLC method outperform other computational methods in terms of mean, median, variance, and standard deviation. Comparison between regular FLC and upgraded FLC shows that the suggested FLC-based GBO design provides a dependable MPPT solution in PEMFCs.
Article
Computer Science, Information Systems
Mahmoud N. Ali, Karar Mahmoud, Matti Lehtonen, Mohamed M. F. Darwish
Summary: This paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for photovoltaic (PV) generation units. The method introduces five effective regions around the point of maximum PV power, and uses a fuzzy logic system to adjust the step size of the duty cycle. By enhancing the coordination between the fuzzy logic based algorithm and the INC method, the MPPT efficiency is improved, as demonstrated in simulations of a grid-connected PV system model.
Article
Computer Science, Artificial Intelligence
Mario Collotta, Renato Ferrero, Edoardo Giusto, Mohammad Ghazi Vakili, Jacopo Grecuccio, Xiangjie Kong, Ilsun You
Summary: Efficient energy consumption and good communication quality are important for Internet of Vehicles applications. The fuzzy control system proposed in the paper can increase battery life while maintaining a good throughput to workload ratio. This technique has shown network lifetime improvement ranging from 30% to 40% in experiments with different medium access control protocols.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Geochemistry & Geophysics
Adrian S. Barfod, Lea Levy, Jakob Juul Larsen
Summary: Processing geophysical data is time-consuming, but by employing machine learning techniques, such as neural networks, the process can be automated and accelerated. The study demonstrates the effectiveness of using neural networks to process induced polarization data, with the addition of an outlier curve detection algorithm to enhance accuracy. The automatic processing approach significantly reduces processing times and improves consistency compared to manual methods.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2021)
Article
Chemistry, Analytical
Carlos Antonio Alves de Araujo Junior, Juan Moises Mauricio Villanueva, Rodrigo Jose Silva de Almeida, Isaac Emmanuel Azevedo de Medeiros
Summary: The study focuses on developing digital twins to assist power plant operators in determining the correct number of fans to optimize the operation of water cooling systems. The robustness of the model was validated, with experimental results showing low average errors in various scenarios.
Article
Engineering, Electrical & Electronic
Dragana J. Petrovic, Miroslav M. Lazic, Bojana V. Jovanovic Lazic, Branko D. Blanusa, Stanko O. Aleksic
Summary: This paper introduces a novel power supply system that utilizes fuzzy inference logic to enhance the control of renewable energy sources. The system integrates solar and wind sources, along with an accumulator battery as an additional power source. By parallel connecting multiple energy sources, the system ensures stable power supply and optimal charging. The system uses two serial converters and a fuzzy logic controller to control the parallel connection of the renewable energy sources, and the reference voltage control of the converters enables optimal utilization of the available energy sources. The accumulated battery compensates for any shortage of solar and wind energies, and excess energy is stored in the battery. Experimental measurements were conducted on a prototype system under real conditions and compared with existing systems of similar nature. This innovative system is primarily designed for remote telecom locations that lack a power distribution network.
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2022)
Article
Chemistry, Physical
Mohamed Benghanem, Nadjwa Chettibi, Adel Mellit, Hamad Almohamadi
Summary: This paper investigates the direct and indirect coupled configuration of a hydrogen production system consisting of a photovoltaic source and an electrolyzer stack. Optimal matching conditions in the directly coupled system can be achieved through modifications in the configuration of the electrolyzer stack or PV generator. Efficiency can be improved by incorporating a buck converter between the PV string and electrolyzer stack. The study proposes a type-2 fuzzy inference system for controlling the operating point of the PV string. The results show that indirect-coupled hydrogen systems achieve higher energy transfer rates and that the type-2 fuzzy logic controller performs satisfactorily in both transient and permanent regimes.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Thermodynamics
Teresa Kurek, Artur Bielecki, Konrad Swirski, Konrad Wojdan, Michal Guzek, Jakub Bialek, Rafal Brzozowski, Rafal Serafin
Summary: This paper presents a complex analysis of heat demand forecasting methods for the Warsaw District Heating Network. Various models were evaluated, with an artificial neural network-based model achieving the best accuracy. Forecasting the intermediate seasons proved to be the most difficult.
Article
Computer Science, Information Systems
Jose De Jesus Rubio, Eduardo Orozco, Daniel Andres Cordova, Marco Antonio Islas, Jaime Pacheco, Guadalupe Juliana Gutierrez, Alejandro Zacarias, Luis Arturo Soriano, Jesus Alberto Meda-Campana, Dante Mujica-Vargas
Summary: In this study, a modified linear technique is proposed for the controllability and observability of robotic arms. The technique involves transforming a nonlinear model into a quasi-linear model, evaluating it at the origin, and testing its controllability and observability using rank condition. The modified linear technique is superior to both the traditional linear technique and linearization technique, as it can be applied to robotic arms with different types of joints.
Article
Computer Science, Information Systems
Jose de Jesus Rubio, Marco Antonio Islas, Genaro Ochoa, David Ricardo Cruz, Enrique Garcia, Jaime Pacheco
Summary: The article proposes a convergent Newton method that combines the Newton method and convergent gradient steepest descent for neural network adaptation, incorporating second-order partial derivatives into time-varying adaptation rates. The method ensures error convergence and minimum finding, with satisfactory results shown in electric energy usage data prediction.
INFORMATION SCIENCES
(2022)
Article
Multidisciplinary Sciences
Ricardo Balcazar, Jose de Jesus Rubio, Eduardo Orozco, Daniel Andres Cordova, Genaro Ochoa, Enrique Garcia, Jaime Pacheco, Guadalupe Juliana Gutierrez, Dante Mujica-Vargas, Carlos Aguilar-Ibanez
Summary: This research compares different types of regulators for the regulation of two mathematical models. It shows that the second-order sliding-mode regulator greatly improves the convergence time and solves the chattering problem in the first-order sliding-mode regulator.
Article
Computer Science, Information Systems
Tonatiuh Hernandez-Cortes, Miguel Amador-Macias, Ricardo Tapia-Herrera, Jesus Alberto Meda-Campana
Summary: This paper focuses on solving the output regulation problem using the new Francis equations for arbitrary reference/disturbance signals. The model is obtained by High-Gain observers, allowing regulation of unmodeled but measurable reference signals. The design involves fixing a globally attractive steady state using LMIs to control the decay rate within input bounds, while the regulation problem is solved by computing the steady-state input based on a modified set of regulation equations. The proposed approach is illustrated using the Furuta pendulum.
Article
Computer Science, Information Systems
Antonio Luna-Alvarez, Dante Mujica-Vargas, Arturo Rendon-Castro, Manuel Matuz-Cruz, Jean Marie Vianney Kinani
Summary: In the field of self-driving vehicles, steering control is the process of transforming sensor information into commands to steer the vehicle and avoid obstacles. This research proposes a data fusion approach using a neurofuzzy aggregation deep learning layer, which combines fuzzy measures, the Choquet fuzzy integral, and a fuzzy backpropagation algorithm to process data from different sources. A self-driving neural model based on the aggregation of steering control and obstacle detection models is also implemented and tested. The proposed approach achieves an average autonomy of 95% and improves driving smoothness by 9% compared to other state-of-the-art methods, as shown in the experiments conducted in a simulation environment and with a scale prototype.
Article
Mathematics, Interdisciplinary Applications
Alfredo Roldan-Caballero, J. Humberto Perez-Cruz, Eduardo Hernandez-Marquez, Jose Rafael Garcia-Sanchez, Mario Ponce-Silva, Jose de Jesus Rubio, Miguel Gabriel Villarreal-Cervantes, Jesus Martinez-Martinez, Enrique Garcia-Trinidad, Alejandro Mendoza-Chegue
Summary: This paper presents the design of an adaptive controller to solve the synchronization control problem of two identical Nwachioma chaotic systems in a master-slave configuration. The closed-loop stability is guaranteed by a Lyapunov-like analysis. Numerical simulations comparing the proposed approach with an active control algorithm are conducted to verify feasibility and performance. Experimental testing of the master-slave Nwachioma chaotic system using two personal computers and two low-cost Arduino UNO boards demonstrates both the effectiveness of the adaptive control and the suitability of Arduino UNO boards for the experimental setup.
Article
Automation & Control Systems
Jesus Alberto Meda-Campana, Ricardo Ismael Ancona-Bravo, Jonathan Omega Escobedo-Alva, Tonatiuh Hernandez-Cortes, Ricardo Tapia-Herrera
Summary: Based on the regulation theory and high-gain observers, this paper designs a controller to track and/or reject unmodeled but measurable signals. It proves that the missing dynamical models for such signals can be estimated by high-gain observers of dimensions equal to or greater than one, and embeds these observers into an auxiliary system known as the exosystem. The proposed controller is robust and able to track/reject any bounded and smooth signal as long as the estimations of the high-gain observers are accurate enough.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jose de Jesus Rubio
Summary: From the control theory perspective, the best control gain achieves a balance between trajectory tracking accuracy and control energy consumption. The bat algorithm is proposed as an alternative for finding the optimal control gain. This paper presents a bat algorithm based control approach to reduce control energy consumption in robots, and a modified bat algorithm based control approach to improve trajectory tracking accuracy in robots. A comparison is made between these two approaches and the simplex based control with regards to trajectory tracking accuracy and control energy consumption in two robots.
Article
Chemistry, Analytical
Abigail Elizabeth Pallares-Calvo, Blanca Esther Carvajal-Gamez, Octavio Gutierrez-Frias, Dante Mujica-Vargas
Summary: This paper focuses on the use of 125 kHz RFID technology in the communication layer of a network of mobile and static nodes in marine environments, specifically the UIoT. The analysis includes characterizing the penetration depth and evaluating the probabilities of data reception between antennas. The results demonstrate the suitability of RFID technology for data communication in marine environments and provide insights into optimizing the system for expanding the monitoring area.
Article
Physics, Multidisciplinary
Armando Adrian Miranda-Gonzalez, Alberto Jorge Rosales-Silva, Dante Mujica-Vargas, Ponciano Jorge Escamilla-Ambrosio, Francisco Javier Gallegos-Funes, Jean Marie Vianney-Kinani, Erick Velazquez-Lozada, Luis Manuel Perez-Hernandez, Lucero Veronica Lozano-Vazquez
Summary: “Noise suppression algorithms have been widely used in various tasks, and this research proposes an unsupervised neural network architecture for Gaussian denoising. The proposed method outperforms other types of neural networks in suppressing image noise, as shown by objective numerical results.”
Article
Physics, Multidisciplinary
Angel Arturo Rendon-Castro, Dante Mujica-Vargas, Antonio Luna-Alvarez, Jean Marie Vianney Kinani
Summary: In this paper, an efficient method is proposed to remove noise in image processing using redescending M-estimators within the framework of Wiener estimation. This method effectively suppresses impulsive, additive, and multiplicative noise at various densities and works on grayscale and color images.
Article
Thermodynamics
Jose de Jesus Rubio, Donaldo Garcia, Humberto Sossa, Ivan Garcia, Alejandro Zacarias, Dante Mujica-Vargas
Summary: In this research, a convolutional radial basis function network is used for energy processes prediction, which utilizes convolution operation to reduce the complexity and improve efficiency by reducing the dimensionality of the input dataset, resulting in more accurate prediction.
Article
Computer Science, Artificial Intelligence
Jose de Jesus Rubio, Mario Alberto Hernandez, Francisco Javier Rosas, Eduardo Orozco, Ricardo Balcazar, Jaime Pacheco
Summary: This study suggests using different observer and controller gains to improve the position and velocity perturbation attenuation based on control theory. High-gain, genetic high-gain, and compact high-gain controllers are proposed to improve the perturbation attenuation in inverted pendulums. These controllers are compared with other controllers.
