A Developed Asymmetric Multilevel Inverter with Lower Number of Components by Y. Naderi-Zarnaghi, M. Karimi, M. R. Jannati-Oskuee, S.H. Hosseini, S. Najafi-Ravadanegh

Get full text

Designing an explainable bio-inspired model for suspended sediment load estimation: eXtreme Gradient Boosting coupled with Marine Predators Algorithm by Roozbeh Moazenzadeh, Okan Mert Katipoğlu, Ahmadreza Shateri, Hamid Nasiri, Mohammed Abdallah

“…While Xtreme Gradient Boosting (XGB), a powerful ensemble machine learning (ML) model, has been employed in previous studies, the novelty of this research lies in the introduction of a hybrid approach that synergistically combines XGB with the bio-inspired Marine Predators Algorithm (XGB-MPA) to estimate SL in the Yeşilirmak River (Turkey). …”
Get full text

Elbow Joint Angle Estimation Using a Low-Cost and Low-Power Single Inertial Device for Daily Home-Based Self-Rehabilitation by Manon Fourniol, Rémy Vauché, Guillaume Rao, Eric Watelain, Edith Kussener

“…In this work, a reduction in the data rate from 100 Hz to 10 Hz increased the RMSE by a factor of 1.8 but could reduce the power consumption associated with the sensor and the algorithm’s computation by a factor of 10. …”
Get full text

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads by Chuan Tian, Shunli Jiang, Shuai Li, Guohui Feng, Bin Yu

“…The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. …”
Get full text

A Novel Voltage–Current Characteristic Model for Understanding of Electric Arc Furnace Behavior Using Experimental Data and Grey Wolf Optimization Algorithm by Mustafa Şeker, Emre Ünsal, Ahmet Aksoz, Mahir Dursun

“…The proposed model integrates polynomial curve fitting, the modified Heidler function, and double S-curves, with the grey wolf optimization (GWO) algorithm applied for parameter optimization, enhancing accuracy in predicting arc dynamics. …”
Get full text

Advanced Solar Irradiance Forecasting Using Hybrid Ensemble Deep Learning and Multisite Data Analytics for Optimal Solar-Hydro Hybrid Power Plants by Sudharshan Konduru, Naveen C., Jagabar Sathik M.

“…This research aims to predict high solar irradiance on hydropower plants to maximize active power generation. A novel hybrid decomposed residual ensembling model for deep learning (SBLTSRARW) using models such as autoregressive integrated moving average (ARIMA) and seasonal-trend decomposition using loess (STL) along with prediction and optimization models such as Bidirectional LSTM (Bi-LSTM), and Whale Optimization Algorithm (WOA) methods are used to predict the irradiances. …”
Get full text

Random forest algorithm integrated with an initial basic feasible solution in buckling analysis of a two-dimensional functionally graded porous taper beam by Ravikiran Chinthalapudi, Jagadesh Kumar Jatavallabhula, Geetha Narayanan Kannaiyan, Bridjesh Pappula, Seshibe Makgato

“…To overcome these limitations, a hybrid analytical computational methodology is proposed that integrates the novel Initial Basic Feasible Solution approach with the Random Forest algorithm. The beam is modelled using hyperbolic shear deformation theory to account for transverse shear effects, while material properties vary along both the length and thickness following a power-law distribution. …”
Get full text

Using data mining techniques to improve inflation rate management by Fatemeh Zahra Abedi Samakoosh, Soheila Karbasi

“…Inflation decreases the purchasing power of households, although this decrease in purchasing power will not be the same in all commodities, and this makes it difficult to predict economic and analytical conditions and investment opportunities.Investigating the consequences of inflation in countries, such as the reduction of purchasing power, using different techniques such as data mining techniques, seems necessary. …”
Get full text

Multi-objective particle swarm optimization algorithm-based method for optimal placement and sizing of distributed generations and shunt capacitors in a radial distribution network by Pinank Patel, Nagaraj Patil, Ahmed Mohsen, Aditya Kashyap, Nofal Adrees Hasan, Karthikeyan A, Dhirendra Nath Thatoi, Deepak Gupta, Alireza kamranfar

Get full text

Low-Loss Optical Nyquist Pulse Train Generation Using Nonauxiliary Wavelength Selective Switch in Communication Band by Koji Takahashi, Yu Yamasaki, Takashi Inoue, Tsuyoshi Konishi

Get full text

Reconstruction of Temperature Distribution by Acoustic Tomography Based on Principal Component Analysis and Deep Neural Network by ZHANG Lifeng, LI Jing, WANG Zhi

“…In order to obtain the online monitoring information of boiler furnace temperature field in thermal power plant quickly and accurately, a temperature field reconstruction algorithm of acoustic tomography (AT) based on deep neural network (DNN) was proposed. …”
Get full text

Transmission and Generation Expansion Planning Considering Virtual Power Lines/Plants, Distributed Energy Injection and Demand Response Flexibility from TSO-DSO Interface by Flávio Arthur Leal Ferreira, Clodomiro Unsihuay-Vila, Rafael A. Núñez-Rodríguez

“…This article presents a computational model for transmission and generation expansion planning considering the impact of virtual power lines, which consists of the investment in energy storage in the transmission system as well as being able to determine the reduction and postponement of investments in transmission lines. …”
Get full text

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism by Wei Liu, Tao Wang, Tianlei Zang, Zhu Huang, Jun Wang, Tao Huang, Xiaoguang Wei, Chuan Li

“…We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. …”
Get full text

Optimizing Virtual Power Plant Operations in Energy and Frequency Regulation Reserve Markets: A Risk-Averse Two-Stage Scenario-Oriented Stochastic Approach by Asad Mujeeb, Zechun Hu, Jianxiao Wang, Rui Diao, Likai Liu, Zhiyuan Bao

“…Then, a novel fast forward selection and simultaneous reduction (FFS&SR) algorithm is proposed, which efficiently generates and refines scenarios, ensuring computational feasibility without compromising accuracy. …”
Get full text

A Reduced-Order Algorithm for a Digital Twin Model of Ultra-High-Voltage Valve-Side Bushing Considering Spatio-Temporal Non-Uniformity by Yongsheng He, Yongfu Li, Xiangcheng Li, Yanan Yuan, Fan Yang, Zongxiang Lu

Get full text

Comprehensive Exploration of Limitations of Simplified Machine Learning Algorithm for Fault Diagnosis Under Fault and Ground Resistances of Multiterminal High-Voltage Direct Curren... by Raheel Muzzammel

“…It is found from the simulation analysis that the preprocessing based on mean and differences in featured data extracted for fault current is required to reduce the impacts of the accuracy of machine learning algorithms. The preprocessing not only retains the accuracy of the machine learning algorithm in different cases of faults, but also minimizes the reduction in accuracy in some fault cases. …”
Get full text

Optimal Protection Coordination for Grid-Connected and Islanded Microgrids Assisted by the Crow Search Algorithm: Application of Dual-Setting Overcurrent Relays and Fault Current L... by Hossien Shad, Hamid Amini Khanavandi, Saeed Abrisham Foroushan Asl, Ali Aranizadeh, Behrooz Vahidi, Mirpouya Mirmozaffari

“…To account for the inherent uncertainties in load and DG power generation, scenario-based stochastic programming (SBSP) is used to model these variations effectively. …”
Get full text

Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…A case study simulating a 30-day mission near Shackleton Crater evaluates the effectiveness of the AI–driven WPT system, demonstrating a 54.6% reduction in energy downtime and a 41.3% improvement in beam alignment efficiency compared to static power scheduling methods. …”
Get full text

A dual-layer planning method based on improved MOPSO for distribution networks considering source–load temporal uncertainty by Guilian Wu, Guilian Wu, Jia Lin, Jinlin Liao

Get full text

Optical OTFS waveform PAPR analysis for high order modulation employing CNN, DNN, and AE machine learning algorithms under a variety of channel scenarios by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…This paper proposes an algorithm for machine-learning (ML)-based PAPR reduction dedicated to optical OTFS under varying channel conditions, such as turbulence and multipath fading. …”
Get full text