SP-Pillars: An Efficient LiDAR 3D Objects Detection Framework With Multi-Scale Feature Perception and Optimization by Tingshuai Chen, Ye Yuan, Bingyang Yin, Yuanhong Liao

“…The experimental evaluation results on the KITTI dataset indicate that the proposed algorithm is reliable and effective. Compared with other related algorithms, this algorithm exhibits excellent detection performance, slightly improving detection speed while maintaining high accuracy, meeting the requirements of real-time processing, and has important application value in optimizing autonomous driving technology.…”
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Smart home power management algorithm using real-time model predictive control for a stand-alone PV system with battery energy storage by Aziz Watil

“…The overall battery efficiency reached 96.45%, demonstrating the algorithm’s ability to optimize power flow, ensure a reliable energy supply, and maintain battery safety under varying weather conditions.…”
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Image optimization method for ablation defects in high-voltage cable buffer layers based on X-ray detection technique by Baiyuan Liu, Weifeng Wang, Tengfei Liu, Zhen Xu, Xiangchen Kong, Xu Zhou, Yanan Cheng

“…The research optimizes the Speeded-Up Robust Features(SURF) detection algorithm by adjusting the Hessian matrix threshold to enhance sensitivity to low-contrast defects. …”
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An artificial intelligence and machine learning-driven CFD simulation for optimizing thermal performance of blood-integrated ternary nano-fluid by Mohib Hussain, Du Lin, Hassan Waqas, Qasem M. Al-Mdallal

“…However, conventional methods for modelling and optimizing these frameworks frequently encounter challenges owing to their intricacy and the multitude of interconnected variables. …”
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Toward a linear-ramp QAOA protocol: evidence of a scaling advantage in solving some combinatorial optimization problems by J. A. Montañez-Barrera, Kristel Michielsen

“…Abstract The quantum approximate optimization algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $${\{{\gamma }_{i},{\beta }_{i}\}}_{i = 0}^{p-1}$$ { γ i , β i } i = 0 p − 1 . …”
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Network Optimization of Fresh Products Cold Chain Considering Supply Disruption and Demand Fluctuation Under the Dual-Carbon Policy by Haojie Ran, Dichen He, Huajun Tang

“…This study focuses on introducing the key optimization goals of the cold chain network for fresh products: maximizing the service level while minimizing operating costs and carbon emissions. …”
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Adaptive polarimetric optimization for ground deformation monitoring using multi-temporal InSAR with dual-polarization sentinel-1 by Leixin Zhang, Feng Zhao, Yunjia Wang, Jordi J. Mallorqui, Teng Wang, Yuxuan Zhang, Zhongbo Hu, Sen Du, José Fernández

“…Additionally, it demonstrates enhanced ground deformation monitoring accuracy, as evidenced by a lower average RMSE compared to the VV and minimum mean square error (MMSE) methods when comparing against GPS data. While achieving a nearly equivalent number of monitoring pixels as the optimal exhaustive search polarimetric optimization (ESPO) algorithm, ADCMD-PolMTI operates 235 and 13 times faster for PSs and DSs, respectively. …”
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Lightweight and rapid adaptive super-resolution interpolation reconstruction algorithm for super-pixel segmentation of super-wide field-of-view long-wave infrared images by Dongdong Shi, Xia Cai, Limin Liu, Fuyu Huang, Kangli Xu, Yuanbo Wang, Yudan Chen

“…Evaluation of the reconstructed images following a 2× reconstruction of dataset 1 reveals that the energy of gradient function of the proposed algorithm exhibits a 21.93% improvement compared to the sub-optimal algorithm. …”
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Optimizing downlink of CR-NOMA resource allocation: A fairness index approach to balancing system performance and energy efficiency by Nahla Nur Elmadina, Rashid A. Saeed, Elmustafa Sayed Ali, Elsadig Saeid, Rania A. Mokhtar

“…To address these limitations, we propose the Fairness Index Average Rate (FIAR) algorithm, an iterative power allocation technique that maximizes network sum rate while meeting minimum user Signal-to-Interference-plus-Noise Ratio (SINR) constraints. …”
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Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron by Yacine Karmi, Haithem Boumediri, Omar Reffas, Yazid Chetbani, Sabbah Ataya, Rashid Khan, Mohamed Athmane Yallese, Aissa Laouissi

“…Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). …”
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A multi-objective master–slave methodology for optimally integrating and operating photovoltaic generators in urban and rural electrical networks by Jhony Andrés Guzmán-Henao, Rubén Iván Bolaños, Brandon Cortés-Caicedo, Luis Fernando Grisales-Noreña, Oscar Danilo Montoya, Jesús C. Hernández

“…Its master stage uses one out of three different algorithms—Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), or the Multi-Objective Ant Lion Optimizer (MOALO)—while the slave stage is always performed by a load flow analyzer. …”
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Crest factor minimization of multisine signals based on the Chebyshev norm approximation method: With application to wafer stage FRF identification by Chen Zhang, Fazhi Song, Yang Liu

“…The algorithm utilizes Lp-based norms (a type of p-norm) of multisine signals as the optimization objective with increasing values of p. …”
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Outage reduction in 5G uplink cooperative NOMA using PSO-optimized relay selection and successive interference cancelation by David S. Bhatti, M. Asif Haroon, Haeung Choi, Seungmin Kim, Heung-No Lee

“…The inclusion of PSO-based relay selection demonstrates a substantial improvement in network performance. We compared the performance of particle swarm optimization (PSO) with that of the genetic algorithm (GA) and deep reinforcement learning (DRL). …”
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Lightweight and Efficient CSI-Based Human Activity Recognition via Bayesian Optimization-Guided Architecture Search and Structured Pruning by Sungkwan Youm, Sunghyun Go

“…NAS identifies optimal network configurations, while pruning systematically removes redundant parameters, preserving accuracy. …”
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Risk-managed economic dispatch in hybrid hydrothermal-wind-solar systems: a novel multi-objective optimization approach by Zhe Wang, Tao Sun, Na Liu

“…A Multi-Objective Artificial Rabbits Optimization (MOARO) algorithm, incorporating Pareto criteria and fuzzy theory, is applied to optimize dispatch decisions while balancing cost and risk. …”
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Distribution Generation Network Arrangement by Capacitor Placement and Sizing in Renewable Energy Sources with Uncertainties Based on Self-adaption Kho-Kho Optimizer by Lu Xingxing

“…Using a self-adaptive Kho-Kho optimizer, the research aims to minimize operational costs while improving technical parameters, such as voltage stability and loss reduction. …”
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Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems by Dinesh Kumar Nishad, Vandna Rani Verma, Pushkar Rajput, Sandeep Gupta, Anurag Dwivedi, Dharti Raj Shah

“…Abstract Mobile Edge Computing (MEC) systems face critical challenges in optimizing computation offloading decisions while maintaining quality of experience (QoE) and energy efficiency, particularly in dynamic multi-user environments. …”
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Research on fuzzy proportional–integral–derivative (PID) control of bolt tightening torque based on particle swarm optimization (PSO) by X. Cheng, Z. Guo, J. Ke

“…To enhance the precision of bolt tightening, this paper introduces a particle swarm optimization (PSO) algorithm to optimize a fuzzy proportional–integral–derivative (FPID) controller. …”
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Dung beetle optimizer based on mean fitness distance balance and multi-strategy fusion for solving practical engineering problems by Wanru Tang, Haoze Qin, Shuang Kang

“…Abstract As a swarm intelligence algorithm, Dung beetle optimizer (DBO) was inspired by the behavior pattern of dung beetles for survival. …”
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Power Adaptation for Optimizing Secrecy Energy Efficiency in NOMA-Enabled Underlay Cognitive Radio Networks and DNN-Based Evaluation by P. P. Hema, A. V. Babu

“…Firstly, analytical models are formulated to evaluate the SEE and secrecy sum rate (SSR) of the secondary network in NOMA-UCRN, considering residual hardware impairments, imperfect successive interference cancellation conditions, and interference power constraints at the primary receiver. Subsequently, joint optimal transmit power allocation (JOTPA) is ascertained for the secondary users (SUs) at the secondary transmitter and the secondary relay, with the aim of maximizing the SEE while satisfying constraints on tolerable interference power at the primary receiver and minimum data rates for the SUs. …”
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