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CRASHWORTHINESS DESIGN AND OPTIMIZATION FOR COLLISION POST OF TRAIN
Published 2022-01-01“…In order to improve the crashworthiness of the collision post structure, the optimal cross-section configuration of the collision post was obtained based on the topology optimization method, and the impacting finite element model of the collision post was established in the explicit dynamics software Ls-Dyna. …”
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Predicting excavation-induced lateral displacement using improved particle swarm optimization and extreme learning machine with sparse measurements
Published 2025-08-01“…This study presents a novel prediction method using an extreme learning machine (ELM) optimized by an improved particle swarm optimization (IPSO) algorithm. …”
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83
Comprehensive recognition algorithm of RS code based on fast code root trial
Published 2022-11-01“…In order to solve the problem of high computation and high missed alarm probability of RS (Reed-Solomon) codes for recognition, comprehensive recognition algorithm of RS codes based on fast code root trial was proposed.Firstly, the check relationship was solved in binary equivalently and fast code root trial was used to check parameters in sequence.Secondly, according to distribution characteristics of the combined code roots, m-level primitive polynomial field and error correction ability was associatively determined.Finally, the short codes and long codes were given different confidence weights and the determined parameters were comprehensively analyzed.The optimal parameter was selected and the generate polynomial was calculated.The proposed algorithm did not need prior information such as signal-to-noise ratio (SNR), and had good adaptability.The simulation results show that the proposed algorithm can effectively reduce the missed alarm probability under the condition of low complexity.Compared with the conventional hard decision algorithm, the performance of the proposed algorithm is improved, and the parameter recognition of RS codes can be completed quickly.…”
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A Robust Salp Swarm Algorithm for Photovoltaic Maximum Power Point Tracking Under Partial Shading Conditions
Published 2024-12-01“…Finally, the integration with P&O facilitates a meticulous search with a small step size, ensuring swift convergence and further mitigating post-convergence power oscillations. Both the simulations and the experimental results indicate that the proposed algorithm outperforms particle swarm optimization (PSO) and grey wolf optimization (GWO) in terms of convergence velocity, tracking precision, and the reduction in iteration power oscillation magnitude.…”
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Prediction of Breakdown Voltage of Long Air Gaps Under Switching Impulse Voltage Based on the ISSA-XGBoost Model
Published 2025-04-01“…To address this issue, this paper proposes a novel prediction model based on the Improved Sparrow Search Algorithm-optimized XGBoost (ISSA-XGBoost). …”
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Improving energy efficiency for intelligent reflecting surface assisted PD-NOMA in EH relaying network
Published 2025-02-01Get full text
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An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration
Published 2025-04-01“…Next, the collected received signal strength indication (RSSI) data are processed using Kalman filtering and Min-Max normalization, suppressing signal fluctuations and accelerating the gradient descent convergence of the distance measurement model. Finally, the improved black kite algorithm (IBKA) is enhanced with tent chaotic mapping, a lens imaging reverse learning strategy, and the golden sine strategy to optimize the weights and biases of the BP neural network, developing an RSSI-based ranging algorithm using the IBKA-BP neural network. …”
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90
Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM)
Published 2022-01-01“…At the same time, the improved artificial bee colony algorithm is used to optimize the kernel width σ and regularization parameter λ of WLSSVM, which improves the prediction accuracy and convergence rate of WLSSVM. …”
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Achieving comprehensive water productivity improvement: A multi-objective simulation-optimization model for water productivity-oriented irrigation water management
Published 2025-03-01“…The Non-dominated Sorting Genetic Algorithm-III (NSGA-III) and Elite Opposition-Based Learning (EOBL) strategy are used to solve the problem and enhance the diversity of the randomly generated initial population accordingly, thus optimal solutions can be generated for supporting high-efficiency irrigation water use. …”
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95
(IoT) Network intrusion detection system using optimization algorithms
Published 2025-07-01“…Abstract To address the complex requirements of network intrusion detection in IoT environments, this study proposes a hybrid intelligent framework that integrates the Whale Optimization Algorithm (WOA) and the Grey Wolf Optimization (GWO) algorithm—referred to as WOA-GWO. …”
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Robust reinforcement learning algorithm based on pigeon-inspired optimization
Published 2022-10-01Get full text
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97
ALGORITHM FOR OPTIMIZING ORGANIC CARBON AND NITROGEN FLOWS ON RECLAIMED LANDS
Published 2024-11-01Get full text
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98
Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms
Published 2024-11-01“…Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. …”
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Automated Calibration of SWMM for Improved Stormwater Model Development and Application
Published 2025-05-01“…The tool also supports parallelized optimization algorithms and utilizes Application Programming Interfaces (APIs) to dynamically update SWMM model parameters, accelerating both model execution and convergence. …”
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RRMSE-enhanced weighted voting regressor for improved ensemble regression.
Published 2025-01-01“…This uniform weighting approach doesn't consider that some models may perform better than others on different datasets, leaving room for improvement in optimizing ensemble performance. To overcome this limitation, we propose the RRMSE (Relative Root Mean Square Error) Voting Regressor, a new ensemble regression technique that assigns weights to each base model based on their relative error rates. …”
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