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  1. 1941

    Investigation on the Role of Artificial Intelligence in Measurement System by P. A. Rezvy, Venkata Lakshmi Narayana Komanapalli

    Published 2025-01-01
    “…Hardware approach with soft computation has reduced non linearity error by 84.63% for thermocouple linearization, meanwhile novel hybrid approach using genetic algorithm (GA) and particle swarm optimization (PSO) combined with back propagation neural network (BPNN) have reduced mean absolute percentage error to 1.2 % for industrial weir than conventional hardware approaches using sensors and signal conditioning circuits but at higher computational cost. …”
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    Article
  2. 1942

    Multimodal Control by Variable-Structure Neural Network Modeling for Coagulant Dosing in Water Purification Process by Jun Zhang, Da-Yong Luo

    Published 2020-01-01
    “…In this paper, combined with rule base, through the PCA method, an improved multimodal variable-structure random-vector neural network algorithm (MM-P-VSRVNN) is proposed for coagulant dosing, which is a key production process in water purification process. …”
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    Article
  3. 1943

    Orchard Navigation Method Based on RS-SC Loop Frame Search Method and SLAM Technology by Ning Xu, Qingshan Meng, Fengping Liu, Zhihe Li, Guangming Wang, Na Guo, Wenxuan Wu

    Published 2025-01-01
    “…In the loop frame matching, an optimization algorithm combining normal distribution transformation and iterative nearest point is used to reduce the cumulative error significantly. …”
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    Article
  4. 1944

    A METHOD FOR SOLVING THE CANONICAL PROBLEM OF TRANSPORT LOGISTICS IN CONDITIONS OF UNCERTAINTY by Lev Raskin, Yurii Parfeniuk, Larysa Sukhomlyn, Mykhailo Kravtsov, Leonid Surkov

    Published 2021-07-01
    “…Development of an accurate algorithm for solving this problem according to the probabilistic criterion in the assumption of the random nature of transportation costs has been done. …”
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    Article
  5. 1945

    Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model by WANG Ding1, PAN Miao2, WU Ying1

    Published 2011-01-01
    “…Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method(MLM) for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound(CRB) for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.…”
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    Article
  6. 1946

    Scheduling and Evaluation of a Power-Concentrated EMU on a Conventional Intercity Railway Based on the Minimum Connection Time by Yinan Wang, Limin Xu, Xiao Yang, Jingjing Bao, Feng Lin, Yiwei Guo, Yixiang Yue

    Published 2025-02-01
    “…Moreover, they have certain cost advantages and practical operational value for improving the market competitiveness of conventional railways. …”
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    Article
  7. 1947

    RFID-embedded mattress for sleep disorder detection for athletes in sports psychology by Metin Pekgor, Aydolu Algin, Turhan Toros

    Published 2025-04-01
    “…This approach shows significant potential for sports psychology applications, enabling personalized recovery strategies and performance optimization. Future work will focus on expanding the dataset, integrating additional biometric sensors, and refining algorithms to improve diagnostic accuracy and real-time usability in clinical and home settings.…”
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    Article
  8. 1948

    PRIMARY CARE: HOW TO INCREASE PHYSICAL ACTIVITY IN YOUR PATIENTS by A. L. Slobodyanyuk, I. A. Кrylova, V. I. Kupaev

    Published 2019-07-01
    “…The variant of rational outpatient counseling with the help of the algorithm of organization of physical activity mode, providing stratification of patients, planning, optimization and control of personal motor activity was presented. …”
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    Article
  9. 1949

    Research on Behavior Recognition and Online Monitoring System for Liaoning Cashmere Goats Based on Deep Learning by Geng Chen, Zhiyu Yuan, Xinhui Luo, Jinxin Liang, Chunxin Wang

    Published 2024-11-01
    “…YOLOv8n demonstrated superior performance, converging within 50 epochs with an average accuracy of 95.31%, making it a baseline for further improvements. We improved YOLOv8n through dataset expansion, algorithm lightweighting, attention mechanism integration, and loss function optimization. …”
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    Article
  10. 1950

    Minimum Error Integration Method for Quadrilateral Flat Plate Fitting in Steel Construction by Zhuoju Huang, Jiemin Ding

    Published 2025-04-01
    “…Our results demonstrate at most a 75.5% reduction in fitting errors for analytical curved plates with particularly significant improvements in biconvex curvature scenarios. A practical engineering validation reveals that the method increases the proportion of planarizable plates from 27% to 45% under identical tolerance criteria, effectively reducing curved-plate fabrication demands and thus reducing cost and carbon emissions. …”
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    Article
  11. 1951

    A dynamic service migration strategy based on mobility prediction in edge computing by Lanlan Rui, Shuyun Wang, Zhili Wang, Ao Xiong, Huiyong Liu

    Published 2021-02-01
    “…Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. …”
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    Article
  12. 1952

    Maximum likelihood self-calibration for direction-dependent gain-phase errors with carry-on instrumental sensors:case of deterministic signal model by WANG Ding1, PAN Miao2, WU Ying1

    Published 2011-01-01
    “…Aim at the self-calibration of direction-dependent gain-phase errors in case of deterministic signal model,the maximum likelihood method(MLM) for calibrating the direction-dependent gain-phase errors with carry-on instrumental sensors was presented.In order to maximize the high-dimensional nonlinear cost function appearing in the MLM,an improved alternative projection iteration algorithm,which could optimize the azimuths and direction-dependent gain-phase errors was proposed.The closed-form expressions of the Cramér-Rao bound(CRB) for azimuths and gain-phase errors were derived.Simulation experiments show the effectiveness and advantage of the novel method.…”
    Get full text
    Article
  13. 1953

    Simulation analysis of path planning for workpiece clamping robots based on digital twin technology by Xin PAN, Min LIANG, Yanchao YIN, Zhong CHEN

    Published 2025-06-01
    “…These results underscore the ability of the algorithm to efficiently and cost-effectively navigate complex obstacle configurations. …”
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    Article
  14. 1954

    A comprehensive techno-economic analysis for a PHEV-integrated microgrid system involving wind uncertainty and diverse demand side management policies by Bishwajit Dey, Laishram Khumanleima Chanu, Gulshan Sharma, Pitshou N. Bokoro

    Published 2025-06-01
    “…The research investigation employed the Differential Evolution (DE) algorithm as an optimization technique. Numerical results show that the total operating cost (TOC) of the MG system reduced from $25,575 during the base load model to $24,521 when the proposed hybrid DSM was implemented. …”
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    Article
  15. 1955

    MC64-ClustalWP2: a highly-parallel hybrid strategy to align multiple sequences in many-core architectures. by David Díaz, Francisco J Esteban, Pilar Hernández, Juan Antonio Caballero, Antonio Guevara, Gabriel Dorado, Sergio Gálvez

    Published 2014-01-01
    “…The new parallelization approach has focused into the most time-consuming stages of this algorithm. In particular, the so-called progressive alignment has drastically improved the performance, due to a fine-grained approach where the forward and backward loops were unrolled and parallelized. …”
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  16. 1956

    Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

    Published 2025-01-01
    “…We found a strong correlation (r = 0.93) between the sensitivity of ET estimates to machine-learned parameters and model error (root-mean-square error; RMSE), indicating that reduced sensitivity minimizes error propagation and improves performance. …”
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  17. 1957

    Empc-based V2G scheduling strategy for multi-attribute EVs aggregator by Haoyang Tang, Zhilu Liu, Lin Zheng, Jianfeng Zheng, Hao Hu, Jinpei Lu, Zhijian Hu

    Published 2025-10-01
    “…The results show that compared with other strategies, the proposed EMPC algorithm can achieve 4–47.4 % reduction in charging costs, significantly reduce the peak valley difference and variance of load, and improve the load curve.…”
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  18. 1958

    A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning by Yuzhu Yang, Hongda Li, Miao Sun, Xingyu Liu, Liying Cao

    Published 2024-09-01
    “…Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). …”
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    Article
  19. 1959

    Underwater acoustic signal denoising method based on DBO–VMD and singular value decomposition by A. Weiyi Chen, B. Shizhe Wang, C. Zongji Li, D. Li Dong

    Published 2025-05-01
    “…This method utilizes the Dung Beetle Optimization (DBO) algorithm to optimize Variational Mode Decomposition (VMD) and combines it with Singular Value Decomposition (SVD). …”
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    Article
  20. 1960

    Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

    Published 2025-02-01
    “…The dataset was preprocessed using the Standard Scaler to standardize it, ensuring each feature has a mean of zero and a standard deviation of one, followed by outlier detection with Cook’s distance. Hyperparameter optimization made using the Differential Evolution (DE) method improved the performance of models. …”
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    Article