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    Electric Vehicle and Soft Open Points Co-Planning for Active Distribution Grid Flexibility Enhancement by Jie Fang, Wenwu Li, Dunchu Chen

    Published 2025-02-01
    “…This paper proposes co-planning of electric vehicles (EVs) and soft opening points (SOPs) to improve the flexibility of the active distribution network, thereby improving the economics and flexibility of the distribution network. …”
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  3. 183

    Joining Application of Unmanned Aerial Vehicle Imagery with GIS for Monitoring of Soft Cliff Linear Habitats by Egidijus Jurkus, Julius Taminskas, Ramūnas Povilanskas, Arvydas Urbis, Jovita Mėžinė, Domantas Urbis

    Published 2025-01-01
    “…The study’s main objective was to assess short-term behavior of soft cliffs as littoral linear habitats during calm season storm events in the example of the Olandų Kepurė cliff, located on a peri-urban protected seashore (Baltic Sea, Lithuania). …”
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  4. 184
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    Improved Motion Correction in Dynamic Contrast-Enhanced MRI Using Low Rank With Soft Weighting by Jichang Zhang, Faisal Najeeb, Yulin Wang, Xinpei Wang, Pengfei Xu, Hammad Omer, Jianjun Zheng, Jingfeng Zhang, Sue Francis, Paul Glover, Richard Bowtell, Thomas Meersmann, Chengbo Wang

    Published 2025-01-01
    “…This paper introduces a motion-corrected, free-breathing dynamic contrast-enhanced (DCE) MRI reconstruction method, termed low-rank plus sparse (L+S) with soft weighting. We designed a soft weighting matrix that smoothly transitions spokes between the target and other motion states to suppress motion blurring. …”
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  6. 186

    Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis by Xiaotian Bai, Xiao Hou, Dazhi Lv, Dazhi Lv, Jialin Wei, Jialin Wei, Yiling Song, Zhengyan Tang, Hongfeng Huo, Hongfeng Huo, Jingmin Liu

    Published 2025-01-01
    “…A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. …”
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    Soft Sensor Modeling of Key Effluent Parameters in Wastewater Treatment Process Based on SAE-NN by Yousuf Babiker M. Osman, Wei Li

    Published 2020-01-01
    “…In this research work, we propose a soft sensor model based on deep learning which combines stacked autoencoders with neural network (SAE-NN). …”
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  10. 190

    Complex q-Rung Orthopair Fuzzy N-Soft Sets: A New Model with Applications by Muhammad Akram, Faiza Wasim, Ahmad N. Al-Kenani

    Published 2021-01-01
    “…To that purpose, the proposed theory integrates complex q-rung orthopair fuzzy sets with the parametric structure of N-soft sets. The framework that arises captures information beyond the confined space of complex intuitionistic fuzzy N-soft sets and complex Pythagorean fuzzy N-soft sets, with the assistance of a parameter q. …”
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    RLS Beamforming Algorithm Based on Wavelet Transform by Jihong Zhao, Pei Lei, Weihua Wang, Hua Qu, Dan He

    Published 2015-02-01
    “…This algorithm uses soft thresholding wavelet transform for signal denoising, solves the problem that the traditional Adaptive beamforming algorithm is sensitive to noise and the steady-state error is big under the environment of the low signal to noise ratio(SNR)and small forgetting factor. …”
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    Trajectory Tracking Control for Robotic Manipulator Based on Soft Actor–Critic and Generative Adversarial Imitation Learning by Jintao Hu, Fujie Wang, Xing Li, Yi Qin, Fang Guo, Ming Jiang

    Published 2024-12-01
    “…Finally, it is demonstrated by the simulation results that the end effector of the robot tracking task is effectively accomplished by the proposed SL-GAIL algorithm, and more superior stability is exhibited in a test environment with interference compared with other algorithms.…”
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  20. 200

    Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning by Bo Wang, Jun Wei, Le Zhang, Hui Jiang, Cheng Jin, Shaowen Huang

    Published 2024-12-01
    “…Abstract Background Aiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model. Methods This paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. …”
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