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  1. 81
  2. 82

    An Improved Diagnosis Approach for Short-Circuit Fault Diagnosis in MPC-Based Current Source Inverter System by Jonggrist Jongudomkarn, Pirat Khunkitti, Apirat Siritaratiwat

    Published 2025-01-01
    “…It then leverages the cost function results from the MPC algorithm as the key diagnostic parameter derived through a comparative analysis of predicted system behavior under normal and fault conditions. …”
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    Article
  3. 83

    DRBD-YOLOv8: A Lightweight and Efficient Anti-UAV Detection Model by Panpan Jiang, Xiaohua Yang, Yaping Wan, Tiejun Zeng, Mingxing Nie, Zhenghai Liu

    Published 2024-11-01
    “…Achieving real-time detection with high accuracy, while accommodating the limited resources of edge-computing devices poses a significant challenge for anti-UAV detection. …”
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  4. 84

    Attraction and aversion of noctuid moths to fermented food sources coordinated by olfactory receptors from distinct gene families by Xiao-Qing Hou, Dan-Dan Zhang, Hanbo Zhao, Yang Liu, Christer Löfstedt, Guirong Wang

    Published 2025-01-01
    “…Conclusions Our findings suggest that this functional conservation is an olfactory adaptation that has allowed noctuid moths to extend their diet to fermented food sources.…”
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  5. 85
  6. 86

    Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection by Qifan Yang, Yu-Ang Chen, Yifan Shi

    Published 2025-07-01
    “…Multi-view OCC approaches usually require collecting multi-view traffic data from all sources and have difficulty detecting intrusion independently in each view. …”
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    Article
  7. 87

    Dual‐Function Ceramic Pigments for Energy‐Efficient and Secure Autonomous Vehicles by Ik Hoon Jeong, Yong‐Jun Seo, Jum Soo Hwang, Geun Hyeong Kim, Yeong Jae Kim, Gil Ju Lee

    Published 2025-07-01
    “…Consequently, a bi‐function ceramic pigment (BFCP) is fabricated with both superior RC performance and heightened LiDAR detectability compared to commercial pigments. …”
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  8. 88
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    YOLOGX: an improved forest fire detection algorithm based on YOLOv8 by Caixiong Li, Yue Du, Xing Zhang, Peng Wu

    Published 2025-01-01
    “…To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX. …”
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  10. 90
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    An Attomolar-Level Optical Device for Monitoring Receptor–Analyte Interactions Without Functionalization Steps: A Case Study of Cytokine Detection by Nunzio Cennamo, Francesco Arcadio, Chiara Marzano, Rosalba Pitruzzella, Mimimorena Seggio, Maria Pesavento, Stefano Toldo, Antonio Abbate, Luigi Zeni

    Published 2025-02-01
    “…The POF-based device was proven to be effective for detecting several interleukins at the attomolar level in a few minutes and without functionalization processes.…”
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    Article
  12. 92

    LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

    Published 2025-01-01
    “…Underwater object detection plays a significant role in fisheries resource assessment and ecological environment protection. …”
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  13. 93

    KronNet a lightweight Kronecker enhanced feed forward neural network for efficient IoT intrusion detection by Saeed Ullah, Junsheng Wu, Mian Muhammad Kamal, Abdul Khader Jilani Saudagar

    Published 2025-07-01
    “…Abstract The rapid expansion of Internet of Things (IoT) networks necessitates efficient intrusion detection systems (IDS) capable of operating within the stringent resource constraints of IoT devices. …”
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  14. 94

    An Optimized FPGA-Based FDIR System for Sensor Fault Detection in Satellite Attitude Estimation by Xianliang Chen, Zhicheng Xie, Jiashu Wu, Xiaofeng Wu

    Published 2025-01-01
    “…To solve this problem, a Fault Detection, Isolation, and Recovery (FDIR) was proposed, which integrates an adaptive unscented Kalman filter (AUKF), a radial basis function (RBF) neural network for fault detection, and a QUEST-based estimator. …”
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  15. 95

    MTAHG and MTBHG: Modified Approaches for Interpreting Gravity Data by Hazel Deniz Toktay, Hanbing Ai, Ahmad Alvandi, Kejia Su, Jinlei Li

    Published 2025-04-01
    “…This paper proposes two effective edge detection tools: one combining the balanced total horizontal gradient (BHG), and the hyperbolic tangent function, abbreviated as “MTBHG”; and the other combining the tilt angle of the total horizontal gradient (TAHG) and the hyperbolic tangent function, abbreviated as “MTAHG.” …”
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  16. 96

    Western spotted skunk spatial ecology in the temperate rainforests of the Pacific Northwest by Marie I. Tosa, Damon B. Lesmeister, Taal Levi

    Published 2024-08-01
    “…Using these home ranges, we fitted a resource selection function using environmental covariates that we assigned to various hypotheses such as resources, predator avoidance, thermal tolerance, and disturbance. …”
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    Research on multi-sensor fusion detection and tracking technology for autonomous-rail rapid transit tram by PAN Wenbo, YUAN Xiwen, LIN Jun, XIE Guotao, LONG Teng, HUANG Wenyu

    Published 2022-07-01
    “…Firstly, a millimeter-wave radar and LiDAR are used to perceive by single sensor, and the sensing results were merged by using a multi-source fusion algorithm, to generate more accurate detection results. …”
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  19. 99

    YOLOv11-GSF: an optimized deep learning model for strawberry ripeness detection in agriculture by Haoran Ma, Qian Zhao, Runqing Zhang, Chunxu Hao, Wenhui Dong, Xiaoying Zhang, Fuzhong Li, Xiaoqin Xue, Gongqing Sun

    Published 2025-08-01
    “…To overcome these limitations, this paper introduces YOLOv11-GSF, a real-time strawberry ripeness detection algorithm based on YOLOv11, which incorporates several innovative features: a Ghost Convolution (GhostConv) convolution method for generating rich feature maps through lightweight linear transformations, thereby reducing computational overhead and enhancing resource utilization; a C3K2-SG module that combines self-moving point convolution (SMPConv) and convolutional gated linear units (CGLU) to better capture the local features of strawberry ripeness; and a F-PIoUv2 loss function inspired by Focaler IoU and PIoUv2, utilizing adaptive penalty factors and interval mapping to expedite model convergence and optimize ripeness classification. …”
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