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  • 1.
    Paul, Satyam
    et al.
    Gas Turbine and Transmission Research Centre, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK.
    Turnbull, Rob
    Gas Turbine and Transmission Research Centre, Faculty of Engineering, University of Nottingham, Nottingham, NG7 2RD, United Kingdom.
    Khodadad, Davood
    Department of Applied Physics and Electronics, Umeå Universitet, Umeå, 90187, Sweden.
    Löfstrand, Magnus
    School of Science and Technology, Orebro University, Orebro, 70182, Sweden.
    A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic2022In: Algorithms, E-ISSN 1999-4893, Vol. 15, no 8, p. 284-284Article in journal (Refereed)
    Abstract [en]

    The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stabilty analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.

  • 2.
    Zhang, L.
    et al.
    Department of Computer Engineering, Taiyuan Institute of Technology, Taiyuan, 030008, China.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Pan, X.
    School of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, China.
    Yue, X.
    Artificial Intelligence Institute of Shanghai University, Shanghai University, Shanghai, 200444, China .
    Wu, P.
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
    Guo, C.
    Department of Computer Engineering, Taiyuan Institute of Technology, Taiyuan, 030008, China.
    Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery2023In: Algorithms, E-ISSN 1999-4893, Vol. 16, no 11, article id 520Article in journal (Refereed)
    Abstract [en]

    In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection. 

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