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  • MDPI AG  (3)
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  • MDPI AG  (3)
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  • 1
    In: Chemosensors, MDPI AG, Vol. 10, No. 6 ( 2022-06-20), p. 233-
    Abstract: Ferroptosis is an iron−dependent form of regulated cell death. It has attracted more and more research interests since it was found because of its potential physiological and pathological roles. In recent years, many efforts have been made for the developments and applications of selective fluorescence probes for real−time and in situ tracking of bioactive species during ferroptosis process, which is necessary and significant to further study the modulation mechanisms and pathological functions of ferroptosis. In this review, we will focus on summarizing the newly developed fluorescence probes that have been applied for ferroptosis imaging in the recent years, and comprehensively discussing their design strategies, including the probes for iron, reactive oxygen species, biothiols and intracellular microenvironmental factors.
    Type of Medium: Online Resource
    ISSN: 2227-9040
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704218-2
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  Applied Sciences Vol. 11, No. 13 ( 2021-06-22), p. 5783-
    In: Applied Sciences, MDPI AG, Vol. 11, No. 13 ( 2021-06-22), p. 5783-
    Abstract: Aiming at addressing the problem that the joints are easily destroyed by the impact torque during the process of space robot on-orbit capturing a non-cooperative spacecraft, a reinforcement learning control algorithm combined with a compliant mechanism is proposed to achieve buffer compliance control. The compliant mechanism can not only absorb the impact energy through the deformation of its internal spring, but also limit the impact torque to a safe range by combining with the compliance control strategy. First of all, the dynamic models of the space robot and the target spacecraft before capture are obtained by using the Lagrange approach and Newton-Euler method. After that, based on the law of conservation of momentum, the constraints of kinematics and velocity, the integrated dynamic model of the post-capture hybrid system is derived. Considering the unstable hybrid system, a buffer compliance control based on reinforcement learning is proposed for the stable control. The associative search network is employed to approximate unknown nonlinear functions, an adaptive critic network is utilized to construct reinforcement signal to tune the associative search network. The numerical simulation shows that the proposed control scheme can reduce the impact torque acting on joints by 76.6% at the maximum and 58.7% at the minimum in the capturing operation phase. And in the stable control phase, the impact torque acting on the joints were limited within the safety threshold, which can avoid overload and damage of the joint actuators.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2704225-X
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  • 3
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 13 ( 2023-06-30), p. 2936-
    In: Mathematics, MDPI AG, Vol. 11, No. 13 ( 2023-06-30), p. 2936-
    Abstract: Visual-based object detection systems are essential components of intelligent equipment for water surface environments. The diversity of water surface target types, uneven distribution of sizes, and difficulties in dataset construction pose significant challenges for water surface object detection. This article proposes an improved YOLOv5 target detection method to address the characteristics of diverse types, large quantities, and multiple scales of actual water surface targets. The improved YOLOv5 model optimizes the extraction of bounding boxes using K-means++ to obtain a broader distribution of predefined bounding boxes, thereby enhancing the detection accuracy for multi-scale targets. We introduce the GAMAttention mechanism into the backbone network of the model to alleviate the significant performance difference between large and small targets caused by their multi-scale nature. The spatial pyramid pooling module in the backbone network is replaced to enhance the perception ability of the model in segmenting targets of different scales. Finally, the Focal loss classification loss function is incorporated to address the issues of overfitting and poor accuracy caused by imbalanced class distribution in the training data. We conduct comparative tests on a self-constructed dataset comprising ten categories of water surface targets using four algorithms: Faster R-CNN, YOLOv4, YOLOv5, and the proposed improved YOLOv5. The experimental results demonstrate that the improved model achieves the best detection accuracy, with an 8% improvement in mAP@0.5 compared to the original YOLOv5 in multi-scale water surface object detection.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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