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Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary. [ABSTRACT FROM AUTHOR]

In the modern era, patient health is no longer the only concern for hospitals. Hospitals have to bear the responsibility of the patient safety and the patient comfort. However, due to the overcrowded and understaffed clinical areas with complex workflow patterns, there are different forms of medical care errors arisen in the healthcare system. According to recent statistic from World Health Organization, ten of milions of patient around the world are injured or dead every yearas a result of incorrect medical care. Rroughly 10% patients admitted to hospital in developed countries suffered some forms of medical care errors or medical adverse events. It is about 1.4 million of world population. In order to enhance the patient safety and patient comfort in the current medical service system, an RFID-based Patient Tracking and Mobile Alert System integrated with information communications technology was designed and developed. Apart from identifying different patient's whereabouts, instant alerts are generated through mobile devices so as to enhance patients' safety and comfort. With a view of investigating the feasibility of RFID and mobile technology in the healthcare environment, a pilot study was conducted in Mindset Club in the Castle Peak Hospital in Hong Kong. [ABSTRACT FROM AUTHOR]