This paper presents a robust output tracking control design method for a linear brushless DC motor with modeling uncertainties. Two frequency-domain specifications directly related to the mixed sensitivity function and control energy consumption are imposed to ensure stability and performance robustness. With regard to time-domain specifications, the rise time, maximum overshoot and steady-state error of the step response are considered. A generalized two-parameters proportional, integral, and derivative (PID) control framework is developed via a genetic searching approach ensuring the specifications imposed. The proposed design method is intuitive and practical that offers an effective way to implement simple but robust solutions covering a wide range of plant perturbation and, in addition, provides excellent tracking performance without resorting to excessive control. Extensive experimental and numerical results for a linear brushless motor confirm the proposed control design approach.

This paper presents a method of block-layout design between successive stations for mass rapid transit systems (MRTSs). The aim is to save energy under the framework of the fixed-block signaling (FBS) system and the equi-block principle. Unlike past research regarding the energy savings of train operation, this paper proposes a combinatorial optimization model to reduce the computation time. In the presented approach, the problem of minimizing the energy consumption between successive stations is first formulated as a combinatorial optimization problem. Then, the train-speed trajectory for saving energy is optimized by a MAX-MIN ant system (MMAS) of ant colony optimization (ACO) algorithms. Finally, the block layout is designed in accordance with the shortest block length under the equi-block principle. It is shown that the method presents a significant improvement for the reduction of computational burden on the block-layout design. The feasibility and benefits are verified via simulation study. Analyses and discussions are also given.

The major drawback of the battery charging in traditional electric vehicles is the use of plug-in charging devices. The aim of this paper is to propose a wireless battery charging method, in addition to power transfer, data related to battery status, vehicle ID code, or emergency messages can be simultaneously transferred between the grid and vehicle. This work applies inductive power transfer to complete the charging system. The proposed control system can monitor the operating status on the secondary (vehicle) side in real time and adjust charging current depending on the battery status. Furthermore, the proposed mechanism is able to make an immediate stop, if there is any contingency, such as overcharging voltage or current. This will be beneficial to efficient and safety concern during the charging process.

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO₂) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.