Objective: Normalizing clinical mentions to concepts in standardized medical terminologies, in general, is challenging due to the complexity and variety of the terms in narrative medical records. In this article, we introduce our work on a clinical natural language processing (NLP) system to automatically normalize clinical mentions to concept unique identifier in the Unified Medical Language System. This work was part of the 2019 n2c2 (National NLP Clinical Challenges) Shared-Task and Workshop on Clinical Concept Normalization.Materials and Methods: We developed a hybrid clinical NLP system that combines a generic multilevel matching framework, customizable matching components, and machine learning ranking systems. We explored 2 machine leaning ranking systems based on either ensemble of various similarity features extracted from pretrained encoders or a Siamese attention network, targeting at efficient and fast semantic searching/ranking. Besides, we also evaluated the performance of a general-purpose clinical NLP system based on Unstructured Information Management Architecture.Results: The systems were evaluated as part of the 2019 n2c2 challenge, and our original best system in the challenge obtained an accuracy of 0.8101, ranked fifth in the challenge. The improved system with newly designed machine learning ranking based on Siamese attention network improved the accuracy to 0.8209.Conclusions: We demonstrate the successful practice of combining multilevel matching and machine learning ranking for clinical concept normalization. Our results indicate the capability and interpretability of our proposed approach, as well as the limitation, suggesting the opportunities of achieving better performance by combining general clinical NLP systems.

Sport and game industry has grown rapidly in recent years due to the application of novel sensors and algorithms for quantitative analysis. For example, flying speed and spin estimation is essential to help players to improve their skills in table tennis. However, the spin estimation for a table tennis ball is challenging, as it is difficult to observe using cameras and model the aerodynamics of ball flight with spin. This article proposes a generalized aerodynamic model with variable aerodynamic coefficients to accurately represent the flying state of a table tennis ball. Analytical solutions for the aerodynamic coefficients and the acceleration due to the Magnus force are also developed for accurate ball spin estimation using pre- and postrebounding flight trajectories. The experimental results showed that compared to current state-of-the-art methods, the proposed method has achieved the best performance in angular velocity magnitude estimation for topspinning and backspinning balls. It also achieved an error of below 10° in angular velocity amplitude estimation. Using the proposed spin estimation method, our table tennis robot could strike balls with either topspin or backspin with a high success rate of up to 84.6 $%$ . Besides, the experimental results also demonstrated the potential of the proposed method in the area of table tennis training and sports-broadcasting.

Scanning probe microscopy (SPM) based nanomanipulators always experience probe accuracy issues caused by uncertainties such as scanner hysteresis, drifts, and interaction forces. Although some SPMs contain internal position sensors, they can hardly sense real probe position due to above uncertainties, especially when implementation scales down below 100 nm. Recently, we proposed a local-image-based non-vector space (NVS) control strategy for regulating nanomanipulator to enhance its accuracy (positioning error is generally maintained below 0.2% of the operation range). Since the previous NVS control strategy employs only local image and pixel-wise information (neglecting global location and local coherence information), it can hardly track trajectories with relatively large "jump" and heavy noise, which might be inefficient for practical implementation. To tackle these problems, this study proposes a novel enhanced NVS (ENVS) control strategy, which performs rough global positioning control through dynamic hysteresis compensation for SPM scanners to solve the "jump" problem; to realize fine positioning control with possible heavily noisy feedback, the ENVS controller utilizes patch-wise-based rather than pixel-wisebased set feedback strategy, and the ENVS is built based on a newly established shape Lyapunov function. Testing results show that the ENVS control scheme can precisely track references with jump changes, such as the step signal. Furthermore, the ENVS controller is able to maintain its accuracy even with extremely noisy feedback.

Vision-based manipulation has been largely used in various robot applications. Normally, in order to obtain the spatial information of the operated target, a carefully calibrated stereo vision system is required. However, it limits the application of robots in the unstructured environment which limits both the number and the pose of the camera. In this study, a calibration-free monocular vision-based robot manipulation approach is proposed based on domain randomization and deep reinforcement learning (DRL). Firstly, a learning strategy combined domain randomization is developed to estimate the spatial information of the target from a single monocular camera arbitrarily mounted in a large area of the manipulation environment. Secondly, to address the monocular occlusion problem which regularly happens during robot manipulations, an occlusion awareness DRL policy has been designed to control the robot to avoid occlusions actively in the manipulation tasks. The performance of our method has been evaluated on two common manipulation tasks, reaching and lifting of a target building block, which show the efficiency and effectiveness of our proposed approach.

Table-tennis robots still have a long way to defeat human players because of the rapid decrease in control accuracy when the ball flying speed and spin velocity increase. In this case, the challenge is to control the robot accurately within a limited time. To overcome this challenge, this article introduces a receding horizon optimization method to solve the accuracy problem of spin estimation. With the help of accurate spin information, a single-parameter-optimization-based stroke approach is developed for the robot to guarantee accuracy and efficiency simultaneously. In terms of accuracy, the proposed method achieves the best performance compared with other spin estimation algorithms, and it also achieves a stroke error of 25.13 cm and 23.93 cm for topspinning and backspinning balls, with the passing-net height error being 12.27 cm and 10.57 cm, respectively. Due to the high efficiency of the method proposed, our robot can hit a ball that has a speed of up to 10 m/s, which is a new record for the maximum ball speed that a table-tennis robot can handle to the best of our knowledge. Using the method proposed in this article achieves success rates of 90.0 $%$ and 85.0 $%$ for striking topspin and backspin balls, surpassing the respective success rates of 40.0 $%$ and 13.1 $%$ obtained by using the previous method.