Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology ...
详细信息
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, obj...
详细信息
Lidars are commonly employed in mobile robot SLAM applications due to their high stability and high measuring accuracy. For a conventional 2D Lidar, only the planar points can be detected in once scan, which limits th...
详细信息
Lidars are commonly employed in mobile robot SLAM applications due to their high stability and high measuring accuracy. For a conventional 2D Lidar, only the planar points can be detected in once scan, which limits the robustness of SLAM algorithms. Omni-directional mobile robots with powered caster wheels have 3-DOF planar motion capabilities so that they are widely deployed in narrowly constrained environment. This paper presents a novel design of a 2.5D Lidar device, in which a 2D Lidar is mounted onto a 1-DOF liner stage that moves along the vertical direction. As a result, the 2.5D Lidar device can also perform vertical scanning within the motion range of the liner stage. Consequently, a robust SLAM algorithm based on the 2.5D Lidar device is proposed for the omni-directional mobile robot, where a frame-to-frame scan matching using ICP algorithm is employed to improve the robustness and accuracy of the scan matching. As the 2.5D points cloud is actually located in a belt-shaped space, the map can be finally constructed in a 2D mode to reduce the computational complexity. In addition, when an incorrect matching occurs, the odometry information of the mobile robot will be employed to further improve the robustness and accuracy of the scan matching algorithm. The effectiveness of the proposed approach is validated through computer simulations.
The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The con...
详细信息
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data inten...
详细信息
Active disturbance rejection control(ADRC) exemplifies the spirit of the data-driven control(DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertai...
详细信息
ISBN:
(纸本)9781509054626
Active disturbance rejection control(ADRC) exemplifies the spirit of the data-driven control(DDC) design strategy and shows much promise in obtaining consistent applications in industrial control systems with uncertainties, without the premise that the detailed mathematical model of the controlled system is given. Instead, it is shown that the information needed for the control system to work at high level of effectiveness can be extracted from the input-output data by the use of the extended state observer(ESO). On the other hand, it is shown in this paper that the robustness of ADRC depends on the effectiveness of ESO. Furthermore, taking advantage of the rich body of knowledge in the existing field of robust control, the estimation error in ESO is analysed and, for the purpose of improved robustness, a unique nonlinear component is added to the conventional ADRC law. The modified ADRC which is a kind of robust ADRC law is validated in simulation for a nonlinear time-varying system with parametric and functional uncertainties. It is shown that the proposed robust ADRC law provides more effective tracking performance than the conventional ADRC when the bandwidth of ESO is not wide enough.
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive w...
详细信息
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segme...
详细信息
Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (...
详细信息
暂无评论