Some time ago, an interferometer-based metrological scanning probe microscope (SPM) was developed at the Institute of Process Measurement and Sensor Technology of the Ilmenau University of Technology, Germany. The specialty of this SPM is the combined deflection detection system that comprises an interferometer and a beam deflection. Due to this system it is possible to simultaneously measure the displacement, bending and torsion of the probe (cantilever). The SPM is integrated into a nanopositioning and nanomeasuring machine (NPM machine) and allows measurements with a resolution of 0.1 nm over a range of 25 mm × 25 mm × 5 mm. Excellent results were achieved for measurements of calibrated step height and lateral standards and these results are comparable to the calibration values from the Physikalisch-Technische Bundesanstalt (PTB) (Dorozhovets N et al 2007 Proc. SPIE 6616 661624–1–7). The disadvantage was a low attainable scanning speed and accordingly large expenditure of time. Control dynamics and scanning speed are limited because of the high masses of the stage and corner mirror of the machine. For the vertical axis an additional high-speed piezoelectric drive is integrated in the SPM in order to increase the measuring dynamics. The movement of the piezoelectric drive is controlled and traceable measured by the interferometer. Hence, nonlinearity and hysteresis in the actuator do not affect the measurement. The outcome of this is an improvement of the bending control of the cantilever and much higher scan speeds of up to 200 µm s−1.
A procedure to design the memoryless state-estimation feedback controller for uncertain dynamic systems with time-varying delay is presented. The uncertainties are time varying and are bounded. The design procedure involves solving two linear matrix inequalities. If there exist two positive definite matrices which satisfy these two linear matrix inequalities, then the asymptotic stabilisability of the closed-loop system is guaranteed. Furthermore, the state observer and control law can be constructed from these solutions. An example is given to illustrate the results.
Abstract Purpose: Femoral neck fracture is a common form of hip fracture in the elderly. Minimally invasive surgery is very popular in recent years. This study was to investigate the clinical efficacy and advantages of the SuperPath approach to total hip arthroplasty in the treatment of femoral neck fractures in the elderly. Methods: From February 2018 to March 2019, 120 patients were random divided into two groups with 60 patients each: the SuperPath group and conventional group. The results evaluated the general operation situation, Serum markers, blood loss, pain score, hip function and prosthesis location analysis. Results: There was no difference demographically between two groups. Compared with the conventional group, the SuperPath group had a shorter operation time (78.4 vs 93.0 min), smaller incision length (5.8 vs 12.5 cm), less intraoperative blood loss (121.5 vs 178.8 ml), shorter hospitalization time (8.0 vs 10.8 day) and less drainage volume (77.8 vs 141.2 ml). The creatine kinase (CK) in the SuperPath group was significantly lower, while there was no difference in the C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). The visual analog scale (VAS) was lower in one month and Harris Hip Score (HHS) were higher in three months in the SuperPath group. There was no difference in the cup abduction angle and anteversion angle of the two groups. Conclusion: We found the better clinical efficacy after the SuperPath approach with less muscle damage, less postoperative pain and better postoperative function compared with the modified Hardinge approach. Trial registration: Retrospectively registered. The randomized clinical trial was retrospective registered at Chinese Clinical Trial Registry on December 31, 2020 (ChiCTR-2000041583, http://www.chictr.org.cn/showproj.aspx?proj=57008).
Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results.In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results.
Animal pose estimation is crucial for animal health assessment, species protection, and behavior analysis. It is an inevitable and unstoppable trend to apply deep learning to animal pose estimation. In many practical application scenarios, pose estimation models must be deployed on edge devices with limited resource. Therefore, it is essential to strike a balance between model complexity and accuracy. To address this issue, we propose a lightweight network model, i.e., MPE-HRNet.L, by improving Lite-HRNet. The improvements are threefold. Firstly, we improve Spatial Pyramid Pooling-Fast and apply it and the improved version to different branches. Secondly, we construct a feature extraction module based on a mixed pooling module and a dual spatial and channel attention mechanism, and take the feature extraction module as the basic module of MPE-HRNet.L. Thirdly, we introduce a feature enhancement stage to enhance important features. The experimental results on the AP-10K dataset and the Animal Pose dataset verify the effectiveness and efficiency of MPE-HRNet.L.
Traffic sign detection plays a vital role in assisted driving and automatic driving. YOLOv5, as a one-stage object detection solution, is very suitable for Traffic sign detection. However, it suffers from the problem of false detection and missed detection of small objects. To address this issue, we have made improvements to YOLOv5 and subsequently introduced YOLOv5-TS in this work. In YOLOv5-TS, a spatial pyramid with depth-wise convolution is proposed by replacing maximum pooling operations in spatial pyramid pooling with depth-wise convolutions. It is applied to the backbone to extract multi-scale features at the same time prevent feature loss. A Multiple Feature Fusion module is proposed to fuse multi-scale feature maps multiple times with the purpose of enhancing both the semantic expression ability and the detail expression ability of feature maps. To improve the accuracy in detecting small even extra small objects, a specialized detection layer is introduced by utilizing the highest-resolution feature map. Besides, a new method based on k-means++ is proposed to generate stable anchor boxes. The experiments on the data set verify the usefulness and effectiveness of our work.
The parallel transportation system based on the method of ACP (Artificial systems, Computing experiments, Parallel Control) will promote the level of city traffic intelligent decision and scientific management. A key problem in the system is how to design a computing experiment method to predict and evaluate the traffic state by real-time and accuracy. This paper introduces the discrete-time queuing model to analyze the traffic flow at the signalized intersection and gives the evaluation conditions of the traffic state. Then, the evaluation conditions are applied to judge the traffic state based on the prediction data of traffic flows from the grey model. Experiments show the method is effective and feasible.
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