A Chattering Free Discrete-Time Global Sliding Mode Controller for Optoelectronic Tracking System

Aiming at the uncertainties including parameter variations and external disturbances in optoelectronic tracking system, a discrete-time global sliding mode controller (DGSMC) is proposed. By the design of nonlinear switching function, the initial state of control system is set on the switching surface. An adaptive discrete-time reaching law is introduced to suppress the high-frequency chattering at control input, and a linear extrapolation method is employed to estimate the unknown uncertainties and commands. The global reachability for sliding mode and the chattering-free property are proven by means of mathematical derivation. Numerical simulation presents that the proposed DGSMC scheme not only ensures strong robustness against system uncertainties and small tracking error, but also suppresses the high-frequency chattering at control input effectively, compared with the SMC scheme using conventional discrete-time reaching law

Structural design of the data base on the internet of things for product quality control

Database management system structure and function, providing data definition language DDL and data manipulation language DML, and some other user functions. Product quality control database structure design, including logical structure design and physical structure design

A novel approach to surface defect detection

Defects or flaws in highly loaded structures have a significant impact on the structural integrity. Early inspection of faults can reduce the likelihood of occurrence of potential disasters and limit the damaging effects of destructions. According to our previous work, a novel approach called as Quantitative Detection of Fourier Transform (QDFT) using guided ultrasonic waves is developed in this paper for efficiently detecting defects in pipeline structures. Details of this fast method consist of three steps: First, an in-house finite element code has been developed to calculate reflection coefficients of guided waves travelling in the pipe. Then, based on boundary integral equations and Fourier transform of space-wavenumber domain, theoretical formulations of the quantitative detection are derived as a function of wavenumber using Born approximation. This lays a solid foundation for QDFT method, in which a reference model in a problem with a known defect is utilized to effectively evaluate the unknown defects. Finally, the location and shape of the unknown defect are reconstructed using signal processing for noise removal. Several examples are presented to demonstrate the correctness and efficiency of the proposed methodology. It is concluded that the general two-dimensional surface defects can be detected with high level of accuracy by this fast approach

Circumferential defect detection using ultrasonic guided waves: An efficient quantitative technique for pipeline inspection

Purpose: Quantitatively detecting surface defects in a circular annulus with high levels of accuracy and efficiency has been paid more attention by researchers. The purpose of this study is to investigate the theoretical dispersion equations for circumferential guided waves and then develop an efficient technique for accurate reconstruction of defects in pipes. Design/methodology/approach: The methodology applied to determine defects in pipelines includes four steps. First, the theoretical work is carried out by developing the appropriate dispersion equations for circumferential guided waves in a pipe. In this phase, formulations of strain-displacement relations are derived in a general equidistant surface coordinate. Following that, a semi-analytical finite element method (SAFEM) is applied to solve the dispersion equations. Then, the scattered fields in a circular annulus are calculated using the developed hybrid finite element method and simulation results are in accord with the law of conservation of energy. Finally, the quantitative detection of Fourier transform (QDFT) approach is further enhanced to efficiently reconstruct the defects in the circular annuli, which have been widely used for engineering applications. Findings: Results obtained from four numerical examples of flaw detection problems demonstrate the correctness of the developed QDFT approach in terms of accuracy and efficiency. Reconstruction of circumferential surface defects using the extended QDFT method can be performed without involving the analytical formulations. Therefore, the streamlined process of inspecting surface defects is well established and this leads to the reduced time in practical engineering tests. Originality/value: In this paper, the general dispersion equations for circumferential ultrasonic guided waves have been derived using an equidistant surface coordinate and solved by the SAFEM technique to discover the relationship between wavenumber of a wave and its frequency. To reconstruct defects with high levels of accuracy and efficiency, the QDFT approach has been further enhanced to inspect defects in the annular structure

Bufalin Induces Lung Cancer Cell Apoptosis via the Inhibition of PI3K/Akt Pathway

Bufalin is a class of toxic steroids which could induce the differentiation and apoptosis of leukemia cells, and induce the apoptosis of gastric, colon and breast cancer cells. However, the anti-tumor effects of bufalin have not been demonstrated in lung cancer. In this study we used A549 human lung adenocarcinoma epithelial cell line as the experimental model to evaluate the potential of bufalin in lung cancer chemotherapy. A549 cells were treated with bufalin, then the proliferation was detected by MTT assay and apoptosis was detected by flow cytometry analysis and Giemsa staining. In addition, A549 cells were treated by Akt inhibitor LY294002 in combination with bufalin and the activation of Akt and Caspase-3 as well as the expression levels of Bax, Bcl-2 and livin were examined by Western blot analysis. The results showed that Bufalin inhibited the proliferation of A549 cells and induced the apoptosis of A549 cells in a dose and time dependent manner. Mechanistically, we found that bufalin inhibited the activation of Akt. Moreover, bufalin synergized with Akt inhibitor to induce the apoptosis of A549 cells and this was associated with the upregulation of Bax expression, the downregulation of Bcl-2 and livin expression, and the activation of Caspase-3. In conclusion, our findings demonstrate that bufalin induces lung cancer cell apoptosis via the inhibition of PI3K/Akt pathway and suggest that bufalin is a potential regimen for combined chemotherapy to overcome the resistance of lung cancer cells to chemotherapeutics induced apoptosis

Visual analysis of discrimination in machine learning

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning

Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification, and the introduction of intelligent wearable ECG devices has enabled daily monitoring. However, due to the need for professional expertise in the ECGs interpretation, general public access has once again been restricted, prompting the need for the development of advanced diagnostic algorithms. Classic rule-based algorithms are now completely outperformed by deep learning based methods. But the advancement of smart diagnostic algorithms is hampered by issues like small dataset, inconsistent data labeling, inefficient use of local and global ECG information, memory and inference time consuming deployment of multiple models, and lack of information transfer between tasks. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout, with the help of the development of low-resolution high-level semantic information, by capitalizing on both local morphological information and global rhythm information. From the perspective of effective data leverage and inter-task knowledge transfer, we develop a parameter isolation based ECG continual learning (ECG-CL) approach. We evaluated our model's performance on four open-access datasets by designing segmentation-to-classification for cross-domain incremental learning, minority-to-majority class for category incremental learning, and small-to-large sample for task incremental learning. Our approach is shown to successfully extract informative morphological and rhythmic features from ECG segmentation, leading to higher quality classification results. From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.Comment: 10 page

Optimization of a Parallel CFD Code and Its Performance Evaluation on Tianhe-1A

This paper describes performance tuning experiences with a parallel CFD code to enhance its performance and flexibility on large scale parallel computers. The code solves the incompressible Navier-Stokes equations based on the novel Slightly Compressible Model on three-dimensional structure grids. High level loop transformations and argument based code specialization are utilized to optimize its uniprocessor performance. Static arrays are converted into dynamically allocated arrays to improve the flexibility. The grid generator is coupled with the flow solver so that they can exchange grid data in the memory. A detailed performance evaluation is performed. The results show that our uniprocessor optimizations improve the performance of the flow solver for 1.38 times to 3.93 times on Tianhe-1A supercomputer. In memory grid data exchange optimization speeds up the application startup time by nearly two magnitudes. The optimized code exhibits an excellent parallel scalability running realistic test cases. On 4 096 CPU cores, it achieves a strong scaling parallel efficiency of 77.39 % and a maximum performance of 4.01 Tflops