Study on the Integration Strategy of Online EOL Testing of Pure Electric Vehicle Power Battery.

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation.

A survey of brain network analysis by electroencephalographic signals.

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Association of shorter mean telomere length with large artery stiffness in patients with coronary heart disease.

BACKGROUND: Accumulating evidence implicates leukocyte telomere length (LTL) shortening as a potential risk predictor for cardiovascular disease. Arterial stiffness chronicles the cumulative burden of cardiovascular disease risk factors. Therefore, the capacity of LTL to predict arterial stiffness was examined. METHODS: A total of 275 unrelated Chinese males: 163 patients with coronary artery disease (CAD) and 112 healthy controls, 40-73 years of age were included in this study. The relative telomere length of leukocytes was determined by a real-time fluorescence quantitative polymerase chain reaction (PCR). Large artery stiffness was measured with carotid-femoral pulse wave velocity (PWV). RESULTS: The relative telomere length (T/S) ratio was significantly shorter in patients with CAD (0.79 +/- 0.26) than in control subjects (1.08 +/- 0.22) (p<0.001). The correlation between LTL and PWV in patients with CAD was stronger than that in the controls (r= -0.467, r(2)=0.227, p<0.001 for patients with CAD versus r= -0.223; r(2)=0.050; p=0.018 for controls). The log(e)-transformed T/S ratio was inversely correlated with age (r= -0.345; p<0.001), PWV (r= -0.326; p<0.001) and C-reactive protein ( r= -0.133; p=0.027). CONCLUSIONS: The data show an association of leukocyte telomere length shortening with increased arterial stiffness and cardiovascular burden, suggesting that telomere length is a biomarker of large artery elasticity and CAD. Further studies are warranted to study the role of LTL dynamics in the pathogenesis of atherosclerosis.

Robust H∞ state-feedback control for linear systems.

This paper investigates the problem of robust H∞ control for linear systems. First, the state-feedback closed-loop control algorithm is designed. Second, by employing the geometric progression theory, a modified augmented Lyapunov-Krasovskii functional (LKF) with the geometric integral interval is established. Then, parameter uncertainties and the derivative of the delay are flexibly described by introducing the convex combination skill. This technique can eliminate the unnecessary enlargement of the LKF derivative estimation, which gives less conservatism. In addition, the designed controller can ensure that the linear systems are globally asymptotically stable with a guaranteed H∞ performance in the presence of a disturbance input and parameter uncertainties. A liquid monopropellant rocket motor with a pressure feeding system is evaluated in a simulation example. It shows that this proposed state-feedback control approach achieves the expected results for linear systems in the sense of the prescribed H∞ performance.