Abstract:In many real-time computing environments, there are some tasks that are time-critical while others are not. To ensure that every critical task can be completed before its deadline, it is necessary to reject some non-critical tasks to entry into the ready queue. We address this problem in the framework of controller synthesis. Our goal is to come up with an admission controller which admits or rejects a task request. With such a controller, no admitted tasks will miss their deadline and the admitted patterns of task releases satisfy a quality-of-service constraint in the form of a linear time temporal logic specification. We prove that it is decidable to determine if such an admission controller exists. Furthermore, if the answer is positive, it is possible to effectively construct a controller in the form of a finite timed controller.

Abstract:CD317 (also known as tetherin, BST-2, or HM1.24) was first discovered from a human plasma cell line in 1994 and defined as a novel terminal B-cell-restricted antigen. It was first reported to be a potent interferon-induced host antiviral factor in 2008, and was demonstrated that it inhibited the release of HIV-1 viral particles deficient in the viral membrane protein Vpu. Since that time, numerous reports have been published regarding to the structure, the antiviral activity and immunological properties of this protein. Moreover, some new functions of CD317, such as involvement with tumor development and exosome tetherin, have been found recently. Thus, CD317 function is not just limited to the antiviral field. In this review, the antiviral function, oncobiology and signal transduction of CD317 were summarized, which will enhance the overall knowledge of CD317 function during viral infection and cancer metastasis, possibly leading to unique therapeutic applications for these diseases.

Abstract:Automatic drive is an important application field of artificial intelligence. In this paper, a novel training strategy for self-driving vehicles was investigated based on the deep reinforcement learning model. The proposed method involves a Q-learning algorithm with filtered experience replay and pre-training with experiences from professional drivers, which accelerates the training process due to reduced exploration spaces. By resampling the input state after clustering, generalization ability of the strategy can be improved due to the individual and independent distribution of the samples. Experimental results show that, in comparison with conventional neural fitted Q-iteration algorithm, the training efficiency and controlling stability can be improved more than 90% and 30% respectively by the proposed approach. Experimental results with more complex testing tracks show that, average travel distance can be improved more than 70% in comparison with the Q-learning algorithm by the proposed method.

Abstract:Clustering is an important research topic in data mining domain for data preprocessing. Clustering is an unsupervised learning method that tries to find out some obvious clusters in the unlabeled data. It is usually performed by maximizing the similarity of inner-clusters and minimizing the similarity of inter-clusters. A lot of clustering algorithms have been proposed to solve various tasks and data properties in the past decades. However, all existing clustering methods have their own pros and cons, and there still lack of a clustering method with universality. Traditional clustering methods are usually classified into partitioning methods, hierarchical methods, density-based methods, grid-based methods and model-based methods. With a brief review to classical clustering methods, we put emphasis on introducing some recent emerging clustering methods like synchronization clustering algorithm, affinity propagation algorithm and density peaks algorithm. Based on the analysis and comparison of these algorithms, their potential applications and research directions are also discussed.

Abstract:In this paper, a real-time running targets detection method was investigated based on a patrol robot system. The convolutional neural network method was used as the classifier. Running targets with various poses under different camera viewpoints and backgrounds were collected for the training of the neural network. To discriminate the foreground target and the changing background caused by the robot motion, an optical flowbased method was applied. Optical flow of two successive frames taken by on-board camera was used to extract region of interest. To boost the detection efficiency and accuracy, both appearance and motion information of the target are used as input of the convolutional neural network. Experimental results show that under real outdoor scenarios, the detection accuracy can reach 85% with a running efficiency of 20 frames per second.

Abstract:In this paper, a new roll-to-roll ultraviolet imprint lithography (RtR UV IL) technology was proposed and an equipment for large area super-hydrophobic materials fabrication was developed. By using the RtR micro/nano imprint method and fast UV curing technology, the micro patterns could be transferred to a flexible substrate without complex fabrication process in a clean room. Through this way, large area superhydrophobic film could be fabricated rapidly and efficiently. This paper focuses on discussing the RtR UV IL technology and the related mechanical parts of the equipment which is used for the superhydrophobic film fabrication. After optimizating, a 20 μm×40 μm×17 μm (diameter×pitch×height) micro-structure array was successfully transferred by using modified UVresist. The water contact angle reached up to 150° after fluoride treatment. Finally, we attempted to fabricate T-shaped micro-pillar array by using this equipment to obtain super hydrophobic materials.

Abstract:In this paper, a framework which applies the piecewise linear quadratic regulator (PLQR) control based on linear time-varying system was presented to solve the problem of input saturations in the tracking control of wheeled mobile robot (WMR). Based on the Lyapunov method, it is proved that the designed trajectory tracking controller PLQR can satisfy the input saturation condition, and guarantee the stability of closed-loop system. With the Matlab simulation experiments, the results show that the PLQR based wheeled mobile robot has good tracking performance with different initial positions and various reference trajectories.

Abstract:In modern aircraft design, numerical simulation becomes an important way to study the aerodynamics of aircraft because of its low cost, high efficiency and high flexibility. In the aerodynamic analysis of rotor unmanned aerial vehicles (UAVs), due to the interaction between rotor and fuselage, we have to model the full rotor UAVs, including the rotor and fuselage, to obtain accurate simulation results. In this kind of simulation, a key step is to effectively model the relative motion between the rotor and fuselage, which is a great challenge. In this paper, a highly scalable parallel computing method based on unstructured sliding meshes for the aerodynamic simulation of rotor UAVs was designed. In the proposed method, an unstructured moving mesh finite element method was used to discretize the governing equations in space, a fully implicit second-order backward differentiation formula was adopted for the temporal discretization, and finaly a parallel Newton-Krylov-Schwarz method was introduced to solve the discritized nonlinear equations. As a case study, we have tested the algorithm on the Tianhe II supercomputer for a rotor UAV in the hover state, and obtained some detailed flow information. Performance results show a nearly linear speedup for up to 4 096 processor cores, suggesting that our solution lays a good foundation for fast and high-fidelity aerodynamic simulation of rotor UAVs.