A motor multi-objective inverse optimization system based on genetic algorithm is designed, which can optimize induction motor and permanent magnet motor. Three design examples are used to verify the effectiveness and...
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A motor multi-objective inverse optimization system based on genetic algorithm is designed, which can optimize induction motor and permanent magnet motor. Three design examples are used to verify the effectiveness and the convenience of the system.
This paper develops a vector Lyapunov function based approach to the stability of continuous and discrete 2D nonlinear systems with Markovian jumps. Nonlinear continuous- time 2D systems described by a Roesser model a...
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The paper introduces the localization problem of sensor networks using relative position measurements. It is assumed that relative positions are measured in local coordinate frames of individual sensors, for which dif...
The paper introduces the localization problem of sensor networks using relative position measurements. It is assumed that relative positions are measured in local coordinate frames of individual sensors, for which different sensors may have different orientations of their local frames and the orientation errors with respect to the global coordinate frame are not known. A new necessary and sufficient condition is developed for localizability of such sensor networks that are modeled as directed graphs. That is, every sensor node should be 2-reachable from the anchor nodes. Moreover, for a localizable sensor network, a distributed, linear, and iterative scheme based on the graph Laplacian of the sensor network is developed to solve the coordinates of the sensor network in the global coordinate frame.
Robust fault detection for a networked control system (NCS) including model uncertainty and stochastic data packet dropout is investigated, the NCS model with stochastic variable is established. Based on the observer ...
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Robust fault detection for a networked control system (NCS) including model uncertainty and stochastic data packet dropout is investigated, the NCS model with stochastic variable is established. Based on the observer method, a robust fault detection filter can generate a residual signal, which has good robustness to the model uncertainty, external disturbance, data packet dropout, and sensitivity to the fault. Then, the sufficient condition of asymptotically mean-square stable for the residual dynamical system is achieved. The desired robust fault detection filter is obtained, which is constructed in terms of certain linear matrix inequality (LMI). In the residual evaluation part, a threshold involved false alarm rate is presented, and the problem of estimating false alarm rate is analyzed. Finally, two numerical examples illustrate the effectiveness of the proposed approach.
DNS provides a critical function in directing Internet traffic. Traditional rule-based anomaly or intrusion detection methods are not able to update the rules dynamically. Data mining based approaches can find various...
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DNS provides a critical function in directing Internet traffic. Traditional rule-based anomaly or intrusion detection methods are not able to update the rules dynamically. Data mining based approaches can find various patterns in massive dynamic query traffic data. In this paper, a novel periodic trend mining method is proposed, as well as a periodic trend pattern based traffic prediction method. Clustering is adopted to partition numerous domain names into separate groups by the characteristics of their query traffic time series. Experimental results on a real-word DNS log indicate data mining based approaches are promising in the domain of DNS service.
Scale Invariance Feature Transform (SIFT) is quite suitable for image matching because of its invariance to image scaling, rotation and slight changes in illumination or viewpoint. However, due to high computation com...
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Scale Invariance Feature Transform (SIFT) is quite suitable for image matching because of its invariance to image scaling, rotation and slight changes in illumination or viewpoint. However, due to high computation complexity it's technically challenging to deploy SIFT in real time application situations. To address this problem, we propose CLSIFT, an OpenCL based highly speeded up and performance portable SIFT solution. Important optimization techniques employed in CLSIFT such as: (1) For less global memory traffic, independent logical functions are merged into the same kernel to reuse data. (2) loop buffers are introduced in for data and intermediate results reusing. (3)Task queue used to schedule threads in the same branch to remove branch divergences. (4) Data partition is based on the statics patterns for workload balance among workgroups. (5) Overlap of CPU time and better parallel strategies are used too. With all mentioned efforts, CLSIFT processes lena. jpg at 74.2 FPS and 43.4FPS respectively on NVidia and AMD GPUS, much higher than CPU's nearly 10 FPS and the known fastest SIFTGPU's 39.8 FPS and 13FPS. Moreover in a quantitative comparison approach we analyze those successful strategies beating SIFTGPU, a famous existing GPU implementation. Additionally, we observe and conclude that NVidia GPU achieves better occupancy and performance due to some factors. Finally, we summarize some techniques and empirical guiding principles that may be shared by other applications on GPU.
Research into conventional therapy and motor learning theory provides evidence that the intensity of practice of a task and feedback are important for iterative control of health care. Iterative learning control (ILC)...
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Research into conventional therapy and motor learning theory provides evidence that the intensity of practice of a task and feedback are important for iterative control of health care. Iterative learning control (ILC) is one model-based approach to stroke rehabilitation that has progressed to a program of clinical trials, which constitute the first major stage towards the eventual transfer into practice. ILC exploits the repeating nature of the patients' tasks to improve performance by learning from past experience as compared with other approaches employed to control FES. ILC is able to respond to physiological changes in the system, such as spasticity and the presence of a patient's voluntary effort by updating the control input using data collected over previous attempts at the task. ILC can also closely regulate the amount of stimulation supplied, ensuring that minimum assistance is provided, promoting the patient's maximum voluntary contribution to the task completion.
The design, characterization and control of a novel 2-DOF MEMS nanopositioner is presented, with Z-shaped electrothermal actuators being used to position the device's central stage. Whereas the more commonly-used ...
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The design, characterization and control of a novel 2-DOF MEMS nanopositioner is presented, with Z-shaped electrothermal actuators being used to position the device's central stage. Whereas the more commonly-used V-shaped electrothermal actuator only allows displacements in one direction, the design of the Z-shaped beams used in the presented device allows two actuators to be coupled back-to-back to achieve bidirectional motion along each of the two axes. Testing of the device shows that stage displacements in excess of ϕ5 μm are achievable for both the x and y axes. The device features integrated displacement sensors based on polysilicon electrothermal heaters, which are supplied with an electrical bias voltage that results in Joule heating. The resistance of each heater varies depending on the position of the central stage, with two heaters being used per axis in a differential configuration. The displacement measurements are utilized as part of an implemented closed-loop control scheme that uses both feedforward and feedback mechanisms based on the principle of internal model control. Experimental testing shows that the use of the controller enhances the static and dynamic performance of the system, with particular improvements being seen in the device's reference tracking, response time and cross-coupling rejection.
There are many types of bio-signals with various control application prospects. In this work possible control application domain of electroencephalographic signal obtained from an easily available, inexpensive EEG hea...
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There are many types of bio-signals with various control application prospects. In this work possible control application domain of electroencephalographic signal obtained from an easily available, inexpensive EEG headset - Emotiv EPOC was presented. This work also involved application of an embedded system platform. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. Potential implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. In this work traditional, statistical methods were neither used nor described.
Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we co...
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Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we continue the research of normal forms for spiking neural P systems. Specifically, we prove that the degree of spiking neural P systems without delay can be decreased to two without losing the computational completeness (both in the generating and accepting modes).
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