Drones in flight make a variety of vibration frequencies. Therefore, gimbal system is needed to get clean images from drone-mounted cameras. The system consists of two things: one is a structure to support a camera mo...
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Our research group, interested in outdoor scenes, has developed a methodology to register a CAD model of a city, with images taken with video cameras installed in a car, while driving city streets. So, we can merge vi...
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Dynamic singularities make it difficult to plan the Cartesian path of free-floating robot. In order to avoid its effect, the direct kinematic equations are used for path planning in the paper. Here, the joint position...
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Dynamic singularities make it difficult to plan the Cartesian path of free-floating robot. In order to avoid its effect, the direct kinematic equations are used for path planning in the paper. Here, the joint position, rate and acceleration are bounded. Firstly, the joint trajectories are parameterized by polynomial or sinusoidal functions. And the two parametric functions are compared in details. It is the first contribution of the paper that polynomial functions can be used when the joint angles are limited (In the similar work of other researchers, only sinusoidla functions could be used). Secondly, the joint functions are normalized and the system of equations about the parameters is established by integrating the differential kinematics equations. Normalization is another contribution of the paper. After normalization, the boundary of the parameters is determined beforehand, and the general criterion to assign the initial guess of the unknown parameters is supplied. The criterion is independent on the planning conditions such as the total time tf. Finally, the parametes are solved by the iterative Newtonian method. Modification of tf may not result in the recalculation of the parameters. Simulation results verify the path planning method.
During the last decades, numerous simulation tools have been proposed to faithfully reproduce the different entities of the grid together with the inclusion of new elements that make the grid "smart". Often,...
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A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is...
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A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which (1) is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects: convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.
The current Grover quantum searching algorithm cannot identify the difference in importance of the search targets when it is applied to an unsorted quantum database, and the probability for each search target is equal...
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The current Grover quantum searching algorithm cannot identify the difference in importance of the search targets when it is applied to an unsorted quantum database, and the probability for each search target is equal. To solve this problem, a Grover searching algorithm based on weighted targets is proposed. First, each target is endowed a weight coefficient according to its importance. Applying these different weight coefficients, the targets are represented as quantum superposition states. Second, the novel Grover searching algorithm based on the quantum superposition of the weighted targets is constructed. Using this algorithm, the probability of getting each target can be approximated to the corresponding weight coefficient, which shows the flexibility of this algorithm. Finally, the validity of the algorithm is proved by a simple searching example.
An increasing number of applications of dynamic neural networks has been developed for digital signal processing (DSP) , dynamic neural networks are feedforward neural networks with commonly used scalar synapses repla...
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ISBN:
(纸本)9780791802977
An increasing number of applications of dynamic neural networks has been developed for digital signal processing (DSP) , dynamic neural networks are feedforward neural networks with commonly used scalar synapses replaced by linear filters. This provides feedforward neural networks with the capability of performing dynamic mappings, which depend on past input values, dynamic neural networks are suitable for time series prediction, nonlinear system identification, and signal processing applications. Their most popular types are Finite Impulse Response (FIR) neural networks, which are obtained by replacing synapses with finite impulse response filters. Due to their guaranteed stability characteristic and easy to minimize error surface they have been used with great success in many applications such as signal enhancement, noise cancellation, classification of input patterns, system identification, prediction, and control.. Most of the works on system identification using neural networks are based on multilayer feedforward neural networks with backpropagation learning or more efficient variations of this algorithm, an elegant method for training layered networks. This paper is based on work in a Dynamic System Modeling (DYSMO) and as an application for speed control of DC motor drive.
The climate in modern livestock production buildings is controlled using a simple state controller. State controllers are typically not equipped to handle abnormal situations, e.g. sensors providing false or no readin...
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As the voltage and current waveforms are deformed due to transient during faults, their pattern changes according to the type of fault. The Artificial Neural Network (ANN) can then be used for fault detection due to i...
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ISBN:
(纸本)0780386108
As the voltage and current waveforms are deformed due to transient during faults, their pattern changes according to the type of fault. The Artificial Neural Network (ANN) can then be used for fault detection due to its distinguished behavior in pattern recognition. In order to minimize the structure and timing of the ANN, preprocessing of the voltage and current waveforms was done. The data delivered from a simulated power system using PSCAD (EMTP with cad system) was used for training and testing the ANN. An experimental setup, consists of a 3 phase power supply module and transmission line module, is utilized. A set of signal conditioning circuits is designed and implemented in order to transfer data to a PC which is used as an on-line relay for fault detection. This is done via a data acquisition card (CIO-DAS1602/12). The Matlab program captures and processes real data for training the ANN. Applying different types of faults for testing the system, right tripping action was taken and the type of fault was correctly identified. The suggested artificial neural network algorithm has been found simple and effective hence could be implemented in practical application.
The analysis and implementation of UDP-based cross-platform data shows that the advantages of UDP protocol are better understood and it's the best places of application. Further, master the knowledge of data trans...
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