This paper presents a novel load variation compensation algorithm in a sensor-less BLDC motor drive for a reciprocating compressor. Periodic load variation of a reciprocating compressor leads to commutation position e...
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ISBN:
(纸本)9781424424900
This paper presents a novel load variation compensation algorithm in a sensor-less BLDC motor drive for a reciprocating compressor. Periodic load variation of a reciprocating compressor leads to commutation position error and instantaneous speed variation and causes large peak currents. The major concept of this study includes tracking the pattern of load torque and controlling the motor input voltage by the commutation point and PWM depending on the tracked load torque. The suggested method can reduce the cost because this method does not require additional current sensing or voltage comparison circuit. The utility of the suggested algorithm was confirmed by simulation and experimental results of a sensor-less BLDC motor drive.
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.
In this paper, we study the servomechanism problem for positive LTI systems. In particular, we consider the robust servomechanism problem of nonnegative constant reference signals for stable SISO positive LTI systems ...
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This paper analyzes the performance of a baseband multiple-input single-output (MISO) time reversal ultra-wideband system (TR-UWB) over the IEEE 802.15.3a channel model. Two scenarios are considered, CM1 based on LOS ...
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In this paper a baseband multiple-input single-output (MISO) time reversal ultra-wideband system (TR-UWB) incorporating a decision feedback equalizer (DFE) is evaluated over the scenarios CM1 and CM3 of the IEEE 802.1...
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To study the design problem of robust reliable guaranteed cost controller for nonlinear singular stochastic systems, the Takagi-Sugeno (T-S) fuzzy model is used to represent a nonlinear singular stochastic system wi...
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To study the design problem of robust reliable guaranteed cost controller for nonlinear singular stochastic systems, the Takagi-Sugeno (T-S) fuzzy model is used to represent a nonlinear singular stochastic system with norm-bounded parameter uncertainties and time delay. Based on the linear matrix inequality (LMI) techniques and stability theory of stochastic differential equations, a stochastic Lyapunov function method is adopted to design a state feedback fuzzy controller. The resulting closed-loop fuzzy system is robustly reliable stochastically stable, and the corresponding quadratic cost function is guaranteed to be no more than a certain upper bound for all admissible uncertainties, as well as different actuator fault cases. A sufficient condition of existence and design method of robust reliable guaranteed cost controller is presented. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed method.
Markov models are a well established technique widely used for modeling deterioration processes of the electric power equipment and in reliability analysis. Recently, several papers using Markov and semi-Markov models...
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ISBN:
(纸本)9781934325216
Markov models are a well established technique widely used for modeling deterioration processes of the electric power equipment and in reliability analysis. Recently, several papers using Markov and semi-Markov models have been published addressing the issue of the calculation of the rlife, future failure rates and the probability of failure of power equipment. This paper focuses on one such model and addresses an issue of accuracy of Markov model analysis. The paper presents a method of model adjustment and discusses implementation of three numerical algorithms solving the problem of parameter approximation. A practical example confirms validity of the approach and illustrates its efficiency.
This paper proposes a robust algorithm for detecting interest points based on the NonsubSampled Contourlet Transform (NSCT). The NSCT provides multiscale decomposition with directional filters at each scale. Furthermo...
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ISBN:
(纸本)9780769534404
This paper proposes a robust algorithm for detecting interest points based on the NonsubSampled Contourlet Transform (NSCT). The NSCT provides multiscale decomposition with directional filters at each scale. Furthermore, NSCT is very efficient in extracting the geometric information of images and therefore it has very good feature localization. The NSCT-based point detector is compared to the widely used Harris and Difference of Gaussian (DoG) interest point detectors. The experimental results reveal the robustness of the proposed algorithm to rotation, scale and viewpoint changes.
This paper provides a methodology to thermally manage an electronic control unit while reducing its radiated emissions. Measurements of the radiated emission levels for a particular electronic control unit revealed ex...
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