In this paper, an adaptive dynamic programming(ADP) algorithm based on value iteration is proposed to tackle the stochastic linear quadratic(SLQ) optimal tracking control problem for discrete-time systems subject to m...
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In this paper, an adaptive dynamic programming(ADP) algorithm based on value iteration is proposed to tackle the stochastic linear quadratic(SLQ) optimal tracking control problem for discrete-time systems subject to multiplicative ***, an augmented system made up of the original system and the command generator is constructed and the condition of well-posedness for SLQ problem is given. Next, the SLQ problem is converted into the deterministic problem through system transformation. Then, ADP algorithm is utilized to solve the SLQ problem with convergence analysis. In the iteration process of ADP algorithm, the system dynamics are not essential to solve the Bellman equation but are needed to update the control gain matrix. Finally, simulation results have shown that the proposed scheme for the problem gives good tracking performance.
It is of great significance for accurate and fast magnetic measurement of high magneto-crystalline anisotropy film materials. In this paper, an surface magneto-optical Kerr effect automatic measurement system based on...
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It is of great significance for accurate and fast magnetic measurement of high magneto-crystalline anisotropy film materials. In this paper, an surface magneto-optical Kerr effect automatic measurement system based on LabVIEW is designed according to the practical requirements of hysteresis loop measurement of magnetic films. The system takes the SR830 lock-in amplifier as core, combined with virtual instrument technology and correlative detection technology, which achieves the effective measurement of the weak Kerr signal of Ni Fe soft magnetic film material under strong noise environment, and automatically draws its hysteresis loop. The measurement system has high resolution, high output signal-to-noise ratio, high automation, and has good application value.
Protein complexes are key molecular entities that play an integral role in human life activities. Systematic identification of protein complexes is an important application of data mining in the biological sciences. E...
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Protein complexes are key molecular entities that play an integral role in human life activities. Systematic identification of protein complexes is an important application of data mining in the biological sciences. Existing multi-label learning algorithms can effectively label nodes belonging to different complexes in protein-protein interaction network to identify overlapping complexes. However, the protein complexes formed by the stochastic strategy may have unstable results and insufficient community quality. To solve these problems, this paper proposes a novel protein complex identification method based on multisource fused data and the multi-label learning algorithm. The descending order of the potential influence of the nodes is used as the node selection order to solve the problem of unstable partitioning of the composite results. The comprehensive similarity obtained by the link correlation and the similarity of the gene annotations is used as the node label update strategy to improve the quality of the composite. The experimental results show that the new proposed method is much more effective and feasible,and has higher precision and biological significance.
Calligraphy is an important humanistic symbol of Chinese civilization. However, most of the calligraphy is incomplete, which has only a small number of Chinese characters circulating in the world. How to use these sam...
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Calligraphy is an important humanistic symbol of Chinese civilization. However, most of the calligraphy is incomplete, which has only a small number of Chinese characters circulating in the world. How to use these samples to efficiently restore the remaining calligraphy characters has always been considered as a difficult task. In this work, we propose Densenetpix2 pix model based style transfer method to solve this problem. By training some samples to learn the rules of transferring the printed font images to the calligraphy characters images, Densenet-pix2 pix can predict the remaining calligraphy characters. Our method modify the generation network and optimization strategies of style transfer, which improves the generation quality of the calligraphy characters and the stability of the model. In addition, we use pre-trained feature extraction models to extract content information and style information, and scientifically evaluate the quality of our generated calligraphy characters from these two aspects. We compared our method with several other baseline methods. The experimental results show that our method can effectively restore the remaining calligraphy characters, and the generated Chinese characters are more delicate.
As a unique property of the object surface, the spectral reflectance plays an important role in computer vision applications and in realistic material reproduction. To determine the influence of the light source on th...
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As a unique property of the object surface, the spectral reflectance plays an important role in computer vision applications and in realistic material reproduction. To determine the influence of the light source on the spectral reflectance reconstruction accuracy, the pseudo-inverse method is adopted to reconstruct the spectral reflectance. Further, the genetic algorithm is used to optimize the light source for improving the reconstruction accuracy. The experiment results show that the spectral reflectance reconstruction accuracy is highly affected by the light source spectra, and the light source spectra optimized by the proposed strategy significantly outperforms several commonly used illumination source.
Based on the structure and dynamic tuning characteristics of the microwave cavity filter, the method of vision assisting mechanical arm motion is used for tuning. In order to meet the flexibility and high precision re...
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Based on the structure and dynamic tuning characteristics of the microwave cavity filter, the method of vision assisting mechanical arm motion is used for tuning. In order to meet the flexibility and high precision requirements of the microwave cavity filter tuning system, the thesis improves image recognition and hand-eye coordination methods. Image recognition using Otsu method for image segmentation, the relative 3 d position coordinates of the target screw are obtained by edge fitting circle and image sharpness, and the screw angle was extracted by dynamically obtaining the screw groove thread area, which was used to improve the applicability and accuracy of screw positioning and thread angle extraction. The hand-eye coordination part proposes a hand-eye coordination method based on image feedback. Through the feedback of the image, the PID control robot arm is continuously moved to make the screw center and the image center overlap to achieve hand-eye coordination. The experimental results show that compared with the traditional hand-eye calibration method, the method takes less time and has lower hand-eye coordination error, which greatly improves the flexibility and precision of the tuning system. Based on the improved method of image recognition and hand-eye coordination, it can realize accurate and fast tuning of microwave cavity filter, and it also has certain guiding significance for the tuning of similar industrial equipment.
We propose a position and posture measurement method based on active binocular vision to improve localization accuracy and stability of bolts in substation fittings. Firstly, we obtain the color image and the depth im...
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We propose a position and posture measurement method based on active binocular vision to improve localization accuracy and stability of bolts in substation fittings. Firstly, we obtain the color image and the depth image of bolts and use shape-based template matching method to obtain the image coordinates of the bolts. Secondly, we obtain the 3 D coordinates of the bolts based on the binocular camera stereo vision model. Finally, based on the point-cloud information of bolts plane, we obtain the posture information of the bolts by using the least square method. The experimental results show that the binocular system designed in this paper can accurately identify the bolts and has good stability. Within 500 mm measurement range, the position measurement error rate of bolts is less than 0.8 %, and the angle measurement error of the fitting plane is less than 1°.
Recently, compressed sensing(CS) has been widely used in ground penetrating radar(GPR) imaging due to its low sampling time, high quality performance and high quality imaging results in underground imaging. However, f...
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Recently, compressed sensing(CS) has been widely used in ground penetrating radar(GPR) imaging due to its low sampling time, high quality performance and high quality imaging results in underground imaging. However, for densely distributed underground multi-target, the error of GPR imaging based on CS is large. So, this paper proposes a filter matrix,which can effectively reduce the energy of B-scan hyperbolic arms, increase the hyperbolic apex energy, and reduce the error of GPR imaging based on CS in densely distributed multi-target environment. The algorithm also takes into account the distance between the antenna and the ground. At first, a method of the normalized is used to calculate the echo data. Then the filter matrix designed in this paper is used. Experimental results present that the filter matrix can effectively reduce the imaging error in multi-target environment.
Aiming at the phenomenon that the target particle tracking algorithm is affected by illumination changes and occlusion in the target tracking process, the goal is lost. A target tracking algorithm based on convolution...
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Aiming at the phenomenon that the target particle tracking algorithm is affected by illumination changes and occlusion in the target tracking process, the goal is lost. A target tracking algorithm based on convolutional neural network and particle filtering is proposed. The algorithm uses the convolutional neural network to automatically learn the depth features of the target,and extracts the more abstract semantic information of the target. The semantic information makes the algorithm robust to the apparent changes of the target, which can alleviate the drift problem to some extent. The algorithm can effectively combine the target apparent model based on convolutional neural network with the particle filter framework. The experimental results show that compared with the other five algorithms in the particle filter framework, the algorithm can track the moving targets with partial occlusion and morphological changes more robustly.
In this paper, the problem of finite-time fault detection(FD) is investigated for networked discrete-time singularly perturbed systems under the stochastic communication protocol(SCP). To alleviate the undesired data ...
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In this paper, the problem of finite-time fault detection(FD) is investigated for networked discrete-time singularly perturbed systems under the stochastic communication protocol(SCP). To alleviate the undesired data collisions caused by largescale data transmissions via the bandwidth-limited communication network, the SCP modeled by a Markov chain is *** to practical factors such as observation error, the Markov modes of the SCP are assumed to be available to the fault detection filter(FDF) according to a hidden Markov process. Our aim is to design a hidden-Markov-model-based FDF as the residual generator such that the resulting augmented system is stochastically H∞ finite-time bounded. A sufficient condition in terms of linear matrix inequalities(LMIs) is derived which ensures the existence of such an FDF. The parameters of the desired FDF are explicitly given when there exist feasible solutions to these LMIs. Finally, the effectiveness of the FD method is demonstrated by a numerical example.
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