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°.
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.
In the process of urban sewage treating, reducing the energy consumption and improving the quality of the effluent are significantly meaningful. According to the activated sludge method, the key factors affecting the ...
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In the process of urban sewage treating, reducing the energy consumption and improving the quality of the effluent are significantly meaningful. According to the activated sludge method, the key factors affecting the energy consumption and water quality of wastewater treatment are determined. In order to minimize the energy consumption of the activated sludge process and maximize the quality of the effluent, four different objective functions are modeled [i.e., the airflow rate, the carbonaceous biochemical oxygen demand(CBOD) of the effluent, the total phosphorus(TP) of the effluent, and the ammonia nitrogen of the effluent(NH4-N)]. These models are developed using a back propagation(BP) neural network based on industrial data, and dissolved oxygen(DO) is the controlled variable. A multi-objective model was evaluated by six evaluation indicators. Based on the analysis of the model and the mechanism of activated sludge process, the multi-objective particle swarm optimization(MOPSO)algorithm was used to optimize the energy consumption and water quality of the activated sludge process. The experimental results show that eventually reduce aeration energy consumption by 17%.
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.
Aiming at the problem that the target occlusion and target loss in the target tracking process can not be solved by most tracking algorithms, anti-occlusion correlation filter tracking based on multi-peaks response is...
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Aiming at the problem that the target occlusion and target loss in the target tracking process can not be solved by most tracking algorithms, anti-occlusion correlation filter tracking based on multi-peaks response is proposed. When the target is occluded, the response of the multi-peaks and the highest peaks exceeds the set thresholds, and the two-dimensional matrix of the Gaussian distribution is used to perform a point multiplication operation with the confidence map to obtain a new confidence map, and the current frame model update is stopped. Experiments show that compared with other algorithms, the algorithm has a significant improvement in tracking accuracy and speed when dealing with video sequences whose targets are occluded.
This paper mainly studies the self-adaptation of Pulse Coupled Neural Network(PCNN) and the application in handwritten digit recognition. First, the edge extraction algorithm of image using PCNN and maximum entropy is...
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This paper mainly studies the self-adaptation of Pulse Coupled Neural Network(PCNN) and the application in handwritten digit recognition. First, the edge extraction algorithm of image using PCNN and maximum entropy is proposed, the parameters' optimization of PCNN is realized by Simple Genetic Algorithm. Then, the foveation algorithm based on PCNN is used to extract the feature points of handwritten digits. Finally, a BP neural network with two hidden layers is used to recognize the images of handwritten digits which have been preprocessed. Experimental results on handwritten digit recognition demonstrated that the proposed method reached good performance on feature extraction and the recognition has better accuracy compared with the original method using BP neural network directly.
This paper is concerned with the asymptotical synchronization of two identical chaotic Lur'e systems using sampleddata control. Firstly, by defining the synchronization error, the synchronization of original syste...
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This paper is concerned with the asymptotical synchronization of two identical chaotic Lur'e systems using sampleddata control. Firstly, by defining the synchronization error, the synchronization of original systems is converted into the stabilization design of the error system. Then, an augmented Lyapunov-Krasovskii functional with new terms is constructed and its derivative is estimated by using Wirtinger-based integral inequality and the reciprocally convex matrix inequality. As a result,a less conservative synchronization criterion is established to design the sampled-data scheme. Finally, the advantage of the proposed method is demonstrated by a numerical example.
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.
Video saliency detection aims to extract salient objects in video. In order to resolve the problems of incomplete extraction salient area and the mixing between background and salient objects at boundary, this paper p...
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Video saliency detection aims to extract salient objects in video. In order to resolve the problems of incomplete extraction salient area and the mixing between background and salient objects at boundary, this paper proposes a video saliency detection method via multi-features fusion based on Boolean Map. The main innovation in this method is that global topological relationship of video frames are utilized to compute saliency values. Firstly, boolean maps are generated according to the Boolean Map theory via combine motion features and color features in videos, and then attention maps are calculated by masking unsurroundedness area, finally, video saliency maps are obtained by fusing all attention maps. Experiments on Segtrack V2 and Fukuchi benchmark datesets show that the proposed method successfully obtains complete and clear boundary salient area, and it outperforms the other general models.
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.
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