Video target tracking is an important research topic in computer vision, and has been widely used in video surveillance, robot, human-computer interaction and so on. The emergence of large data age and the emergence o...
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Video target tracking is an important research topic in computer vision, and has been widely used in video surveillance, robot, human-computer interaction and so on. The emergence of large data age and the emergence of in-depth learning methods provide a new opportunity for the study of video target tracking. This paper first analyzes the research problems of video target tracking at present, analyzes the characteristics and trends of video target tracking in the new period, introduces the emerging recursive neural network frame structure, combined with Kalman filter And the experimental results show that the accuracy and robustness of the target tracking based on the convolution neural network algorithm are all good.
Maximum power point tracking controller is essential to obtain the maximum power from a solar array in the photovoltaic systems as the PV power module varies with the temperature and solar irradiation. In the DC/DC ci...
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Maximum power point tracking controller is essential to obtain the maximum power from a solar array in the photovoltaic systems as the PV power module varies with the temperature and solar irradiation. In the DC/DC circuit, the maximum power point tracking algorithm based on parabolic approximation method is used. On the basis of analyzing the principle of various tracking methods, the key technology of parabola approximation can be found to find the exact maximum power point.
After reviewing the development of industrial manufacturing, a novel concept called social manufacturing(SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization e...
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After reviewing the development of industrial manufacturing, a novel concept called social manufacturing(SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer's requirements of "from mind to products", and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend,and is expected to become popular in more and more ***, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies,like Internet of Things(Io T), social networks, cloud computing,3 D printing, and intelligentsystems, are introduced and analyzed,and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3 D printing are suggested for different user cases.
Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization...
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Compared with a traditional manufacturing process, 3D printing has advantages of performance and cost in personalized customization and has been applied in many fields. The problem of 3D model orientation optimization is a crucial one in practice. In this paper, based on the mathematical relationship between model orientation and printing time, surface quality, and supporting area, the model orientation problem is transformed into a multi-objective optimization problem with goal of minimizing printing time, surface quality, and supporting area. Ordinal Optimization (OO) is not only applicable to problems with random factors, but also to solve complex deterministic problems. The model orientation is a complex deterministic problem. We solve it with OO in this paper and use linear weighting to convert the multi-objective optimization problem into single-objective one. Finally, we compare the experimental results of solving 3D model orientation problems solved by OO and Genetic Algorithm (GA). The results show that OO requires less calculation time than GA while achieving comparable performance.
In recent years, with the rapid development of computer technology, facial expression recognition technology has gradually been applied to primary and secondary schools. This article first introduces the application o...
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In recent years, with the rapid development of computer technology, facial expression recognition technology has gradually been applied to primary and secondary schools. This article first introduces the application of facial expression recognition in education. Then the development of facial expression recognition and the four basic processes of facial expressions are described. After that, the methods and algorithms used in face detection and localization of face expression recognition and classification are summarized, feature extraction and classification are summarized. Finally, the current application of facial expression recognition technology in education and the existing problems and future development are pointed out.
This paper focuses on an accelerating method for partitioning the loops in the structure of the adaptive dynamic programming（ADP）. ADP contains critic-actor structure which involves the iterations of the value funct...
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This paper focuses on an accelerating method for partitioning the loops in the structure of the adaptive dynamic programming（ADP）. ADP contains critic-actor structure which involves the iterations of the value function. When the system needs to be stable, the value function generally needs to iterate thousands of times, the high computation burden which hinders the iterations will be generated. In order to reduce the computation burden, we introduce a hyperparallelepiped based loop partitioning（H-LP） method which splits the iterations of the value function and reduces the communication traffic calculated by the data footprint. The experiment results show that the computation performance will be enhanced when the H-LP method is introduced. The proposed method has an important practical significance.
This paper presents a position control strategy based on the iterative method for a planar *** control objective of the system is to move the end-point from any initial equilibrium point to a target equilibrium *** pr...
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This paper presents a position control strategy based on the iterative method for a planar *** control objective of the system is to move the end-point from any initial equilibrium point to a target equilibrium *** presented method is based on the iterative steering,where a converging control law is applied *** order to compute such a control law,the dynamic equations of the system are transformed via partial feedback linearization and nilpotent ***,the simulation results demonstrate that the position control objective is realized by using this control strategy.
In order to avoid the linear inversion method falling into local minima and slow convergence speed of the global optimization inversion method, the article proposed the simplex-simulated annealing algorithm for transi...
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In order to avoid the linear inversion method falling into local minima and slow convergence speed of the global optimization inversion method, the article proposed the simplex-simulated annealing algorithm for transient electromagnetic inversion research which combines advantages of the simplex method and the simulated annealing algorithm. The simplex method is used to obtain local minimum value which is relatively close to the actual value, then the simulated annealing algorithm is used to obtain the global optimal solution which can better reflect structural characteristics of the real stratigraphic *** the comparison of the noise inversion results and noise free inversion results about K-type, H-type, KH-type and HKH-type stratigraphic models, it can be proved that the simplex-simulated annealing algorithm can suppress some noise. The comparison of the simulated annealing method and the simplex-simulated annealing algorithm shows that the simplex-simulated annealing algorithm has the characteristics of global searching ability and fast convergence speed.
In the process of image acquisition and transmission, the image always generates noise due to internal and external interference. Noise reduces the quality of the image, and makes it difficult for subsequent image pro...
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In the process of image acquisition and transmission, the image always generates noise due to internal and external interference. Noise reduces the quality of the image, and makes it difficult for subsequent image processing. Therefore, image denoising is very important in image processing. Wavelet denoising can effectively filter out noise and retain high-frequency information of the image, this method has the characteristics of fast operation speed and has become an important branch of image denoising. Threshold functions commonly used in wavelet threshold denoising include hard threshold function and soft threshold function. The hard threshold function is not continuous as a whole. Although the soft threshold function has good continuity, there is always a constant deviation between the processed coefficient and the original coefficient when the wavelet coefficient is large. In response to these deficiencies, this paper establishes a new improved threshold function based on traditional soft and hard threshold functions. By processing the thresholds of wavelet coefficients, a reasonable balance between smoothing and edge oscillations can be achieved after image denoising. The improved threshold function not only overcomes the shortcomings of the soft and hard threshold functions, but also provides more flexibility in the processing of image *** MATLAB simulation, the denoising effects of the soft, hard threshold functions and the threshold function constructed in this paper are compared in terms of signal-to-noise ratio（SNR） and root mean square error（MSE）. The MATLAB simulation results show that compared with the traditional threshold function, the improved threshold function has a higher signal-to-noise ratio（SNR = 26.27709） and a smaller mean square error（MSE = 153.4579）, and it has a good noise reduction effect.
In this paper,the problem on guaranteed H∞ performance state estimation for static neural networks with a timevarying delay is investigated and the corresponding criterion is ***,a novel augmented Lyapunov-Krasovskii...
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In this paper,the problem on guaranteed H∞ performance state estimation for static neural networks with a timevarying delay is investigated and the corresponding criterion is ***,a novel augmented Lyapunov-Krasovskii functional(LKF) is *** the derivative of the LKF is estimated by the relaxed integral ***,the state estimator can be calculated by solving a set of linear matrix ***,an example is used to illustrate the effectiveness of the proposed method.
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