Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers’ motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers’ actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, including entry, pull, exit and recovery. The experiment shows that our method can provide comprehensive motion evaluation information under real on-water scenario, and the phase identification of kayaker’s motions is up to 98% validated by videography method. The proposed approach can provide quantitative information for coaches and athletes, which can be used to improve the training effects.
The slanted-edge method for modulation transfer function (MTF) measurement uses edge target images whose gray values are often affected by noise and other factors, decreasing its accuracy. We first analyze the ill-posedness in the edge spread function (ESF) regression caused by noise. Second, we propose a regularized slanted-edge method to solve this problem by incorporating a Tikhonov regularization term. Combined with varying precision weights, the ESF is solved using the variational principle, and the MTF is estimated using the regularized ESF. The regularized slanted-edge method is verified for Gaussian, gamma, and Rayleigh noise. The results show that our method improves the accuracy by 0.01-9.02% and 4.33% on average. The proposed method is more robust to noise and accurate than the slanted-edge method.
Most commonly used camera characterization methods do not use a deep learning‐based artificial neural network approach at present. This article proposes a colorimetric characterization method for color imaging systems based on the multi‐input particle swarm optimization backpropagation neural network. Combined with a particle swarm optimization algorithm for global search and a 19‐input vector, this method not only overcomes the effects of local extrema on the multi‐input backpropagation neural network, but also improves the accuracy of the common input backpropagation neural network. Images of a ColorChecker SG chart were collected using a Canon EOS 1000D camera for experimental verification, and the color differences were used to evaluate the characterization results. The results show that the color differences of the multi‐input particle swarm optimization backpropagation neural network (structure: 19‐7‐3) model are substantially better than those of the multi‐input backpropagation neural network (structure: 19‐7‐3) and common input backpropagation neural network (structure: 3‐4‐3) models. Its performance is close to that of the weighted nonlinear regression model. The multi‐input particle swarm optimization backpropagation neural network is hence an effective method for colorimetric characterization with good prediction accuracy.
A method for polynomial fitting of Wassermann-Wolf surfaces and the results are presented. Conformal optics by which the missile dome is shaped to aerodynamic requirements generally introduces various kinds of aberrations as targe as tens to hundreds of wave lengths across the field of regard. Through polynomial fitting of Wassermann-Wolf surfaces and using Zernike polynomials instead of traditional merit function, the conformal optical system is implemented and an example is demonstrated. The results show that MTF of the optical system approaches to the diffraction limit across the field of view.
China Time-honored Brand " is a treasure of Chinese traditional culture, and it have received extensive social recognition because of their distinctive national characteristics and profound historical and cultural connotations. However, in recent years, the marketing status of some Time-honored Brand enterprises are not optimistic. On the one hand, these are not good at using self-Media to promote their own brands, resulting in the post-90s and post-00s are not familiar with or even forgotten about these Time-honored Brand enterprises, thus resulting in the phenomenon of consumer disruption. On the other hand, Time-honored Brand enterprises are not good at self-Media platform application and fail to give full play to the advantages of micro-marketing. At present, there are 117 Time-honored Brand enterprises in Beijing. The author mainly analyses the current situation of micro-marketing of these 117 Time-honored Brand enterprises, summarizing their existing problems, and gives corresponding micro-marketing strategies. 2. The present situation and problems of micro-marketing in Time-honored Brand enterprises 2.1 Present situation of micro-marketing in Time-honored Brand enterprises According to the official data of China Time-honored Brand Information Management, a unified platform of business system of the Ministry of Commerce, there are 1128 China Time-honored Brand enterprises up to now, of which 117 are in Beijing. Next, the application status of micromarketing tools in 117 Time-honored Brand enterprises in Beijing is investigated, such as official websites, official flagship stores, WeChat, micro-blog and so on. The following is a statistics of the number of self-Media platforms owned by Beijing's Time-honored Brand enterprises, as shown in Figure 1.
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