Flexure hinges are susceptible to fatigue damage under cyclic loading, resulting in performance degradation. This paper investigates the stiffness degradation of the right circular flexure hinges ...(RCFHs) under cyclic loading. Fatigue damage experiments are conducted to obtain the stiffness degradation curves, which can be divided into several stages by feature points. A relationship between feature lives and alternating stress amplitudes is established. A fatigue damage stiffness degradation piecewise curve model for RCFHs is proposed. The effect of notch stress concentration on fatigue damage is analyzed. Fatigue damage experiments under non-zero mean stress are conducted, and an equivalent fatigue stress equation is obtained. Finally, a generalized fatigue damage stiffness degradation model for RCFHs is developed, which establishes a relationship between residual stiffness and cycle number. On this basis, a fatigue damage performance modeling method for flexure hinge mechanisms is proposed. The fatigue damage performance of a compliant bridge mechanism was modeled and tested. The experimental results of input stiffness degradation are generally in agreement with the predicted results, which verify the validity of the method.
To design a flexure hinge with high precision and high natural frequency, the sinusoidal flexure hinge is proposed in this article. First, the formulae for the compliance and precision factors of the ...hinge were derived based on the Euler–Bernoulli beam theory and the Gauss–Legendre quadrature formula. The natural frequency was also investigated based on the transfer matrix method. Compared with the simulation results of ANSYS Workbench, the results show that the modeling error is less than 6.7%. Second, the influence of structural parameters on compliance, precision factor, compliance precision ratio, and natural frequency was analyzed. The results show that compliance and precision are often contradictory, and the minimum thickness significantly influences the hinge's performance. Compared with conic flexure hinges in terms of compliance, precision, compliance precision ratios, and natural frequency, the sinusoidal flexure hinges have a better comprehensive performance. Finally, a flexure hinge was manufactured, and compliance was measured. The experimental results show that the error between the experimental value and the modeling value is 7.8%. Both simulation and experimental results verify the effectiveness of the sinusoidal flexure hinge model.
A new type of sinusoidal flexure hinge is proposed in this article. The compliance and precision factors formulas of the hinge were derived based on the Euler–Bernoulli beam theory and the Gauss–Legendre quadrature formula. The natural frequency was also investigated based on the transfer matrix method.
Lilium concolor Salisb. is a perennial herb with high ornamental and medicinal value in China. The complete chloroplast genome sequence of L. concolor was assembled using high-throughput sequencing ...data. The chloroplast genome of L. concolor is 152,625 bp in length and consists of large single-copy (82,056 bp) and small single-copy (17,585 bp) regions, and a pair of inverted repeat (26,492 bp) regions. A total of 131 genes were annotated, these included 85 protein-coding, 38 tRNA, and eight rRNA genes, with an overall GC content of 37.0%. Phylogenetic analysis with 48 chloroplast genomes fully resolved L. concolor in a clade with L. amabile, L. callosum, and L. pumilum. This study further confirmed that chloroplast genomes in the genus Lilium are highly conserved, which supports the conclusions from previous reports.
Intellectual property rights are becoming increasingly important for the survival and development of hi-tech enterprises. Nowadays, there exist various problems in the protection and management of ...intellectual property, which lead to numerous losses. Therefore, it is advisable to improve the legal system about the administrative protection of intellectual property rights in hi-tech enterprises, as well as strengthen the intellectual property management system, establish an effective and rigorous system of intellectual property protection. Meanwhile, we should, by employing scientific strategic measures of directing staff to make the most of intellectual property, attaching great importance to patent documents, enhancing innovative ability, implementing patent strategy, as well as forming technology alliance, to protect hi-tech intellectual property rights and make full use of them.
Many investigations have revealed that transition of melt structure can effectively influence the final solidification microstructures. In this study, ultrasonic treatment was applied to AI-20%Si ...melt and Sr-modified AI-20%Si melt at 720 ℃ （i.e. above liquidus of about 690℃） for 60 s, and then the melt was quickly quenched to room temperature. Experimental results show that ultrasonic treatment can refine the primary Si phase and a-AI of AI-20%Si alloy; strontium can make the morphology of Si phase spheroidized and refined as Sr addition changes the faceted growth characteristic of Si phase; however, the refinement effect of ultrasonic treatment on the primary Si phase and α-AI is weakened by Sr addition.
Cephalopods play key roles in global marine ecosystems as both predators and preys. Regressive estimation of original size and weight of cephalopod from beak measurements is a powerful tool of ...interrogating the feeding ecology of predators at higher trophic levels. In this study, regressive relationships among beak measurements and body length and weight were determined for an octopus species （Octopus variabilis）, an important endemic cephalopod species in the northwest Pacific Ocean. A total of 193 indi- viduals （63 males and 130 females） were collected at a monthly interval from Jiaozhou Bay, China. Regressive relationships among 6 beak measurements （upper hood length, UHL; upper crest length, UCL; lower hood length, LHL; lower crest length, LCL; and upper and lower beak weights） and mantle length （ML）, total length （TL） and body weight （W） were determined. Results showed that the relationships between beak size and TL and beak size and ML were linearly regressive, while those between beak size and W fitted a power function model. LHL and UCL were the most useful measurements for estimating the size and biomass of O. variabilis. The relationships among beak measurements and body length （either ML or TL） were not significantly different between two sexes; while those among several beak measurements （UHL, LHL and LBW） and body weight （W） were sexually different. Since male individu- als of this species have a slightly greater body weight distribution than female individuals, the body weight was not an appropriate measurement for estimating size and biomass, especially when the sex of individuals in the stomachs of predators was unknown. These relationships provided essential information for future use in size and biomass estimation of O. variabilis, as well as the esti- mation of predator/prey size ratios in the diet of top predators.
Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers ...new opportunities for data-driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE-Net 2.0, to discover (time-dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE-Net 2.0 is based on our earlier work 1 where the original version of PDE-Net was proposed. PDE-Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi-layer neural network for model recovery. Comparing with existing approaches, PDE-Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE-Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.
•The proposal of a numeric-symbolic hybrid deep network to recover PDEs from observed dynamic data.•The symbolic network is able to recover concise analytic form of the hidden PDE model.•Our approach only requires minor prior knowledge on the mechanism of the observed dynamic data.•The network can perform accurate long-term prediction without re-training for new initial conditions.
In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in ...remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds.
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