Academic literature on the topic 'Force histogram'

In order to describe directly interphase properties of composite, force modulation of atomic force microscopy is adopted to study the cross-section of unidirectional carbon fiber/epoxy composites systematically. Research results indicate that in force modulation mode of AFM, relative stiffness of various phases distinct in distribution, which is described by probability histogram of relative stiffness. By comparison of probability histogram of relative stiffness nearby interphase of untreated and oxidation treated by ozone composites, the relative stiffness change oxidation treated one was more obviously to be found than the one untreated. Indirect show that obvious interphase formed by oxidation treatment. This method plays a valuable role in assessment of interphase strength of carbon fiber/epoxy, as well as in instruction composite production technology.

We propose a study of the Adaptive Biasing Force method’s robustness under generic (possibly non-conservative) forces. We first ensure the flat histogram property is satisfied in all cases. We then introduce a fixed point problem yielding the existence of a stationary state for both the Adaptive Biasing Force and Projected Adapted Biasing Force algorithms, relying on generic bounds on the invariant probability measures of homogeneous diffusions. Using classical entropy techniques, we prove the exponential convergence of both biasing force and law as time goes to infinity, for both the Adaptive Biasing Force and the Projected Adaptive Biasing Force methods.

Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.

Chapple, William. Regulation of muscle stiffness during periodic length changes in the isolated abdomen of the hermit crab. J. Neurophysiol. 78: 1491–1503, 1997. Reflex activation of the ventral superficial muscles (VSM) in the abdomen of the hermit crab, Pagurus pollicarus, was studied using sinusoidal and stochastic longitudinal vibration of the muscle while recording the length and force of the muscle and the spike times of three exciter motoneurons. In the absence of vibration, the interspike interval histograms of the two larger motoneurons were bimodal; cutting sensory nerves containing most of the mechanoreceptor input removed the short interval peak in the histogram, indicating that the receptors are important in maintaining tonic firing. Vibration of the muscle evoked a reflex increase in motoneuron frequency that habituated after an initial peak but remained above control levels for the duration of stimulation. Motoneuron frequency increased with root mean square (rms) stimulus amplitude. Average stiffness during stimulation was about two times the stiffness of passive muscle. The reflex did not alter muscle dynamics. Estimated transfer functions were calculated from the fast Fourier transform of length and force signals. Coherence was >0.9 for the frequency range of 3–35 Hz. Stiffness magnitude gradually increased over this range in both reflex activated and passive muscle; phase was between 10 and 20°. Reflex stiffness decreased with increasing stimulus amplitudes, but at larger amplitudes, this decrease was much less pronounced; in this range stiffness was regulated by the reflex. The sinusoidal frequency at which reflex bursts were elicited was ∼6 Hz, consistent with previous measurements using ramp stretch. During reflex excitation, there was an increase in amplitude of the short interval peak in the interspike interval histogram; this was reduced when the majority of afferent pathways was removed. A phase histogram of motoneuron firing during sinusoidal vibration had a peak at ∼110 ms, also suggesting that an important component of the reflex is via direct projections from the mechanoreceptors. These results are consistent with the hypothesis that a robust feedforward regulation of abdominal stiffness during continuous disturbances is achieved by mechanoreceptors signalling the absolute value of changing forces; habituation of the reflex, its high-threshold for low frequency disturbances and the activation kinetics of the muscle further modify reflex dynamics.

Purpose The purpose of this paper is to solve the problem that the relationship between loading forces, which were applied at different positions on a plane, and output values of load-sharing dynamometer is non-linear. Design/methodology/approach First, the analytical model of ISPM (isodynamic surface proportional mapping method) method, which is used to calibrate dynamometer, was established. Then, a series of axial force calibration tests were performed on a load-sharing dynamometer at different loading positions. Finally, according to output values, calibration forces at different loading positions were calculated by ISPM method, and corresponding distribution histogram of calibration force error was generated. Findings The largest error between calculated force and standard force is 2.92 per cent, and the probability of calculated force error within 1 per cent is 91.03 per cent, which verify that the ISPM method is reliable for non-linear calibration of dynamometers. Originality/value The proposed ISPM method can achieve non-linear calibration between measured force and output signal of load-sharing dynamometer at different positions. In addition, ISPM method can also solve some complex non-linear problems, such as prediction of plane cutting force under the influence of multiple parameters, the force measurement of multi-degree-of-freedom platform and so on.

Sign language image recognition is also a very interesting research topic. Because it can be applied to help normal people understand and use it as a communication tool for the hearing impaired. The objectives of this research were to: 1) study and analyze Thai Sign Language image recognition image data for hearing impaired, 2) develop Thai Sign Language image recognition system for hearing impaired by using new techniques, and 3) measure the efficiency of Thai sign language image recognition for hearing impaired using Radial Inverse Force Histogram and the Maximum and Minimum boundary values. The results of the research were as follows: 1) The study and analysis of image data of Thai Sign Language Image Recognition In this research, 62,694 sign language images were used, divided into 2 parts: 1) American Sign Language images, which consisted of 36 groups of images, namely 26 groups of letters (AZ) and 10 groups of numbers (0-9), and Part 2) Picture of Thai Sign Language consisting of 61 groups of images, including 44 groups of letters (ก-ฮ), 7 groups of vowels and 10 groups of numbers (0-9). Each group of pictures is rotated, enlarged, and Image promotion There were 6 sub-groups of images in various forms, divided into 2 parts: 70% of the images for training and 30% of the images for testing. 3) The results of measuring the efficiency of image recognition. It is divided into two parts: American Sign Language Image Recognition and Thai Sign Language Image Recognition. Compared with the Angular histogram method, the mean image accuracy was 0.86, the recall of the mean American sign language was 0.91, and the accuracy of the Thai Sign Language was 0.78. The recognition performance for Thai Sign Language images averaged 0.89, while the recognition efficiency was achieved when using radial inverse force histograms in combination with image similarity measurements with maximum-minimum boundary values. Accuracy for Mean American Sign Language was 0.99 and Remembrance for Mean American Sign was 1.00, while Accuracy for Mean Thai Sign Language was 0.89. Mean Remembrance for Thai Sign Language was 0.96. The results of the visual recognition performance measurement of both the American Sign Language and the Thai Sign Language images were very good compared to the Angular Histogram method.