Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently ...depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.
A new method is described to derive mixing-height time series directly from aerosol-layer height data available from a Vaisala CL51 ceilometer. As complete as possible mixing-height time series are ...calculated by avoiding outliers, filling data gaps by linear interpolation, and smoothing. In addition, large aerosol-layer heights at night that can be interpreted as residual layers are not assigned as mixing heights. The resulting mixing-height time series, converted to an appropriate data format, can be used as input for dispersion calculations. Two case examples demonstrate in detail how the method works. The mixing heights calculated using ceilometer data are compared with values determined from radiosounding data at Vienna by applying the parcel, Heffter, and Richardson methods. The results of the parcel method, obtained from radiosonde profiles at noon, show the best fit to the ceilometer-derived mixing heights. For midnight radiosoundings, larger deviations between mixing heights from the ceilometer and those deduced from the potential temperature profiles of the soundings are found. We use data from two Vaisala CL51 ceilometers, operating in the Vienna area at an urban and rural site, respectively, during an overlapping period of about 1 year. In addition to the case studies, the calculated mixing-height time series are also statistically evaluated and compared, demonstrating that the ceilometer-based mixing height follows an expected daily and seasonal course.
As tree rings can reveal various information regarding climate and environmental factors, increasing research is being conducted on them. Although tree ring analysis software such as Windendro has ...been applied, research on the development of analysis software using image preprocessing algorithms and deep learning is recently being attempted as computer vision technology. In this study, Mask R-CNN and linear interpolation were applied from images collected using a smartphone (SM-G973, Samsung, Suwon) and a scanner (CanoScan 9000F, Canon, Otaku) to propose an effective method for detecting tree ring boundaries. Pitch pine (Pinus rigida), Korean pine (Pinus koraiensis), white birch (Betula platyphylla), and cork oak (Quercus variabilis) were selected as tree species. Of the 300 images, 240 were classified as training data and 60 as validation data. As a result of learning, smartphones detected 86.0 % (381 ring boundaries) of the rings in pitch pine, 82.1 % (367 ring boundaries) in Korean pine, 84.7 % (309 ring boundaries) in white birch, and 78.7 % (318 ring boundaries) in cork oak. The scanner detected 93.2 % (413 ring boundaries) of the rings in pitch pine, 90.8 % (405 ring boundaries) in Korean pine, 88.2 % (322 ring boundaries) in white birch, and 89.4 % (361 ring boundaries) in cork oak. In particular, the smartphone showed satisfactory results of 84.7 % and 78.7 % for detecting tree ring boundaries of white birch and cork oak, where the boundaries of the rings were unclear. In the annual growth analysis results, both smartphones and scanners were statistically insignificant, and there was no difference compared with those of Windendro. Therefore, Mask R-CNN might be an effective approach for tree ring boundary detection as it showed satisfactory results, even with smartphones. In addition, although there was distortion in cases where images were acquired with a circular lens, there was no statistically significant difference from Windendro results. Thus, Mask R-CNN and linear interpolation can be used for tree ring boundary detection and growth measurement.
To accurately estimate the battery state of health (SOH) is crucial to enhance the performance of a battery-powered system. This paper proposes a SOH estimation method that can predict the battery ...current SOH when the battery is fully charged. When the state of charge (SOC) is 100%, the AC impedance of the Li-ion battery at each frequency can be obtained by electrochemical impedance spectroscopy (EIS) and used as the training data of the neural network. The result has been validated by another aging battery and it shows that with the best performance neural network, the maximum relative error of the estimated SOH is only 1.31 %. Besides, compared to the linear interpolation method, the maximum relative error has improved by 1.74 %, MRE by 0.50 %, MAE by 0.42 %, and MSE by 0.86 %. Furthermore, this paper discusses a technique that can estimate the battery SOH under different temperatures without collecting the AC impedance through the whole cycle life test.
•An active SOH estimation method has been established by ANN.•AC impedance of the battery is utilized as the training data of ANN.•Removing the non-linear data reduces the scale of ANN and improves its performance.•The MAE, MRE and MSE of proposed ANN are 0.35, 0.41 % and 0.21 %, respectively.•The sensitivity of the neural network to the temperature has been validated.
We analyse the strong approximation of the Cox–Ingersoll–Ross (CIR) process in the regime where the process does not hit zero by a positivity preserving drift-implicit Eulertype method. As an error ...criterion, we use the pth mean of the maximum distance between the CIR process and its approximation on a finite time interval. We show that under mild assumptions on the parameters of the CIR process, the proposed method attains, up to a logarithmic term, the convergence of order 1/2. This agrees with the standard rate of the strong convergence for global approximations of stochastic differential equations with Lipschitz coefficients, despite the fact that the CIR process has a non-Lipschitz diffusion coefficient.
Due to the compute-intensiveness and the lack of robustness of the algorithms for reconstruction of meshes and spline surfaces from point clouds, there is a need for further research in the topic of ...direct tool-path planning based on point clouds. In this paper, a novel approach for planning iso-parametric tool-path from a point cloud is presented. Since such planning falls into the iso-parametric category, it intrinsically depends on the parameterization of point clouds. Accordingly, a point-based conformal map is employed to build the parameterization. Based on it, formulas of computing path parameters are derived, which are much simpler than the conventional ones. By regularizing parameter domain and on the basis of the previous formulas, boundary conformed tool-path can be generated with forward and side step calculated against specified chord deviation and scallop height, respectively. Experimental results are given to illustrate the effectiveness of the proposed methods.
•A point-based iso-parametric tool-path planning method was proposed.•The formulas for computing forward and side step were simplified significantly.•Boundary conformed tool-path was generated for point clouds.
This paper studies the bifurcation case for the planar phase space long-term density propagation problem, and presents an improved multi-segment continuum method for accurate and efficient long-term ...density propagation, by introducing the multi-segment method to the alpha shape triangulation-based linear interpolation method. The density evolution equation is formulated for the continuum density propagation under the influence of the solar radiation pressure and Earth’s oblateness using semi-analytical equations. For the overall highly deformed and elongated density distribution for the bifurcation case, the multi-segment method is introduced to the alpha shape-based linear interpolation method to get accurate interpolated density, by dividing the overall density distribution into multiple segments and performing the linear interpolation within the actual non-convex hull of the sample distribution for each segment. Four segments are divided for the overall density distribution considering the Hamiltonian dynamic constraints on the solar angle domain. The superiority of the improved multi-segment alpha shape-based continuum method is demonstrated for accurate and efficient density propagation for the bifurcation case in the context of the high-altitude and high area-to-mass ratio satellite long-term propagation.
We develop new distribution discontinuity tests conditional on multiple explanatory variables for analyzing meet-or-just-beat behavior around benchmarks. These tests combine Burgstahler and Dichev's ...(1997) meet-or-just-beat intuition with a flexible statistical model that addresses important limitations of the existing tests. Our method considerably outperforms logit-based tests of distribution discontinuity determinants and changes the interpretation of a major finding in the earnings discontinuity literature. As a secondary benefit, it also has slightly higher statistical power than histogram-based tests of distribution discontinuity existence. Our method is robust, easy to implement using our publicly available Stata command, and could benefit researchers in many fields.
•We develop distribution discontinuity tests with multiple explanatory variables.•Our tests measure the determinants of distribution discontinuity much better than the standard logit approach.•Our tests detect the existence of discontinuity a little better than standard histogram-based tests.•Our tests change a major finding in the earnings discontinuity literature.•Our distribution discontinuity tests are flexible, robust, and easy to implement with our custom Stata estimation command.
Individuals spend time on online video-sharing platforms searching for videos. Video summarization helps search through many videos efficiently and quickly. In this paper, we propose an unsupervised ...video summarization method based on deep reinforcement learning with an interpolation method. To train the video summarization network efficiently, we used the graph-level features and designed a reinforcement learning-based video summarization framework with a temporal consistency reward function and other reward functions. Our temporal consistency reward function helped to select keyframes uniformly. We present a lightweight video summarization network with transformer and CNN networks to capture the global and local contexts to efficiently predict the keyframe-level importance score of the video in a short length. The output importance score of the network was interpolated to fit the video length. Using the predicted importance score, we calculated the reward based on the reward functions, which helped select interesting keyframes efficiently and uniformly. We evaluated the proposed method on two datasets, SumMe and TVSum. The experimental results illustrate that the proposed method showed a state-of-the-art performance compared to the latest unsupervised video summarization methods, which we demonstrate and analyze experimentally.
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