Неразрушающий контроль стальных тосов с использованием оптимизированного метода опорных векторов

In this paper, to address the problems of poor signal noise reduction and low recognition rate in wire rope leakage magnetic detection, we propose the algorithm MSVDW, which uses a combination of median filtering, singular value decomposition (SVD) and wavelet transform, to denoise the collected three-dimensional MFL signals. Then, false color is used to enhance the image. The image is then localized and segmented using the modulus maximum method. The color moments are extracted from the images and used as the input of the particle swarm algorithm optimized support vector machine (PSO-SVM) for training and recognition. The experimental results show that the noise reduction algorithm proposed in this paper effectively reduces the noise of the leakage signal, the false color image enhances the defect image information, and the algorithm of PSO-SVM greatly improves the recognition rate of defects.

Evaluating Operational and Experimental HRRR model forecasts of Atmospheric River events in California

Improved forecasts of Atmospheric River (AR) events, which provide up to half the annual precipitation in California, may reduce impacts to water supply, lives, and property. We evaluate Quantitative Precipitation Forecasts (QPF) from the High-Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) for five AR events that occurred in Feb-Mar 2019 and compare them to Quantitative Precipitation Estimates (QPE) from Stage IV and Mesonet products. Both HRRR versions forecast spatial patterns of precipitation reasonably well, but are drier than QPE products in the Bay Area and wetter in the Sierra Nevada range. The HRRR dry bias in the Bay Area may be related to biases in the model temperature profile, while IWV, wind speed, and wind direction compare reasonably well. In the Sierra Nevada range, QPE and QPF agree well at temperatures above freezing. Below freezing, the discrepancies are due in part to errors in the QPE products, which are known to underestimate frozen precipitation in mountainous terrain. HRRR frozen QPF accuracy is difficult to quantify, but the model does have wind speed and wind direction biases near the Sierra Nevada range. HRRRv4 is overall more accurate than HRRRv3, likely due to data assimilation improvements, and possibly physics improvements. Applying a Neighborhood Maximum method impacted performance metrics, but did not alter general conclusions, suggesting closest grid box evaluations may be adequate for these types of events. Improvements to QPF in the Bay Area and QPE/QPF in the Sierra Nevada range would be particularly useful to provide better understanding of AR events.

A Class of Exponentiated Regression Model for Non Negative Censored Data with an Application to Antibody Response to Vaccine

In this paper, an asymmetric regression model for censored non-negative data based on the centred exponentiated log-skew-normal and Bernoulli distributions mixture is introduced. To connect the discrete part with the continuous distribution, the logit link function is used. The parameters of the model are estimated by using the likelihood maximum method. The score function and the information matrix are shown in detail. Antibody data from a study of the measles vaccine are used to illustrate applicability of the proposed model, and it was found the best fit to the data with respect to an others models used in the literature.

Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017

The level of poverty in a Regency/city in South Sulawesi in 2017 is different. The grouping of poverty status can be done based on the value of the HeadCount Index (HCI) of South Sulawesi. Factors affecting poverty will differ for each area being observed. The statistical modeling method developed for data analysis by taking into account the location factor is semiparametric Geographical Weighted Logistic Regression (GWLR). The GWLR semiparametric Model consists of parameters that are affected by the location and not affected by the location. The parameter estimator of the GWLR semiparametric model used in this research was obtained using the maximum method likelihood estimation. The result of a semiparametric model of GWLR each district/city in South Sulawesi in 2017 has the value Estimator parameter for global parameters is the same value for each location, namely, a3 = 0.1724, a4 = 0.0204, and a6 = 0.0261 whereas the parameter estimator for local parameters has different values so that GWLR semiparametric model of each district/city.

Improved AEB algorithm combined with estimating the adhesion coefficient of road ahead and considering the performance of EHB

This paper proposes an improved autonomous emergency braking (AEB) algorithm intended for intelligent vehicle. Featuring a combination with the estimation of road adhesion coefficient, the proposed approach takes into account the performance of electronic hydraulic brake. In order for the accurate yet fast estimate of road ahead adhesion coefficient, the expectation maximization framework is applied depending on the reflectivity of ground extracted by multiple beams lidar in four major steps, which are the rough extraction of ground points based on 3 σ criterion, the accurate extraction of ground points through principal component analysis (PCA), the main distribution characteristics of ground as extracted using the expectation maximum method (EM) and the estimation of road adhesion coefficient via joint probability. In order to describe the performance of EHB, the response characteristics, as well as the forward and adverse models of both braking pressure and acceleration are obtained. Then, with two typical roads including single homogeneous road and fragment pavement, the safe distance of improved AEB is modeled. To validate the algorithm developed in this paper, various tests have been conducted. According to the test results, the reflectivity of laser point cloud is effective in estimating the road adhesion coefficient. Moreover, considering the performance of EHB system, the improved AEB algorithm is deemed more consistent with the practicalities.

Learning a Convolutional Autoencoder for Nighttime Image Dehazing

Currently, haze removal of images captured at night for foggy scenes rely on the traditional, prior-based methods, but these methods are frequently ineffective at dealing with night hazy images. In addition, the light sources at night are complicated and there is a problem of inconsistent brightness. This makes the estimation of the transmission map complicated in the night scene. Based on the above analysis, we propose an autoencoder method to solve the problem of overestimation or underestimation of transmission captured by the traditional, prior-based methods. For nighttime hazy images, we first remove the color effect of the haze image with an edge-preserving maximum reflectance prior (MRP) method. Then, the hazy image without color influence is input into the self-encoder network with skip connections to obtain the transmission map. Moreover, instead of using the local maximum method, we estimate the ambient illumination through a guiding image filtering. In order to highlight the effectiveness of our experiments, a large number of comparison experiments were conducted between our method and the state-of-the-art methods. The results show that our method can effectively suppress the halo effect and reduce the effectiveness of glow. In the experimental part, we calculate that the average Peak Signal to Noise Ratio (PSNR) is 21.0968 and the average Structural Similarity (SSIM) is 0.6802.

THE CALCULATION OF BACKLASH IN THE GEARS BY USING THE MONTE CARLO METHOD

This paper is devoted to ensuring normal lateral clearance during the normalization of manufacturing errors of gear wheels and non-gear transmission elements. The task is complicated by the fact that GOST 1643–81 sets tolerances and maximum deviations relative to the working axles of the gears. The methods of calculating the lateral clearance of spur gears are considered. As the influencing factors on the lateral clearance, the thermal expansion of the link materials, the deviation of the interaxial distance of the wheels, the deviation of the engagement pitch, the radial run-out of the gear crowns, the error in the direction of the tooth, the parallelism and skew of the axles of the wheels are taken into account. The transmission accuracy parameters are read randomly. The results of calculation by the minimum-maximum method and the probabilistic method are compared. As a probabilistic calculation method, the Monte Carlo method is adopted. The input calculation parameters are taken equal to the maximum allowable values from GOST 1643–81, the parameters of the kinematic error of the wheels for calculation by the probabilistic method are considered distributed according to the equally probable and normal distribution laws.

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection

Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.

Author Correction: A full-width half-maximum method to assess retinal vascular structural changes in patients with ischemic heart disease and microvascular angina

An amendment to this paper has been published and can be accessed via a link at the top of the paper.