Single Gaussian Model Research Articles

Research on multi-source information fusion tool wear monitoring based on MKW-GPR model

To enhance intelligent manufacturing capabilities and achieve the goal of online real-time high-precision monitoring of tool wear with small sample data, this paper proposes a multi-source information fusion method for turning tool wear monitoring based on the multi-kernel weighted Gaussian process regression (MKW-GPR) model. Firstly, the framework and process of multi-source information fusion for turning tool wear monitoring are established, and the experimental platform is constructed. Secondly, a full-life cutting experiment of the turning tool is conducted. During the experiment, tool wear image data and multi-sensor signal data, including cutting force and vibration signals, are collected. Then, the experimental data were processed to construct a dataset with multi-source information fusion, leading to the establishment of the MKW-GPR model. Subsequently, the experimental dataset was employed to validate the effectiveness of the turning tool wear monitoring model. The results demonstrate that, in recognizing the wear state, the MKW-GPR model exhibited superior recognition performance compared to the single kernel function Gaussian process regression model. Furthermore, in predicting the wear value, the MKW-GPR model is better than the long short-term memory (LSTM), random forest (RF), and scalable end-to-end tree boosting (XGBoost) models. During the small sample size a priori point data prediction performance test, the MKW-GPR model achieved the highest accuracy, with a maximum relative error of only 1.92% in predicting tool life during the severe wear stage. These findings substantiate the feasibility and effectiveness of the proposed turning tool wear monitoring method.

Improving population-level refractive error monitoring via mixture distributions.

Sampling and describing the distribution of refractive error in populations is critical to understanding eye care needs, refractive differences between groups and factors affecting refractive development. We investigated the ability of mixture models to describe refractive error distributions. We used key informants to identify raw refractive error datasets and a systematic search strategy to identify published binned datasets of community-representative refractive error. Mixture models combine various component distributions via weighting to describe an observed distribution. We modelled raw refractive error data with a single-Gaussian (normal) distribution, mixtures of two to six Gaussian distributions and an additive model of an exponential and Gaussian (ex-Gaussian) distribution. We tested the relative fitting accuracy of each method via Bayesian Information Criterion (BIC) and then compared the ability of selected models to predict the observed prevalence of refractive error across a range of cut-points for both the raw and binned refractive data. We obtained large raw refractive error datasets from the United States and Korea. The ability of our models to fit the data improved significantly from a single-Gaussian to a two-Gaussian-component additive model and then remained stable with ≥3-Gaussian-component mixture models. Means and standard deviations for BIC relative to 1 for the single-Gaussian model, where lower is better, were 0.89 ± 0.05, 0.88 ± 0.06, 0.89 ± 0.06, 0.89 ± 0.06 and 0.90 ± 0.06 for two-, three-, four-, five- and six-Gaussian-component models, respectively, tested across US and Korean raw data grouped by age decade. Means and standard deviations for the difference between observed and model-based estimates of refractive error prevalence across a range of cut-points for the raw data were -3.0% ± 6.3, 0.5% ± 1.9, 0.6% ± 1.5 and -1.8% ± 4.0 for one-, two- and three-Gaussian-component and ex-Gaussian models, respectively. Mixture models appear able to describe the population distribution of refractive error accurately, offering significant advantages over commonly quoted simple summary statistics such as mean, standard deviation and prevalence.

Validating a double Gaussian source model for small proton fields in a commercial Monte-Carlo dose calculation engine

PurposeThe primary fluence of a proton pencil beam exiting the accelerator is enveloped by a region of secondaries, commonly called “spray”. Although small in magnitude, this spray may affect dose distributions in pencil beam scanning mode e.g., in the calculation of the small field output, if not modelled properly in a treatment planning system (TPS). The purpose of this study was to dosimetrically benchmark the Monte Carlo (MC) dose engine of the RayStation TPS (v.10A) in small proton fields and systematically compare single Gaussian (SG) and double Gaussian (DG) modeling of initial proton fluence providing a more accurate representation of the nozzle spray. MethodsThe initial proton fluence distribution for SG/DG beam modeling was deduced from two-dimensional measurements in air with a scintillation screen with electronic readout. The DG model was either based on direct fits of the two Gaussians to the measured profiles, or by an iterative optimization procedure, which uses the measured profiles to mimic in-air scan-field factor (SF) measurements. To validate the DG beam models SFs, i.e. relative doses to a 10 × 10 cm2 field, were measured in water for three different initial proton energies (100MeV, 160MeV, 226.7MeV) and square field sizes from 1×1cm2 to 10×10cm2 using a small field ionization chamber (IBA CC01) and an IBA ProteusPlus system (universal nozzle). Furthermore, the dose to the center of spherical target volumes (diameters: 1cm to 10cm) was determined using the same small volume ionization chamber (IC). A comprehensive uncertainty analysis was performed, including estimates of influence factors typical for small field dosimetry deduced from a simple two-dimensional analytical model of the relative fluence distribution. Measurements were compared to the predictions of the RayStation TPS. ResultsSFs deviated by more than 2% from TPS predictions in all fields <4×4cm2 with a maximum deviation of 5.8% for SG modeling. In contrast, deviations were smaller than 2% for all field-sizes and proton energies when using the directly fitted DG model. The optimized DG model performed similarly except for slightly larger deviations in the 1×1cm2 scan-fields. The uncertainty estimates showed a significant impact of pencil beam size variations (±5%) resulting in up to 5.0% standard uncertainty. The point doses within spherical irradiation volumes deviated from calculations by up to 3.3% for the SG model and 2.0% for the DG model. ConclusionProperly representing nozzle spray in RayStation’s MC-based dose engine using a DG beam model was found to reduce the deviation to measurements in small spherical targets to below 2%. A thorough uncertainty analysis shows a similar magnitude for the combined standard uncertainty of such measurements.

ALMA Reveals Extended Cool Gas and Hot Ionized Outflows in a Typical Star-forming Galaxy at Z = 7.13

We present spatially resolved morphological properties of [C II] 158 μm, [O III] 88 μm, dust, and rest-frame ultraviolet (UV) continuum emission for A1689-zD1, a strongly lensed, sub-L* galaxy at z = 7.13, by utilizing deep Atacama Large Millimeter/submillimeter Array (ALMA) and Hubble Space Telescope (HST) observations. While the [O III] line and UV continuum are compact, the [C II] line is extended up to a radius of r ∼ 12 kpc. Using multi-band rest-frame far-infrared continuum data ranging from 52 to 400 μm, we find an average dust temperature and emissivity index of K and , respectively, across the galaxy. We find slight differences in the dust continuum profiles at different wavelengths, which may indicate that the dust temperature decreases with distance. We map the star formation rate (SFR) via IR and UV luminosities and determine a total SFR of 37 ± 1M ⊙yr−1 with an obscured fraction of 87%. While the [O III] line is a good tracer of the SFR, the [C II] line shows deviation from the local L [C II]-SFR relations in the outskirts of the galaxy. Finally, we observe a clear difference in the line profile between [C II] and [O III], with significant residuals (∼5σ) in the [O III] line spectrum after subtracting a single Gaussian model. This suggests a possible origin of the extended [C II] structure from the cooling of hot ionized outflows. The extended [C II] and high-velocity [O III] emission may both contribute in part to the high L [O III]/L [C II] ratios recently reported in z > 6 galaxies.

Validation and testing of a novel pencil-beam model derived from Monte Carlo simulations in carbon-ion treatment planning for different scenarios

PurposeThe calculation ability of the newly-proposed accurate beam model, the double Gaussian-logistic (DG-L) model, was validated in both homogeneous and heterogeneous phantoms to provide helpful information for its future application in clinical carbon-ion treatment planning system (TPS). MethodsMatRad was used as the new algorithm test platform. Based on Monte Carlo (MC) method, the basic database in matRad was generated, then comparative dosimetric analyses between the single Gaussian (SG), double Gaussian (DG) and DG-L models against the MC recalculations were performed on the treatment plans of a cubic water phantom, a TG119 phantom and a liver patient scenario. Absolute dose differences, dose-volume histograms (DVHs) and global γ-index analyses derived from the treatment plans were evaluated. ResultsCalculated with the DG-L model, the deviations of the target dose coverage (D95) for the cubic water phantom, the TG119 phantom and the liver patient case against the MC recalculations could be reduced from −2.5%, −4.6% and −6.4% to −0.3%, −2.0% and −4.5% respectively compared to the SG model, while the γ pass rates (3%/3mm) could be enhanced from 98.0%, 90.6% and 90.1% to 99.8%, 95.7% and 91.6%, respectively. The novel beam model also shows improved performance compared with the DG model, without substantially increasing the computation time. ConclusionsThe DG-L model could effectively improve the dose calculation accuracy and mitigate the delivered dose deficiency in target volumes compared to the SG and DG models. The lateral heterogeneities should be considered for its future implementation in a clinical TPS.

Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model

Hand gesture recognition is an area of study that attempts to identify human gestures through mathematical algorithms, and can be used in several fields, such as communication between deaf-mute people, human–computer interaction, intelligent driving, and virtual reality. However, changes in scale and angle, as well as complex skin-like backgrounds, make gesture recognition quite challenging. In this paper, we propose a robust recognition approach for multi-scale as well as multi-angle hand gestures against complex backgrounds. First, hand gestures are segmented from complex backgrounds using the single Gaussian model and K-means algorithm. Then, the HOG feature and an improved 9ULBP feature are fused into the HOG-9ULBP feature, which is invariant in scale and rotation and enables accurate feature extraction. Finally, SVM is adopted to complete the hand gesture classification. Experimental results show that the proposed method achieves the highest accuracy of 99.01%, 97.50%, and 98.72% on the self-collected dataset, the NUS dataset, and the MU HandImages ASL dataset, respectively.

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.

A fast radar target recognition based on single Gaussian background model

A fast radar target recognition based on single Gaussian background model

A fast radar target recognition based on single Gaussian background model

A fast radar target recognition based on single Gaussian background model

Learned Compression Framework With Pyramidal Features and Quality Enhancement for SAR Images

Current image compression algorithms based on transforms can achieve ideal performance for natural images, but do not do well with synthetic aperture radar (SAR) images. We propose a learned compression framework with pyramidal features and quality enhancement to fully exploit the redundancy among image pixels and to improve the compression bitrate and reconstruction quality. Based on the variational autoencoder (VAE) architecture, pyramidal decomposition is performed at the first autoencoder to extract both global and coarse feature maps. The latent distribution is modeled by the second hyperprior autoencoder with a single-Gaussian model for more accurate and flexible entropy estimation. Universal quantization is applied to consolidate the entropy estimation accuracy of the hyperprior network. To further improve reconstruction quality, a residual dense network (RDN) is adopted to fully capture local and global features. Experimental results demonstrate that the proposed framework provides a better rate-distortion tradeoff than standard codecs such as JPEG, JPEG2000, and learning-based methods on both the Sandia and ICEYE datasets.

Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

Objective The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

Recognition of Persian Sign Language Alphabet Using Gaussian Distribution, Radial Distance and Centroid-Radii

Aims: Presenting a novel approach to identify Persian static symptoms in that the proposed sign language recognition system consists of two segmentation and feature extraction phases. In the segmentation phase, the hand region is separated by an effective segmentation method from the original image. Objective: (1) Introducing an effective framework to solve sensitivity to light conditions in identifying and recognizing letters of the Persian sign language alphabet. (2) Proposing a new feature extraction method to eliminate sensitivity to rotation and scale changes. (3) Creating a new dataset includes 480 images of the sign language alphabet symptoms in order to show the performance of our method. Methods: The proposed sign language recognition system uses two segmentation and feature extraction phases. In the segmentation phase, the hand region is separated by an effective segmentation method from the original image. This method is based on the unique Gaussian model in the YCbCr color space. The Bayes rule is used to precisely identify the hand region too. In the feature extraction phase, the radial model is used to obtain a one-dimensional function to display the hand region boundary and to compute the combined feature vector. In order to normalize this method, the Fourier Transformation method is applied. Results: The system was trained and tested using 480 image samples of Persian sign language characters, 15 images per sign, with the .jpg extension. Extensive experimental evaluations indicate that the proposed recognition system is less susceptible to displacement, scale, and rotation, and can detect symptoms at an accuracy of 95.62%. Conclusion: In this article, a new system was proposed for recognizing the alphabet letters in Persian sign language. This system consists of two main phases: the segmentation phase or the hand region identification and feature extraction phase. In the segmentation phase of the detection system, at first, the hand region is separated by an effective segmentation method from the original image. This method is based on the single Gaussian model in the color space of the YCbCr and the Bayes rule. Applying this method in hand segmentation, enables the system, to recognize the hand region well. In this method, after hand region detection, using the Sobel edge detector, the image edge is extracted. In the feature extraction phase, the radial distance model was used to obtain a one-dimensional function display of the hand region boundary and compute its feature vector. This model is based on the edge and image centroid and computes the centroid distance to the shape boundary as a function of the angle. Since this model by itself is sensitive to the scale variations and tilt, in order to normalize this method to tilt variations, the Fourier transformation was applied to feature vector computed by the radial model. Then the vector elements are divided by the largest vector element, thus, using this method the computed feature vector will be distinct for different images and will show less sensitivity to displacement, scale, and gradient. A database consist of 480 images of Persian sign symptoms was created to the overall accuracy evaluation of the system, the results show that the proposed recognition system can identify 32 letters of the Persian sign language alphabet with a detection rate 95.62%.

Identification methodology of special behaviors for fish school based on spatial behavior characteristics

The identification of special behaviors for fish school is an effective approach to monitor fish welfare and improve the informatization level of aquaculture. In order to monitor and detect special behaviors of fish school, such as stress and feeding behavior, a scheme based on simulation feature point selection (SFPS) is proposed. In this scheme, feature points extraction and special behaviors identification are integrated seamlessly. In order to avoid the issues of poor stability and complex operation led by foreground segmentation, an image is divided into several sub-images with the help of sliding window. Feature points and velocities of sub-images are then obtained by the Harris corner detection and Lucas-Kanade optical flow. Moreover, a model coupled normal behavior characteristic matrix and the detection of special behaviors is designed to identify special behaviors of fish school. Furthermore, comparisons among SFPS, Single Gaussian Model (SGM) and the kinetic energy model are carried out. The results show that the scheme can effectively monitor the special behaviors of fish school, and the detection accuracy of feeding behavior can achieve to 96.02%. Comparing with SGM and the kinetic energy model, the accuracy of stress behavior and feeding behavior for SFPS scheme are improved by 13.25%, 4.21%, 30.24%, and 4.32% respectively. Also, the operation time is reduced by 98.73% without the foreground segmentation. The SFPS scheme proposed in this paper is not only conducive to the development of special behaviors identification technology for fish school, but also provides a feasible solution for the application of this technology in practical engineering.

Cloud robotic grasping of Gaussian mixture model based on point cloud projection under occlusion

Purpose In multi-robot cooperation, the cloud can share sensor data, which can help robots better perceive the environment. For cloud robotics, robot grasping is an important ability that must be mastered. Usually, the information source of grasping mainly comes from visual sensors. However, due to the uncertainty of the working environment, the information acquisition of the vision sensor may encounter the situation of being blocked by unknown objects. This paper aims to propose a solution to the problem in robot grasping when the vision sensor information is blocked by sharing the information of multi-vision sensors in the cloud. Design/methodology/approach First, the random sampling consensus algorithm and principal component analysis (PCA) algorithms are used to detect the desktop range. Then, the minimum bounding rectangle of the occlusion area is obtained by the PCA algorithm. The candidate camera view range is obtained by plane segmentation. Then the candidate camera view range is combined with the manipulator workspace to obtain the camera posture and drive the arm to take pictures of the desktop occlusion area. Finally, the Gaussian mixture model (GMM) is used to approximate the shape of the object projection and for every single Gaussian model, the grabbing rectangle is generated and evaluated to get the most suitable one. Findings In this paper, a variety of cloud robotic being blocked are tested. Experimental results show that the proposed algorithm can capture the image of the occluded desktop and grab the objects in the occluded area successfully. Originality/value In the existing work, there are few research studies on using active multi-sensor to solve the occlusion problem. This paper presents a new solution to the occlusion problem. The proposed method can be applied to the multi-cloud robotics working environment through cloud sharing, which helps the robot to perceive the environment better. In addition, this paper proposes a method to obtain the object-grabbing rectangle based on GMM shape approximation of point cloud projection. Experiments show that the proposed methods can work well.

Gaussian Mixture Model Clustering-Based Knock Threshold Learning in Automotive Engines

In this article, a Gaussian mixture model (GMM) clustering-based method is proposed to learn the optimal threshold of the knock intensity metric, which minimizes the probability of the judgment error of the knock event. First, statistical analysis of knock intensity on an experimental database measured from a gasoline engine test bench is conducted and the results show that GMM has better fitting performance than the single Gaussian model. Then, the learning problem which consists of two subproblems is formulated based on the assumption that the probability distribution model of knock intensity is a two-component GMM. The first subproblem is a clustering problem in which parameters of the two-component GMM are optimized to maximize the likelihood of obtaining the measurements. The second subproblem is also an optimization problem in which the probability of the judgment error is formulated as a function of the threshold. The solution to the problem is the required threshold decision. The convergence analysis of the proposed method is implemented. Furthermore, the proposed method is also validated in the experimental database. The proposed method is expected to be applied in the real-world application to save time, labor effort on the knock threshold calibration.

Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment

The health monitoring and management have been accepted in modern industrial machinery for an intelligent industrial production. To timely and reliably assess the bearing performance degradation, a novel health monitoring method called feature clustering analysis (FCA) has been proposed in this study. Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. First, the data provided by the reference state (healthy data) and the one from the monitor state (monitor data) are fused together to construct a reference model, which is to explore the active role of healthy status and activate the difference between healthy status and unhealthy status. Manifold learning is later implemented to mine the discriminated features for good class-separable clustering measure. In this manner, heterogeneous information hidden in this reference model will appear once degradation happened. Finally, a clustering quantification factor, named as feature clustering indicator (FCI), is calculated to assess distribution evolution and migration of the monitor status as compared to the consistent healthy status. Furthermore, a single Gaussian model (SGM) based on these FCIs is used to provide a smooth estimate of the healthy condition level. The corresponding negative log likelihood probability (NLLP) and the fault occurrence alarm are developed for an accurate and reliable FCC. And it can well depict a comprehensibility of the real bearing performance degradation process for its whole life. Meanwhile, as compared to other health profiles based on the classical health indicators, the proposed FCC has provided a much more accurate degradation level and rather monotonic profile. The experimental results show the potential in machine health performance degradation assessment.

GATE MODELING OF LATERAL SCATTERING OF PROTON PENCIL BEAMS.

To validate the GATE Monte Carlo simulation code and to investigate the lateral scattering of proton pencil beams in the major body tissue elements in the therapeutic energy range. In this study, GATE Monte Carlo simulation code was used to compute absorbed dose and fluence of protons in a water cubic phantom for the clinical energy range. To apply the suitable physics model for simulation, different physics lists were investigated. The present research also investigated the optimal value of the water ionization potential as a simulation parameter. Thereafter, the lateral beam profile of proton pencil beams were simulated at different energies and depths in body tissue elements. The range results obtained using the QGSP_BIC_EMY physics showed the best compatibility with the NIST database data. Moreover, it was found that the 76eV is the optimal value for the water ionization potential. In the next step, it was shown that the beam halo can be described by adding a supplementary Gaussian function to the standard single-Gaussian model, which currently is used by treatment planning systems (TPS).

Stressing of Migration Matrices for IFRS 9 and ICAAP Calculations

Rating transition matrices have become a workhorse of the IFRS 9 expected credit loss and ICAAP stress test modelling. The standard method to stress a through-the-cycle transition matrix is based on a single factor Gaussian model with a correlation parameter that is usually estimated on the level of a product pool. The goal of the paper is to generalize the model allowing for more general distributional assumptions and to test empirically the sensitivity of the results with respect to these assumptions and different possible approaches to the correlation parameter estimation. We are not aware of any such empirical study in the literature. The results show that this dependence is very strong with the standard approach underestimating the results, as we argue, of a more precise calculation many times. Therefore, there is a significant model risk that needs to be taken into account in ICAAP/IFRS 9 implementation and dealt with in further research.

Pore-throat characterization of unconsolidated porous media using watershed-segmentation algorithm

Pore-throat characteristics of porous media play important roles in various flow processes. However, pore size distribution (PSD) and throat size distribution (TSD) of various porous media have rarely been reported. Watershed-segmentation (WS) is a promising pore-throat identification method. To avoid over- and under-segmentations, a trial-and-error method was devised and validated using artificially ordered packings. Afterwards, we analyzed porous media with various characteristics, and comparisons with artificially ordered packing were made. More angular particles generated larger pores and throats. TSD of spherical particles was bimodal, whereas other particles resembled a normal distribution. Pore surface area and volume relation demonstrated a fractal behavior. Compared with medial axis (MA) method, PSD and TSD from WS method were larger but exhibited similar distribution trends. Normalizing the PSD with the median pore size yielded a single Gaussian model, demonstrating a possible PSD model. However, normalization to TSD data does not produce a single Gaussian model.

A method to describe energy changes in UV–vis absorbance spectra during the aggregation process of conjugated polymer solutions

We have defined average photon energy (Eave) to describe energy changes in UV–vis absorbance spectra during the aggregation of conjugated polymer solutions quantitatively. During the aggregation process of P3HS (poly(3-hexylselenophene)), Eave always decreases and maximum aggregation absorbance (ABSmagg) always increases, and relationships between (Eave and ABSmagg) and time are single exponential models. The relationship between ABSmagg and Eave of P3HS is a single Gaussian model, so Eave can be used to predict the amount of aggregate. Eave and ABSmagg of P3HT (Poly(3-hexylthiophene)) follow similar rules of P3HS. This work describes energy changes and predicts the amount of aggregate in UV–vis spectra during the aggregation process.