Gaussian Mixture Components Research Articles

A novel gradient boosting approach for imbalanced regression

Data imbalance is prevalent in the real world and has received significant attention in classification tasks, both theoretically and practically. However, imbalanced regression remains underexplored. It involves continuous labels, which is laborious and technically difficult due to the absence of distinct boundaries between different targets. To address this issue, we leverage the balanced mean square error (Balanced MSE) from a statistical perspective into the gradient boosting algorithm framework to present the imbalanced regression gradient boosting algorithm (IMr-GB). This algorithm could adapt to imbalanced prior distributions and achieve tradeoffs between frequent and rare labels, thus delivering balanced estimation and effectively reducing adverse effects on underrepresented datasets. In addition, we devise a Bayes variant of IMr-GB, denoted as IMr-bay-GB, to eliminate the complexity of the number of Gaussian mixture components and acquire robustness and optimal performance. Our strategies are extensively tested on ten real-world data sets to demonstrate their superior performance.

An analysis of likely germline events by tumor tissue testing on large somatic panels.

e22524 Background: The increase in large panel testing has facilitated the detection of numerous events, including those in genes associated with inherited cancer. Further, large panel sequencing may uncover variants in these genes in non-inherited cancers. Hence the frequency of pathogenic/likely pathogenic (P/LP) events in some common germline genes was studied across cancers in the Indian population. Methods: A statistical model was used to derive a method to label variants as likely germline. Variants in hereditary breast and ovarian cancer (HBOC) and mismatch repair (MMR) genes from 1553 cases sequenced at our referral laboratory on the TruSight Oncology 500 panel (TSO500) were analyzed for their variant allele frequency (VAF) distribution. A 3 component Gaussian mixture model (intended to separate somatic, germline heterozygous, homozygous variants) was fit to this VAF distribution and a cutoff was determined such that the area under the curve of the somatic component above the cutoff was below 0.01. This gave a threshold value of 40% VAF for determining likely germline variants (LGVs). Data from 1713 TSO500 cases was then analyzed to identify LGVs. Results: The dataset of 1713 cases comprised 20.67% NSCLC, 12.61% breast, 12.49% ovarian and 10.5% colorectal cancer and < 10% of other cancer types including 2.39% pancreatic and 2.04% prostate cancer. 23.8% of ovarian, 17.14% of prostate, 12.5% of breast and 12.2% of pancreatic cancer cases had variants in BRCA1/BRCA2. Among these, 18.69% of ovarian, 17.14% of prostate, 8.33% of breast and 7.32% of pancreatic cancers had LGVs. LGVs in BRCA1/2 were also found in 3.33% of colorectal and 1.13% of NSCLC. The BRCA1/BRCA2 LGV ratio was found to be 3.0 in ovarian, 1.0 in breast and 2.0 in pancreatic but 0.2 in prostate and colorectal and 0.33 in NSCLC cancer. Adding other HBOC genes (ATM, BRCA1/2, BRIP1, CHEK2, PALB2, RAD51C, RAD51D) increased the LGV frequency to 22.43% of ovarian, 22.86% of prostate, 11.11% of breast, 9.76% of pancreatic, 4.44% of colorectal and 2.26% of NSCLC. MMR gene LGVs were seen in 1.23% of all cancers mainly in 10.71% of endometrial and 4.44% of colorectal cancers. Unlike the HBOC genes, MMR gene variants were not observed across multiple cancers and did not show a predominantly germline distribution (somatic/LGV 1:1). In contrast, the background P/LP frequency calculated from gnomAD v4 South Asian data (excluding those with conflicting labels) was 0.74% for BRCA1/2, 1.82% for the extended HBOC list and 0.5% for the MMR genes. Conclusions: Somatic testing was used to identify LGVs and determine the frequencies of HBOC and MMR genes in cancers in the Indian population. Somatic testing also identified LGVs in multiple tissues at frequencies above the background P/LP frequency for this population, suggesting a possible role for these genes in other cancers. This information underscores the need for further study and may be useful to define a clinically relevant subset of patients.

Acoustic Echo Cancellation using Adaptive Filter for Quranic Accent Signals

Quranic recordings andechoed portions of the emphasis are susceptible to signal reverberation, particularly when being listened to in a conference room. Tajweed and Quranic verse rule identification are susceptible to additive noise, which could lower classification accuracy. In order to reflect the most correct rate following pattern categorization, this study suggested the appropriate use of three adaptive algorithms: Affine Projection (AP), Least Mean Square (LMS), and Recursive Least Squares (RLS). For feature extraction, Mel Frequency Cepstral Coefficient is used together with Probabilities Principal Component Analysis (PPCA), K-Neural Network (KNN) and Gaussian Mixture Model (GMM). AP indicates 93.9% for all of the classification algorithm in used, while for LMS and RLS the results are differed varies on different pattern classification algorithm stated whereby with LMS and PPCA classification, 96.9 % for accuracy and 84.8% accuracy for LMS and KNN. While for RLS and GMM, 96.9% was achieved and the results were reduced for both KNN and PPCA. The analysis has resulted for both on accuracies within different filtering algirithm and classification for accuracy and ERLE(dB).Towards this research it is hope will embark more understanding towards echo cancellation and quality of sound recordings that may affected even to the Quranic recordings.KeywordsAdaptive filtering, acoustic echo cancellation, recursive least squares, least mean square, affine projection, accuracy rate1.IntroductionUnusually big peaks have been produced by frequency response, masking, and emerging peaks from sound systems. Noise can be introduced into music recordings from the surrounding environment, during the recording system when audio signals change, or during the indexing process. These noise signals can disrupt and lower the quality and efficiency of the recording. Quranic recordings may also be affected by echoes, which can interfere with the recording process. The speaker, microphone, and transmission path are among the variables that affect.

A joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction

Abstract Due to the extensive access of renewable sources in distribution networks, this research proposes a joint configuration method for multiple flexible resources in low carbon distribution networks based on massive scene dimensionality reduction to maximize the access capacity of renewable sources. The dimensionality reduction clustering is carried out on the wind-light-load mass high-dimensional scenes by the principal component Gaussian mixture clustering algorithm, and the typical scene set of wind-power-loads is obtained. Hereafter, a joint configuration model for multiple flexible resources of distribution network for massive scenarios is constructed, and using the second-order cone relaxation technique to convert the non-convex constraints in the model to convex processing. Last, a Portugal 54 distribution system is employed to verify the proposed method.

Bayesian Inversion, Uncertainty Analysis and Interrogation Using Boosting Variational Inference

AbstractGeoscientists use observed data to estimate properties of the Earth's interior. This often requires non‐linear inverse problems to be solved and uncertainties to be estimated. Bayesian inference solves inverse problems under a probabilistic framework, in which uncertainty is represented by a so‐called posterior probability distribution. Recently, variational inference has emerged as an efficient method to estimate Bayesian solutions. By seeking the closest approximation to the posterior distribution within any chosen family of distributions, variational inference yields a fully probabilistic solution. It is important to define expressive variational families so that the posterior distribution can be represented accurately. We introduce boosting variational inference (BVI) as a computationally efficient means to construct a flexible approximating family comprising all possible finite mixtures of simpler component distributions. We use Gaussian mixture components due to their fully parametric nature and the ease with which they can be optimized. We apply BVI to seismic travel time tomography and full waveform inversion, comparing its performance with other methods of solution. The results demonstrate that BVI achieves reasonable efficiency and accuracy while enabling the construction of a fully analytic expression for the posterior distribution. Samples that represent major components of uncertainty in the solution can be obtained analytically from each mixture component. We demonstrate that these samples can be used to solve an interrogation problem: to assess the size of a subsurface target structure. To the best of our knowledge, this is the first method in geophysics that provides both analytic and reasonably accurate probabilistic solutions to fully non‐linear, high‐dimensional Bayesian full waveform inversion problems.

Space Target Tracking with the HRRP Characteristic-Aided Filter via Space-Based Radar

Approaching space target tracking is a typical and challenging mission in the space situational awareness (SSA) field. As the space-based radar is able to monitor the space targets of interest full-weather all-time, the space-based radar system is utilized in this paper. However, most multi-target tracking (MTT) filters in target tracking studies merely utilize the location or narrow measurements, and many potentially valuable electromagnetic scattering characteristics are missed, which leads to space target false tracking problems. The space-based radar transmits a wide-band signal, and the measured high-resolution range profile (HRRP) information is an effective characteristic for different target discrimination. Therefore, the HRRP characteristics of space targets are implemented into the update recursion of the MTT filter, which can be utilized to improve the tracking performance. Then, to predict the target HRRP sequence, the geometrical theory of diffraction (GTD) model is utilized. Additionally, a modified spatial spectrum method with a novel covariance matrix is designed to improve the scattering parameter estimation accuracy. Finally, an adapting threshold is devised for merging the Gaussian mixture (GM) components weights. The proposed threshold is on the basis of the proposed HRRP characteristic-aided probability hypothesis density (PHD) filter, and it can tackle the problem of space target discrimination. Simulation results validate the effectiveness and robustness of the proposed probability hypothesis density (HGI-PHD) filter aided by HRRP information and improved with GM weights.

Vehicular visible light communications noise analysis and modeling.

Vehicular visible light communications (VVLC) is promising intelligent transportation systems technology with the utilization of light-emitting diodes. The main degrading factor for the performance of VVLC systems is noise. Traditional VVLC systems noise modeling is based on the additive white Gaussian noise assumption in the form of shot and thermal noise. In this paper, to investigate both time correlated and white noise components of the VVLC channel noise, we propose a noise analysis based on Allan variance, which provides a time-series analysis method to identify noise from the data. The results show that white noise and random walk are observed in the VVLC systems. We also propose a motion detection algorithm based on the adaptive Gaussian mixture (GM) model to generate a double Gaussian model of VVLC channel noise. We further present a study on the error performance of a VVLC system considering channel noise to be a mixture of Gaussian components. We derive the analytical expressions of probability of error for binary phase-shift keying and quadrature phase-shift keying constellations. It has been observed that, in the presence of GM noise, the system performance degrades significantly from the usual one expected in a Gaussian noise environment and becomes a function of the mixing coefficients of the GM distribution.

Reduction and reconstruction strategy of active thermal control system based on unsupervised learning and thermo-optics for spaceborne high-resolution remote sensor

The development of small Earth observation satellites with high performance optical remote sensors has received much attention in recent years due to its responsiveness and immediacy. As one of the most resource-intensive subsystems of spacecrafts, the active thermal control system (ATCS) should be optimized; however, manual thermal design does not guarantee an optimal solution and requires months. For this purpose, we presented a ATCS’s reduction and reconstruction strategy based on an unsupervised learning framework, in which factor loadings are applied to combine kernel-based principal component analysis and Gaussian mixture model clustering. This strategy was implemented on a spaceborne high-resolution remote sensor (HRS) with 0.5 m ground sample distance. As a result, with the cumulative variance contribution rate > 90 % and 10 Gaussian distributions, the reconstructed ATCS of HRS had a 78.6 % reduction rate and took only 3 h, which suggests the high efficiency and low computational cost of the proposed strategy. Finally, the performed orbital thermal and thermo-optical integrated analyses indicated that temperature fluctuations were within ± 0.02–0.6 °C and optical surface errors were within the tolerance. The average modulation transfer function in the tangential and sagittal directions decreased by only 2 % and 2.5 %, respectively, and the radius of the spot root mean square was considerably smaller than the size of one pixel (8.75 μm). These results prove the feasibility and validity of the proposed strategy. This study solved the problem of identifying the optimal ATCS for optical remote sensors and significantly reducing the ATCS’s resource occupancy, which provided a reliable research basis for the development of high-performance small Earth observation satellites. Additionally, the proposed strategy is versatile and can be used in thermal engineering applications of various complex spacecrafts and payloads.

Unsupervised convolutional autoencoders for 4D transperineal ultrasound classification.

4D Transperineal ultrasound (TPUS) is used to examine female pelvic floor disorders. Muscle movement, like performing a muscle contraction or a Valsalva maneuver, can be captured on TPUS. Our work investigates the possibility for unsupervised analysis and classification of the TPUS data. An unsupervised 3D-convolutional autoencoder is trained to compress TPUS volume frames into a latent feature vector (LFV) of 128 elements. The (co)variance of the features are analyzed and statistical tests are performed to analyze how features contribute in storing contraction and Valsalva information. Further dimensionality reduction is applied (principal component analysis or a 2D-convolutional autoencoder) to the LFVs of the frames of the TPUS movie to compress the data and analyze the interframe movement. Clustering algorithms ( -means clustering and Gaussian mixture models) are applied to this representation of the data to investigate the possibilities of unsupervised classification. The majority of the features show a significant difference between contraction and Valsalva. The (co)variance of the features from the LFVs was investigated and features most prominent in capturing muscle movement were identified. Furthermore, the first principal component of the frames from a single TPUS movie can be used to identify movement between the frames. The best classification results were obtained after applying principal component analysis and Gaussian mixture models to the LFVs of the TPUS movies, yielding a 91.2% accuracy. Unsupervised analysis and classification of TPUS data yields relevant information about the type and amount of muscle movement present.

Gaussian Mixture Midway-Merge for Object SLAM With Pose Ambiguity

In this letter, we propose a novel method to merge a Gaussian mixture on matrix Lie groups and present its application for a simultaneous localization and mapping problem with symmetric objects. The key idea is to predetermine the weighted mean called a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">midway point</i> and merge Gaussian mixture components at the associated tangent space. Through this rule, the covariance matrix captures the original density more accurately, and the need for the back-projection is spared when compared to the conventional merge. We highlight the midway-merge by numerically evaluating dissimilarity metrics of density functions before and after the merge on the rotational group. Furthermore, we experimentally discover that the rotational error of symmetric objects follows heavy-tailed behavior and formulate the Gaussian sum filter to model it by a Gaussian mixture noise. The effectiveness of our approach is validated through virtual and real-world datasets.

ScSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder

Single cell sequencing is a technology for high-throughput sequencing analysis of genome, transcriptome and epigenome at the single cell level. It can improve the shortcomings of traditional methods, reveal the gene structure and gene expression state of a single cell, and reflect the heterogeneity between cells. Among them, the clustering analysis of single-cell RNA data is a very important step, but the clustering of single-cell RNA data is faced with two difficulties, dropout events and dimension curse. At present, many methods are only driven by data, and do not make full use of the existing biological information. In this work, we propose scSSA, a clustering model based on semi-supervised autoencoder, fast independent component analysis (FastICA) and Gaussian mixture clustering. Firstly, the semi-supervised autoencoder imputes and denoises the scRNA-seq data, and then get the low-dimensional latent representation. Secondly, the low-dimensional representation is reduced the dimension and clustered by FastICA and Gaussian mixture model respectively. Finally, scSSA is compared with Seurat, CIDR and other methods on 10 public scRNA-seq datasets. The results show that scSSA has superior performance in cell clustering on 10 public datasets. In conclusion, scSSA can accurately identify the cell types and is generally applicable to all kinds of single cell datasets. scSSA has great application potential in the field of scRNA-seq data analysis. Details in the code have been uploaded to the website https://github.com/houtongshuai123/scSSA/.

Hourly air temperature projection in future urban area by coupling climate change and urban heat island effect

Global climate change and urban heat island (UHI) effect are changing the urban microclimate and ecosystem of building and outdoor environment. In this study, CMIP5 family, which was found to have the most consistent projection with the current worldwide observation till now, was chosen to evaluate climate change’s impact on urban climate. This research uses Principal Component Analysis and Gaussian Mixture Model based building type clustering method to obtain urban morphological data and input parameters of the UHI model, and the validated UHI model is then coupled to the projections from various global climate models (GCMs).It is found that future urban microclimate prediction after considering the UHI effect can significantly improve the prediction accuracy of air temperature when downscaling future weather data from various selected GCMs, indicating the UHI shall be considered in predicting future weather data in urban tissue. After using the best-fit climate change prediction model MRI-CGCM3 to couple with the UHI model, the projections of the heating degree days (HDD) and cooling degree days (CDD) of Shenzhen from 2020 to 2099 has shown that comparing with the typical meteorological year, the average HDD in Shenzhen will be reduced by 57.5% in 2020–2099 under the RCP8.5 scenario, and the CDD will increase by 25.1%, which will exert considerable impacts on building energy use in urban tissue.

A Methodologically Updated De-Novo Extraction of Copy Number Signatures in Multiple Myeloma: Clinical Significance and Putative Aetiologies

BACKGROUND: The tumor genomes of most Multiple Myeloma (MM) patients are heavily burdened with highly heterogenous copy number (CN) alterations, as detected by multiple molecular methods including whole genome sequencing (WGS), and have been shown to have a strong prognostic and predictive significance for patients' survival. Cutting-edge computational developments have made it possible to analyze complex genomic CN changes by identifying CN alterations' recurrent patterns among large cohorts of patients, named CN signatures (CNS), that are the result of cumulative chromosomic instability (CIN) processes throughout the course of cancer cells evolution. In fact, a robust methodological framework for CNS computation, along with a large compendium of CNS aetiologies have been recently published for many cancer types, notably not including MM (Drews 2022). To our knowledge, the only study that analyzes CNS in MM focused on the role of CNS in predicting chromothripsis events, but remarkably it did not include neither CNS aetiologies nor complete CNS characterization for all the discovered CNS (Maclachlan 2021). AIM: To define a novel methodological framework, based on previous studies, aimed at the identification of CNS in MM, in order to assess both the aetiologies and the biological significance of MM CNS and to evaluate their prognostic impact on MM clinical outcome. The newly extracted CNS defined at diagnosis will be also used as novel disease biomarkers, to develop an improved, aetiology-based MM patients' stratification in different molecular subtypes. METHODS: We calculated the MM genomic distributions of the six essential CN features (segment length, breakpoint per 10 Mb, breakpoint per chromosome arm, segment CN change, CN value, length of oscillating CN states) that have been previously shown to encode patterns of CN alterations, underlying the observed tumor CIN. Starting from 886 WGS generated CN profiles, included in the CoMMpass study, the above mentioned features were computed. Features were then categorized into components, by using mixture models' decomposition and CNS were finally extracted from the components, by using both a Hierarchical Dirichlet Process (HDP) and a Non-Negative Matrix Factorization (NNMF) approach. RESULTS: The main novel characteristics of the developed methodological framework aimed at CNS assessment were 1) the use of a "continuous CN value" feature, which enabled the evaluation of sub-clonal events and 2) the use of a logarithmic scale in "segment length" feature, which favored a higher resolution for categorizing focal and/or gene level CN events, that are very common in MM. Thanks to these implementations, 33 Gaussian mixture components were identified (as compared to 28 detected in Maclachlan 2021). After deriving a Sample x Component - Sum of Posteriors Matrix, the signatures were extracted by applying two parallel state-of-art approaches, namely HDP and NNMF. This allowed the extraction of 9 CNS that were characterized by their component's composition. The signature's exposure levels were correlated to well-known MM biomarkers (e.g. TP53 mut and/or del, 1q CN gain, 13q CN loss, t-IgH, hyperdiploidy), showing that all signatures correlated to at least one of the well known MM biomarkers; in particular, CN.SIG5 exposures was found to correlate to high-risk MM biomarkers (TP53 p<0.001, 1q CN gain p<0.001, t(4;14) p<0.001), thus suggesting its possible involvement in the aetiology of this peculiar genomic configuration. Finally, a survival analysis was performed in patients characterized by high exposure (4th quartile) to the CN.SIG5 (75 patients), as compared to the others (811 patients), showing a significant negative impact of this CNS on both overall (OS p<0.001)) and progression free survivals (PFS p<0.001). Cox-analysis revealed an OS HR= 1.37 p<0.001, PFS HR= 1.16, p<0.001, per 5% increase in exposure. CONCLUSION: By employing a novel bio-informatic approach, based on the use of continuous CN data for CNS extraction, 33 feature's components were identified. We observed that CN.SIG5 significantly affected patients carrying well-known high-risk genomic features, and patients highly exposed to this CNS had decreased PFS and OS. Additional characterizations are needed to unveil the biological meaning of CNS exposure; however, MM CNS, while informing on disease outcome, might be considered as new comprehensive biomarkers in this disease. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Ensemble-based unsupervised machine learning method for membership determination of open clusters using Mahalanobis distance

ABSTRACT We present an improved method for the determination of membership of an open cluster using ensemble-based unsupervised machine learning techniques. The working principle of this method relies on two stages: (i) choosing a suitable range of three astrometric parameters (π, μαcos δ, μδ) using k-nearest neighbour (kNN) algorithm on the data downloaded for the cluster within a smaller search radius; (ii) application of two component Gaussian mixture modelling (GMM) on the resulting one dimensional Gaussian distribution of Mahalanobis distance (MD) of stars using the range of parameters obtained from the earlier step, but with the data downloaded within a bigger search radius. MD is calculated from the mean of each of the parameters in three dimensions. Thus the use of MD reduces the input of the GMM from the 3D parameter space into a 1D parameter space for the cluster membership determination. The method has been tested on a few clusters including those which have overlaps in some/all the parameters using the data obtained from the Gaia DR3 data base. It is found that the approach can easily separate the cluster members from the field stars. The clean colour-magnitude diagrams and similar direction of proper motions of the member stars obtained for the clusters shows that this method is very efficient and robust in segregating the cluster members from the field stars.

Sharp optimal recovery in the two component Gaussian mixture model

In this paper, we study the problem of clustering in the Two component Gaussian mixture model when the centers are separated by some Δ>0. We present a nonasymptotic lower bound for the corresponding minimax Hamming risk improving on existing results. We also propose an optimal, efficient and adaptive procedure that is minimax rate optimal. The rate optimality is moreover sharp in the asymptotics when the sample size goes to infinity. Our procedure is based on a variant of Lloyd’s iterations initialized by a spectral method. As a consequence of nonasymptotic results, we find a sharp phase transition for the problem of exact recovery in the Gaussian mixture model. We prove that the phase transition occurs around the critical threshold Δ¯ given by Δ¯2=σ2(1+ 1+2p nlogn)logn.

A New Standardized Type Drought Indicators Based Hybrid Procedure for Strengthening Drought Monitoring System

Drought is occurring recurrently in various climatic zones around the world. Therefore, accurate and continuous drought monitoring are essential for reliable drought mitigation policies. In past research, several drought monitoring indicators have been developed. Regardless of their scopes and applicabilities, every indicator has certain amount of error regarding accurate determination of drought classes. In addition, climate change and complex features of meteorological variables also reduce the performance of each indicator. Consequently, accurate drought monitoring is a challenging task in hydrology and water management research. The objective of this research is to enhance the accuracies in drought characterization by employing multiple drought indicators, simultaneously. This article proposes a new aggregative index – the Seasonal Mixture Standardized Drought Index (SMSDI). The procedure of SMSDI is mainly based on the integration of Principle Component Analysis (PCA) and K- Component Gaussian Mixture Distribution (K-CGMD). In preliminary analysis, aggregation of three multi-scalar Standardized Drought Indices (SDIs) is made for three meteorological gauge stations of Pakistan. For comparative assessment, individual SDI has used to investigate the association and consistency with SMSDI. Outcome associated with this research shows that the SMSDI have significant correlation with individual SDIs. We conclude that instead of using individual indicator, the proposed aggregative approach enhances the scope and capacity of drought indicators for extracting reliable information related to future drought.

Visualizing Realistic Benchmarked IDS Dataset: CIRA-CIC-DoHBrw-2020

Intrusion Detection System (IDS) dataset is crucial to detect lateral movement of cyber-attacks. IDS dataset will help to train the IDS classifier model to achieve earliest detection. A good near-realism public dataset is essential to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been under scrutiny for its practicality to reflect real low-footprint cyber threats, render real-time network scenario, reflect recent malware attack over newly developed DoH protocol, disregard layer 3 information and finally publish contradictory results of classification and analysis between various studies which makes it non-reproducible and without shareable results. This problem can be resolved by sophisticatedly visualizing a new realistic, real-time, low footprint and up-to-date benchmarked dataset. Visualization helps to detect data deformation before designing the optimized and highly accurate classifier model. Therefore, this study aims to review a new realistic benchmarked IDS dataset and apply sophisticated technique to visualize them. The review starts by carefully examining production network features. These are then compared with various well-established public IDS datasets. Many of them are static, unrealistic meta-features and disregard source and destination Internet Protocol (IP) information except CIRA-CIC-DoHBrw-2020 dataset. The study then applies Eigen Centrality (EC) technique from the graph theory to visualize this layer 3 (L3) information. Finally, using various visualization techniques such as Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM), the study further analyzes and subsequently visualizes the data. Results show that the CIRA-CIC-DoHBrw-2020 simulated recent malware attack and has a very imbalanced dataset which reflects the realistic low-footprint cyber-attacks. The centrality graph clearly visualizes IPs that are compromised by recent DoH attack in real-time, and the study concludes decisively that smaller packet length of size 1000 to 2000 bytes is to fit an attack trait.

Gaussian sum reapproximation applied to the probability of collision calculations

In predicting the collision of space debris, the propagated orbital uncertainty may not follow a Gaussian distribution if the initial orbital uncertainty is large or the propagation time is long. In this paper, a Gaussian mixture uncertainty propagation method developed by (Psiaki et al., 2015) is used to calculate the collision probability. The initial Gaussian distribution is fitted by the weighted Gaussian mixture components. The linear matrix inequality is optimized to prevent the covariance matrix of Gaussian mixture components from being too small, and an appropriate number of Gaussian mixture components is used to approximate the initial orbital covariance. At the same time, this paper provides a method to calculate the collision probability of two objects in which a Gaussian mixture is used to represent the distribution of orbital uncertainty. The linear method and the unscented Kalman filter (UKF) method for propagating the Gaussian covariance are analysed. The results of numerical simulations show that compared with the linear covariance propagation method, UKF method, and high-precision Monte Carlo covariance propagation method for space objects with a large initial orbital uncertainty, the Gaussian mixture method can be effectively applied to capture the non-Gaussian characteristics of the predicted nonlinear orbital dynamic uncertainty. Compared with the univariate splitting method, the advantage of this Gaussian mixture method is that it does not need to search for the most nonlinear direction. The accuracy of the collision probability calculation is improved from 1.460 × 10−3 to 1.663 × 10−3. A comparison of the computational burden between the Gaussian mixture algorithm and Vittaldev’s algorithm to achieve the same results is presented. The calculation burden of the Gaussian mixture method is approximately 3 times that of the univariate Gaussian method.

Chemometric approach to characterization of the selected grape seed oils based on their fatty acids composition and FTIR spectroscopy

Addressing the issues arising from the production and trade of low-quality foods necessitates developing new quality control methods. Cooking oils, especially those produced from the grape seeds, are an example of food products that often suffer from questionable quality due to various adulterations and low-quality fruits used for their production. Among many methods allowing for fast and efficient food quality control, the combination of experimental and advanced mathematical approaches seems most reliable. In this work a method for grape seed oils compositional characterization based on the infrared (FTIR) spectroscopy and fatty acids profile is reported. Also, the relevant parameters of oils are characterized using a combination of standard techniques such as the Principal Component Analysis, k-Means, and Gaussian Mixture Model (GMM) fitting parameters. Two different approaches to perform unsupervised clustering using GMM were investigated. The first approach relies on the profile of fatty acids, while the second is FT-IR spectroscopy-based. The GMM fitting parameters in both approaches were compared. The results obtained from both approaches are consistent and complementary and provide the tools to address the characterization and clustering issues in grape seed oils.

Tracking of Multiple Maneuvering Random Hypersurface Extended Objects Using High Resolution Sensors

With the increased resolution capability of modern sensors, an object should be considered as extended if the target extent is larger than the sensor resolution. Multiple maneuvering extended object tracking (MMEOT) uses not only measurements of the target centroid but also high-resolution sensor measurements which may resolve individual features or measurement sources. MMEOT aims to jointly estimate object number, centroid states, and extension states. However, unknown and time-varying maneuvers of multiple objects produce difficulties in terms of accurate estimation. For multiple maneuvering star-convex extended objects using random hypersurface models (RHMs) in particular, their complex maneuvering behaviors are difficult to be described accurately and handled effectively. To deal with these problems, this paper proposes an interacting multiple model Gaussian mixture probability hypothesis density (IMM-GMPHD) filter for multiple maneuvering extended object tracking. In this filter, linear maneuver models derived from RHMs are utilized to describe different turn maneuvers of star-convex extended objects accurately. Based on these, an IMM-GMPHD filtering recursive form is given by deriving new update and merging formulas of model probabilities for extended objects. Gaussian mixture components of different posterior intensities are also pruned and merged accurately. More importantly, the geometrical significance of object extension states is fully considered and exploited in this filter. This contributes to the accurate estimation of object extensions. Simulation results demonstrate the effectiveness of the proposed tracking approach—it can obtain the joint estimation of object number, kinematic states, and object extensions in complex maneuvering scenarios.