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Artykuły w czasopismach na temat „Mixture of Gaussian (MoG)”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Kim, Yongho. "Fast MOG (Mixture of Gaussian) Algorithm based on Predicting Model Parameters." TECHART: Journal of Arts and Imaging Science 2, no. 1 (2015): 41. http://dx.doi.org/10.15323/techart.2015.02.2.1.41.

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Kong, Long, Symeon Chatzinotas, and Bjorn Ottersten. "Unified Framework for Secrecy Characteristics With Mixture of Gaussian (MoG) Distribution." IEEE Wireless Communications Letters 9, no. 10 (2020): 1625–28. http://dx.doi.org/10.1109/lwc.2020.2999361.

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Matczak, Grzegorz, and Przemyslaw Mazurek. "Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection." Remote Sensing 13, no. 5 (2021): 870. http://dx.doi.org/10.3390/rs13050870.

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Background estimation algorithms are important in UAV (Unmanned Aerial Vehicle) vision tracking systems. Incorrect selection of an algorithm and its parameters leads to false detections that must be filtered by the tracking algorithm of objects, even if there is only one UAV within the visibility range. This paper shows that, with the use of genetic optimization, it is possible to select an algorithm and its parameters automatically. Background estimation algorithms (CNT (CouNT), GMG (Godbehere-Matsukawa-Goldberg), GSOC (Google Summer of Code 2017), MOG (Mixture of Gaussian), KNN (K–Nearest Ne

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Wang, Biao, Chunhao Chen, Zhe Jiang, and Yu Zhao. "ROV State Estimation Using Mixture of Gaussian Based on Expectation-Maximization Cubature Particle Filter." Applied Sciences 13, no. 10 (2023): 5885. http://dx.doi.org/10.3390/app13105885.

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The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. For the accurate and real-time observation of ROV climbing, and, at the same time, to reduce the influence of the uncertainty of the noise distribution, the ROV state filter is designed based on the mixture of Gaussian model theory with the expectation-maximization cubature particle filter (EM-MOGCPF). The EM-MOGCPF conside

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Fatima, Ezzahra Sloukia, Bouarfa Rajae, Medromi Hicham, and Wahbi Mohammed. "BEARINGS PROGNOSTIC USING MIXTURE OF GAUSSIANS HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE." International Journal of Network Security & Its Applications (IJNSA) 5, no. 3 (2013): 85–97. https://doi.org/10.5281/zenodo.4326213.

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Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven prognostics method which is based on the transformation of the data provided by the sensors into models that are able to characterize the behavior of the degradation of bearings. For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently publi

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Setiawan, Ariyono, I. Gede Susrama Mas Diyasa, Moch Hatta, and Eva Yulia Puspaningrum. "Mixture gaussian V2 based microscopic movement detection of human spermatozoa." International Journal of Advances in Intelligent Informatics 6, no. 2 (2020): 210. http://dx.doi.org/10.26555/ijain.v6i2.507.

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Healthy and superior sperm is the main requirement for a woman to get pregnant. To find out how the quality of sperm is needed several checks. One of them is a sperm analysis test to see the movement of sperm objects, the analysis is observed using a microscope and calculated manually. The first step in analyzing the scheme is detecting and separating sperm objects. This research is detecting and calculating sperm movements in video data. To detect moving sperm, the background processing of sperm video data is essential for the success of the next process. This research aims to apply and compa

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7

Yao, Li, and Miaogen Ling. "An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/424050.

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Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG) background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very cro

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Bhaggiaraj., S., Kumar. C. Ranjeeth, Vijay. K. S. Rahul, and Prabhu. A. Vignesh. "Vehicle Surveillance and Tracking using Background Segmentation." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 164–69. https://doi.org/10.35940/ijeat.F1310.089620.

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A significant initial step for video investigation is Background Subtraction and it is utilized to find the objects of enthusiasm for additional prerequisites. Foundation deduction approach is a general technique for movement recognition strategy, which proficiently utilizes the distinction of the current picture and the foundation picture to recognize moving articles. Here the proposed calculation is known as Mixture of Gaussian (MOG) process. This goes under a quality investigation calculation for pictures, which could be handled in the recordings and casings. A methodology is utilized along

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N., Satish Kumar, and G. Shobha. "HYBRID APPROACH FOR KEY FRAME EXTRACTION FROM VIDEO SEQUENCE." INTERNATIONAL JOURNAL OF RESEARCH- GRANTHAALAYAH 5, no. 4 RACSIT (2017): 97–104. https://doi.org/10.5281/zenodo.583896.

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This paper proposed and developed hybrid approach for extraction of key-frames from video sequences from stationary camera. This method first uses histogram difference to extract the candidate key frames from the video sequences, later using Background subtraction algorithm (Mixture of Gaussian) was used to fine tune the final key frames from the video sequences. This developed approach show considerable improvement over the state-of-the art techniques and same is reported in this paper.

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Cao, Xiangyong, Zongben Xu, and Deyu Meng. "Spectral-Spatial Hyperspectral Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field." Remote Sensing 11, no. 13 (2019): 1565. http://dx.doi.org/10.3390/rs11131565.

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In this paper, a new supervised classification algorithm which simultaneously considers spectral and spatial information of a hyperspectral image (HSI) is proposed. Since HSI always contains complex noise (such as mixture of Gaussian and sparse noise), the quality of the extracted feature inclines to be decreased. To tackle this issue, we utilize the low-rank property of local three-dimensional, patch and adopt complex noise strategy to model the noise embedded in each local patch. Specifically, we firstly use the mixture of Gaussian (MoG) based low-rank matrix factorization (LRMF) method to s

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Xu, Pengfei, Tianhao Cui, and Lei Chen. "ANLoC: An Anomaly-Aware Node Localization Algorithm for WSNs in Complex Environments." Sensors 19, no. 8 (2019): 1912. http://dx.doi.org/10.3390/s19081912.

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Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed

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12

孙, 树平. "ECG Classification Based on Incremental Gaussian Mixture Model." Modeling and Simulation 09, no. 02 (2020): 105–15. http://dx.doi.org/10.12677/mos.2020.92012.

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Sun, Yang, Jungang Yang, Miao Li, and Wei An. "Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density." Remote Sensing 11, no. 23 (2019): 2831. http://dx.doi.org/10.3390/rs11232831.

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The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies

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Ana, Marcela Herrera Navarro, Alejandro Romero González Julio, Alberto Olmos Trejo Carlos, Margarita Córdoba Esparza Diana, and Jiménez Hernández Hugo. "Persons Characterization into Image Sequences Using a Shape Measure." International Journal of Advanced and Innovative Research 7, no. 2 (2018): 82–85. https://doi.org/10.5281/zenodo.1193995.

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The characterizing of persons in real time is a topic of interest in computer vision. In this work we develop an methodof persons characterization. Particularly, the proposal is divided in 3 stages: 1) In a first stage, Mixture of Gaussians (MOG) is used to detected each objectin movement,the contour of the foreground isanalysed with the radius distribution. Finally,  for each block a measure of dispersionbased  in radius distribution, considering the maximum sparse criterion  is proposed.  

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15

Mák, Fruzsina. "Szezonális előrejelzési bizonytalanság a villamosenergia-piacon." Statisztikai Szemle 101, no. 5 (2023): 403–39. http://dx.doi.org/10.20311/stat2023.05.hu0403.

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A tanulmányban a hazai villamosenergia-rendszer terhelésének előrejelzési bizonytalanságát vizsgálom, Gauss-keverékregresszió (Gaussian Mixture Regression) felhasználásával. A rendszerterhelés heteroszkedasztikus viselkedésének leírása a Gauss-keverékmodellre (Gaussian Mixture Model) épülően kézenfekvő, azonban az energiaszektort érintő szakirodalomban elsősorban a nemlineáris (és interakciós) kapcsolatok modellezésére történő alkalmazása szerepel. Ez többek között azzal magyarázható, hogy a villamosenergia-fogyasztás bizonytalanságának explicit modellezése iránti igény az elmúlt években jelen

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Ye, Xulun, Jieyu Zhao, and Yu Chen. "A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency." Entropy 20, no. 11 (2018): 830. http://dx.doi.org/10.3390/e20110830.

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Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generative clustering approach within the Bayesian nonparametric framework has been proposed. Specifically, our manifold method automatically selects the cluster number with a Dirichlet Process (DP) prior. Then, a DP-based mixture model with constrained Mixture of Gaussians (MoG) is constructed to handle the

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Yoshioka, Taku, Ken-ichi Morishige, Mitsuo Kawato, and Masa-aki Sato. "Gaussian mixture prior distribution on artifactual current for MEG inverse problem." Neuroscience Research 68 (January 2010): e332. http://dx.doi.org/10.1016/j.neures.2010.07.1472.

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Cahya, Habib Dwi, and Agus Harjoko. "Otomasi Kamera Perangkap Menggunakan Deteksi Gerak dan Komputer Papan Tunggal." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 1 (2019): 11. http://dx.doi.org/10.22146/ijeis.36102.

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USB camera is currently used in daily life for various purposes. On its development, the use of USB camera can be used to create camara traps and can be used to observe the development of animal with integrated systems. In this research, motion detection was used to observe animals online using Single Board Computer (SBC) Camera trap in this research using Single Board camera in form of raspberry pi 3 B. Python proggramming language is used with OpenCV library. The method used to detect motion is the Mixture of Gaussian (MOG). The result image gained by motion detection will be uploaded to the

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Kim, Hyoun Woo, Seung Hyun Shim, and Jong Woo Lee. "Sn-Catalyzed Growth of MgO Nanowires." Journal of Nanoscience and Nanotechnology 7, no. 12 (2007): 4434–38. http://dx.doi.org/10.1166/jnn.2007.880.

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We reported the fabrication and characterization of MgO nanowires, which were grown by thermal evaporation of the mixture of MgB2 and Sn powders at 800 °C through a vapor-liquid-solid (VLS) process. We characterized as-synthesized MgO nanowires using X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Sn nanoparticles were located at the tips of the nanowires, serving as catalyst for the growth of MgO nanowires. The produced nanowires were of cubic MgO structures with diameters in the range of 10–170 nm. The PL measurement with a Gaussian fitting exhibited vi

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Feng, Yi, Weijun Li, Kai Zhang, Xianling Li, Wenfang Cai, and Ruonan Liu. "Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing." Machines 10, no. 6 (2022): 435. http://dx.doi.org/10.3390/machines10060435.

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Reliability is of great significance in ensuring the safe operation of modern industry, which mainly relies on data analysis and life tests. However, as the life of mechanical systems becomes increasingly longer with the rapid development of the manufacturing industry, the collection of historical failure data becomes progressively more time-consuming. In this paper, a few-shot reliability assessment approach is proposed in order to overcome the dependence on historical data. Firstly, the vibration response of a bearing was illustrated. Then, based on a vibration response analysis, a morpholog

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Wu, Xingbang, Fangchen Hu, Peng Zou, Xingyu Lu, and Nan Chi. "The performance improvement of visible light communication systems under strong nonlinearities based on Gaussian mixture model." Microwave and Optical Technology Letters 62, no. 2 (2019): 547–54. http://dx.doi.org/10.1002/mop.32080.

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Ma, Tian-Hui, Zongben Xu, and Deyu Meng. "Remote Sensing Image Denoising via Low-Rank Tensor Approximation and Robust Noise Modeling." Remote Sensing 12, no. 8 (2020): 1278. http://dx.doi.org/10.3390/rs12081278.

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Noise removal is a fundamental problem in remote sensing image processing. Most existing methods, however, have not yet attained sufficient robustness in practice, due to more or less neglecting the intrinsic structures of remote sensing images and/or underestimating the complexity of realistic noise. In this paper, we propose a new remote sensing image denoising method by integrating intrinsic image characterization and robust noise modeling. Specifically, we use low-Tucker-rank tensor approximation to capture the global multi-factor correlation within the underlying image, and adopt a non-id

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KIM, TAEHO, and KANG-HYUN JO. "REAL-TIME OBJECT DETECTION USING TWO BACKGROUND MODELS UNDER SHAKING CAMERA." International Journal of Information Acquisition 06, no. 01 (2009): 13–21. http://dx.doi.org/10.1142/s0219878909001783.

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In this paper, we propose a novel approach to detect moving objects by two background models, multiple background model (MBM) and temporal median background (TMB), from hand-taken image sequence. For this purpose, we record image sequences by hand-held camera without tripod so every frame has variation between consecutive frames. A pixel-based background model is fragile while image sequence has variation. Therefore we calculate the camera movement using correlation between two consecutive images and it helps us to generate MBM under shaking camera. The computational cost of correlation quickl

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Li, Ke, Huiying Gong, Jinyu Qiu, et al. "Neuron Contact Detection Based on Pipette Precise Positioning for Robotic Brain-Slice Patch Clamps." Sensors 23, no. 19 (2023): 8144. http://dx.doi.org/10.3390/s23198144.

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A patch clamp is the “gold standard” method for studying ion-channel biophysics and pharmacology. Due to the complexity of the operation and the heavy reliance on experimenter experience, more and more researchers are focusing on patch-clamp automation. The existing automated patch-clamp system focuses on the process of completing the experiment; the detection method in each step is relatively simple, and the robustness of the complex brain film environment is lacking, which will increase the detection error in the microscopic environment, affecting the success rate of the automated patch clam

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Htun, Swe Nwe Nwe, Thi Thi Zin, and Hiromitsu Hama. "Virtual Grounding Point Concept for Detecting Abnormal and Normal Events in Home Care Monitoring Systems." Applied Sciences 10, no. 9 (2020): 3005. http://dx.doi.org/10.3390/app10093005.

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In this paper, an innovative home care video monitoring system for detecting abnormal and normal events is proposed by introducing a virtual grounding point (VGP) concept. To be specific, the proposed system is composed of four main image processing components: (1) visual object detection, (2) feature extraction, (3) abnormal and normal event analysis, and (4) the decision-making process. In the object detection component, background subtraction is first achieved using a specific mixture of Gaussians (MoG) to model the foreground in the form of a low-rank matrix factorization. Then, a theory o

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Liu, Yue, and Bufang Li. "Bayesian hierarchical K-means clustering." Intelligent Data Analysis 24, no. 5 (2020): 977–92. http://dx.doi.org/10.3233/ida-194807.

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Clustering algorithm is the foundation and important technology in data mining. In fact, in the real world, the data itself often has a hierarchical structure. Hierarchical clustering aims at constructing a cluster tree, which reveals the underlying modal structure of a complex density. Due to its inherent complexity, most existing hierarchical clustering algorithms are usually designed heuristically without an explicit objective function, which limits its utilization and analysis. K-means clustering, the well-known simple yet effective algorithm which can be expressed from the view of probabi

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Wang, Shuai, Xiaoyu Li, Wei Chen, Weiqiang Fan, and Zijian Tian. "An Intelligent Vision-Based Method of Worker Identification for Industrial Internet of Things (IoT)." Wireless Communications and Mobile Computing 2022 (January 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/8641096.

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With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker’s head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel

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Agarwal, Manan, Khushboo K. Rao, Kaushar Vaidya, and Souradeep Bhattacharya. "ML-MOC: Machine Learning (kNN and GMM) based Membership determination for Open Clusters." Monthly Notices of the Royal Astronomical Society 502, no. 2 (2021): 2582–99. http://dx.doi.org/10.1093/mnras/stab118.

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ABSTRACT The existing open-cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present ml-moc, a new machine-learning-based approach to identify likely members of open clusters using the Gaia DR2 data and no a priori information about cluster parameters. We use the k-nearest neighbour (kNN) algorithm and the Gaussian mixture model (GMM) on high-precision proper motions and parallax measurements from the Gaia DR2 data to determine the membership probabilities of individ

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Zaporozhets, Oleksandr, Kateryna Synylo, Sergii Karpenko, and Andriy Krupko. "Improvementof the computer model of air pollution estimation due to emissions of stationary sources of airports and compressor stations." Eastern-European Journal of Enterprise Technologies 3, no. 10(111) (2021): 54–64. http://dx.doi.org/10.15587/1729-4061.2021.236125.

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Emission sources at airports and compressor stations have the potential to emit pollutants, the effects of which can degrade local air quality. In most cases, the basis of gas pumping units includes either aircraft engines that have exhausted their flight life, or their targeted modifications to fulfill the tasks of gas pumping units and compressor stations in various gas transportation systems. The methodology for calculating the concentration of pollutants contained in the emissions of enterprises does not take into account all possible features of emission sources, in terms of passive

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Escudero, Carlos G., Favio R. Faifer, Analía V. Smith Castelli, Mark A. Norris, and Juan C. Forte. "Field/isolated lenticular galaxies with high SN values: the case of NGC 4546 and its globular cluster system." Monthly Notices of the Royal Astronomical Society 493, no. 2 (2020): 2253–70. http://dx.doi.org/10.1093/mnras/staa392.

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ABSTRACT We present a photometric study of the field lenticular galaxy NGC 4546 using Gemini/GMOS imaging in g′r′i′z′. We perform a 2D image decomposition of the surface brightness distribution of the galaxy using galfit, finding that four components adequately describe it. The subtraction of this model from our images and the construction of a colour map allow us to examine in great detail the asymmetric dust structures around the galactic centre. In addition, we perform a detailed analysis of the globular cluster (GC) system of NGC 4546. Using a Gaussian Mixture Model algorithm in the colour

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Oleksandr, Zaporozhets, Synylo Kateryna, Karpenko Sergii, and Krupko Andriy. "Improvement of the computer model of air pollution estimation due to emissions of stationary sources of airports and compressor stations." Eastern-European Journal of Enterprise Technologies 3, no. 10 (111) (2021): 54–64. https://doi.org/10.15587/1729-4061.2021.236125.

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Emission sources at airports and compressor stations have the potential to emit pollutants, the effects of which can degrade local air quality. In most cases, the basis of gas pumping units includes either aircraft engines that have exhausted their flight life, or their targeted modifications to fulfill the tasks of gas pumping units and compressor stations in various gas transportation systems. The methodology for calculating the concentration of pollutants contained in the emissions of enterprises does not take into account all possible features of emission sources, in terms of passive stati

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Bailer-Jones, Coryn A. L., Morgan Fouesneau, and Rene Andrae. "Quasar and galaxy classification in Gaia Data Release 2." Monthly Notices of the Royal Astronomical Society 490, no. 4 (2019): 5615–33. http://dx.doi.org/10.1093/mnras/stz2947.

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ABSTRACT We construct a supervised classifier based on Gaussian Mixture Models to probabilistically classify objects in Gaia data release 2 (GDR2) using only photometric and astrometric data in that release. The model is trained empirically to classify objects into three classes – star, quasar, galaxy – for G ≥ 14.5 mag down to the Gaia magnitude limit of G = 21.0 mag. Galaxies and quasars are identified for the training set by a cross-match to objects with spectroscopic classifications from the Sloan Digital Sky Survey. Stars are defined directly from GDR2. When allowing for the expectation t

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Luo, Xiaoyue, Yanhui Wang, Benhe Cai, and Zhanxing Li. "Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold." ISPRS International Journal of Geo-Information 10, no. 11 (2021): 742. http://dx.doi.org/10.3390/ijgi10110742.

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Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving o

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Kang, Wooseok, Ho Seong Hwang, Nobuhiro Okabe, and Changbom Park. "A Redshift Survey of the Coma Cluster (A1656): Understanding the Nature of Subhalos in the Weak-lensing Map." Astrophysical Journal Supplement Series 278, no. 2 (2025): 51. https://doi.org/10.3847/1538-4365/adcac8.

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Abstract We study the physical properties of weak-lensing subhalos in the Coma cluster of galaxies using data from galaxy redshift surveys. The data include 12,989 galaxies with measured spectroscopic redshifts (2184 from our MMT/Hectospec observations and 10,807 from the literature). The r-band magnitude limit at which the differential spectroscopic completeness drops below 50% is 20.2 mag, which is spatially uniform in a region of 4.5 deg2 where the weak-lensing map of N. Okabe et al. exists. We identify 1337 member galaxies in this field and use them to understand the nature of 32 subhalos

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Daemi, Atefeh, Hariprasad Kodamana, and Biao Huang. "Gaussian process modelling with Gaussian mixture likelihood." Journal of Process Control 81 (September 2019): 209–20. http://dx.doi.org/10.1016/j.jprocont.2019.06.007.

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Fabisch, Alexander. "gmr: Gaussian Mixture Regression." Journal of Open Source Software 6, no. 62 (2021): 3054. http://dx.doi.org/10.21105/joss.03054.

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Ali-Loytty, Simo Sakari. "Box Gaussian Mixture Filter $ $." IEEE Transactions on Automatic Control 55, no. 9 (2010): 2165–69. http://dx.doi.org/10.1109/tac.2010.2051486.

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Maragakis, Paul, Arjan van der Vaart, and Martin Karplus. "Gaussian-Mixture Umbrella Sampling." Journal of Physical Chemistry B 113, no. 14 (2009): 4664–73. http://dx.doi.org/10.1021/jp808381s.

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Freitas, Breno L., Renato M. Silva, and Tiago A. Almeida. "Gaussian Mixture Descriptors Learner." Knowledge-Based Systems 188 (January 2020): 105039. http://dx.doi.org/10.1016/j.knosys.2019.105039.

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Ju, Zhaojie, and Honghai Liu. "Fuzzy Gaussian Mixture Models." Pattern Recognition 45, no. 3 (2012): 1146–58. http://dx.doi.org/10.1016/j.patcog.2011.08.028.

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McNicholas, Paul David, and Thomas Brendan Murphy. "Parsimonious Gaussian mixture models." Statistics and Computing 18, no. 3 (2008): 285–96. http://dx.doi.org/10.1007/s11222-008-9056-0.

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Viroli, Cinzia, and Geoffrey J. McLachlan. "Deep Gaussian mixture models." Statistics and Computing 29, no. 1 (2017): 43–51. http://dx.doi.org/10.1007/s11222-017-9793-z.

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Kalliovirta, Leena, Mika Meitz, and Pentti Saikkonen. "Gaussian mixture vector autoregression." Journal of Econometrics 192, no. 2 (2016): 485–98. http://dx.doi.org/10.1016/j.jeconom.2016.02.012.

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Feng, Chunhua, Zhuang Liu, Weidong Li, Xin Lu, Yanguo Jing, and Yongsheng Ma. "Improved Gaussian mixture model and Gaussian mixture regression for learning from demonstration based on Gaussian noise scattering." Advanced Engineering Informatics 65 (May 2025): 103192. https://doi.org/10.1016/j.aei.2025.103192.

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Wei, Hui, and Wei Zheng. "Image Denoising Based on Improved Gaussian Mixture Model." Scientific Programming 2021 (September 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/7982645.

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Streszczenie:

An image denoising method is proposed based on the improved Gaussian mixture model to reduce the noises and enhance the image quality. Unlike the traditional image denoising methods, the proposed method models the pixel information in the neighborhood around each pixel in the image. The Gaussian mixture model is employed to measure the similarity between pixels by calculating the L2 norm between the Gaussian mixture models corresponding to the two pixels. The Gaussian mixture model can model the statistical information such as the mean and variance of the pixel information in the image area. T

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Mirra, J., and S. Abdullah. "Bayesian gaussian finite mixture model." Journal of Physics: Conference Series 1725 (January 2021): 012084. http://dx.doi.org/10.1088/1742-6596/1725/1/012084.

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Wichert, Andreas. "Quantum-like Gaussian mixture model." Soft Computing 25, no. 15 (2021): 10067–81. http://dx.doi.org/10.1007/s00500-021-05941-9.

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Fallah, Afshin. "Gaussian mixture analysis of covariance." Journal of Statistical Computation and Simulation 86, no. 16 (2016): 3158–74. http://dx.doi.org/10.1080/00949655.2016.1151519.

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Lotsi, Anani, and Ernst Wit. "Sparse Gaussian graphical mixture model." Afrika Statistika 11, no. 2 (2016): 1041–59. http://dx.doi.org/10.16929/as/2016.1041.91.

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Platanios, Emmanouil A., and Sotirios P. Chatzis. "Gaussian Process-Mixture Conditional Heteroscedasticity." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 5 (2014): 888–900. http://dx.doi.org/10.1109/tpami.2013.183.

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