scholarly journals Robust estimation of gaussian mixtures from noisy input data

2008 ◽  
Author(s):  
Shaobo Hou ◽  
Aphrodite Galata
Keyword(s):  
Robust Estimation ◽  
Input Data ◽  
Gaussian Mixtures ◽  
Noisy Input

scholarly journals Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 15-21
Author(s):  
Dmitrienko V. D ◽  
Yu. Zakovorotnyi A ◽  
Yu. Leonov S ◽  
Khavina I. P
Keyword(s):  
Neural Networks ◽  
Multiple Solutions ◽  
Input Data ◽  
Learning Algorithms ◽  
Adaptive Resonance Theory ◽  
Classification Methods ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Noisy Input ◽  
New Algorithms

A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.

scholarly journals Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3222
Author(s):  
Sami M. Saliba ◽  
Benjamin D. Bowes ◽  
Stephen Adams ◽  
Peter A. Beling ◽  
Jonathan L. Goodall
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Real World ◽  
Input Data ◽  
Flood Control ◽  
Sequential Optimization ◽  
System Control ◽  
Passive System ◽  
General Technique ◽  
Noisy Input

Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

scholarly journals An Analysis of Rhythmic Patterns with Unsupervised Learning

2019 ◽  
Vol 10 (1) ◽  
pp. 178 ◽  
Author(s):  
Matevž Pesek ◽  
Aleš Leonardis ◽  
Matija Marolt
Keyword(s):  
Unsupervised Learning ◽  
Time Complexity ◽  
Input Data ◽  
Music Genres ◽  
Music Signal ◽  
Noisy Input ◽  
Complex Patterns ◽  
Rhythmic Structures

This paper presents a model capable of learning the rhythmic characteristics of a music signal through unsupervised learning. The model learns a multi-layer hierarchy of rhythmic patterns ranging from simple structures on lower layers to more complex patterns on higher layers. The learned hierarchy is fully transparent, which enables observation and explanation of the structure of the learned patterns. The model employs tempo-invariant encoding of patterns and can thus learn and perform inference on tempo-varying and noisy input data. We demonstrate the model’s capabilities of learning distinctive rhythmic structures of different music genres using unsupervised learning. To test its robustness, we show how the model can efficiently extract rhythmic structures in songs with changing time signatures and live recordings. Additionally, the model’s time-complexity is empirically tested to show its usability for analysis-related applications.

scholarly journals Estimation and computations for Gaussian mixtures with uniform noise under separation constraints

2021 ◽  
Author(s):  
Pietro Coretto
Keyword(s):  
Data Analysis ◽  
Density Estimation ◽  
Robust Estimation ◽  
Data Distribution ◽  
Gaussian Mixture ◽  
Gaussian Mixtures ◽  
Outlier Identification ◽  
Uniform Noise ◽  
Type Algorithm ◽  
Ml Estimator

AbstractIn this paper we study a finite Gaussian mixture model with an additional uniform component that has the role to catch points in the tails of the data distribution. An adaptive constraint enforces a certain level of separation between the Gaussian mixture components and the uniform component representing noise and outliers in the tail of the distribution. The latter makes the proposed tool particularly useful for robust estimation and outlier identification. A constrained ML estimator is introduced for which existence and consistency is shown. One of the attractive features of the methodology is that the noise level is estimated from data. We also develop an EM-type algorithm with proven convergence. Based on numerical evidence we show how the methods developed in this paper are useful for several fundamental data analysis tasks: outlier identification, robust location-scale estimation, clustering, and density estimation.

scholarly journals wUUNet: Advanced Fully Convolutional Neural Network for Multiclass Fire Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 98
Author(s):  
Vladimir Sergeevich Bochkov ◽  
Liliya Yurievna Kataeva
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Input Data ◽  
Final Section ◽  
State Of The Art ◽  
Sliding Window ◽  
Input Image ◽  
Gaussian Mixtures ◽  
Flame Suppression ◽  
Segmentation Task

This article describes an AI-based solution to multiclass fire segmentation. The flame contours are divided into red, yellow, and orange areas. This separation is necessary to identify the hottest regions for flame suppression. Flame objects can have a wide variety of shapes (convex and non-convex). In that case, the segmentation task is more applicable than object detection because the center of the fire is much more accurate and reliable information than the center of the bounding box and, therefore, can be used by robotics systems for aiming. The UNet model is used as a baseline for the initial solution because it is the best open-source convolutional neural network. There is no available open dataset for multiclass fire segmentation. Hence, a custom dataset was developed and used in the current study, including 6250 samples from 36 videos. We compared the trained UNet models with several configurations of input data. The first comparison is shown between the calculation schemes of fitting the frame to one window and obtaining non-intersected areas of sliding window over the input image. Secondarily, we chose the best main metric of the loss function (soft Dice and Jaccard). We addressed the problem of detecting flame regions at the boundaries of non-intersected regions, and introduced new combinational methods of obtaining output signal based on weighted summarization and Gaussian mixtures of half-intersected areas as a solution. In the final section, we present UUNet-concatenative and wUUNet models that demonstrate significant improvements in accuracy and are considered to be state-of-the-art. All models use the original UNet-backbone at the encoder layers (i.e., VGG16) to demonstrate the superiority of the proposed architectures. The results can be applied to many robotic firefighting systems.

scholarly journals Mitigation of Flooding in Stormwater Systems Utilizing Imperfect Forecasting and Sensor Data with Deep Deterministic Policy Gradient Reinforcement Learning

2020 ◽  
Author(s):  
Sami Saliba ◽  
Benjamin Bowes ◽  
Stephen Adams ◽  
Peter Beling ◽  
Jonathan Goodall
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Real World ◽  
Input Data ◽  
Flood Control ◽  
Sensor Data ◽  
Sequential Optimization ◽  
Passive System ◽  
Noisy Input ◽  
Policy Gradient

Climate change and development have increased urban flooding, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information, are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

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