Bayesian approach to incremental batch learning on forest cover sensor data for multiclass classification

2021 ◽  
pp. 1063293X2110584
Author(s):  
Venkata Vara Prasad D ◽  
Lokeswari Y Venkataramana ◽  
Saraswathi S ◽  
Sarah Mathew ◽  
Snigdha V
Keyword(s):  
Bayesian Approach ◽  
Incremental Learning ◽  
Bayesian Model ◽  
Forest Cover ◽  
Bayesian Framework ◽  
Training Data ◽  
Sensor Data ◽  
Learning Approaches ◽  
New Class ◽  
Multiple Levels

Deep neural networks can be used to perform nonlinear operations at multiple levels, such as a neural network that is composed of many hidden layers. Although deep learning approaches show good results, they have a drawback called catastrophic forgetting, which is a reduction in performance when a new class is added. Incremental learning is a learning method where existing knowledge should be retained even when new data is acquired. It involves learning with multiple batches of training data and the newer learning sessions do not require the data used in the previous iterations. The Bayesian approach to incremental learning uses the concept of the probability distribution of weights. The key idea of Bayes theorem is to find an updated distribution of weights and biases. In the Bayesian framework, the beliefs can be updated iteratively as the new data comes in. Bayesian framework allows to update the beliefs iteratively in real-time as data comes in. The Bayesian model for incremental learning showed an accuracy of 82%. The execution time for the Bayesian model was lesser on GPU (670 s) when compared to CPU (1165 s).

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

scholarly journals Deep Learning of Appearance Affinity for Multi-Object Tracking and Re-Identification: A Comparative View

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1757
Author(s):  
María J. Gómez-Silva ◽  
Arturo de la Escalera ◽  
José M. Armingol
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Loss Function ◽  
Neural Model ◽  
Training Data ◽  
Learning Approaches ◽  
The Core ◽  
Triplet Loss ◽  
Affinity Model

Recognizing the identity of a query individual in a surveillance sequence is the core of Multi-Object Tracking (MOT) and Re-Identification (Re-Id) algorithms. Both tasks can be addressed by measuring the appearance affinity between people observations with a deep neural model. Nevertheless, the differences in their specifications and, consequently, in the characteristics and constraints of the available training data for each one of these tasks, arise from the necessity of employing different learning approaches to attain each one of them. This article offers a comparative view of the Double-Margin-Contrastive and the Triplet loss function, and analyzes the benefits and drawbacks of applying each one of them to learn an Appearance Affinity model for Tracking and Re-Identification. A batch of experiments have been conducted, and their results support the hypothesis concluded from the presented study: Triplet loss function is more effective than the Contrastive one when an Re-Id model is learnt, and, conversely, in the MOT domain, the Contrastive loss can better discriminate between pairs of images rendering the same person or not.

New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 ◽  
Vol 69 (4) ◽  
pp. 297-306
Author(s):  
Julius Krause ◽  
Maurice Günder ◽  
Daniel Schulz ◽  
Robin Gruna
Keyword(s):  
Active Learning ◽  
Near Infrared ◽  
Agricultural Products ◽  
Training Data ◽  
Calibration Model ◽  
Learning Approaches ◽  
Training Samples ◽  
Agricultural Applications ◽  
Selection Of

Abstract The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

scholarly journals Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 38 ◽  
Author(s):  
Martin Jänicke ◽  
Bernhard Sick ◽  
Sven Tomforde
Keyword(s):  
Activity Recognition ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Training Data ◽  
Sensor Data ◽  
Activity Data ◽  
High Loss ◽  
New Sensors ◽  
Self Adaptive

Personal wearables such as smartphones or smartwatches are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on the creation of a self-adaptive activity recognition system based on IMU that includes new sensors during runtime. Starting with a classifier based on GMM, the density model is adapted to new sensor data fully autonomously by issuing the marginalization property of normal distributions. To create a classifier from that, label inference is done, either based on the initial classifier or based on the training data. For evaluation, we used more than 10 h of annotated activity data from the publicly available PAMAP2 benchmark dataset. Using the data, we showed the feasibility of our approach and performed 9720 experiments, to get resilient numbers. One approach performed reasonably well, leading to a system improvement on average, with an increase in the F-score of 0.0053, while the other one shows clear drawbacks due to a high loss of information during label inference. Furthermore, a comparison with state of the art techniques shows the necessity for further experiments in this area.

scholarly journals Multimodal Sensor Data Fusion for Activity Recognition Using Filtered Classifier

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1262 ◽  
Author(s):  
Muhammad Razzaq ◽  
Ian Cleland ◽  
Chris Nugent ◽  
Sungyoung Lee
Keyword(s):  
Activity Recognition ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sensor Data ◽  
Test Dataset ◽  
Sensory Data ◽  
Multimodal Sensors ◽  
Imbalanced Class Distribution ◽  
Single Modality

Activity recognition (AR) is a subtask in pervasive computing and context-aware systems, which presents the physical state of human in real-time. These systems offer a new dimension to the widely spread applications by fusing recognized activities obtained from the raw sensory data generated by the obtrusive as well as unobtrusive revolutionary digital technologies. In recent years, an exponential growth has been observed for AR technologies and much literature exists focusing on applying machine learning algorithms on obtrusive single modality sensor devices. However, University of Jaén Ambient Intelligence (UJAmI), a Smart Lab in Spain has initiated a 1st UCAmI Cup challenge by sharing aforementioned varieties of the sensory data in order to recognize the human activities in the smart environment. This paper presents the fusion, both at the feature level and decision level for multimodal sensors by preprocessing and predicting the activities within the context of training and test datasets. Though it achieves 94% accuracy for training data and 47% accuracy for test data. However, this study further evaluates post-confusion matrix also and draws a conclusion for various discrepancies such as imbalanced class distribution within the training and test dataset. Additionally, this study also highlights challenges associated with the datasets for which, could improve further analysis.

scholarly journals Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

2016 ◽  
Vol 113 (47) ◽  
pp. 13301-13306 ◽  
Author(s):  
Dezhen Xue ◽  
Prasanna V. Balachandran ◽  
Ruihao Yuan ◽  
Tao Hu ◽  
Xiaoning Qian ◽  
...  
Keyword(s):  
Solid Solution ◽  
Phase Boundary ◽  
Morphotropic Phase Boundary ◽  
Bayesian Approach ◽  
Bayesian Learning ◽  
Piezoelectric Properties ◽  
Training Data ◽  
Materials Informatics ◽  
Initial Training ◽  
New Materials

An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau–Devonshire theory, we demonstrate our approach for BaTiO3-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba0.5Ca0.5)TiO3-Ba(Ti0.7Zr0.3)O3, with piezoelectric properties that show better temperature reliability than other BaTiO3-based piezoelectrics in our initial training data.

scholarly journals SYNTHETIC VISION SYSTEM CALIBRATION FOR CONFORM PROJECTION ON THE PILOT’S HEAD-UP DISPLAY

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 575-581
Author(s):  
S. Danilov ◽  
M. Kozyrev ◽  
M. Grechanichenko ◽  
L. Grodzitskiy ◽  
V. Mizginov ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Vision System ◽  
Digital Terrain Model ◽  
3D Models ◽  
Sensor Data ◽  
Training Dataset ◽  
Learning Approaches ◽  
Terrain Model ◽  
Front Window ◽  
Calibration Algorithm

Abstract. Situational awareness of the crew is critical for the safety of the air flight. Head-up display allows providing all required flight information in front of the pilot over the cockpit view visible through the cockpit’s front window. This device has been created for solving the problem of informational overload during piloting of an aircraft. While computer graphics such as scales and digital terrain model can be easily presented on the such display, errors in the Head-up display alignment for correct presenting of sensor data pose challenges. The main problem arises from the parallax between the pilot’s eyes and the position of the camera. This paper is focused on the development of an online calibration algorithm for conform projection of the 3D terrain and runway models on the pilot’s head-up display. The aim of our algorithm is to align the objects visible through the cockpit glass with their projections on the Head-up display. To improve the projection accuracy, we use an additional optical sensor installed on the aircraft. We combine classical photogrammetric techniques with modern deep learning approaches. Specifically, we use an object detection neural network model to find the runway area and align runway projection with its actual location. Secondly, we re-project the sensor’s image onto the 3D model of the terrain to eliminate errors caused by the parallax. We developed an environment simulator to evaluate our algorithm. Using the simulator we prepared a large training dataset. The dataset includes 2000 images of video sequences representing aircraft’s motion during takeoff, landing and taxi. The results of the evaluation are encouraging and demonstrate both qualitatively and quantitatively that the proposed algorithm is capable of precise alignment of the 3D models projected on a Head-up display.

scholarly journals 3D Convolutional Neural Networks and a CrossDocked Dataset for Structure-Based Drug Design

2020 ◽  
Author(s):  
Paul Francoeur ◽  
Tomohide Masuda ◽  
David R. Koes
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Mean Squared Error ◽  
Comprehensive Evaluation ◽  
Training Data ◽  
Learning Approaches ◽  
Neural Network Models ◽  
Structure Based Drug Design ◽  
Affinity Prediction

One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measuring generalization to new targets, and there does not exist a standard dataset of sufficient size to compare performance between models. We present a new dataset for structure-based machine learning, the CrossDocked2020 set, with 22.5 million poses of ligands docked into multiple similar binding pockets across the Protein Data Bank and perform a comprehensive evaluation of grid-based convolutional neural network models on this dataset. We also demonstrate how the partitioning of the training data and test data can impact the results of models trained with the PDBbind dataset, how performance improves by adding more, lower-quality training data, and how training with docked poses imparts pose sensitivity to the predicted affinity of a complex. Our best performing model, an ensemble of 5 densely connected convolutional newtworks, achieves a root mean squared error of 1.42 and Pearson R of 0.612 on the affinity prediction task, an AUC of 0.956 at binding pose classification, and a 68.4% accuracy at pose selection on the CrossDocked2020 set. By providing data splits for clustered cross-validation and the raw data for the CrossDocked2020 set, we establish the first standardized dataset for training machine learning models to recognize ligands in non-cognate target structures while also greatly expanding the number of poses available for training. In order to facilitate community adoption of this dataset for benchmarking protein-ligand binding affinity prediction, we provide our models, weights, and the CrossDocked2020 set at https://github.com/gnina/models.

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