scholarly journals Anomalies in the Sky: Experiments with traffic densities and airport runway use

10.29007/3lks ◽  
2019 ◽  
Author(s):  
Axel Tanner ◽  
Martin Strohmeier
Keyword(s):  
Anomaly Detection ◽  
Gini Index ◽  
Ground Truth ◽  
Air Traffic ◽  
Learning Approaches ◽  
Traffic Data ◽  
Traffic Patterns ◽  
Ground Truth Data ◽  
Political Events ◽  
Airport Runway

Anomalies in the airspace can provide an indicator of critical events and changes which go beyond aviation. Devising techniques, which can detect abnormal patterns can provide intelligence and information ranging from weather to political events. This work presents our latest findings in detecting such anomalies in air traffic patterns using ADS-B data provided by the OpenSky network [8]. After discussion of specific problems in anomaly detection in air traffic data, we show an experiment in a regional setting, evaluating air traffic densities with the Gini index, and a second experiment investigating the runway use at Zurich airport. In the latter case, strong available ground truth data allows to better understand and confirm findings of different learning approaches.

Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection

2020 ◽  
Vol 47 (8) ◽  
pp. 982-997
Author(s):  
Mohamed H. Zaki ◽  
Tarek Sayed ◽  
Moataz Billeh
Keyword(s):  
Data Collection ◽  
Video Analysis ◽  
Three Dimensional ◽  
Ground Truth ◽  
Traffic Data ◽  
Ground Truth Data ◽  
The Road ◽  
Traffic Data Collection ◽  
Bounding Boxes ◽  
Automated Video Analysis

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements.

scholarly journals Unsupervised learning trajectory anomaly detection algorithm based on deep representation

2020 ◽  
Vol 16 (12) ◽  
pp. 155014772097150
Author(s):  
Zhongqiu Wang ◽  
Guan Yuan ◽  
Haoran Pei ◽  
Yanmei Zhang ◽  
Xiao Liu
Keyword(s):  
Anomaly Detection ◽  
Unsupervised Learning ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
Ground Truth ◽  
Real Data ◽  
Detection Algorithm ◽  
Learning Trajectory ◽  
Fusion Model ◽  
Ground Truth Data

Without ground-truth data, trajectory anomaly detection is a hard work and the result lacks of interpretability. Moreover, in most current methods, trajectories are represented by geometric features or their low-dimensional linear combination, and some hidden features and high-dimensional combined features cannot be found efficiently. Meanwhile, traditional methods still cannot get rid of the limitation of space attributes. Therefore, a novel trajectory anomaly detection algorithm is present in this article. Unsupervised learning mechanism is used to overcome nonground-truth problem and deep representation method is used to represent trajectories in a comprehensive way. First, each trajectory is partitioned into segments according to its open angles, then the shallow features at each point of a segment are extracted and. In this way, each segment is represented as a feature sequence. Second, shallow features are integrated into auto-encoder-based deep feature fusion model, and the fusion feature sequences can be extracted. Third, these fused feature sequences are grouped into different clusters using a unsupervised clustering algorithm, and then segments which quite differ from others are detected as anomalies. Finally, comprehensive experiments are conducted on both synthetic and real data sets, which demonstrate the efficiency of our work.

scholarly journals Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Laura K. Young ◽  
Hannah E. Smithson
Keyword(s):  
Adaptive Optics ◽  
Retinal Image ◽  
Imaging System ◽  
Ground Truth ◽  
Learning Approaches ◽  
Image Capture ◽  
Ground Truth Data ◽  
Cone Mosaic ◽  
Synthetic Images

AbstractHigh resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical research and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground-truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.

scholarly journals Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Sanket Kadulkar ◽  
Michael Howard ◽  
Thomas Truskett ◽  
Venkat Ganesan
Keyword(s):  
Optimal Transport ◽  
Kinetic Monte Carlo ◽  
Ground Truth ◽  
Spatial Arrangement ◽  
Spherical Particles ◽  
Learning Approaches ◽  
Structure Property ◽  
Spatial Correlations ◽  
Ground Truth Data ◽  
Modeling Strategy

<pre>We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.</pre>

scholarly journals Creating a universal SNP and small indel variant caller with deep neural networks

2016 ◽  
Author(s):  
Ryan Poplin ◽  
Pi-Chuan Chang ◽  
David Alexander ◽  
Scott Schwartz ◽  
Thomas Colthurst ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Mammalian Species ◽  
Variant Calling ◽  
Ground Truth ◽  
Learning Approaches ◽  
Next Generation ◽  
Ground Truth Data ◽  
Sequencing Technologies ◽  
Significant Step ◽  
Generation Sequencing

AbstractNext-generation sequencing (NGS) is a rapidly evolving set of technologies that can be used to determine the sequence of an individual’s genome1 by calling genetic variants present in an individual using billions of short, errorful sequence reads2. Despite more than a decade of effort and thousands of dedicated researchers, the hand-crafted and parameterized statistical models used for variant calling still produce thousands of errors and missed variants in each genome3,4. Here we show that a deep convolutional neural network5 can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls. This approach, called DeepVariant, outperforms existing tools, even winning the “highest performance” award for SNPs in a FDA-administered variant calling challenge. The learned model generalizes across genome builds and even to other mammalian species, allowing non-human sequencing projects to benefit from the wealth of human ground truth data. We further show that, unlike existing tools which perform well on only a specific technology, DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, from deep whole genomes from 10X Genomics to Ion Ampliseq exomes. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.

scholarly journals Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Sanket Kadulkar ◽  
Michael Howard ◽  
Thomas Truskett ◽  
Venkat Ganesan
Keyword(s):  
Optimal Transport ◽  
Kinetic Monte Carlo ◽  
Ground Truth ◽  
Spatial Arrangement ◽  
Spherical Particles ◽  
Learning Approaches ◽  
Structure Property ◽  
Spatial Correlations ◽  
Ground Truth Data ◽  
Modeling Strategy

<pre>We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.</pre>

scholarly journals Review of Current Machine Learning Approaches for Anomaly Detection in Network Traffic

2020 ◽  
Vol 8 (4) ◽  
pp. 64-95
Author(s):  
Wasim Ahmed Ali ◽  
Manasa K N ◽  
Mohammed Aljunid ◽  
Malika Bendechache ◽  
P. Sandhya
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
High Performance ◽  
Large Network ◽  
Learning Approaches ◽  
Traffic Data ◽  
Industrial Control Systems ◽  
Broad Perspective ◽  
Comprehensive Survey ◽  
Network Technologies

Due to the advance in network technologies, the number of network users is growing rapidly, which leads to the generation of large network traffic data. This large network traffic data is prone to attacks and intrusions. Therefore, the network needs to be secured and protected by detecting anomalies as well as to prevent intrusions into networks. Network security has gained attention from researchers and network laboratories. In this paper, a comprehensive survey was completed to give a broad perspective of what recently has been done in the area of anomaly detection. Newly published studies in the last five years have been investigated to explore modern techniques with future opportunities. In this regard, the related literature on anomaly detection systems in network traffic has been discussed, with a variety of typical applications such as WSNs, IoT, high-performance computing, industrial control systems (ICS), and software-defined network (SDN) environments. Finally, we underlined diverse open issues to improve the detection of anomaly systems.

scholarly journals Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation

2021 ◽  
Vol 11 (20) ◽  
pp. 9724
Author(s):  
Junuk Cha ◽  
Muhammad Saqlain ◽  
Changhwa Lee ◽  
Seongyeong Lee ◽  
Seungeun Lee ◽  
...  
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Ground Truth ◽  
Small Subset ◽  
Learning Approaches ◽  
Shape Estimation ◽  
Camera System ◽  
Ground Truth Data ◽  
Human Pose ◽  
2D Images

Three-dimensional human pose and shape estimation is an important problem in the computer vision community, with numerous applications such as augmented reality, virtual reality, human computer interaction, and so on. However, training accurate 3D human pose and shape estimators based on deep learning approaches requires a large number of images and corresponding 3D ground-truth pose pairs, which are costly to collect. To relieve this constraint, various types of weakly or self-supervised pose estimation approaches have been proposed. Nevertheless, these methods still involve supervision signals, which require effort to collect, such as unpaired large-scale 3D ground truth data, a small subset of 3D labeled data, video priors, and so on. Often, they require installing equipment such as a calibrated multi-camera system to acquire strong multi-view priors. In this paper, we propose a self-supervised learning framework for 3D human pose and shape estimation that does not require other forms of supervision signals while using only single 2D images. Our framework inputs single 2D images, estimates human 3D meshes in the intermediate layers, and is trained to solve four types of self-supervision tasks (i.e., three image manipulation tasks and one neural rendering task) whose ground-truths are all based on the single 2D images themselves. Through experiments, we demonstrate the effectiveness of our approach on 3D human pose benchmark datasets (i.e., Human3.6M, 3DPW, and LSP), where we present the new state-of-the-art among weakly/self-supervised methods.

scholarly journals Learning to Calibrate Battery Models in Real-Time with Deep Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1361
Author(s):  
Ajaykumar Unagar ◽  
Yuan Tian ◽  
Manuel Arias Chao ◽  
Olga Fink
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Computational Models ◽  
Lithium Ion ◽  
Ground Truth ◽  
Model Parameters ◽  
Learning Approaches ◽  
Ground Truth Data ◽  
Practical Applications ◽  
Physical Assets

Lithium-ion (Li-I) batteries have recently become pervasive and are used in many physical assets. For the effective management of the batteries, reliable predictions of the end-of-discharge (EOD) and end-of-life (EOL) are essential. Many detailed electrochemical models have been developed for the batteries. Their parameters are calibrated before they are taken into operation and are typically not re-calibrated during operation. However, the degradation of batteries increases the reality gap between the computational models and the physical systems and leads to inaccurate predictions of EOD/EOL. The current calibration approaches are either computationally expensive (model-based calibration) or require large amounts of ground truth data for degradation parameters (supervised data-driven calibration). This is often infeasible for many practical applications. In this paper, we introduce a reinforcement learning-based framework for reliably inferring calibration parameters of battery models in real time. Most importantly, the proposed methodology does not need any labeled data samples of observations and the ground truth parameters. The experimental results demonstrate that our framework is capable of inferring the model parameters in real time with better accuracy compared to approaches based on unscented Kalman filters. Furthermore, our results show better generalizability than supervised learning approaches even though our methodology does not rely on ground truth information during training.

Evaluation of Traffic Data Collection Method

2015 ◽  
Vol 764-765 ◽  
pp. 905-909
Author(s):  
Won Ho Suh ◽  
James Anderson ◽  
Angshuman Guin ◽  
Michael Hunter
Keyword(s):  
Performance Monitoring ◽  
Detection System ◽  
Ground Truth ◽  
Traffic Data ◽  
Accurate Data ◽  
Annual Average ◽  
Collection Method ◽  
Ground Truth Data ◽  
Traffic Data Collection ◽  
Annual Average Daily Traffic

Traffic counts are one of the fundamental data sources for the Highway Performance Monitoring System (HPMS). Automatic Traffic Recorders (ATRs) are used to provide continuous traffic count coverage at selected locations to estimate annual average daily traffic (AADT). However, ATR data is often unavailable. This paper investigated the feasibility of using Video Detection System (VDS) technology when ATR data is not available. An Android Tablet-based manual traffic counting application was developed to acquire manual count based ground truth data. The performance of VDS was evaluated under various conditions including mounting styles, heights, and roadway offsets. The results indicated that VDS data presents reasonably accurate data, although the data exhibits more variability compared to ATR data.

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