scholarly journals A Novel Decomposition Model for Visual Rail Surface Inspection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1271
Author(s):  
Ziwen Zhang ◽  
Mangui Liang ◽  
Zhiyu Liu
Keyword(s):  
Large Scale ◽  
Low Rank ◽  
Surface Inspection ◽  
Type I ◽  
Dynamic Background ◽  
Practical Applications ◽  
Decomposition Model ◽  
Vision Inspection ◽  
Defect Area ◽  
Track Surface

Rail surface inspection plays a pivotal role in large-scale railway construction and development. However, accurately identifying possible defects involving a large variety of visual appearances and their dynamic illuminations remains challenging. In this paper, we fully explore and use the essential attributes of our defect structure data and the inherent temporal and spatial characteristics of the track to establish a general theoretical framework for practical applications. As such, our framework can overcome the bottleneck associated with machine vision inspection technology in complex rail environments. In particular, we consider a differential regular term for background rather than a traditional low-rank constraint to ensure that the model can tolerate dynamic background changes without losing sensitivity when detecting defects. To better capture the compactness and completeness of a defect, we introduce a tree-shaped hierarchical structure of sparse induction norms to encode the spatial structure of the defect area. The proposed model is evaluated with respect to two newly released Type-I/II rail surfaces discrete defects (RSDD) data sets and a practical rail line. Qualitative and quantitative evaluations show that the decomposition model can handle the dynamics of the track surface well and that the model can be used for structural detection of the defect area.

Bullying, School Violence, and Climate in Evolving Contexts

2018 ◽  
Author(s):  
Ron Avi Astor ◽  
Rami Benbenisthty
Keyword(s):  
School Violence ◽  
School Safety ◽  
Media Coverage ◽  
Large Scale ◽  
Ecological Model ◽  
Empirical Studies ◽  
Practical Applications ◽  
Teacher Student ◽  
Area Of Interest ◽  
New Research

Since 2005, the bullying, school violence, and school safety literatures have expanded dramatically in content, disciplines, and empirical studies. However, with this massive expansion of research, there is also a surprising lack of theoretical and empirical direction to guide efforts on how to advance our basic science and practical applications of this growing scientific area of interest. Parallel to this surge in interest, cultural norms, media coverage, and policies to address school safety and bullying have evolved at a remarkably quick pace over the past 13 years. For example, behaviors and populations that just a decade ago were not included in the school violence, bullying, and school safety discourse are now accepted areas of inquiry. These include, for instance, cyberbullying, sexting, social media shaming, teacher–student and student–teacher bullying, sexual harassment and assault, homicide, and suicide. Populations in schools not previously explored, such as lesbian, gay, bisexual, transgender, and queer students and educators and military- and veteran-connected students, become the foci of new research, policies, and programs. As a result, all US states and most industrialized countries now have a complex quilt of new school safety and bullying legislation and policies. Large-scale research and intervention funding programs are often linked to these policies. This book suggests an empirically driven unifying model that brings together these previously distinct literatures. This book presents an ecological model of school violence, bullying, and safety in evolving contexts that integrates all we have learned in the 13 years, and suggests ways to move forward.

scholarly journals Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Moritz Mercker ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Leonie Enners ◽  
Stefan Garthe
Keyword(s):  
Habitat Selection ◽  
Large Scale ◽  
Error Rates ◽  
Process Models ◽  
Statistical Hypothesis ◽  
Type I ◽  
Tracking Data ◽  
Selection Models ◽  
Animal Tracking ◽  
Step Selection

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

scholarly journals 2D Nanomaterials for Effective Energy Scavenging

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Md Al Mahadi Hasan ◽  
Yuanhao Wang ◽  
Chris R. Bowen ◽  
Ya Yang
Keyword(s):  
Large Scale ◽  
Waste Heat ◽  
Material Selection ◽  
Power Supplies ◽  
Small Scale ◽  
Energy Scavenging ◽  
Power Sources ◽  
Practical Applications ◽  
2D Nanomaterials ◽  
Self Powered

AbstractThe development of a nation is deeply related to its energy consumption. 2D nanomaterials have become a spotlight for energy harvesting applications from the small-scale of low-power electronics to a large-scale for industry-level applications, such as self-powered sensor devices, environmental monitoring, and large-scale power generation. Scientists from around the world are working to utilize their engrossing properties to overcome the challenges in material selection and fabrication technologies for compact energy scavenging devices to replace batteries and traditional power sources. In this review, the variety of techniques for scavenging energies from sustainable sources such as solar, air, waste heat, and surrounding mechanical forces are discussed that exploit the fascinating properties of 2D nanomaterials. In addition, practical applications of these fabricated power generating devices and their performance as an alternative to conventional power supplies are discussed with the future pertinence to solve the energy problems in various fields and applications.

scholarly journals Application of Novel Non-Thermal Physical Technologies to Degrade Mycotoxins

2021 ◽  
Vol 7 (5) ◽  
pp. 395
Author(s):  
Mohammad Yousefi ◽  
Masoud Aman Mohammadi ◽  
Maryam Zabihzadeh Khajavi ◽  
Ali Ehsani ◽  
Vladimír Scholtz
Keyword(s):  
Electron Beam ◽  
Food Systems ◽  
Large Scale ◽  
Plasma Electron ◽  
Pulsed Light ◽  
Beam Radiation ◽  
Practical Applications ◽  
Electron Beam Radiation ◽  
Non Thermal Plasma ◽  
Agriculture And Food

Mycotoxins cause adverse effects on human health. Therefore, it is of the utmost importance to confront them, particularly in agriculture and food systems. Non-thermal plasma, electron beam radiation, and pulsed light are possible novel non-thermal technologies offering promising results in degrading mycotoxins with potential for practical applications. In this paper, the available publications are reviewed—some of them report efficiency of more than 90%, sometimes almost 100%. The mechanisms of action, advantages, efficacy, limitations, and undesirable effects are reviewed and discussed. The first foretastes of plasma and electron beam application in the industry are in the developing stages, while pulsed light has not been employed in large-scale application yet.

scholarly journals A Short Review on Recent Advances of Hydrogel-Based Adsorbents for Heavy Metal Ions

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 864
Author(s):  
Suguna Perumal ◽  
Raji Atchudan ◽  
Thomas Nesakumar Jebakumar Immanuel Edison ◽  
Rajendran Suresh Babu ◽  
Petchimuthu Karpagavinayagam ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Large Scale ◽  
Water Bodies ◽  
Dimensional Structure ◽  
High Porosity ◽  
Adsorption Method ◽  
Toxic Heavy Metal ◽  
Practical Applications

The growth of industry fulfills our necessity and promotes economic development. However, pollutants from such industries pollute water bodies which pose a high risk for living organisms. Thus, researchers have been urged to develop an efficient method to remove toxic heavy metal ions from water bodies. The adsorption method shows promising results for the removal of heavy metal ions and is easy to operate on a large scale, thus can be applied to practical applications. Numerous adsorbents were developed and reported, among them hydrogels, which attract great attention because of the reusability, ease of preparation, and handling. Hydrogels are generally prepared by the cross-linking of polymers that result in a three-dimensional structure, showing high porosity and high functionality. They are hydrophilic in nature because of the functional groups, and are non-toxic. Thus, this review provides various methods of hydrogel adsorbents preparation and summarizes recent progress in the use of hydrogel adsorbents for the removal of heavy metal ions. Further, the mechanism involved in the removal of heavy metal ions is briefly discussed. The most recent studies about the adsorption method for the treatment of heavy metal ions contaminated water are presented.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

A Model-Based Factored Bayesian Reinforcement Learning Approach

2014 ◽  
Vol 513-517 ◽  
pp. 1092-1095
Author(s):  
Bo Wu ◽  
Yan Peng Feng ◽  
Hong Yan Zheng
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Practical Applications ◽  
Model Based ◽  
Online Planning ◽  
Bayesian Reinforcement Learning ◽  
Bayesian Inference Method ◽  
Unknown Structure

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.

scholarly journals Domain-Adversarial Training of Self-Attention-Based Networks for Land Cover Classification Using Multi-Temporal Sentinel-2 Satellite Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2564
Author(s):  
Mauro Martini ◽  
Vittorio Mazzia ◽  
Aleem Khaliq ◽  
Marcello Chiaberge
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Domain Adaptation ◽  
Learning Approaches ◽  
Atmospheric Conditions ◽  
Practical Applications ◽  
Area Of Interest ◽  
Multi Temporal ◽  
Significant Performance ◽  
Adversarial Training

The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have shown promising results in processing long temporal sequences in the multi-spectral domain with a contained computational request. Nevertheless, most practical applications cannot rely on labeled data, and in the field, surveys are a time-consuming solution that pose strict limitations to the number of collected samples. Moreover, atmospheric conditions and specific geographical region characteristics constitute a relevant domain gap that does not allow direct applicability of a trained model on the available dataset to the area of interest. In this paper, we investigate adversarial training of deep neural networks to bridge the domain discrepancy between distinct geographical zones. In particular, we perform a thorough analysis of domain adaptation applied to challenging multi-spectral, multi-temporal data, accurately highlighting the advantages of adapting state-of-the-art self-attention-based models for LC&CC to different target zones where labeled data are not available. Extensive experimentation demonstrated significant performance and generalization gain in applying domain-adversarial training to source and target regions with marked dissimilarities between the distribution of extracted features.

hIPPYlib

2021 ◽  
Vol 47 (2) ◽  
pp. 1-34
Author(s):  
Umberto Villa ◽  
Noemi Petra ◽  
Omar Ghattas
Keyword(s):  
Inverse Problems ◽  
Large Scale ◽  
Low Rank ◽  
Scalable Algorithms ◽  
Low Rank Approximation ◽  
Dimensional Parameter ◽  
Infinite Dimensional ◽  
Bayesian Inverse Problems ◽  
Rank Approximation ◽  
Extensible Software

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.

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