scholarly journals Fatigue lifetime correction of structural joints of opencast mining machinery

2021 ◽  
Vol 23 (3) ◽  
pp. 530-539
Author(s):  
Paweł Grabowski ◽  
Artur Jankowiak ◽  
Witold Marowski
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Opencast Mining ◽  
Fatigue Lifetime ◽  
Current State ◽  
Structural Joints ◽  
Mining Machinery ◽  
Unique Method ◽  
Long Life ◽  
Strength And Durability

Opencast mining machinery represents a group of large-scale individually manufactured technical objects operated with long-life requests. Since their manufacturers are obliged to provide product that will reach declared time of life, fatigue strength and durability conditions have to be taken into account for superstructures to meet the requirements. The paper highlights main problems occurring while assessing fatigue lifetime during design. Firstly, the short survey of current state of the art regarding the approach to this problem is presented. Secondly, the most important reasons of unsatisfactory accuracy of the assessments are discussed. As a main objective of the study, the authors introduce the unique method of continuous fatigue lifetime correction for the welded superstructures during the machine lifecycle, as a remedy for this group of machinery. Furthermore, results and experience from adapting the approach in real object are presented, including fatigue lifetime correction due to the real intensity of loading acquired from a bucket-wheel excavator during its longlasting operation. It is expected that proposed procedure can help to improve credibility of fatigue lifetime assessment of heavy earthmoving machinery

scholarly journals Using plants to remediate or manage metal-polluted soils: an overview on the current state of phytotechnologies

2021 ◽  
Vol 43 ◽  
pp. e58283
Author(s):  
Clístenes Williams Araújo do Nascimento ◽  
Caroline Miranda Biondi ◽  
Fernando Bruno Vieira da Silva ◽  
Luiz Henrique Vieira Lima
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Cost Effective ◽  
Time Frame ◽  
Contaminated Site ◽  
Polluted Soils ◽  
Mining Sites ◽  
Current State ◽  
Remedial Actions ◽  
The Tropics

Soil contamination by metals threatens both the environment and human health and hence requires remedial actions. The conventional approach of removing polluted soils and replacing them with clean soils (excavation) is very costly for low-value sites and not feasible on a large scale. In this scenario, phytoremediation emerged as a promising cost-effective and environmentally-friendly technology to render metals less bioavailable (phytostabilization) or clean up metal-polluted soils (phytoextraction). Phytostabilization has demonstrable successes in mining sites and brownfields. On the other hand, phytoextraction still has few examples of successful applications. Either by using hyperaccumulating plants or high biomass plants induced to accumulate metals through chelator addition to the soil, major phytoextraction bottlenecks remain, mainly the extended time frame to remediation and lack of revenue from the land during the process. Due to these drawbacks, phytomanagement has been proposed to provide economic, environmental, and social benefits until the contaminated site returns to productive usage. Here, we review the evolution, promises, and limitations of these phytotechnologies. Despite the lack of commercial phytoextraction operations, there have been significant advances in understanding phytotechnologies' main constraints. Further investigation on new plant species, especially in the tropics, and soil amendments can potentially provide the basis to transform phytoextraction into an operational metal clean-up technology in the future. However, at the current state of the art, phytotechnology is moving the focus from remediation technologies to pollution attenuation and palliative cares.

AN APPLICATION CONCEPT OF THE FIBRE-OPTICAL TECHNOLOGY IN GEODETIC MONITORING OF ENGINEERING OBJECTS

2017 ◽  
Vol 166 (46) ◽  
pp. 60-74
Author(s):  
Krzysztof Karsznia ◽  
Konrad Podawca
Keyword(s):  
Risk Management ◽  
Large Scale ◽  
State Of The Art ◽  
Monitoring Systems ◽  
Management Approach ◽  
Engineering Structures ◽  
Geoinformation Systems ◽  
Current State ◽  
Modern Engineering ◽  
Integrated Risk

Monitoring of structures and other different field objects undoubtedly belongs to the main issues of modern engineering. The use of technologies making it possible to implement structural monitoring makes it possible to build an integrated risk management approach combining instrumental solutions with geoinformation systems. In the studies of engineering structures, there is physical monitoring mainly used for examining the physical state of the object - so-called SHM ("Structural Health Monitoring"). However, very important role is also played by geodetic monitoring systems (GMS). The progress observed in the field of IT and automatics has opened new possibilities of using integrated systems on other, often large-scale objects. Based on the current state-of-the-art, the article presents the concept of integration approaches of physical and geodetic monitoring systems in order to develop useful guidelines for further construction of an expert risk management system.

Real Time Simulation for High Fidelity Multibody Dynamics and Mechatronic Systems

2014 ◽  
Author(s):  
William Prescott
Keyword(s):  
Real Time ◽  
Multibody Dynamics ◽  
Large Scale ◽  
State Of The Art ◽  
High Fidelity ◽  
Implicit Integration ◽  
Mechatronic Systems ◽  
Vehicle Simulation ◽  
Current State ◽  
Time Simulation

This paper will investigate the use of large scale multibody dynamics (MBD) models for real-time vehicle simulation. Current state of the art in the real-time solution of vehicle uses 15 degree of freedom models, but there is a need for higher-fidelity systems. To increase the fidelity of models uses this paper will propose the use of the following techniques: implicit integration, parallel processing and co-simulation in a real-time environment.

scholarly journals Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

2021 ◽  
Vol 13 (22) ◽  
pp. 4599
Author(s):  
Félix Quinton ◽  
Loic Landrieu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Crop Rotation ◽  
Large Scale ◽  
State Of The Art ◽  
Crop Rotations ◽  
Learning Approach ◽  
Type Mapping ◽  
Current State ◽  
Crop Type

While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

scholarly journals Fast consensus in a large-scale multi-agent system with directed graphs using time-delayed measurements

2019 ◽  
Vol 377 (2153) ◽  
pp. 20180130 ◽  
Author(s):  
Adrián Ramírez ◽  
Rifat Sipahi ◽  
Sabine Mondié ◽  
Rubén Garrido
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Directed Graphs ◽  
Graph Laplacian ◽  
Delay Systems ◽  
Multi Agent Systems ◽  
Current State ◽  
Complex Eigenvalues ◽  
Multi Agent ◽  
Delayed Measurements

This article is on fast-consensus reaching in a class of multi-agent systems (MAS). We present an analytical approach to tune controllers for the agents based on the premise that delayed measurements in the controller can be preferable to standard controllers relying only on current measurements. Controller tuning in this setting is however challenging due to the presence of delays. To tackle this problem, we propose an analytic geometry approach. The key contribution is that the tuning can be implemented for complex eigenvalues of the arising graph Laplacian of the network, complementing the current state of the art, which is limited to real eigenvalues. Results, therefore, extend our knowledge beyond symmetric graphs and enable the study of the MAS under directed graphs. This article is part of the theme issue ‘Nonlinear dynamics of delay systems’.

scholarly journals Scalable batch-correction method for integrating large-scale single-cell transcriptomes

2021 ◽  
Author(s):  
Xiangchun Li ◽  
Xilin Shen
Keyword(s):  
Single Cell ◽  
Expression Profile ◽  
Large Scale ◽  
State Of The Art ◽  
Correction Method ◽  
Feature Representation ◽  
Batch Correction ◽  
Data Alignment ◽  
Current State ◽  
Core Idea

Integration of the evolving large-scale single-cell transcriptomes requires scalable batch-correction approaches. Here we propose a simple batch-correction method that is scalable for integrating super large-scale single-cell transcriptomes from diverse sources. The core idea of the method is encoding batch information of each cell as a trainable parameter and added to its expression profile; subsequently, a contrastive learning approach is used to learn feature representation of the additive expression profile. We demonstrate the scalability of the proposed method by integrating 18 million cells obtained from the Human Cell Atlas. Our benchmark comparisons with current state-of-the-art single-cell integration methods demonstrated that our method could achieve comparable data alignment and cluster preservation. Our study would facilitate the integration of super large-scale single-cell transcriptomes. The source code is available at https://github.com/xilinshen/Fugue.

scholarly journals Language-Agnostic Visual-Semantic Embeddings

2021 ◽  
Author(s):  
Jônatas Wehrmann ◽  
Rodrigo C. Barros
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Visual Representations ◽  
Predictive Performance ◽  
Word Embedding ◽  
Retrieval Models ◽  
New Methods ◽  
Current State ◽  
Effective Use ◽  
Cross Language

We propose a framework for training language-invariant cross-modal retrieval models. We introduce four novel text encoding approaches, as well as a character-based word-embedding approach, allowing the model to project similar words across languages into the same word-embedding space. In addition, by performing cross-modal retrieval at the character level, the storage requirements for a text encoder decrease substantially, allowing for lighter and more scalable retrieval architectures. The proposed language-invariant textual encoder based on characters is virtually unaffected in terms of storage requirements when novel languages are added to the system. Contributions include new methods for building character-level-based word-embeddings, an improved loss function, and a novel cross-language alignment module that not only makes the architecture language-invariant, but also presents better predictive performance. Moreover, we introduce a module called \adapt, which is responsible for providing query-aware visual representations that generate large improvements in terms of recall for four widely-used large-scale image-text datasets. We show that our models outperform the current state-of-the-art all scenarios. This thesis can serve as a new path on retrieval research, now allowing for the effective use of captions in multiple-language scenarios.

Big Data Analytics for Train Delay Prediction

2018 ◽  
pp. 320-348 ◽  
Author(s):  
Emanuele Fumeo ◽  
Luca Oneto ◽  
Giorgio Clerico ◽  
Renzo Canepa ◽  
Federico Papa ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Fast Learning ◽  
Current State ◽  
Big Data Technologies ◽  
Large Scale Data Processing ◽  
Prediction Systems ◽  
Delay Prediction

Current Train Delay Prediction Systems (TDPSs) do not take advantage of state-of-the-art tools and techniques for extracting useful insights from large amounts of historical data collected by the railway information systems. Instead, these systems rely on static rules, based on classical univariate statistic, built by experts of the railway infrastructure. The purpose of this book chapter is to build a data-driven TDPS for large-scale railway networks, which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.

Beyond User-Specificity for EMG Decoding Using Multiresolution Muscle Synergy Analysis

2013 ◽  
Author(s):  
Mark R. Ison ◽  
Panagiotis Artemiadis
Keyword(s):  
Large Scale ◽  
Muscle Activation ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Muscle Synergy ◽  
Current State ◽  
Classifier Performance ◽  
Subject Variability ◽  
Leave One Out ◽  
Validation Testing

Electromyographic (EMG) processing is a vital step towards converting noisy muscle activation signals into robust features that can be decoded and applied to applications such as prosthetics, exoskeletons, and human-machine interfaces. Current state of the art processing methods involve collecting a dense set of features which are sensitive to many of the intra- and inter-subject variability ubiquitous in EMG signals. As a result, state of the art decoding methods have been unable to obtain subject independence. This paper presents a novel multiresolution muscle synergy (MRMS) feature extraction technique which represents a set of EMG signals in a sparse domain robust to the inherent variability of EMG signals. The robust features, which can be extracted in real time, are used to train a neural network and demonstrate a highly accurate and user-independent classifier. Leave-one-out validation testing achieves mean accuracy of 81.9± 3.9% and area under the receiver operating characteristic curve (AUC), a measure of overall classifier performance over all possible thresholds, of 92.4± 8.9%. The results show the ability of sparse MRMS features to achieve subject independence in decoders, providing opportunities for large-scale studies and more robust EMG-driven applications.

scholarly journals Halo exclusion criteria impacts halo statistics

2019 ◽  
Vol 489 (3) ◽  
pp. 4170-4175 ◽  
Author(s):  
Rafael García ◽  
Eduardo Rozo
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Mass Function ◽  
Exclusion Criteria ◽  
Simulation Data ◽  
Dark Energy Survey ◽  
Current State ◽  
Statistical Precision ◽  
Energy Survey ◽  
Cluster Abundance

ABSTRACT Every halo-finding algorithm must make a critical yet relatively arbitrary choice: it must decide which structures are parent haloes, and which structures are subhaloes of larger haloes. We refer to this choice as percolation. We demonstrate that the choice of percolation impacts the statistical properties of the resulting halo catalogue. Specifically, we modify the halo-finding algorithm rockstar to construct three different halo catalogues from the same simulation data, each with identical mass definitions, but different choice of percolation. The resulting haloes exhibit significant differences in both halo abundance and clustering properties. Differences in the halo mass function reach 6 per cent for haloes of mass $10^{13}\ h^{-1}\ {\rm {\rm M}_{\odot }}$, larger than the few per cent precision necessary for current cluster abundance experiments such as the Dark Energy Survey. Comparable differences are observed in the large-scale clustering bias, while differences in the halo–matter correlation function reach 30 per cent on translinear scales. These effects can bias weak-lensing estimates of cluster masses at a level comparable to the statistical precision of current state-of-the-art experiments.

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