Over-the-row harvester damage evaluation in super-high-density olive orchard by on-board sensing techniques

2017 ◽  
Vol 8 (2) ◽  
pp. 487-491 ◽  
Author(s):  
J. Martinez-Guanter ◽  
M. Garrido-Izard ◽  
J. Agüera ◽  
C. Valero ◽  
M. Pérez-Ruiz
Keyword(s):  
Structural Damage ◽  
Image Data ◽  
High Density ◽  
Tree Structure ◽  
Light Detection And Ranging ◽  
Olive Grove ◽  
Damage Evaluation ◽  
Olive Fruit ◽  
Before And After ◽  
Extra Stress

New Super-High-Density (SHD) olive orchards designed for mechanical harvesting are increasing very rapidly in Spain. Most studies have focused in effectively removing the olive fruit, however the machine needs to put significant amount of energy on the canopy that could result in structural damage or extra stress on the trees. During harvest, a series of 3-axis accelerometers were installed on the tree structure in order to register vibration patterns. A LiDAR (Light Detection and Ranging) and a camera sensing device were also mounted on a tractor. Before and after harvest measurements showed significant differences in the LiDAR and image data. A fast estimate of the damage produced by an over-the-row harvester with contactless sensing could be useful information for adjusting the machine parameters in each olive grove automatically in the future.

Nonlinear Structural Damage Detection Based on Adaptive Volterra Filter Model

2018 ◽  
Vol 18 (02) ◽  
pp. 1871003 ◽  
Author(s):  
J. Prawin ◽  
A. Rama Mohan Rao
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Linear Models ◽  
Time History ◽  
Diagnostic Techniques ◽  
Filter Model ◽  
Volterra Filter ◽  
Damage Indices ◽  
Before And After ◽  
Nonlinear Time History

The majority of the existing damage diagnostic techniques are based on linear models. Changes in the state of the dynamics of these models, before and after damage in the structure based on the vibration measurements, are popularly used as damage indicators. However, the system may initially behave linearly and subsequently exhibit nonlinearity due to the incipience of damage. Breathing cracks that exhibit bilinear behavior are one such example of the damage induced due to nonlinearity. Further many real world structures even in their undamaged state are nonlinear. Hence, in this paper, we present a nonlinear damage detection technique based on the adaptive Volterra filter using the nonlinear time history response. Three damage indices based on the adaptive Volterra filter are proposed and their sensitiveness to damage and noise is assessed through two numerically simulated examples. Numerical investigations demonstrate the effectiveness of the adaptive Volterra filter model to detect damage in nonlinear structures even with measurement noise.

scholarly journals Comparative Analysis of Capabilities of Different Types of Cone-Beam Tomography in an Experimental Study of Root Canals of Teeth Filled with High-Density Materials (part 2)

2021 ◽  
pp. 51-61
Author(s):  
A. Yu. Vasil'ev ◽  
V. V. Petrovskaya ◽  
E. A. Nichipor ◽  
V. G. Alpatova ◽  
N. N. Potrakhov ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Experimental Study ◽  
Cone Beam Computed Tomography ◽  
Comparative Assessment ◽  
High Density ◽  
Cone Beam ◽  
Root Canals ◽  
Different Types ◽  
Before And After ◽  
Cone Beam Tomography

During the course of this experimental study tomograms of extracted teeth were analyzed before and after filling the root canals with an endodontic material and fragments of broken metal instruments for root canal treatment. During the first stage of the experiment, untreated extracted teeth were scanned using conebeam computed tomography and microfocus cone-beam computed tomography. A comparative assessment of capabilities of the two methods of cone-beam computed tomography based on examination of untreated root canals was carried out. The second part of the study is dedicated to visualization of root canals that contain foreign high-density materials.

scholarly journals MECHANICAL CHARACTERIZATION OF PA 12 AND HDPE PIPES BEFORE AND AFTER AGING IN WATER AT TWO DIFFERENT TEMPERATURES

2021 ◽  
Vol 9 (1) ◽  
pp. 248-256
Author(s):  
J.A. dos Santos ◽  
R.C. Tucunduva ◽  
J.R.M. D’Almeida
Keyword(s):  
High Density Polyethylene ◽  
Mechanical Performance ◽  
High Density ◽  
Effect Of Temperature ◽  
Polyamide 12 ◽  
Polyethylene Pipes ◽  
Before And After ◽  
Different Temperatures ◽  
Deterioration Process ◽  
Combined Action

Polymer pipes are being widely used by many industrial segments. Although not affected by corrosion, the mechanical performance of these pipes can be reduced due to exposure to temperature, UV radiation and by contact with various fluids. Depending on the deterioration process, embrittlement or plasticization may occur, and the service life of the pipe can be severely reduced. In this work, the combined action of temperature and water upon the mechanical performance of polyamide 12 and high-density polyethylene pipes is evaluated. Destructive and non-destructive techniques were used and the performance of both materials was compared. Both polymers were platicized by the effect of water. However, for high density polyethylene the effect of temperature was more relevant than for polyamide. This behavior was attributed to the dependence of the free volume with the markedly different glass transition temperature of the polymers and the temperatures of testing.

scholarly journals On the Same Skywalk in a Time of Pandemic: How a vulnerable group copes with the new conditions in Hong Kong

2021 ◽  
Vol 5 (17) ◽  
pp. 33-48
Author(s):  
Caterina Villani ◽  
Gianni Talamini ◽  
Zhijian Hu
Keyword(s):  
Hong Kong ◽  
Public Space ◽  
Migrant Workers ◽  
Publishing House ◽  
Domestic Workers ◽  
High Density ◽  
The Public ◽  
Before And After ◽  
Pedestrian Network ◽  
International Publishing

The public space plays a crucial role in providing adequate infrastructure for vulnerable social groups in the context of high-density urban Asia. In this study, a well-known elevated pedestrian network in Hong Kong emerges as a revelatory case for the comparative analysis of the pattern of stationary uses before and after the COVID-19 pandemic out-break. Findings reveal a significant decrease (-20 %) in the total number of users and a shift in the pattern of activities, comprising a significant shrinkage of socially oriented uses and a vast increase of individual behaviors. This study advocates a responsive policymaking that considers the peculiar post-outbreak needs of migrant workers in Hong Kong and in high-density urban Asia Keywords: Covid-19; public space; migrant domestic workers; behavioural mapping eISSN  2514-751X © 2020 The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians / Africans / Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ajebs.v5i17.374

Structural damage evaluation of industrial masonry chimneys

2018 ◽  
Vol 51 (1) ◽  
Author(s):  
David Bru ◽  
Ricardo Reynau ◽  
F. Javier Baeza ◽  
Salvador Ivorra
Keyword(s):  
Structural Damage ◽  
Damage Evaluation

Performance of a Post-Byzantine Triple-Domed Basilica under Near and Far Fault Seismic Loads

2015 ◽  
pp. 831-867 ◽  
Author(s):  
Constantine C. Spyrakos ◽  
Charilaos A. Maniatakis ◽  
Panagiotis Kiriakopoulos ◽  
Alessio Francioso ◽  
Ioannis M. Taflampas
Keyword(s):  
Structural Damage ◽  
New Technology ◽  
Element Model ◽  
Seismic Loads ◽  
Regional Seismicity ◽  
Near Fault ◽  
Collapse Mechanisms ◽  
Before And After ◽  
Out Of Plane ◽  
Structural Parts

In this Chapter a triple-domed basilica constructed at the end of the 19th century is selected as a case study to present a methodology for the selection of the appropriate intervention techniques in monumental structures. The methodology includes in-situ and laboratory testing, application of analytical methods, consideration of geotechnical parameters and regional seismicity. Seismic loads are estimated according to contemporary and older concepts for seismic design. Since the impact of near-fault phenomena on masonry structures has not been thoroughly studied, although considered as responsible for extensive structural damage during major seismic events, a procedure is presented in order to account for the special characteristics of strong ground motion, in the so-called near-fault region. The seismic performance of the structure before and after interventions, using traditional and new technology, is assessed by applying a validated finite element model. Also, the out-of-plane behavior of structural parts is evaluated through kinematic analysis of selective collapse mechanisms.

Convolutional Nonlinear Differential Recurrent Neural Networks for Crowd Scene Understanding

2018 ◽  
Vol 12 (04) ◽  
pp. 481-500 ◽  
Author(s):  
Naifan Zhuang ◽  
The Duc Kieu ◽  
Jun Ye ◽  
Kien A. Hua
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Scene Understanding ◽  
Image Data ◽  
High Density ◽  
Temporal Information ◽  
Deep Model ◽  
End To End ◽  
The Individual

With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility, and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, convolutional nonlinear differential recurrent neural networks (CNDRNNs), for crowd scene understanding. CNDRNNs consist of GoogleNet Inception V3 convolutional neural networks (CNNs) and nonlinear differential recurrent neural networks (RNNs). Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, CNDRNN utilizes a unified deep model to optimize the parameters of CNN and RNN hand in hand. It thus has the potential of generating a more harmonious model. The proposed architecture takes sequential raw image data as input, and does not rely on tracklet or trajectory detection. It thus has clear advantages over the traditional flow-based and trajectory-based methods, especially in challenging crowd scenarios of high density and low mobility. Taking advantage of CNN and RNN, CNDRNN can effectively analyze the crowd semantics. Specifically, CNN is good at modeling the semantic crowd scene information. On the other hand, nonlinear differential RNN models the motion information. The individual and increasing orders of derivative of states (DoS) in differential RNN can progressively build up the ability of the long short-term memory (LSTM) gates to detect different levels of salient dynamical patterns in deeper stacked layers modeling higher orders of DoS. Lastly, existing LSTM-based crowd scene solutions explore deep temporal information and are claimed to be “deep in time.” Our proposed method CNDRNN, however, models the spatial and temporal information in a unified architecture and achieves “deep in space and time.” Extensive performance studies on the Violent-Flows, CUHK Crowd, and NUS-HGA datasets show that the proposed technique significantly outperforms state-of-the-art methods.

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