scholarly journals Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring

2019 ◽  
Vol 4 (4) ◽  
pp. 58 ◽  
Author(s):  
Soilán ◽  
Sánchez-Rodríguez ◽  
Río-Barral ◽  
Perez-Collazo ◽  
Arias ◽  
...  
Keyword(s):  
Remote Monitoring ◽  
Laser Scanning ◽  
State Of The Art ◽  
Light Detection And Ranging ◽  
Infrastructure Monitoring ◽  
Railway Infrastructure ◽  
Risk Vulnerability ◽  
Potential Damage ◽  
Different Levels ◽  
Prediction Capability

Improving the resilience of infrastructures is key to reduce their risk vulnerability and mitigate impact from hazards at different levels (e.g., from increasing extreme events, driven by climate change); or from human-made events such as: accidents, vandalism or terrorist actions. One of the most relevant aspects of resilience is preparation. This is directly related to: (i) the risk prediction capability; (ii) the infrastructure monitoring; and (iii) the systems contributing to anticipate, prevent and prepare the infrastructure for potential damage. This work focuses on those methods and technologies that contribute to more efficient and automated infrastructure monitoring. Therefore, a review that summarizes the state of the art of LiDAR (Light Detection And Ranging)-based data processing is presented, giving a special emphasis to road and railway infrastructure. The most relevant applications related to monitoring and inventory transport infrastructures are discussed. Furthermore, different commercial LiDAR-based terrestrial systems are described and compared to offer a broad scope of the available sensors and tools to remote monitoring infrastructures based on terrestrial systems.

Evidence-Based Social Sciences: A New Emerging Field

2018 ◽  
Vol 5 (2) ◽  
pp. 207-211
Author(s):  
Nazila Zarghi ◽  
Soheil Dastmalchian Khorasani
Keyword(s):  
Social Sciences ◽  
Critical Appraisal ◽  
State Of The Art ◽  
Internal Validity ◽  
Evidence Based ◽  
Multiple Sources ◽  
Social Settings ◽  
Research Findings ◽  
Academic Area ◽  
Different Levels

Abstract Evidence based social sciences, is one of the state-of- the-art area in this field. It is making decisions on the basis of conscientious, explicit and judicious use of the best available evidence from multiple sources. It also could be conducive to evidence based social work, i.e a kind of evidence based practice in some extent. In this new emerging field, the research findings help social workers in different levels of social sciences such as policy making, management, academic area, education, and social settings, etc.When using research in real setting, it is necessary to do critical appraisal, not only for trustingon internal validity or rigor methodology of the paper, but also for knowing in what extent research findings could be applied in real setting. Undoubtedly, the latter it is a kind of subjective judgment. As social sciences findings are highly context bound, it is necessary to pay more attention to this area. The present paper tries to introduce firstly evidence based social sciences and its importance and then propose criteria for critical appraisal of research findings for application in society.

scholarly journals Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Grid Cell ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Inventory Analysis ◽  
Light Detection ◽  
Individual Tree ◽  
Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

scholarly journals Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 674
Author(s):  
Kushani De De Silva ◽  
Carlo Cafaro ◽  
Adom Giffin
Keyword(s):  
State Of The Art ◽  
Estimation Method ◽  
Synthetic Data ◽  
Noisy Data ◽  
Bayesian Framework ◽  
Physical Systems ◽  
Gradient Profile ◽  
Ill Posed ◽  
Profile Estimation ◽  
Different Levels

Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the estimated system. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. This becomes worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the gradient profile of discrete noisy data is presented. The method was developed within a Bayesian framework. Comprehensive numerical experiments were conducted on synthetic data at different levels of noise. The accuracy of the proposed method was quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.

Towards Wireless Sensor Networks for railway infrastructure monitoring

2010 ◽  
Author(s):  
Fran Flammini ◽  
Andrea Gaglione ◽  
Francesco Ottello ◽  
Alfio Pappalardo ◽  
Concerta Pragliola ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Infrastructure Monitoring ◽  
Railway Infrastructure

scholarly journals Saliency Detection by Multilevel Deep Pyramid Model

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Wang ◽  
Lei Dai ◽  
Yingfeng Cai ◽  
Long Chen ◽  
Yong Zhang
Keyword(s):  
Background Noise ◽  
State Of The Art ◽  
Saliency Detection ◽  
Saliency Map ◽  
Multiple Features ◽  
Low Level ◽  
Pyramid Model ◽  
High Level ◽  
Different Levels ◽  
Better Than

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.

scholarly journals Graph Algorithms for Word Sense Disambiguation in Biomedicine

2015 ◽  
Author(s):  
Rodrigo Goulart ◽  
Juliano De Carvalho ◽  
Vera De Lima
Keyword(s):  
Text Mining ◽  
Graph Algorithms ◽  
State Of The Art ◽  
Word Sense Disambiguation ◽  
The State ◽  
Word Sense ◽  
New Approach ◽  
Similar Performance ◽  
Sense Disambiguation ◽  
Different Levels

Word Sense Disambiguation (WSD) is an important task for Biomedicine text-mining. Supervised WSD methods have the best results but they are complex and their cost for testing is too high. This work presents an experiment on WSD using graph-based approaches (unsupervised methods). Three algorithms were tested and compared to the state of the art. Results indicate that similar performance could be reached with different levels of complexity, what may point to a new approach to this problem.

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

scholarly journals Geomorphometric analysis of cave ceiling channels mapped with 3D terrestrial laser scanning

2016 ◽  
Author(s):  
Michal Gallay ◽  
Zdenko Hochmuth ◽  
Ján Kaňuk ◽  
Jaroslav Hofierka
Keyword(s):  
Laser Scanning ◽  
Methodological Approach ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Scalar Fields ◽  
Complex Environments ◽  
Digital Elevation ◽  
Unique Manner ◽  
Elevation Model ◽  
Different Levels

Abstract. The change of hydrological conditions during the evolution of caves in carbonate rocks often results in a complex subterranean geomorphology which comprises specific landforms such as ceiling channels, anastomosing half tubes, or speleothems organised vertically in different levels. Studying such complex environments traditionally requires tedious mapping, however, this is being replaced with terrestrial laser scanning technology. Laser scanning overcomes the problem of reaching high ceilings providing new options to map underground landscapes with unprecedented level of detail and accuracy. The acquired point cloud can be handled conveniently with dedicated software, but applying traditional geomorphometry to analyse the cave surface is limited. This is because geomorphometry has been focused on parameterisation and analysis of surficial terrain. The theoretical and methodological concept has been based on two-dimensional scalar fields which is sufficient for most cases of the surficial terrain. The terrain surface is modelled with a bivariate function of altitude (elevation) and represented by a raster digital elevation model. However, the cave is a three-dimensional entity therefore a different approach is required for geomorphometric analysis. In this paper, we demonstrate the benefits of high resolution cave mapping and 3-D modelling to better understand the palaeohydrography of the Domica cave in Slovakia. This methodological approach adopted traditional geomorphometric methods in a unique manner and also new methods used in 3D computer graphics which can be applied to study other 3-D geomorphological forms

Variance Quantification of Different Additive Manufacturing Processes for Acoustic Meta Materials

2021 ◽  
Vol 263 (4) ◽  
pp. 2708-2723
Author(s):  
Manuel Bopp ◽  
Arn Joerger ◽  
Matthias Behrendt ◽  
Albert Albers
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Production Process ◽  
Laser Scanning ◽  
Mode Shapes ◽  
Manufacturing Processes ◽  
3D Laser Scanning ◽  
Fused Filament Fabrication ◽  
Manufacturing Techniques ◽  
Different Levels

Many concepts for acoustic meta materials rely on additive manufacturing techniques. Depending on the production process and material of choice, different levels of precision and repeatability can be achieved. In addition, different materials have different mechanical properties, many of which are frequency dependent and cannot easily be measured directly. In this contribution the authors have designed different resonator elements, which have been manufactured utilizing Fused Filament Fabrication with ABSplus and PLA, as well as PolyJet Fabrication with VeroWhitePlus. All structures are computed in FEA to obtain the calculated Eigenfrequencies and mode shapes, with the respective literature values for each material. Furthermore, the dynamic behavior of multiple instances of each structure is measured utilizing a 3D-Laser-Scanning Vibrometer under shaker excitation, to obtain the actual Eigenfrequencies and mode shapes. The results are then analyzed in regards to variance between different print instances, and in regards to accordance between measured and calculated results. Based on previous work and this analysis the parameters of the FEA models are updated to improve the result quality.

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