Spatial Interpolation for Periodic Surfaces in Manufacturing Using a Bessel Additive Variogram Model

2018 ◽  
Vol 140 (6) ◽  
Author(s):  
Yuhang Yang ◽  
Chenhui Shao
Keyword(s):  
High Resolution ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Interpolation Method ◽  
Spatial Models ◽  
High Resolution Data ◽  
Periodic Surfaces ◽  
Variogram Model ◽  
Geostatistical Interpolation ◽  
Proposed Model

High-resolution spatial data are essential for characterizing and monitoring surface quality in manufacturing. However, the measurement of high-resolution spatial data is generally expensive and time-consuming. Interpolation based on spatial models is a typical approach to cost-effectively acquire high-resolution data. Conventional modeling methods fail to adequately model the spatial correlation induced by periodicity, and thus their interpolation precision is limited. In this paper, we propose using a Bessel additive periodic variogram model to capture such spatial correlation. When combined with kriging, a geostatistical interpolation method, accurate interpolation performance can be achieved for common periodic surfaces. In addition, parameters of the proposed model provide valuable insights for the characterization and monitoring of spatial processes in manufacturing. Both simulated and real-world case studies are presented to demonstrate the effectiveness of the proposed method.

The influence of resolution on scale-dependent clustering in fracture spacing data

2016 ◽  
Vol 4 (3) ◽  
pp. T387-T394 ◽  
Author(s):  
Ankur Roy ◽  
Atilla Aydin ◽  
Tapan Mukerji
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Reservoir Characterization ◽  
Data Capture ◽  
Low Resolution ◽  
Data Set ◽  
Bootstrap Analysis ◽  
High Resolution Data ◽  
Fracture Spacing ◽  
Resolution Data

It is a common practice to analyze fracture spacing data collected from scanlines and wells at various resolutions for the purposes of aquifer and reservoir characterization. However, the influence of resolution on such analyses is not well-studied. Lacunarity is a parameter that is used for multiscale analysis of spatial data. In quantitative terms, at any given scale, it is a function of the mean and variance of the distribution of masses captured by a gliding a window of that scale (size) across any pattern of interest. We have described the application of lacunarity for delineating differences between scale-dependent clustering attributes of data collected at different resolutions along a scanline. Specifically, we considered data collected at different resolutions from two outcrop exposures, a pavement and a cliff section, of the Cretaceous turbititic sandstones of the Chatsworth Formation widely exposed in southern California. For each scanline, we analyzed data from low-resolution aerial or ground photographs and high-resolution ground measurements for scale-dependent clustering attributes. High-resolution data show larger values of scale-dependent lacunarity than their respective low-resolution counterparts. We further performed a bootstrap analysis for each data set to test for the significance of such clustering differences. We started with generating 300 realizations for each data set and then ran lacunarity analysis on them. It was seen that lacunarity for higher resolution data set lay significantly outside the upper 90th percentile values, thus proving that higher resolution data are distinctly different from random and fractures are clustered. We have therefore postulated that lower resolution data capture fracture zones that had relatively uniform spacing, whereas higher resolution data capture thin and short splay joints and sheared joints that contribute to fracture clustering. Such findings have important implications in terms of understanding organization of fractures in fracture corridors, which in turn is critical for modeling and upscaling exercises.

scholarly journals Application of Nonhydraulic Delineation Method of Flood Hazard Areas Using LiDAR-Based Data

Geosciences ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 338 ◽  
Author(s):  
J. Carl Ureta ◽  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Joan Ureta ◽  
Marzieh Motallebi
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Planning ◽  
Interpolation Method ◽  
Flood Hazard ◽  
Data Gathering ◽  
Development Planning ◽  
Supplementary Information ◽  
High Resolution Data ◽  
Interpolation Procedure

Fluvial dynamics are an important aspect of land-use planning as well as ecosystem conservation. Lack of floodplain and flood inundation maps can cause severe implication on land-use planning and development as well as in disaster management. However, flood hazard delineation traditionally involves hydrologic models and uses hydraulic data or historical flooding frequency. This entails intensive data gathering, which leads to extensive amount of cost, time, and complex models, while typically only covers a small portion of the landscape. Therefore, alternative approaches had to be explored. This study explores an alternative approach in delineating flood hazard areas through a straightforward interpolation process while using high-resolution LiDAR-based datasets. The objectives of this study are: (1) to delineate flood hazard areas through a straightforward, nonhydraulic, and interpolation procedure using high-resolution (LiDAR-based) datasets and (2) to determine whether using high-resolution data, coupled with a straightforward interpolation procedure, will yield reliable potential flood hazard maps. Results showed that a straightforward interpolation method using LiDAR-based data produces a reliable potential flood zone map. The resulting map can be used as supplementary information for rapid analysis of the topography which could have implications in area development planning and ecological management and best practices.

scholarly journals Integration of Terrestrial Laser Scanning and UAS Photogrammetry in Geological Studies: Examples from Croatia

2019 ◽  
Author(s):  
Branko Kordić ◽  
Borna Lužar-Oberiter ◽  
Kristina Pikelj ◽  
Bojan Matoš ◽  
Goran Vlastelica
Keyword(s):  
High Resolution ◽  
Case Studies ◽  
Spatial Data ◽  
Laser Scanning ◽  
Spatial Information ◽  
Terrestrial Laser Scanning ◽  
Structural Aspect ◽  
Unmanned Aircraft ◽  
Added Value ◽  
High Resolution Data

Terrestrial laser scanning (TLS) in combination with Unmanned Aircraft System (UAS) and modern computer based photogrammetry is currently the best approach for the acquisition of high-resolution 3D spatial information. Highly realistic 3D spatial data sets are becoming the basis for detailed geological studies, providing a multidisciplinary approach in the study and research of both underground and above ground sites. To emphasize the variety of possible implementations of these state-of-the-art methodologies, four characteristic and yet quite different case studies are presented where such geodetic techniques are successfully employed. The presented case studies demonstrate that TLS and UAS photogrammetry, as non-contact surveying methods, are able to reduce survey time and total project costs. As added value, they provide high-resolution data that can be analyzed in a virtual environment from a sedimentological or structural aspect. Stored digital documentation also allows future multi-temporal spatial data comparison at any timeframe and scale, thus enhancing any target geological data gathering and analyses at the studied sites.

MODERN TECHNOLOGY IN DUNE COMPLEXES MONITORING ON THE VISTULA SPIT

2017 ◽  
Author(s):  
Aleksandr Danchenkov ◽  
Aleksandr Danchenkov
Keyword(s):  
High Resolution ◽  
Baltic Sea ◽  
Spatial Data ◽  
Modern Technology ◽  
High Potential ◽  
Coastal Processes ◽  
The Baltic Sea ◽  
Widespread Application ◽  
The Baltic ◽  
Modern Technologies

Modern technologies, which provide fast and accurate acquisition of high-resolution spatial data, have found widespread application in the monitoring of coastal processes. This paper reports the results of four years’ monitoring of a huge deflation/blowout/wind-scour basin dynamics at the Vistula Spit (southeast coast of the Baltic Sea). Information about the volume and size dynamics together with deflation/accumulation schemes and 3D elevation maps is presented. Basing on the obtained results, forecast of the deflation basin dynamics for 2016 was proposed. This paper implements the Terrestrial Laserscanning (TLS) method to the coastal processes investigation and demonstrates its high potential in this field.

MODERN TECHNOLOGY IN DUNE COMPLEXES MONITORING ON THE VISTULA SPIT

2017 ◽  
Author(s):  
Aleksandr Danchenkov ◽  
Aleksandr Danchenkov
Keyword(s):  
High Resolution ◽  
Baltic Sea ◽  
Spatial Data ◽  
Modern Technology ◽  
High Potential ◽  
Coastal Processes ◽  
The Baltic Sea ◽  
Widespread Application ◽  
The Baltic ◽  
Modern Technologies

Modern technologies, which provide fast and accurate acquisition of high-resolution spatial data, have found widespread application in the monitoring of coastal processes. This paper reports the results of four years’ monitoring of a huge deflation/blowout/wind-scour basin dynamics at the Vistula Spit (southeast coast of the Baltic Sea). Information about the volume and size dynamics together with deflation/accumulation schemes and 3D elevation maps is presented. Basing on the obtained results, forecast of the deflation basin dynamics for 2016 was proposed. This paper implements the Terrestrial Laserscanning (TLS) method to the coastal processes investigation and demonstrates its high potential in this field.

A Study of Malware Propagation Dynamics in Wireless Sensor Network using Spatially Correlated Security Model

2020 ◽  
Vol 13 ◽  
Author(s):  
Satya Ranjan Biswal ◽  
Santosh Kumar Swain
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Network ◽  
Spatial Correlation ◽  
Malicious Code ◽  
Wireless Sensor ◽  
Epidemic Theory ◽  
Malware Propagation ◽  
Proposed Model ◽  
Propagation Dynamics

: Security is one of the important concern in both types of the network. The network may be wired or wireless. In case of wireless network security provisioning is more difficult in comparison to wired network. Wireless Sensor Network (WSN) is also a type of wireless network. And due to resource constraints WSN is vulnerable against malware attacks. Initially, the malware (virus, worm, malicious code, etc.) targets a single node of WSN for attack. When a node of WSN gets infected then automatically start to spread in the network. If nodes are strongly correlated the malware spreads quickly in the network. On the other hand, if nodes are weakly correlated the speed of malware spread is slow. A mathematical model is proposed for the study of malware propagation dynamics in WSN with combination of spatial correlation and epidemic theory. This model is based on epidemic theory with spatial correlation. The proposed model is Susceptible-Exposed-Infectious-Recover-Dead (SEIRD) with spatial correlation. We deduced the expression of basic reproduction number. It helps in the study of malware propagation dynamics in WSN. The stability analysis of the network has been investigated through proposed model. This model also helps in reduction of redundant information and saving of sensor nodes’ energy in WSN. The theoretical investigation verified by simulation results. A spatial correlation based epidemic model has been formulated for the study of dynamic behaviour of malware attacks in WSN.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 464
Author(s):  
Wei Ma ◽  
Sean Qian
Keyword(s):  
High Resolution ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Data Driven ◽  
Traffic Information ◽  
Flow Density ◽  
Massive Data ◽  
Market Penetration ◽  
High Resolution Data ◽  
Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

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