A Hybrid Approach to Disseminate Large Volume Sensor Data for Monitoring Global Change

2012 ◽  
pp. 165-177
Author(s):  
Theodor Foerster ◽  
Albert Remke ◽  
Georg Kaspar
Keyword(s):  
Global Change ◽  
Large Volume ◽  
Hybrid Approach ◽  
Sensor Data ◽  
Volume Sensor

scholarly journals Exploring big volume sensor data with vroom

2017 ◽  
Vol 10 (12) ◽  
pp. 1973-1976 ◽  
Author(s):  
Oscar Moll ◽  
Aaron Zalewski ◽  
Sudeep Pillai ◽  
Sam Madden ◽  
Michael Stonebraker ◽  
...  
Keyword(s):  
Sensor Data ◽  
Volume Sensor

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

Human Motion Capture with Sparse Inertial Sensors and Video

2020 ◽  
Author(s):  
Timo von Marcard
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Hybrid Approach ◽  
Human Motion ◽  
The Body ◽  
Sensor Data ◽  
Discrete Optimization Problem ◽  
Global Optimization Problem

This thesis explores approaches to capture human motions with a small number of sensors. In the first part of this thesis an approach is presented that reconstructs the body pose from only six inertial sensors. Instead of relying on pre-recorded motion databases, a global optimization problem is solved to maximize the consistency of measurements and model over an entire recording sequence. The second part of this thesis deals with a hybrid approach to fuse visual information from a single hand-held camera with inertial sensor data. First, a discrete optimization problem is solved to automatically associate people detections in the video with inertial sensor data. Then, a global optimization problem is formulated to combine visual and inertial information. The propose  approach enables capturing of multiple interacting people and works even if many more people are visible in the camera image. In addition, systematic inertial sensor errors can be compensated, leading to a substantial in...

scholarly journals Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 34
Author(s):  
Stefan Bosse ◽  
Dennis Weiss ◽  
Daniel Schmidt
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Damage Detection ◽  
High Reliability ◽  
Hybrid Approach ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Low Resource ◽  
Damage Diagnostics ◽  
Single Instance

Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.

scholarly journals 3D CONTENT GENERATION USING HYBRID AERIAL SENSOR DATA

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 297-303
Author(s):  
U. Bacher
Keyword(s):  
Data Acquisition ◽  
Hybrid Approach ◽  
Sensor Data ◽  
Sensor Systems ◽  
Camera System ◽  
New Era ◽  
Urban Modelling ◽  
Aerial Mapping ◽  
The One ◽  
3D Content

Abstract. In aerial data acquisition a new era started with the introduction of the first real hybrid sensor systems, like the Leica CityMapper-2. Hybrid in this context means the combination of an (oblique) camera system with a topographic LiDAR into an integrated aerial mapping system. By combining these complimentary sub-systems into one system the weaknesses of the one system could be compensated by using the alternative data source. An example is the mapping of low-light urban canyons, where image-based systems mostly produce unreliable results. For an LiDAR sensor the geometrical reconstruction of these areas is straight forward and leads to accurate results. The paper gives a detailed overview over the development and technical characteristics of hybrid sensor systems. The process of data acquisition is discussed and strategies for hybrid urban mapping are proposed. A hybrid sensor alone is just a part of the whole procedure to generate 3D content. As important as the senor itself is the workflow to generate the products. Here again a hybrid approach, with the processing of all datasets in one environment, is discussed. Special attention is paid to the hybrid orientation of the data and the integrated generation of base and enhanced products. The paper is rounded off by the discussion of the advantage of LiDAR data for the 3D Mesh generation for urban modelling.

scholarly journals RankGPES: learning to rank for information retrieval using a hybrid genetic programming with evolutionary strategies

2021 ◽  
Author(s):  
Mohammad A. Islam
Keyword(s):  
Information Retrieval ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Large Volume ◽  
Learning To Rank ◽  
Hybrid Approach ◽  
Industrial Applications ◽  
Evolutionary Strategies ◽  
Optimal Result ◽  
Approach To Learning

In recent years, Learning to Rank has not only shown effectiveness and better suitability for modern Web Era needs, but also has proved that it outperforms traditional ranking in terms of accuracy and efficiency. Evolutionary approach to Learning to Rank such as RankGP [37] and RankDE [3] have shown further improvement over non-evolutionary algorithms. However when Evolutionary algorithms have been applied to a large volume of data, often they showed they required so much computational efforts that they were not worth applying to industrial applications. In this thesis, we present RankGPES: a Learning to Rank algorithm based on a hybrid approach combining Genetic Programming with Evolution Strategies. Our results not only showed that it outperformed both RankGP [37] by 20% and RankDE [3] by 6% in terms of accuracy but also it showed it required significant less amount of time to converge to a near-optimal result.

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 75
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

Live Road Condition Assessment with Internal Vehicle Sensors

2021 ◽  
pp. 036119812110168
Author(s):  
Eyal Levenberg ◽  
Asmus Skar ◽  
Shahrzad M. Pour ◽  
Ekkart Kindler ◽  
Matteo Pettinari ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Relevant Information ◽  
Pavement Management ◽  
Sensor Data ◽  
Electric Cars ◽  
Condition Evaluation ◽  
Pavement Management Systems ◽  
Road Condition ◽  
Spatio Temporal ◽  
Vehicle Sensors

Modern cars are equipped with many sensors that measure information about the vehicle and its surroundings. These measurements are therefore related to the ride-surface conditions over which the vehicle is passing. The paper commences by outlining a four-component vision for performing road condition evaluation based on in-vehicle sensor readings and subsequent feeding of pavement management systems (PMSs) with live condition information. This is expected to enrich the functionalities of PMSs, and ultimately lead to improved maintenance and repair decisions. Next the LiRA (Live Road Assessment) project—an ongoing proof-of-concept attempt to realize the vision components—is presented. The project elements and software architecture are described in detail, listing any assumptions, compromises, and challenges. LiRA involves a fleet of 400 electric cars operating in Copenhagen, both within the city streets and nearby highways. Sensor data collection is performed with a customized Internet of Things (IoT) device installed in the cars. Data processing and structuring involve new software tools for quality control, spatio-temporal interpolation, and map matching. A hybrid approach, combining machine learning models with physical (mechanics-based) models, is currently being applied to convert data into relevant information for PMSs. Based on the experience thus far with LiRA, the vision actualization is deemed achievable, workable, and up-scalable.

scholarly journals Fieldwork in landscape ecology

2020 ◽  
Author(s):  
Jesse E. D. Miller ◽  
Carly D. Ziter ◽  
Michael J Koontz
Keyword(s):  
Global Change ◽  
Landscape Ecology ◽  
Critical Role ◽  
Sensor Data ◽  
Next Generation ◽  
Field Methods ◽  
Computational Approaches ◽  
A Priority

Fieldwork has played a critical role in the development of landscape ecology, and it remains essential for addressing contemporary challenges such as understanding the landscape ecology of global change. Advances in technology have expanded the scope of fieldwork to include the deployment of drones and other sensors, and in recent years, researchers have expressed concerns that traditional fieldwork (e.g., organismal observation) may be declining. Continuing to train the next generation of researchers in field methods should be a priority for landscape ecologists. Indeed, there is great potential for combining fieldwork with modern sensor data and computational approaches to advance the field of landscape ecology.

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