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 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.

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.

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 (3) ◽  
pp. 61
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 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 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.

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. 61
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 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 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.

scholarly journals Outlier Detection for Sensor Systems (ODSS): A MATLAB Macro for Evaluating Microphone Sensor Data Quality

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2329 ◽  
Author(s):  
Robert Vasta ◽  
Ian Crandell ◽  
Anthony Millican ◽  
Leanna House ◽  
Eric Smith
Keyword(s):  
Data Quality ◽  
Outlier Detection ◽  
Sensor Data ◽  
Sensor Systems

scholarly journals Design of Early Warning System Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 66
Author(s):  
Gan Bo ◽  
Jin Shan
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sensor Data ◽  
Landslide Monitoring ◽  
Remote Transmission ◽  
Sensor Data Acquisition

In order to solve the shortcomings of the landslide monitoring technology method, a set of landslides monitoring and early warning system is designed. It can achieve real-time sensor data acquisition, remote transmission and query display. In addition, aiming at the harsh environment of landslide monitoring and the performance requirements of the monitoring system, an improved minimum hop routing protocol is proposed. It can reduce network energy consumption, enhance network robustness, and improve node layout and networking flexibility. In order to realize the remote transmission of data, GPRS wireless communication is used to transmit monitoring data. Combined with remote monitoring center, real-time data display, query, preservation and landslide warning and prediction are realized. The results show that the sensor data acquisition system is accurate, the system is stable, and the node network is flexible. Therefore, the monitoring system has a good use value.

scholarly journals Implementation of Oxygen Saturation Sensor Data Acquisition Based on Bluetooth Low Energy Protocol

2021 ◽  
Author(s):  
Muhammad Fahmi Ali Fikri ◽  
Dany Primanita Kartikasari ◽  
Adhitya Bhawiyuga
Keyword(s):  
Data Acquisition ◽  
Data Center ◽  
Low Energy ◽  
Sensor Data ◽  
Data Delivery ◽  
Bluetooth Low Energy ◽  
Data Redundancy ◽  
Iot Devices ◽  
Store And Forward ◽  
Sensor Data Acquisition

Sensor data acquisition is used to obtain sensor data from IoT devices that already provide the required sensor data. To acquire sensor data, we can use Bluetooth Low Energy (BLE) protocol. This data acquisition aims to process further data which will later be sent to the server. Bluetooth Low Energy (BLE) has an architecture consisting of sensors, gateways, and data centers, but with this architecture, there are several weaknesses, namely the failure when sending data to the data center due to not being connected to internet network and data redundancy at the time of data delivery is done. The proposed solution to solve this problem is to create a system that can acquire sensor data using the Bluetooth Low Energy (BLE) protocol with use a store and forward mechanism and checking data redundancy. The proposed system will be implemented using sensors from IoT devices, the gateway used is Android devices, and using the Bluetooth Low Energy protocol to acquire data from sensors. Then the data will be sent to the cloud or server. The results of the test give the results of the system being successfully implemented and IoT devices can be connected to the gateway with a maximum distance of 10 meters. Then when the system stores, for every minute there is an increase in data of 4 kb. Then there is no data redundancy in the system.

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