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Paper des Monats
- ja (1)
Sensor based person tracking is a challenging
topic. The main objective is positioning in areas without
GPS connection, i.e. indoors. A research project is carried
out at BAM, Federal Institute for Materials Research and
Testing, to develop and to validate a multi-sensor system for
3D localization. It combines body motion sensing and a
guard system for the tracking and recording of the status of
persons. The so named BodyGuard system was designed for
sensor-based monitoring and radio-based transmission of
the movement of a person. Algorithms were developed to
transform the sensor data into a spatial coordinate. This
paper describes how the BodyGuard system operates, which
main components were used in the system, how the
individual sensor data are converted into 3D motion data,
with which algorithms the individual sensors are processed,
how individual errors are compensated and how the sensor
data are merged into a 3D Model. Final objective of the
BodyGuard system is to determine the exact location of a
person in a building, e.g. during fire-fighting operations.
Gas source localization with a micro-drone using bio-inspired and particle filter-based algorithms
(2013)
Gas source localization (GSL) with mobile robots is a challenging task due to the unpredictable nature of gas dispersion, the limitations of the currents sensing technologies, and the mobility constraints of ground-based robots. This work proposes an integral solution for the GSL task, including source declaration. We present a novel pseudo-gradient-based plume tracking algorithm and a particle filter-based source declaration approach, and apply it on a gas-sensitive micro-drone. We compare the performance of the proposed system in simulations and real-world experiments against two commonly used tracking algorithms adapted for aerial exploration missions.
In this study a method for early damage detection is investigated based on the directed excitation and measuring of Lamb waves (guided waves). The objective is to make a statement about the practicability of a Lamb wave generator (LWG) and about the application relevant parameters (specimen materials and dimensions, wave characteristics, excitation, transmission und reflection). Guided waves enable an effective differentiation of structural and defective indications as well as a classification of different defect types. For the investigations, a LWG was developed which combines the advantages of the single excitation of a piezoelectric actuator and the possibility of a group excitation. Algorithms were developed to identify and analyze damage patterns (cracks, wholes, weakening).
Limited access to the object of relevance is often an essential problem in testing routines, e.g. for non-destructive testing of industry facilities or components of means of transportation. The presented project is about the development of a non-destructive testing method for application over great distances (up to 100 m). The method is based on directed excitation and measuring of Lamb waves (guided waves). It enables an effective differentiation of structural and defective indications as well as a classification of different defect types.
The advantage of guided waves is the ability of propagation over great distances. This offers innovative possibilities for the investigation of large or difficult to access constructions. The testing method can be helpful in a broad variety of applications. It is useable for the characterization of plane or cylindrical, thin-walled surfaces of metal or fiber laminated materials. Particularly the investigation of aircraft wings and other aerospace components as well as the investigation of pipelines are addressed fields of application.
Multichannel measuring systems are used to measure strains and accelerations during drop tests of containments for dangerous goods. Conventional systems require cabling of each sensor and co-falling of the cable harness, causing problems in the test preparation and execution. Promising results of a single cable measuring system, consisting of measuring modules with data bus connection and local data acquisition were presented in a previous study. This paper follows up with results from laboratory and full-scale testing of an further enhanced single cable system for the application in drop tests.----------------------------------------------------------------------------------------------------------------------------------------------------------------
Vielkanalmesssysteme werden zur Messung von Dehnungen und Beschleunigungen bei Fallversuchen von Gefahrstoffbehältern eingesetzt. Konventionelle Systeme erfordern die Verkabelung jeder einzelnen Messstelle und somit das Mitfallen eines Kabelbaums, wodurch Probleme bei der Versuchsvorbereitung und Durchführung entstehen. In einer vorangegangenen Studie wurden vielversprechende Ergebnisse durch Verwendung eines 1-Kabelmesssystems mit Datenbus und dezentraler Datenspeicherung erzielt. Dieser Artikel beinhaltet weiterführende Ergebnisse aus Labor- und Realversuchen mit einem weiterentwickelten System zur Anwendung bei Fallversuchen.
The Split Hopkinson Pressure Bar (SHPB) or
Kolsky Bar is known as a method for analysing
mechanical properties of a material under dynamic
load. Referring to the name it is a splitted
Hopkinson Pressure Bar. The Hopkinson Bar is
related to its inventor Bertram Hopkinson (1914).
He used it to analyze stone samples under dynamic
load. Later his construction was improved by
Davies (1948) [1] and Kolsky (1949) [2], who had
the idea to split the bar and put a sample in
between.
The technique is used to measure a lot of
different mechanical properties of a material e.g.
the dynamic Youngs modulus, deformation
behaviour or to chart the dynamic stress-strain
diagram. Further applications are the measurement
of the elastic wave and the analysis of the
propagation of the wave [2]. In the last years there
was still an interest in measuring dynamic
properties with a SHPB. Nevertheless no
comprehensive validation of the system exists.
Another problem is that the stress and the strain in
the sample are only calculated with simplified
estimations.
The aim of this paper is to give a first
impression of important points validating the
system. On account of different setups of the
SHPB a validation of the used system is necessary.
Hence it is important to look at the theory. Further
in this paper the measurement results will be
shown in a strain over time diagram.
This article presents the development and validation of an autonomous, gas sensitive microdrone that is capable of estimating the wind vector in real time using only the onboard control unit of the microdrone and performing gas distribution mapping (DM). Two different sampling approaches are suggested to address this problem. On the one hand, a predefined trajectory is used to explore the target area with the microdrone in a real-world gas DM experiment. As an alternative sampling approach, we introduce an adaptive strategy that suggests next sampling points based on an artificial potential field (APF). Initial results in real-world experiments demonstrate the capability of the proposed adaptive sampling strategy for gas DM and its use for gas source localization.
One of the main unsolved issues of CCS is the comprehensive surveillance of CO2 storage areas with reasonable effort and costs. This study presents an approach for distributed subsurface monitoring of gas storage areas. The concept combines different measurement technologies to one multifunctional sensor: membrane based measurement technology for in situ monitoring of gases in soil and fibre optical sensing of temperature and strain (as a measure for structural change). A test field of application-relevant dimensions is built up to validate and optimize the technology.
Carbon capture & storage (CCS) is one of the most promising technologies for greenhouse gas (GHG) management. However, an unsolved issue of CCS is the development of appropriate long-term monitoring systems for leak detection of the stored CO2. To complement already existing monitoring infrastructure for CO2 storage areas, and to increase the granularity of gas concentration measurements, a quickly deployable, mobile measurement device is needed. In this paper, we present an autonomous gas-sensitive micro-drone, which can be used to monitor GHG emissions, more specifically, CO2. Two different measurement strategies are proposed to address this task. First, the use of predefined sensing trajectories is evaluated for the task of gas distribution mapping using the micro-drone. Alternatively, we present an adaptive strategy, which suggests sampling points based on an artificial potential field (APF). The results of real-world experiments demonstrate the feasibility of using gas-sensitive micro-drones for GHG monitoring missions. Thus, we suggest a multi-layered surveillance system for CO2 storage areas.
In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach for GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.
This work focuses on the design and fabrication of low-cost and fast-response of an electronic nose (E-nose) based on semiconductor gas sensors, for discriminating some synthetic gases such as ammonia (NH3), ethanol (C2H5OH), and hydrogen sulfide (H2S). Additionally, the capability of separating different concentration levels of each considered gases was checked. Dataset treatment of E-nose by using Principal Component Analysis (PCA) showed a good discrimination of the different synthetic gases. Furthermore, perfect classification was reached of different concentration levels of the analysed gases by using Discriminant Function Analysis (DFA). In the light of these results, it could be stated that the developed E-nose system constitutes an inexpensive, rapid, simple to use, and efficient tool for synthetic gases detection.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize a 5 cm wide chemical trail, a detection criterion was developed that uses only relative changes in the transient phase of the sensor response. The reduction in signal strength dependence improves the robustness of its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot can correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
This paper presents first advances in the area of aerial chemical trail following. For that purpose, we equipped a palm-size aerial robot, based on the Crazyflie 2.0 quadrocopter, with a small lightweight metal oxide gas sensor for measuring evaporated ethanol from chemical trails. To detect and localize the chemical trail, a novel detection criterion was developed that uses only relative changes in the transient phase of the sensor response, making it more robust in its application. We tested our setup in first crossing-trail experiments showing that our flying ant robot is able to correlate an odor hit with the chemical trail within 0.14 m. Principally, this could enable aerial chemical trail following in the future.
Poor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits a satisfactory discrimination between the odorous air samples. Twenty-four VOCs with known molecular formulas were identified with Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). A validated Partial Least Square (PLS) model foresees good calibration between e-nose measurement and TD-GCMS analysis. The finding indicates that TD-GC–MS approach in conjunction with e-nose unit could be suitable tool for environmental measurement-based odor emissions.
Wastewater contains harmful chemicals and heavy metals that are known to cause various environmental and health problems. Therefore, the water quality control using sensitive, simple, fast, accurate, and portable tools is of great importance. This study aimed to evaluate the pollution parameters of wastewaters collected at different treatment stages from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewaters by using an electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) combined with chemometric techniques. Water and wastewater pollution parameters determination were performed using inductively coupled plasma optical emission spectrometry for the determination of cations, and anions by using ion chromatography. Chemometric techniques, such as Principal Component Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to process the E-nose and E-tongue datasets to describe the similarities between the samples. In addition, Partial Least Squares Regression (PLSR) model was constructed using electronic sensing data to simultaneously predict the concentration values of physicochemical parameters. The obtained correlation coefficient, for training and testing sets, is higher than 0.91 for the prediction of the concentration of all physicochemical parameters, except for iron (Fe) which remains 0.84. These results suggest that simple, portable, and inexpensive tools such as electronic nose and tongue are suitable for wastewater analysis.
Fiber optic sensors have gained increasing importance in recent years and are well established in many areas of industrial applications. In this paper, we introduce a concept of a self-diagnostic fiber optic sensor. The presented sensor is to resolve the problems of embedded fiber optic sensors in complex structures and to enable the validation under operational conditions. For this purpose, different magnetostrictive coated fiber optic sensors were developed and various experiments were performed to verify their mode of Operation and to determine the respective reproducibility. The measuring principle is illustrated by obtained experimental results, which showed a change in wavelength from 1 pm at a magnetic field strength change of 0.25 mT.
In addition, the temperature characteristics of the implemented magnetostrictive sensor were analyzed and an experimental factor of 1.5 compared to a reference fiber optic sensor was determined.
A monitoring method is introduced that creates twodimensional (2D) maps of the soil gas distribution. The method combines linear gas sensing technology for in-situ monitoring of gases in soil with the mapping capabilities of Computed Tomography (CT) to reconstruct spatial and temporal resolved gas distribution maps. A weighted iterative algebraic reconstruction method based on Maximum Likelihood with Expectation Maximization (MLEM) in combination with a source-by-source reconstruction approach is introduced that works with a sparse setup of orthogonally-aligned linear gas sensors. The reconstruction method successfully reduces artifact production, especially when multiple gas sources are present, allowing the discrimination between true and non-existing so-called ghost source locations. Experimental validation by controlled field experiments indicates the high potential of the proposed method for rapid gas leak localization and quantification with respect to Pipeline or underground gas storage issues.
Linear sensor for areal subsurface gas monitoring - calibration routine and validation experiments
(2014)
Membrane based linear gas sensors and fiber optical sensors feature similar geometries and complement each other in quantities to be measured. To the author's best knowledge, it is the first time that these sensors are combined to a multifunctional sensor for distributed measuring of gas concentrations, temperature, and strain. Objective is a comprehensive monitoring of underground gas storage areas. In the presented project a 400 m² test site and a corresponding laboratory system were just built up to characterize, validate, and optimize the combined sensor. Application of the sensor lines in a grid structure should enable spatial resolution of the measurement data and early detection of relevant events, as gas leakage, temperature change, or mechanical impact. A Calibration routine was developed which can be applied subsequent to underground installation. First measurement results indicate the potential of the method, with regard to highly topical energy transport and storage issues.
In this paper, we show that a micro unmanned aerial vehicle (UAV) equipped with commercially available gas sensors can address environmental monitoring and gas source localization (GSL) tasks. To account for the challenges of gas sensing under real-world conditions, we present a probabilistic approach to GSL that is based on a particle filter (PF). Simulation and real-world experiments demonstrate the suitability of this algorithm for micro UAV platforms.