scholarly journals Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 685 ◽  
Author(s):  
Han Fan ◽  
Victor Hernandez Bennetts ◽  
Erik Schaffernicht ◽  
Achim Lilienthal
Keyword(s):  
Emergency Response ◽  
Electronic Nose ◽  
Gas Sensing ◽  
Practical Significance ◽  
Computationally Efficient ◽  
Urban Search And Rescue ◽  
Efficient Manner ◽  
Distribution Maps ◽  
Gas Identification ◽  
Sampling Procedures

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

scholarly journals Computationally Efficient Magnetic Resonance Imaging Based Surface Contact Modeling as a Tool to Evaluate Joint Injuries and Outcomes of Surgical Interventions Compared to Finite Element Modeling

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Joshua E. Johnson ◽  
Phil Lee ◽  
Terence E. McIff ◽  
E. Bruce Toby ◽  
Kenneth J. Fischer
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Joint Mechanics ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Element Modeling ◽  
Joint Injuries ◽  
Surface Contact ◽  
Joint Contact ◽  
Contact Pressures

Joint injuries and the resulting posttraumatic osteoarthritis (OA) are a significant problem. There is still a need for tools to evaluate joint injuries, their effect on joint mechanics, and the relationship between altered mechanics and OA. Better understanding of injuries and their relationship to OA may aid in the development or refinement of treatment methods. This may be partially achieved by monitoring changes in joint mechanics that are a direct consequence of injury. Techniques such as image-based finite element modeling can provide in vivo joint mechanics data but can also be laborious and computationally expensive. Alternate modeling techniques that can provide similar results in a computationally efficient manner are an attractive prospect. It is likely possible to estimate risk of OA due to injury from surface contact mechanics data alone. The objective of this study was to compare joint contact mechanics from image-based surface contact modeling (SCM) and finite element modeling (FEM) in normal, injured (scapholunate ligament tear), and surgically repaired radiocarpal joints. Since FEM is accepted as the gold standard to evaluate joint contact stresses, our assumption was that results obtained using this method would accurately represent the true value. Magnetic resonance images (MRI) of the normal, injured, and postoperative wrists of three subjects were acquired when relaxed and during functional grasp. Surface and volumetric models of the radiolunate and radioscaphoid articulations were constructed from the relaxed images for SCM and FEM analyses, respectively. Kinematic boundary conditions were acquired from image registration between the relaxed and grasp images. For the SCM technique, a linear contact relationship was used to estimate contact outcomes based on interactions of the rigid articular surfaces in contact. For FEM, a pressure-overclosure relationship was used to estimate outcomes based on deformable body contact interactions. The SCM technique was able to evaluate variations in contact outcomes arising from scapholunate ligament injury and also the effects of surgical repair, with similar accuracy to the FEM gold standard. At least 80% of contact forces, peak contact pressures, mean contact pressures and contact areas from SCM were within 10 N, 0.5 MPa, 0.2 MPa, and 15 mm2, respectively, of the results from FEM, regardless of the state of the wrist. Depending on the application, the MRI-based SCM technique has the potential to provide clinically relevant subject-specific results in a computationally efficient manner compared to FEM.

scholarly journals Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases

Chemosensors ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 45 ◽  
Author(s):  
Alphus Dan Wilson
Keyword(s):  
Electronic Nose ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Human Diseases ◽  
Detection Methods ◽  
Research Progress ◽  
Disease Detection ◽  
New Information ◽  
Disease Diagnostics ◽  
Diagnostic Applications

The development of electronic-nose (e-nose) technologies for disease diagnostics was initiated in the biomedical field for detection of biotic (microbial) causes of human diseases during the mid-1980s. The use of e-nose devices for disease-diagnostic applications subsequently was extended to plant and animal hosts through the invention of new gas-sensing instrument types and disease-detection methods with sensor arrays developed and adapted for additional host types and chemical classes of volatile organic compounds (VOCs) closely associated with individual diseases. Considerable progress in animal disease detection using e-noses in combination with metabolomics has been accomplished in the field of veterinary medicine with new important discoveries of biomarker metabolites and aroma profiles for major infectious diseases of livestock, wildlife, and fish from both terrestrial and aquaculture pathology research. Progress in the discovery of new e-nose technologies developed for biomedical applications has exploded with new information and methods for diagnostic sampling and disease detection, identification of key chemical disease biomarkers, improvements in sensor designs, algorithms for discriminant analysis, and greater, more widespread testing of efficacy in clinical trials. This review summarizes progressive advancements in utilizing these specialized gas-sensing devices for numerous diagnostic applications involving noninvasive early detections of plant, animal, and human diseases.

scholarly journals Integrative Bayesian Network Analysis of Genomic Data

2014 ◽  
Vol 13s2 ◽  
pp. CIN.S13786 ◽  
Author(s):  
Yang Ni ◽  
Francesco C. Stingo ◽  
Veerabhadran Baladandayuthapani
Keyword(s):  
Bayesian Network ◽  
Cancer Progression ◽  
Rapid Development ◽  
Genomic Data ◽  
Biological Knowledge ◽  
Network Approach ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
New Genes ◽  
Genome Wide

Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient's clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.

Framework for virtual hybrid simulation of TADAS frames using opensees and abaqus

2016 ◽  
Vol 24 (11) ◽  
pp. 2165-2179 ◽  
Author(s):  
Azin Ghaffary ◽  
Reza Karami Mohammadi
Keyword(s):  
Hybrid Simulation ◽  
Sufficient Accuracy ◽  
Hysteretic Behavior ◽  
Software Framework ◽  
Fe Model ◽  
Computationally Efficient ◽  
Element Analysis ◽  
Efficient Manner ◽  
Coupled Codes ◽  
Comparison Of The Results

Virtual hybrid simulation is a computationally-efficient method that enables coupling of two or more finite element analysis programs. In this study, benefits of this technique in predicting both cyclic and seismic response of a one-story one-bay frame equipped with a Triangular-plate Added Damping and Stiffness (TADAS) damper are evaluated. For this purpose, a detailed FE model of the damper is built in Abaqus to take into account precise modelling of its hysteretic behavior as well as a number of important features related to the geometric characteristics of the including parts of the device, while the remainder of the structure is modelled in OpenSees. Continuous exchange of the data between the coupled codes is conducted through the software framework, OpenFresco. Comparison of the results with experimental outcomes is presented, which proves the ability of the introduced technique in modelling the behavior of such structures in an efficient manner while preserving sufficient accuracy. At the end, a series of dynamic virtual hybrid simulations of the frame are performed which provide useful insights into design of TADAS frames.

Optimal Design of Hybrid Electric Fuel Cell Vehicles Under Uncertainty and Enterprise Considerations

2008 ◽  
Author(s):  
Jeongwoo Han ◽  
Panos Papalambros
Keyword(s):  
Fuel Cell ◽  
Design Optimization ◽  
Consumer Preferences ◽  
New Technology ◽  
Fuel Cell Vehicles ◽  
Optimization Strategy ◽  
Computationally Efficient ◽  
Fuel Price ◽  
Efficient Manner ◽  
Hybrid Electric

System research on Hybrid Electric Fuel Cell Vehicles (HEFCV) explores the tradeoffs among safety, fuel economy, acceleration, and other vehicle attributes. In addition to engineering considerations, inclusion of business aspects is important in a preliminary vehicle design optimization study. For a new technology, such as fuel cells, it is also important to include uncertainties stemming from manufacturing variability to market response to fuel price fluctuations. This paper applies a decomposition-based multidisciplinary design optimization strategy to an HEFCV. Uncertainty propagated throughout the system is accounted for in a computationally efficient manner. The latter is achieved with a new coordination strategy based on sequential linearizations. The hierarchically partitioned HEFCV design model includes enterprise, powertrain, fuel cell, and battery subsystem models. In addition to engineering uncertainties, the model takes into account uncertain behavior by consumers, and the expected maximum profit is calculated using probabilistic consumer preferences while satisfying engineering feasibility constraints.

scholarly journals A New Method for the Comparison of Trend Data with an Application to Water Vapor

2011 ◽  
Vol 24 (12) ◽  
pp. 3124-3141 ◽  
Author(s):  
Sebastian Mieruch ◽  
Stefan Noël ◽  
Maximilian Reuter ◽  
Heinrich Bovensmann ◽  
John P. Burrows ◽  
...  
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Statistical Significance ◽  
Taylor Series Expansion ◽  
Posterior Probabilities ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Trend Data

Abstract Global total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite data and from globally distributed radiosonde measurements, archived and quality controlled by the Deutscher Wetterdienst (DWD). The control of atmospheric water vapor amount by the hydrological cycle plays an important role in determining surface temperature and its response to the increase in man-made greenhouse effect. As a result of its strong infrared absorption, water vapor is the most important naturally occurring greenhouse gas. Without water vapor, the earth surface temperature would be about 20 K lower, making the evolution of life, as we know it, impossible. The monitoring of water vapor and its evolution in time is therefore of utmost importance for our understanding of global climate change. Comparisons of trends derived from independent water vapor measurements from satellite and radiosondes facilitate the assessment of the significance of the observed changes in water vapor. In this manuscript, the authors have compared observed water vapor change and trends, derived from independent instruments, and assessed the statistical significance of their differences. This study deals with an example of the Behrens–Fisher problem, namely, the comparison of samples with different means and different standard deviations, applied to trends from time series. Initially the Behrens–Fisher problem for the derivation of the consolidated change and trends is solved using standard (frequentist) hypothesis testing by performing the Welch test. Second, a Bayesian model selection is applied to solve the Behrens–Fisher problem by integrating the posterior probabilities numerically by using the algorithm Differential Evolution Markov Chain (DEMC). Additionally, an analytical approximative solution of the Bayesian posterior probabilities is derived by means of a quadratic Taylor series expansion applied in a computationally efficient manner to large datasets. The two statistical methods used in the study yield similar results for the comparison of the water vapor changes and trends from the different measurements, yielding a consolidated and consistent behavior.

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