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Artykuły w czasopismach na temat "EXTURE DEFECT DETECTION"
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A, Sowmiya, i Karunamoorthy B. "Fabric Fault and Extra Thread Detection using Convolutional Neural Network". Journal of Soft Computing Paradigm 5, nr 2 (czerwiec 2023): 148–63. http://dx.doi.org/10.36548/jscp.2023.2.005.
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A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws in order to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, ReLU, Pooling, Fully Connected layer. A number of well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect are tested in this study. Finally, The study demonstrates the result by classification and proves how accurately the defects are identified.
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Shingare, Saurabh S., Prabodh Khampariya i Shashikant M. Bakre. "A Survey on the Application of Artificial Neural Network-based Approach for Fault Location in Extra High Voltage (EHV) Network". NeuroQuantology 20, nr 5 (30.04.2022): 61–73. http://dx.doi.org/10.14704/nq.2022.20.5.nq22149.
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Recently, neural networks have acquired substantial relevance in defect location. The widespread use of neural networks began in the late 1980s and early 1990s. Normally, neural networks are used to improve defect detection, classification, and localization. There has been a lot of study and material written on fault location using neural networks. This research covers use of artificial neural networks (ANN) in identification of faults in EHV networks. We choose recent publications to uncover research gaps or expansions in the chosen study field.
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Burglin, T. R., i G. Ruvkun. "Regulation of ectodermal and excretory function by the C. elegans POU homeobox gene ceh-6". Development 128, nr 5 (1.03.2001): 779–90. http://dx.doi.org/10.1242/dev.128.5.779.
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Caenorhabditis elegans has three POU homeobox genes, unc-86, ceh-6 and ceh-18. ceh-6 is the ortholog of vertebrate Brn1, Brn2, SCIP/Oct6 and Brn4 and fly Cf1a/drifter/ventral veinless. Comparison of C. elegans and C. briggsae CEH-6 shows that it is highly conserved. C. elegans has only three POU homeobox genes, while Drosophila has five that fall into four families. Immunofluorescent detection of the CEH-6 protein reveals that it is expressed in particular head and ventral cord neurons, as well as in rectal epithelial cells, and in the excretory cell, which is required for osmoregulation. A deletion of the ceh-6 locus causes 80% embryonic lethality. During morphogenesis, embryos extrude cells in the rectal region of the tail or rupture, indicative of a defect in the rectal epithelial cells that express ceh-6. Those embryos that hatch are sick and develop vacuoles, a phenotype similar to that caused by laser ablation of the excretory cell. A GFP reporter construct expressed in the excretory cell reveals inappropriate canal structures in the ceh-6 null mutant. Members of the POU-III family are expressed in tissues involved in osmoregulation and secretion in a number of species. We propose that one evolutionary conserved function of the POU-III transcription factor class could be the regulation of genes that mediate secretion/osmoregulation.
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Elshimy, Ahmed, Rasha Tolba Khattab i Hend Galal Eldeen Mohamed Ali Hassan. "The role of MDCT in the assessment of cardiac and extra-cardiac vascular defects among Egyptian children with tetralogy of Fallot and its surgical implementation". Egyptian Journal of Radiology and Nuclear Medicine 52, nr 1 (8.02.2021). http://dx.doi.org/10.1186/s43055-021-00426-z.
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Abstract Background Tetralogy of Fallot (TOF) is considered the most common form of cyanotic congenital heart diseases (CHD), accounting for about 10% of cases. It includes four main cardiac defects, in addition to various extra-cardiac anomalies. Aim This study aimed to evaluate cardiac and extra-cardiac vascular defects associated with TOF among Egyptian children, regarding frequency and types with assessment of multi-slice or multi-detector computed tomography (MDCT) role in their diagnosis. Definitely, full detection of these vascular anomalies has utmost importance when evaluating such patients particularly before surgical intervention. Methods This study included 60 pediatric patients diagnosed as TOF, who underwent MDCT examination in our institute during period of 6 months from (March to September 2020), to confirm their trans-thoracic echocardiography (TTE) findings and detect other vascular abnormalities which cannot be precisely detected with TTE before their surgical interventions. Results The incidence of different extra-cardiac vascular defects diagnosed by MDCT among our patients was 85% which was significantly higher than that detected by TTE (55%). Moreover, MDCT was superior to TTE assessment as regards its diagnostic accuracy (96.6% vs. 80%), sensitivity (98% vs. 76.9%), and specificity (88.9% vs. 85.7%), in addition to both positive and negative predictive values. The most common anomalies detected were affecting the pulmonary artery (80%), followed by aorto-pulmonary vessels (45%), then aortic artery (40%), coronary arteries (20%), and lastly vena cava connection (6.7%). Patients’ demographic characteristics and clinical presentations were also presented. Conclusion This study confirmed that many extra-cardiac vascular defects are commonly associating cardiac lesions in TOF and emphasizing the great value of MDCT in their diagnosis. Certainly, proper detection of these anomalies will help decision-making during preoperative evaluation, corrective interventions, and further management of these cases.
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Gardahaut, Audrey, Alexandre Charau, Florian Le Bourdais, Adrien Stolidi i Natalia Sargeeva-Chollet. "Implementation of Non Destructive Testing and in-line monitoring techniques on extra-large structures printed with WAAM technology". e-journal of nondestructive testing 28, nr 9 (wrzesień 2023). http://dx.doi.org/10.58286/28477.
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Grade2XL is a European project funded by the H2020 program that gathers 21 academic and industrial partners. Started in March 2020 for 4 years, the principal objective is to print extra-large structures using the WAAM (Wire Arc Additive Manufacturing) method with a complete control of the fabrication process. Since WAAM is a relatively new technology for printing large specimens, it is important to develop quality assurance methods that can be used during the fabrication and once the structure has been built, which is the focus of CEA List in the project. Regarding methods applied built structures, CEA List has investigated Resonant Ultrasound Spectroscopy (RUS) for the material characterization and more specifically the evaluation of the elastic constants or more conventional ones for the inspection with phased-array ultrasonic techniques (PAUT) or eddy current (EC) for the detection of indications are implemented and optimised them for the inspection of the final structures. Concerning the development of in-line monitoring methods, CEA List has been working on laser Doppler vibrometry (LDV) and X-ray fluorescence (XRF) spectrometry, in order to get structural information during the printing but also detect the appearance of abnormal events and correlate them to the appearance of defects generated during the WAAM process. This proceeding presents the advantages of those methods for the inspection of extra-large structures made by WAAM and discusses the first results obtained in the framework of the project. Keywords: Additive Manufacturing, NDT, Imaging techniques, Eddy current, In-line monitoring, laser vibrometry, X-ray fluorescence spectrometry, Resonant Ultrasound Spectroscopy.
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Korya, Daniel, Saqib Chaudhry, Nile Khan, Ildiko Torok, Bernard Galea, Sonia Gill, Siddhart Mehta, Spozhmy Panezai, Mohammad Moussavi i Jawad F. Kirmani. "Abstract W P190: Contrast Transcranial Doppler Detects More Intra and Extra-Cardiac Right-to-Left Shunts than Trans-Esophageal Echocardiogram". Stroke 46, suppl_1 (luty 2015). http://dx.doi.org/10.1161/str.46.suppl_1.wp190.
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INTRODUCTION/OBJECTIVES: Paradoxical embolism is initially evaluated with a contrast trans-thoracic echocardiogram (cTTE), since the more sensitive method of contrast trans-esophageal echocardiogram (cTEE) is also more invasive. An adequate valsalva is essential to raise intra-thoracic pressures, but since the patient is sedated for the procedure, it is not always possible with cTEE. The amount of RLS through a PFO depends on strain rate and duration of the Valsalva maneuver. CTCD is a non-invasive method of detecting RLS that has been shown to be as good as cTEE. The goal of this study was to evaluate whether or not cTCD could be more effective at detecting RLS. MATERIALS AND METHODS: A review and evaluation of medical records and imaging was done on patients with embolic stroke from 2012-2013 at a university affiliated comprehensive stroke center. Patients had embolic stroke and a cTCD to be included. A subgroup of patients who had cTCD also had a cTEE. Patients who were positive for RLS on cTCD were compared with those who had RLS on cTEE. Statistical analysis was performed to determine significance and potential for future complications. RESULTS: A total of 1,033 patient records were reviewed and of those 130 patients (55% women) had embolic stroke and a cTCD, 35 were positive for a RLS based on the presence of microbubbles in the cerebral circulation, and 95 were negative. Of the 35 patients with positive cTCD, 45% (n=16) also underwent cTEE; however, only 44% (n=7) of those had positive results for RLS. Conversely, 56% (n=9) of the patients who were shown to have RLS on cTCD were missed on cTEE (without cardiac septal defects). Of the 95 patients who had negative cTCD, 19 of them also underwent cTEE and all were negative on cTEE. CONCLUSION: Using cTCD to detect microbubbles in cerebral arteries appears to be more reliable and accurate for discovering RLS of intra or extra-cardiac origin. For embolic ischemic strokes, we recommend using cTCD as a first-line study and suggest it should be the new “gold standard”. Randomized, prospective studies should be conducted to further validate this data.
Rozprawy doktorskie na temat "EXTURE DEFECT DETECTION"
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SHARMA, MONIKA. "AUTOMATED TEXTURE DEFECT DETECTION USING THE NON-EXTENSIVE ENTROPY WITH GAUSSIAN GAIN". Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14881.
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Inefficient processes in industries can cost a lot of time, money and customer satisfaction. Quality assurance is a very important aspect for industries. As a result, to improve their processes and become more efficient at the global level, they have started to automate their certain tasks for which humans were generally considered to perform them. And one such popular task is Industrial Inspection . As a result, intelligent visual inspection systems are developed to ensure high quality of products in production lines. Advancement in image processing and computer vision techniques has led to the development of Automated Visual Inspection Systems. These systems are required in industries to inspect the manufactured products, so as to identify any discrepancies in them. Thus, they play an important role in industries by ensuring that only good quality products enter into the market because selling defective products in market can lead to large losses for goods manufacturing industry. One such industry is Textile Industry where these systems are of great importance. They identify defects occurring in textile cloth. Several algorithms have been developed based on different approaches so as to identify defects in texture patterns. Presence of complex defects is a major hurdle for many such algorithms and that is why new ideas keep on emerging to develop more efficient algorithms which can identify such type of defects in texture patterns. On the same lines, this major project report presents a new algorithm based on a new approach to identify complex defects in texture in a more efficient and accurate manner.
Streszczenia konferencji na temat "EXTURE DEFECT DETECTION"
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Moles, Michael D. C., Fre´de´ric Jacques i Noe¨l Dube´. "Pressure Vessels Inspections Using Ultrasonic Phased Arrays". W ASME 2003 Pressure Vessels and Piping Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/pvp2003-1847.
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Phased arrays offer significant technical advantages for weld inspections over conventional ultrasonics. The phased array beams can be steered, scanned, swept and focused electronically. Beam steering permits the selected beam angles to be optimized ultrasonically by orienting them perpendicular to the predicted defects, especially Lack of Fusion. Electronic (linear) scanning permits very rapid coverage of the welds. Beam steering (usually called sectorial or azimuthal scanning) can be used for mapping welds at appropriate angles to optimize Probability of Detection of defects. Electronic focusing permits optimizing the beam shape and size at the expected defect location, also to optimize Probability of Detection. Overall, the use of phased arrays permits optimizing defect detection while minimizing inspection time. The paper describes the application of phased arrays for inspecting pressure vessel welds. Phased arrays offer significant practical advantages over conventional automated inspections. Thick section weld inspections typically use the established “top, side, end” or “top, side, TOFD” views of the weld. Other displays can be used, e.g. strip charts for zone discrimination scanning of narrow gap welds. Special inspections can be easily performed with phased arrays, e.g. additional beams for extra coverage, multiple angles or inspection set-ups simultaneously, or special scans such as tandem probes. Different delivery systems and instrumentation can be assembled for any required scan. Fitness-For-Service inspections requiring high PoD and accurate sizing can be performed using upscale systems. These phased array inspections can be tailored to any known code requirements.
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Hossain, Md Shahjahan, Russell Krenek, Hossein Taheri i Fadwa Dababneh. "Ultrasonic Phased Array Technique for Defect Detection and Sizing in Heavy-Walled Cast Components". W ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23319.
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Abstract Cast metal parts are extensively found in many engineering products such as pump casing and engines. Thermal effects exhibited during metal casting processes, such as solidification can generate defects in the cast components. Effective nondestructive testing (NDT) for detection and sizing of defects in cast parts prevents extra cost and time associated with repair and maintenance. Surface-breaking cracks and porosity are among the common types of defects in large cast components. There are several limitations in using conventional NDT methods for as built cast parts due to surface conditions, coarse-grain structure, and characteristics of potential defects. Ultrasonic adaptive imaging based on the Phased Array Ultrasonic Testing (PAUT) technology is proposed for coarse-grain heavy-walled cast material inspection. The capability of aperture focusing in PAUT provides the opportunity for better imaging results. A comprehensive understanding about the ultrasonic beam focusing and the selection of an appropriate transducer and wedges is necessary for successful defect characterization. Cast aluminum and iron samples having on-purpose made defects were successfully inspected using the PAUT. Unlike conventional techniques, the results indicated that the PAUT is a promising method for inspecting as-built cast parts with rough surface finish conditions. The proposed method helps to decrease the inspection time, machining requirements, and preparation costs. Moreover, the enhanced defect sizing approach provides useful information for repair and maintenance decision making such as amount of material grinding and post-welding procedure.
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Cummings, Ian T., Erica M. Jacobson, Peter H. Fickenwirth, Eric B. Flynn i Adam J. Wachtor. "In-Process Defect Detection for Additively Manufactured Metal Lattices". W ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24368.
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Abstract The internal lattice structures common to laser powder bed fusion parts pose significant challenges for x-ray computed tomography scans, which are the current default post-process inspection technique. In this work, we present an alternative in-process inspection technique that acquires a full-field ultrasonic response measurement for each layer of the build via ultrasonic excitation of the build plate and non-contact measurement with a scanning laser Doppler vibrometer. We implement a maximum a posteriori detection technique based on those ultrasonic response measurements that can detect malformed lattice elements by combining the information available from the measurements with the ideal part geometry as encoded in the part STL file. We demonstrate successful detection of extra sintered material in the part as well as lattice strut member breakage. Further development of this inspection method in laser powder bed fusion production environments will aide qualification of latticed parts with increased confidence.
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SIEBER, PAUL, KONSTANTINOS AGATHOS, ROHAN SOMAN, WIESLAW OSTACHOWICZWIESLAW OSTACHOWICZ i ELENI CHATZI. "A PARAMETRIZED REDUCED ORDER MODEL FOR RAPID EVALUATION OF FLAWS IN GUIDED WAVE TESTING". W Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36315.
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Data from guided wave propagation in structures, produced by piezoelectric elements, can offer valuable information regarding the possible existence of flaws. Numerical models can be used to complement the attained data for refining the potential for flaw characterization. Unfortunately, evaluation of these models remains computationally expensive, especially for small defects, due to the short wavelength required for detection and, the in turn fine discretization in time and space. This renders real–time simulation infeasible, rendering GW–approaches less attractive for inverse problem formulations, where the forward problem needs to be solved several times. We propose an accelerated computation method, which exploits the properties of guided waves interacting with defects, where an extra band of waves is created, whose phase is differentiated, depending on the location of the flaw (e.g. notch) within the medium. To expedite the actual simulation for the inverse problem, the system is parametrized in terms of the location of the flaw and, in an offline phase, is repeatedly solved to produce snapshots of the system’s response. The snapshots are used to create a physics–informed interpolation of the solution of the wave propagation problem for different flaw locations. The gained information is then used in an inverse setting for localising the defect using an evolution strategy as a means to stochastic, derivative-free numerical optimization. The method is demonstrated in simulations of a 2D slice of a thin plate.
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Arcangeletti, Giorgio, Alessandro Smareglia, Mariella Busco, Paolo Catena, Andrea Esposito, Giulio Monacchi i Natalia Pierozzi. "Integrated Acoustic Unit I.A.U.: A Novel Technology for Continuous Piggability Detection During Pipeline Laying Operation". W Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/32113-ms.
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Abstract In the offshore oil and gas industry the pipeline integrity during the whole life cycle is an aspect of paramount importance. During Installation phase pipeline structural integrity can be compromised by fabrications defects and by the action of external forces in case of accidental events, that could generate pipe instability and, in the worst-case scenario, thepipe collapse. Current offshore regulations provide the requirements to be respected during pipelaying operations in terms of Buckle Detection to meet the pipeline Piggability requirements. The debate on the use of a Mechanical Buckle Detector (MDB), i.e., a gauge plate trolley pulled through the pipeline by a wire connected to the vessel and placed at a certain distance behind the touchdown point, is still open. Indeed, Buckle Detection by deploying the MBD may introducesome operational risks, i.e., the cable break, the stacking during motion and the damage of the internal coating. This may lead toadditional vessel time, extra launch/recovery operations and false readings/bias. With the main target to overcome the above issues, Saipem has developed a tool based on acoustic technique to detect deformations of the internal sections of pipes during installation operations: the Integrated Acoustic Unit, IAU (Patented Technology, see Ref. [1]). The tool works by means of a non-intrusive technique andcan provide the continuous characterization of the whole pipe span up to the touchdown point in a single shot. This paper gives an overview of recent developments of Saipem Integrated Acoustic Unit. It describes the working principle and the available IAU Functions, the pipe defects characterization campaign through modelling (FEA) and laboratory tests, the system validation activity through the experimental campaign, the third-party qualification and the new Saipem Procedure for Technology Qualification that was at the basis of the process.
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Arcangeletti, Giorgio, Alessandro Smareglia, Mariella Busco, Paolo Catena, Andrea Esposito, Giulio Monacchi i Natalia Pierozzi. "Integrated Acoustic Unit I.A.U.: A Novel Technology for Continuous Piggability Detection During Pipeline Laying Operation". W Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/32113-ms.
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Abstract In the offshore oil and gas industry the pipeline integrity during the whole life cycle is an aspect of paramount importance. During Installation phase pipeline structural integrity can be compromised by fabrications defects and by the action of external forces in case of accidental events, that could generate pipe instability and, in the worst-case scenario, thepipe collapse. Current offshore regulations provide the requirements to be respected during pipelaying operations in terms of Buckle Detection to meet the pipeline Piggability requirements. The debate on the use of a Mechanical Buckle Detector (MDB), i.e., a gauge plate trolley pulled through the pipeline by a wire connected to the vessel and placed at a certain distance behind the touchdown point, is still open. Indeed, Buckle Detection by deploying the MBD may introducesome operational risks, i.e., the cable break, the stacking during motion and the damage of the internal coating. This may lead toadditional vessel time, extra launch/recovery operations and false readings/bias. With the main target to overcome the above issues, Saipem has developed a tool based on acoustic technique to detect deformations of the internal sections of pipes during installation operations: the Integrated Acoustic Unit, IAU (Patented Technology, see Ref. [1]). The tool works by means of a non-intrusive technique andcan provide the continuous characterization of the whole pipe span up to the touchdown point in a single shot. This paper gives an overview of recent developments of Saipem Integrated Acoustic Unit. It describes the working principle and the available IAU Functions, the pipe defects characterization campaign through modelling (FEA) and laboratory tests, the system validation activity through the experimental campaign, the third-party qualification and the new Saipem Procedure for Technology Qualification that was at the basis of the process.
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Vishwanatha, C. R., i V. Asha. "Crack Detection of Pharmaceutical Vials Using Agglomerative Clustering Technique". W International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-23rx59.
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Pharmaceutical industries remain very profitable but defects in medicine vials are causing losses and adding extra overhead in quality management. In order to minimize these losses and overheads, companies need to find new ways of doing quality management for every vial produced. This paper presents a method for finding cracks on the vials using Agglomerative Clustering Technique. The technique successfully detects all types of cracks on the vials. The algorithm has achieved 100% accuracy in detection of cracks on the Pharmaceutical Vials and can have potential application in pharmaceutical industries in quality control.
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Zhan, Ying. "Bridge Surface Roughness Detection Based on Double-Vehicle-Double-Pass Technique". W IABSE Conference, Copenhagen 2018: Engineering the Past, to Meet the Needs of the Future. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2018. http://dx.doi.org/10.2749/copenhagen.2018.385.
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Bridge surface roughness encompasses dents, cracks, bulges and other defects due to construction as well as wear and tear in service. It significantly affects the traffic load on bridge, costs extra vehicle fuel consumption and tire wear, and therefore is a major concern of bridge monitoring and maintenance. As it considerably affects the contact force between the vehicle and the bridge, it is also a major obstacle of using vehicle measured data to identify bridge parameters. This paper presents a method to estimate the surface roughness profile of a bridge. With the acceleration data gathered from two different vehicles running on the bridge successively at the same speed, the roughness profile of the bridge can be measured with satisfactory accuracy. The method is verified with finite element simulation.
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Cordero, Chiara Emilia, Erica Liberto, Federico Stilo, Humberto Bizzo, Luis Cuadros Rodriguez, Simone Squara i Stephen Reichenbach. "Artificial Intelligence smelling machines based on multidimensional gas chromatography: Capturing extra-virgin olive oil aroma blueprint and unique identity". W 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/okat9384.
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Comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOF MS) has been recently applied as core technology of a Sensomics-based expert system (SEBES) capable to predict key-aroma signatures of food without using human olfaction. The strategy, also referred to as Artificial Intelligence Smelling, conceptually opens many different opportunities for odorants pattern detection, accurate quantification avoiding time-consuming sample preparation/extraction steps, and samples sensory qualification/discrimination based on computer vision strategies.Â
The contribution illustrates the potentials of GC×GC platforms in the context of Artificial Intelligence Smelling for extra-virgin olive oils selected within high-quality productions from Italy and Brazil. In particular, by accurate quantification of key-aroma compounds and potent odorants strongly correlated to sensory defects, samples’ aroma bluperint is captured and used to discriminate oils based on their peculiar hedonic features.
The application of combined untargeted and targeted (UT) fingerprinting strategy of 2D-data patterns enables effective discrimination between oils produced in different Italian Regions and between oils from Italy and Brazil independently by processing technologies and olives cultivars. This identitation process has great potentials being an effective fingerprinting strategy while providing, at the same time, high level information on chemical composition as a detailed profiling.
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Moghadam, Farid K., i Amir R. Nejad. "Experimental Validation of Angular Velocity Measurements for Wind Turbines Drivetrain Condition Monitoring". W ASME 2019 2nd International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/iowtc2019-7620.
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Abstract Drivetrain bearings are seen as the most common reason of the wind turbine drivetrain system failures and the consequent downtimes. In this study, the angular velocity error function is used for the condition monitoring of the bearings and gears in the wind turbine drivetrain. This approach benefits from using the sensor data and the dedicated communication network which already exist in the turbine for performance monitoring purposes. Minor required modification includes an additional moderate sampling frequency encoder without any need of adding an extra condition monitoring system. The additional encoder is placed on the low speed shaft and can also be used as the backup for the high speed shaft encoder which is critical for turbine control purposes. A theory based on the variations of the energy of response around the defect frequency is suggested to detect abnormalities in the drivetrain operation. The proposed angular velocity based method is compared with the classical vibration-based detection approach based on axial/radial acceleration data, for the faults initiated by different types of excitation sources. The method is experimentally evaluated using the data obtained from the encoders and vibration sensors of an operational wind turbine.