Echtzeit-Bauteilprüfung beim Feinschneiden/Real-time quality analysis during fine blanking – Test rig for sheared part characterization using image processing and neural networks

2020 ◽  
Vol 110 (06) ◽  
pp. 382-388
Author(s):  
Herman Voigts ◽  
Rafael Hild ◽  
Andreas Feuerhack ◽  
Thomas Bergs
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Quality Assessment ◽  
Real Time ◽  
Quality Analysis ◽  
Test Rig ◽  
Fine Blanking ◽  
Real Time Analysis ◽  
Automated Image Processing

Die Schnittteilqualität beim Feinschneiden unterliegt einer Vielzahl von Einflussfaktoren. Derzeit findet die Qualitätsbewertung offline vom Prozess statt. Um eine echtzeitfähige Qualitätsbeurteilung für den Einsatz von Assistenzsystemen zu ermöglichen, wurde eine bildverarbeitende Methodik untersucht. Es wurde ein Prüfstand entwickelt zur Erforschung der Methoden für eine automatisierte bildverarbeitende und echtzeitfähige Analyse der Schnittteilqualität mittels neuronalen Netzen.   The quality of the sheared surface during fine blanking is subject to a large number of influencing factors. Currently, quality assessment is carried out offline. To enable real-time quality assessment based on assistance systems, an image-processing methodology was investigated. A test rig was developed to investigate methods for automated image processing and real-time analysis of the sheared surface quality by means   of neural networks.

scholarly journals ANN analysis in a vision approach for potato inspection

2008 ◽  
Vol 6 (02) ◽  
Author(s):  
R. Rios-Cabrera ◽  
I Lopez-Juarez ◽  
Hsieh Sheng-Jen
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Comparative Analysis ◽  
Physical Properties ◽  
Real Time ◽  
The Other ◽  
Stability And Convergence

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

scholarly journals Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3279
Author(s):  
Maria Habib ◽  
Mohammad Faris ◽  
Raneem Qaddoura ◽  
Manal Alomari ◽  
Alaa Alomari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Quality Assessment ◽  
Transcript Level ◽  
Assessment Process ◽  
Classification Model ◽  
Systematic Evaluation ◽  
Transcript Levels ◽  
Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

scholarly journals Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic

2021 ◽  
Vol 8 ◽  
Author(s):  
Mojtaba Akbari ◽  
Jay Carriere ◽  
Tyler Meyer ◽  
Ron Sloboda ◽  
Siraj Husain ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Real Time ◽  
Close Contact ◽  
Image Features ◽  
Svm Classifier ◽  
Quick Response ◽  
Robot Arm ◽  
Noise Characteristics ◽  
The Us

During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.

scholarly journals Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals

2022 ◽  
Vol 12 ◽  
Author(s):  
Silvia Seoni ◽  
Simeon Beeckman ◽  
Yanlu Li ◽  
Soren Aasmul ◽  
Umberto Morbiducci ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Assessment ◽  
Real Time ◽  
Template Matching ◽  
Quality Metrics ◽  
Laser Doppler ◽  
Signal Quality ◽  
Laser Doppler Vibrometry ◽  
Quality Metric

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

scholarly journals Real Time Estimation of Modal Parameters and Their Quality Assessment

2008 ◽  
Vol 15 (3-4) ◽  
pp. 299-306 ◽  
Author(s):  
Tadeusz Uhl ◽  
Maciej Petko ◽  
Grzegorz Karpiel ◽  
Andrzej Klepka
Keyword(s):  
Laboratory Test ◽  
Quality Assessment ◽  
Real Time ◽  
Time Estimation ◽  
Parameters Estimation ◽  
Modal Parameters ◽  
Recursive Method ◽  
Test Rig ◽  
Confidence Bounds ◽  
Real Time Estimation

In this paper the recursive method for modal parameters estimation is formulated and verified. Formulated algorithms are implemented in the FPGA electronic chip. As a result, the modal parameters and confidence bounds for the modal parameters are obtained in real time. The algorithms and their implementations are tested on laboratory test rig data and applied to – flight modal analysis of an airframe structure.

scholarly journals The development of modern automated image processing and transfer systems for agriculture unmanned aerial vehicles

2020 ◽  
Vol 161 ◽  
pp. 01087 ◽  
Author(s):  
Marina Vasileva ◽  
Ilyas Ismagilov ◽  
Alexander Gerasimov
Keyword(s):  
Image Processing ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Crop Yields ◽  
Walsh Functions ◽  
Aerial Vehicles ◽  
Transmission Of Information ◽  
High Resolution Images ◽  
Automated Image Processing

The paper contains results of analytic research of unmanned aerial vehicles using in agriculture. The main problems arising in the creation and subsequent large volumes of high-resolution images real time transfer in unmanned aerial vehicles are highlighted. The Automated image processing and transfer system using new methods of information compression on unmanned aerial vehicles board is proposed. The paper considers the issues of consider the problems of constructing new orderings of Walsh functions and constructing fast compression algorithms in synthesized systems of discrete Walsh functions. For processing and subsequent transmission of information from UAVs recommended to use the fast DWT procedure, it allows for a hardware implementation capable of the real-time conversion performing due to its simplicity. The introduction of the proposed solutions for UAVs in agriculture allows to increase accurasy of electronic cartographic material, to keep electronic records of agricultural operations, to carry out operational monitoring of the crops state and to respond quickly for violations and deviations, to predict crop yields and plan their activities for short-term and long-term prospects.

scholarly journals Automated high-throughput analysis of B cell spreading on immobilized antibodies with whole slide imaging

2015 ◽  
Vol 1 (1) ◽  
pp. 224-227 ◽  
Author(s):  
Veit Wiesmann ◽  
Dorothea Reimer ◽  
Daniela Franz ◽  
Hanna Hüttmayer ◽  
Dirk Mielenz ◽  
...  
Keyword(s):  
Image Processing ◽  
B Cells ◽  
Quality Analysis ◽  
Imaging Data ◽  
Whole Slide Imaging ◽  
High Quality ◽  
High Throughput Analysis ◽  
Fluorescent Cell ◽  
Immobilized Antibodies ◽  
Automated Image Processing

AbstractAutomated image processing methods enable objective, reproducible and high quality analysis of fluorescent cell images in a reasonable amount of time. Therefore, we propose the application of image processing pipelines based on established segmentation algorithms which can handle massive amounts of whole slide imaging data of multiple fluorescent labeled cells. After automated parameter adaption the segmentation pipelines provide high quality cell delineations revealing significant differences in the spreading of B cells: LPS-activated B cells spread significantly less on anti CD19 mAb than on anti BCR mAb and both processes could be inhibited by the F-actin destabilizing drug Cytochalasin D. Moreover, anti CD19 mAb induce a more symmetrical spreading than anti BCR mAb as reflected by the higher cell circularity.

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

scholarly journals SOFTWARE AND HARDWARE AUTOMATED SYSTEM OF CASTS DEFECTS NON-DESTRUCTIVE MONITORING

2019 ◽  
Vol 62 (2) ◽  
pp. 134-140 ◽  
Author(s):  
S. V. Knyazev ◽  
D. V. Skopich ◽  
E. A. Fat’yanova ◽  
A. A. Usol’tsev ◽  
A. I. Kutsenko
Keyword(s):  
Control System ◽  
Product Quality ◽  
Real Time ◽  
Production Control ◽  
Distributed Database ◽  
Data Block ◽  
Automated System ◽  
Quality Analysis ◽  
Technological Processes

Introduction of the “Automated system for operational control of casts production (OCCP AS)” makes the basis of an integrated automated production control system (APCS). It performs three main tasks: control and recording (production, products, materials, etc.), improving quality of casts and operational management of technological processes. Solution of these tasks was accomplished through automating data collection in real time for all production operations, recording material flows, creating operational communication channels, as well as centralized collection, processing and representation of data by the process information server. The next step in building an effective automated control system is to stabilize product quality in changing external conditions, for example, quality of materials, and to optimize production (technology change in order to reduce costs for constant or higher product quality). The second stage is based on mathematical processing and analysis of data coming from OCCP AS, it allows to determine optimal ranges of parameters of technological processes  – “Automated system for optimization and analysis of production progress (OAPP AS)”. OAPP AS consists of two subsystems: quality analysis and technology management. The first solves the problem of data analysis and modeling, the second – calculation of real-time optimal process parameters and real time prediction. The stages tasks compete for access to different hardware resources. The most critical parameter for OCCP AS is performance of server disk arrays, for OAPP AS it is processor performance. In either case, system scaling is effectively solved by parallelizing operations across different servers, forming a cluster, and across different processors (cores) on the same server. To process defect images and to obtain cause-and-effect characteristics, you can use OpenCV software package, which is an open source computer vision library. In course of processing, Sobel operator, Gauss filter and binarization were used. They are based on processing pixels using matrices. Operations on pixels are independent and can be performed in parallel. The task of clustering is reduced to definition of an expert method or using various mathematical algorithms for defects belonging to a specific cluster (data block) through a set of values of dependent factors. Thus, data blocks are formed by the criterion of the defect cause. Calculation of a data block to which a product defect belongs can be very resource-intensive operation. To increase efficiency of image recognition systems and parallelization ofsearch operations, it makes sense to place data clusters on different servers. As a result, there is a need for a distributed database. This is a special class of DBMS, which requires appropriate software. Generation of OAPPAS based on a multi-node cluster with ApacheCassandra DBMS installed and using Nvidia video cards supporting CUDA technology on each node will be the cheapest and most effective solution. Video card is selected based on required number of graphics processors on the node.

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