scholarly journals Product Quality Monitoring in Hydraulic Presses Using a Minimal Sample of Sensor and Actuator Data

2021 ◽  
Vol 21 (2) ◽  
pp. 1-23
Author(s):  
Iris Weiss ◽  
Birgit Vogel-Heuser ◽  
Emanuel Trunzer ◽  
Simon Kruppa
Keyword(s):  
Product Quality ◽  
Production Systems ◽  
True Positive Rate ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Automated Production ◽  
Minimal Sample ◽  
Positive Rate ◽  
Dynamic Time ◽  
Data Driven Modeling

Machine learning and artificial intelligence provide methods and algorithms to take advantage of sensor and actuator data in automated production systems. Product quality monitoring is one of the promising applications available for data-driven modeling, particularly in cases where the quality parameters cannot be measured with reasonable effort. This is the case for defects such as cracks in workpieces of hydraulic metal powder presses. However, the variety of shapes produced at a powder press requires training of individual models based on a minimal sample size of unlabeled data to adapt to changing settings. Therefore, this article proposes an unsupervised product quality monitoring approach based on dynamic time warping and non-linear regression to detect anomalies in unlabeled sensor and actuator data. A preprocessing step that isolates only the relevant intervals of the process is further introduced, facilitating efficient product quality monitoring. The evaluation on an industrial dataset with 37 samples, generated in test runs, shows a true-positive rate for detected product quality defects of 100% while preserving an acceptable accuracy. Moreover, the approach achieves the output within less than 10 seconds, assuring that the result is available before the next workpiece is processed. In this way, efficient product quality management is possible, reducing time- and cost-intensive quality inspections.

Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction

2018 ◽  
Vol 66 (4) ◽  
pp. 344-355 ◽  
Author(s):  
Iris Weiß ◽  
Birgit Vogel-Heuser
Keyword(s):  
Data Mining ◽  
Product Quality ◽  
Statistical Methods ◽  
Analysis Of Variance ◽  
Production System ◽  
Production Systems ◽  
High Potential ◽  
Quality Prediction ◽  
Automated Production ◽  
Effective Use

AbstractData mining in automated production systems provide high potential to increase the Overall Equipment Effectiveness. Nevertheless, data of such machines/plants include specific characteristics regarding the variance and distribution of the dataset. For modelling product quality prediction, these characteristics have to be analysed to interpret the results correctly. Therefore, an approach for the analysis of variance and distribution of datasets is proposed. The evaluation of this approach validates the developed guidelines, which identify the reasons for inconsistent prediction results based on two different datasets of the same production system.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Research on a product quality monitoring method based on multi scale PP-YOLO

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qipeng Chen ◽  
Qingsong Fan ◽  
Huafeng Quan
Keyword(s):  
Product Quality ◽  
Quality Monitoring ◽  
Monitoring Method ◽  
Multi Scale

Ascertaining an efficient eligibility cut-off for extended Medicare items for eating disorders

2021 ◽  
pp. 103985622110286
Author(s):  
Tracey Wade ◽  
Jamie-Lee Pennesi ◽  
Yuan Zhou
Keyword(s):  
Eating Disorders ◽  
Eating Disorder ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Rate Sensitivity ◽  
True Positive Rate ◽  
Eating Disorder Examination Questionnaire ◽  
Eating Disorder Examination ◽  
Positive Rate ◽  
The Relationship

Objective: Currently eligibility for expanded Medicare items for eating disorders (excluding anorexia nervosa) require a score ⩾ 3 on the 22-item Eating Disorder Examination-Questionnaire (EDE-Q). We compared these EDE-Q “cases” with continuous scores on a validated 7-item version of the EDE-Q (EDE-Q7) to identify an EDE-Q7 cut-off commensurate to 3 on the EDE-Q. Methods: We utilised EDE-Q scores of female university students ( N = 337) at risk of developing an eating disorder. We used a receiver operating characteristic (ROC) curve to assess the relationship between the true-positive rate (sensitivity) and the false-positive rate (1-specificity) of cases ⩾ 3. Results: The area under the curve showed outstanding discrimination of 0.94 (95% CI: .92–.97). We examined two specific cut-off points on the EDE-Q7, which included 100% and 87% of true cases, respectively. Conclusion: Given the EDE-Q cut-off for Medicare is used in conjunction with other criteria, we suggest using the more permissive EDE-Q7 cut-off (⩾2.5) to replace use of the EDE-Q cut-off (⩾3) in eligibility assessments.

scholarly journals Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

2021 ◽  
Vol 10 (7) ◽  
pp. 1543
Author(s):  
Morwenn Le Boulc’h ◽  
Julia Gilhodes ◽  
Zara Steinmeyer ◽  
Sébastien Molière ◽  
Carole Mathelin
Keyword(s):  
Systematic Review ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
False Negative ◽  
Significant Proportion ◽  
False Negative Rate ◽  
True Positive Rate ◽  
Axillary Staging ◽  
Positron Emission ◽  
Positive Rate

Background: This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases. Methods: A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43–59%) and 100% (95% CI: 99–100%) for US, 83% (95% CI: 72–91%) and 85% (95% CI: 72–92%) for MRI, and 49% (95% CI: 39–59%) and 94% (95% CI: 91–96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI. Conclusions: In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

scholarly journals Markerless tracking of an entire honey bee colony

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katarzyna Bozek ◽  
Laetitia Hebert ◽  
Yoann Portugal ◽  
Greg J. Stephens
Keyword(s):  
Honey Bee ◽  
True Positive Rate ◽  
Computational Method ◽  
Visual Features ◽  
Brood Cell ◽  
Collective Behaviors ◽  
Markerless Tracking ◽  
Bee Colony ◽  
Positive Rate ◽  
Group Members

AbstractFrom cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.

scholarly journals Classifying the Biological Status of Honeybee Workers Using Gas Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 166
Author(s):  
Jakub T. Wilk ◽  
Beata Bąk ◽  
Piotr Artiemjew ◽  
Jerzy Wilde ◽  
Maciej Siuda
Keyword(s):  
Gas Sensors ◽  
Laboratory Tests ◽  
Test Chamber ◽  
Ambient Air ◽  
True Positive Rate ◽  
True Positive ◽  
Positive Rate ◽  
Parameters Of Accuracy ◽  
Selection Of

Honeybee workers have a specific smell depending on the age of workers and the biological status of the colony. Laboratory tests were carried out at the Department of Apiculture at UWM Olsztyn, using gas sensors installed in two twin prototype multi-sensor detectors. The study aimed to compare the responses of sensors to the odor of old worker bees (3–6 weeks old), young ones (0–1 days old), and those from long-term queenless colonies. From the experimental colonies, 10 samples of 100 workers were taken for each group and placed successively in the research chambers for the duration of the study. Old workers came from outer nest combs, young workers from hatching out brood in an incubator, and laying worker bees from long-term queenless colonies from brood combs (with laying worker bee’s eggs, humped brood, and drones). Each probe was measured for 10 min, and then immediately for another 10 min ambient air was given to regenerate sensors. The results were analyzed using 10 different classifiers. Research has shown that the devices can distinguish between the biological status of bees. The effectiveness of distinguishing between classes, determined by the parameters of accuracy balanced and true positive rate, of 0.763 and 0.742 in the case of the best euclidean.1nn classifier, may be satisfactory in the context of practical beekeeping. Depending on the environment accompanying the tested objects (a type of insert in the test chamber), the introduction of other classifiers as well as baseline correction methods may be considered, while the selection of the appropriate classifier for the task may be of great importance for the effectiveness of the classification.

scholarly journals Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records

2016 ◽  
Vol 24 (2) ◽  
pp. 263-272 ◽  
Author(s):  
Kosuke Imai ◽  
Kabir Khanna
Keyword(s):  
Mean Squared Error ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Voter Registration ◽  
Racial Groups ◽  
Ecological Inference ◽  
Inference Problem ◽  
Individual Level ◽  
Positive Rate ◽  
Election Results

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

scholarly journals Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 572
Author(s):  
Mads Jochumsen ◽  
Taha Al Muhammadee Janjua ◽  
Juan Carlos Arceo ◽  
Jimmy Lauber ◽  
Emilie Simoneau Buessinger ◽  
...  
Keyword(s):  
Open Source ◽  
Neural Plasticity ◽  
Motor Evoked Potentials ◽  
Low Cost ◽  
True Positive Rate ◽  
Positive Rate ◽  
3D Printed ◽  
Major Factors ◽  
And Control ◽  
Bci System

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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