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Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection.
Sensors (Basel)
January 2025
Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level.
View Article and Find Full Text PDFDetermination of Vibration Properties and Reliable Frequency Estimation for Synchronous Vibrations Through Improved Blade Tip Timing Techniques Without a Once-per-Revolution Sensor.
Sensors (Basel)
January 2025
Institute of Power Plant Technology, Steam and Gas Turbines, RWTH Aachen University, 52062 Aachen, Germany.
Synchronous vibrations, which are caused by periodic excitations, can have a severe impact on the service life of impellers. Blade Tip Timing (BTT) is a promising technique for monitoring synchronous vibrations due to its non-intrusive nature and ability to monitor all blades at once. BTT generally employs a Once-per-Revolution (OPR) sensor that is mounted on the shaft for blade identification and deflection calculation.
View Article and Find Full Text PDFSix-Dimensional Pose Estimation of Molecular Sieve Drying Package Based on Red Green Blue-Depth Camera.
Sensors (Basel)
January 2025
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
This paper aims to address the challenge of precise robotic grasping of molecular sieve drying bags during automated packaging by proposing a six-dimensional (6D) pose estimation method based on an red green blue-depth (RGB-D) camera. The method consists of three components: point cloud pre-segmentation, target extraction, and pose estimation. A minimum bounding box-based pre-segmentation method was designed to minimize the impact of packaging wrinkles and skirt curling.
View Article and Find Full Text PDFReusable Biosensor for Easy RNA Detection from Unfiltered Saliva.
Sensors (Basel)
January 2025
Department of Clinical and Molecular Biochemistry, Pomeranian Medical University in Szczecin, 72 Powstańców Wlkp. Al., 70-111 Szczecin, Poland.
Biosensors are transforming point-of-care diagnostics by simplifying the detection process and enabling rapid, accurate testing. This study introduces a novel, reusable biosensor designed for direct viral RNA detection from unfiltered saliva, targeting SARS-CoV-2. Unlike conventional methods requiring filtration, our biosensor leverages a unique electrode design that prevents interference from saliva debris, allowing precise measurements.
View Article and Find Full Text PDFUltrasound Evaluation of Gastric Emptying and Comparison with Patient-Reported Risk Factors in Elective Surgical Patients.
J Clin Med
January 2025
Department of Anesthesiology and Reanimation, Faculty of Medicine, Recep Tayyip Erdogan University, 53100 Rize, Turkey.
: Despite standard preoperative fasting guidelines, residual gastric content can persist in some patients, increasing the risk of aspiration pneumonitis. Multiple patient-specific factors may predict gastric content retention, but their predictive accuracy is limited. We hypothesized that ultrasound would more reliably identify residual gastric content compared to a comprehensive questionnaire and aimed to determine the most practical approach for risk assessment in elective surgical patients.
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