Automatic pavement crack detection is essential for evaluating maintenance requirements and ensuring driving safety. Crack detection plays a primary role in realising the automatic evaluation of ...pavement condition. Most existing researches on pavement crack detection rely on laborious work, which is a time- and cost-intensive process. Although there has been considerable research on pavement crack detection, it remains a challenging task owing to diverse complex pavement conditions. Recently, deep learning-based algorithms have achieved significant success in computer vision tasks. However, the techniques still have limitations for automatic pavement distress detection. To overcome the current limitations, this study proposes a method for detecting signs of pavement distress based on faster region based convolutional neural network (Faster R-CNN). The study focuses on the detection of longitudinal cracks, transverse cracks, alligator cracks, and partial patching in pavement images. A framework for applying the Faster R-CNN technique to a full-size pavement image is also proposed, which allows the sliding window size to be reduced, thus enabling the detection of larger images. The performance of the proposed method was validated against a dataset containing actual pavement images. The experimental test results show that the proposed method could successfully detect cracks and partial patching with accuracy.
Online Diagnosis of Induction Motors Using MCSA Jung, Jee-Hoon; Lee, Jong-Jae; Kwon, Bong-Hwan
IEEE transactions on industrial electronics (1982),
12/2006, Letnik:
53, Številka:
6
Journal Article
Recenzirano
In this paper, an online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed. MCSA is a method for motor ...diagnosis with stator-current signals. The proposed system diagnoses induction motors having four types of faults such as breakage of rotor bars and end rings, short-circuit of stator windings, bearing cracks, and air-gap eccentricity. Although MCSA is one of the most powerful online methods for diagnosing motor faults, it has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced signal-and-data-processing algorithms are proposed. They are composed of an optimal-slip-estimation algorithm, a proper-sample-selection algorithm, and a frequency auto search algorithm for achieving MCSA efficiently. The proposed system is able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor. Experimental results obtained on 3.7-kW and 30-kW three-phase squirrel-cage induction motors and voltage-source inverters with a vector-control technique are discussed
The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition ...index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.
Hall-effect sensors are used to detect metal surface defects both experimentally and numerically. The gap between the specimen and the sensor, called the liftoff, is assumed to remain constant, while ...a slight misplacement of a sample may lead to incorrect measurements by the Hall-effect sensor. This paper proposes a numerical simulation method to mitigate the liftoff issue. Owing to the complexity of conducting precise finite-element analysis, rather than obtaining the induced current in the Hall sensor, only the magnetic flux leakage is obtained. Thus, to achieve a better approximation, a numerical method capable of obtaining the induced current density in the circumferential direction in terms of the inspection direction is also proposed. Signals of the conventional and proposed approximate numerical methods affected by the sensor liftoff variation were obtained and compared. For small liftoffs, both conventional and proposed numerical methods could identify notch defects, while as the liftoff increased, no defect could be identified using the conventional numerical method. Furthermore, experiments were performed using a variety of liftoff configurations. Based on the results, considering the threshold of the conventional numerical method, defects were detected for greater liftoffs, but misdetection did not occur.
Ground-penetrating radar is a typical sensor system for analyzing underground facilities such as pipelines and rebars. The technique also can be used to detect an underground cavity, which is a ...potential sign of urban sinkholes. Multichannel ground-penetrating radar devices are widely used to detect underground cavities thanks to the capacity of informative three-dimensional data. Nevertheless, the three-dimensional ground-penetrating radar data interpretation is unclear and complicated when recognizing underground cavities because similar ground-penetrating radar data reflected from different underground objects are often mixed with the cavities. As it is prevalently known that the deep learning algorithm-based techniques are powerful at image classification, deep learning-based techniques for underground object detection techniques using two-dimensional GPR (ground-penetrating radar) radargrams have been researched upon in recent years. However, spatial information of underground objects can be characterized better in three-dimensional ground-penetrating radar voxel data than in two-dimensional ground-penetrating radar images. Therefore, in this study, a novel underground object classification technique is proposed by applying deep three-dimensional convolutional neural network on three-dimensional ground-penetrating radar data. First, a deep convolutional neural network architecture was developed using three-dimensional convolutional networks for recognizing spatial underground objects such as, pipe, cavity, manhole, and subsoil. The framework of applying the three-dimensional convolutional neural network into three-dimensional ground-penetrating radar data was then proposed and experimentally validated using real three-dimensional ground-penetrating radar data. In order to do that, three-dimensional ground-penetrating radar block data were used to train the developed three-dimensional convolutional neural network and to classify unclassified three-dimensional ground-penetrating radar data collected from urban roads in Seoul, South Korea. The validation results revealed that four underground objects (pipe, cavity, manhole, and subsoil) are successfully classified, and the average classification accuracy was 97%. In addition, a false alarm was rarely indicated.
Synthesis of metal nanostructures arrays with large amounts of small nano-gaps on a homogenous macroscale is of significant interest and importance in chemistry, biotechnology, physics, and ...nanotechnology because of their enhanced properties. However, the fabrication of uncovered nano-gaps with high-density and uniformity is rather difficult due to the complex and multiple synthetic steps. In this research, a facile and low-cost approach is demonstrated for the synthesis of high-density small nano-gaps (about 3.4 nm) between silver nanostructure array patterns (SNAPs) over a large area. Uniform nano-hole patterns were periodically generated over an entire substrate using nano-imprint lithography. Electrochemical reaction at the high over-potential produced multiple silver nanocrystals inside the nano-hole patterns, generating a high-density of small and uncovered nano-gaps. Finally, we fully demonstrate their application in the rapid detection of rhodamine 6G (R6G) molecules by surface-enhanced Raman scattering (SERS) spectroscopy with a very low detection limit (1 fM) as well as excellent signal uniformity (RSD < 8.0% ± 2.5%), indicating an extraordinary capability for single-molecule detection.
Summary
Background
Tegoprazan is a novel potassium‐competitive acid blocker for the treatment of acid‐related disorders.
Aims
To assess whether tegoprazan is non‐inferior to lansoprazole in terms of ...efficacy and safety in patients with gastric ulcers.
Methods
In this phase 3, double‐blind, active control, multicentre study, 306 gastric ulcer patients were randomised to one of three treatment groups: tegoprazan 50 mg, tegoprazan 100 mg and lansoprazole 30 mg once daily for 4 or 8 weeks. The primary endpoint was the cumulative proportion of patients with healed ulcers confirmed by endoscopy up to 8 weeks from treatment initiation. Symptoms and safety were assessed.
Results
In the full analysis set, the cumulative healing rates at week 8 were 94.8% (91/96) for the tegoprazan 50 mg, 95.0% (94/99) for the tegoprazan 100 mg and 95.7% (89/93) for the lansoprazole 30 mg groups. At week 4, the respective healing rates were 90.6% (87/96), 91.9% (91/99), and 89.2% (83/93). In per protocol analysis, 4‐week healing rates were 95.4% (84/88), 94.6% (88/93) and 92.9% (79/85) for tegoprazan 50 mg, tegoprazan 100 mg and lansoprazole 30 mg, respectively. Both doses of tegoprazan were non‐inferior to lansoprazole in ulcer healing at 4 and 8 weeks. The incidence of drug‐related treatment‐emergent adverse events did not differ among groups. The increase in serum gastrin concentration was not higher in tegoprazan‐treated patients than in lansoprazole‐treated patients.
Conclusions
Tegoprazan 50 or 100 mg were not inferior to lansoprazole 30 mg once daily in the treatment of gastric ulcers.
A bridgeless boost rectifier with low conduction losses and reduced diode reverse-recovery problems is proposed for power-factor correction. The proposed boost rectifier can reduce the conduction ...losses and alleviate the diode reverse-recovery problems by using a coupled inductor and two additional diodes. Zero-current turn-off of the output diodes is achieved, and the reverse-recovery currents of the additional diodes are slowed down to reduce the diode reverse-recovery losses. All inductive components are realized on a single magnetic core by utilizing the leakage inductance of the coupled inductor. Furthermore, for the use of this topology in the practical design, the linear peak current mode control is employed for the proposed boost rectifier. A detailed analysis and a control strategy are presented. Experimental results for a 300-W prototype are also discussed to show the performance of the proposed boost rectifier
H2 receptor antagonists (H2RA) have been used to treat gastritis by inhibiting gastric acid. Proton pump inhibitors (PPIs) are more potent acid suppressants than H2RA. However, the efficacy and ...safety of low-dose PPI for treating gastritis remain unclear. The aim was to investigate the efficacy and safety of low-dose PPI for treating gastritis.
A double-blind, noninferiority, multicenter, phase 3 clinical trial randomly assigned 476 patients with endoscopic erosive gastritis to a group using esomeprazole 10 mg (DW1903) daily and a group using famotidine 20 mg (DW1903R1) daily for 2 weeks. The full-analysis set included 319 patients (DW1903, n=159; DW1903R1, n=160) and the per-protocol set included 298 patients (DW1903, n=147; DW1903R1, n=151). The primary endpoint (erosion improvement rate) and secondary endpoint (erosion and edema cure rates, improvement rates of hemorrhage, erythema, and symptoms) were assessed after the treatment. Adverse events were compared.
According to the full-analysis set, the erosion improvement rates in the DW1903 and DW1903R1 groups were 59.8% and 58.8%, respectively. According to the per-protocol analysis, the erosion improvement rates in the DW1903 and DW1903R1 groups were 61.9% and 59.6%, respectively. Secondary endpoints were not significantly different between two groups except that the hemorrhagic improvement rate was higher in DW1903 with statistical tendency. The number of adverse events were not statistically different.
DW1903 of a low-dose PPI was not inferior to DW1903R1 of H2RA. Thus, lowdose PPI can be a novel option for treating gastritis (ClinicalTrials.gov Identifier: NCT05163756).
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