Defects recognition on X-ray images for weld inspection using SVM

Xiao-guang, Zhang; Juan, Xu; Ge, Guangying

doi:10.1109/icmlc.2004.1380463

Cited by 17 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of computer vision technology, many image-based wheel flat detection systems have been designed [92,93]. In these systems, high-speed cameras were used to acquire a photo of the wheel tread when the train passes by.…”

Section: Image-based Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

View full text Add to dashboard Cite

Wheel flats are amongst the most common local surface defect in railway wheels, which can result in repetitive high wheel–rail contact forces and thus lead to rapid deterioration and possible failure of wheels and rails if not detected at an early stage. The timely and accurate detection of wheel flats is of great significance to ensure the safety of train operation and reduce maintenance costs. In recent years, with the increase of train speed and load capacity, wheel flat detection is facing greater challenges. This paper focuses on the review of wheel flat detection techniques and flat signal processing methods based on wayside deployment in recent years. Commonly used wheel flat detection methods, including sound-based methods, image-based methods, and stress-based methods are introduced and summarized. The advantages and disadvantages of these methods are discussed and concluded. In addition, the flat signal processing methods corresponding to different wheel flat detection techniques are also summarized and discussed. According to the review, we believe that the development direction of the wheel flat detection system is gradually moving towards device simplification, multi-sensor fusion, high algorithm accuracy, and operational intelligence. With continuous development of machine learning algorithms and constant perfection of railway databases, wheel flat detection based on machine learning algorithms will be the development trend in the future.

show abstract

Section: Image-based Methodsmentioning

confidence: 99%

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Feng

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Machine learning method, which is used in many scientific studies in different fields [10,28,29,35] is the basis of many studies in the field of health [36][37][38]. In machine learning method, classification and prediction [36][37][38][39][40] operations can be performed on images such as X-ray films, ultrasound records, MR images, and the same operations can be performed on numerical data [41][42][43][44].…”

Section: Machine Learning Processes and Findings For A1cmentioning

confidence: 99%

Nursing Strategies for Diabetic Patient Management: Predicting Parameter Values Post-Exenatide Treatment with Machine Learning Algorithm

Ersoy,

Gürfidan

2024

Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi

View full text Add to dashboard Cite

The global escalation of DM parallels the rise in obesity rates, with Turkey experiencing a prevalence of 13.7% for diabetes and 32% for obesity among adults. Managing diabetic patients necessitates a comprehensive approach due to the intertwined nature of diabetes and obesity, along with the heightened risk of additional chronic illnesses. Diabet nurses play a pivotal role in diabetic care, encompassing regular assessments, blood glucose monitoring, medication management, patient education. Incretin-mimetic glucagon-like peptide-1 receptor-agonists (GLP-1A) have demonstrated superiority in diabetes, weight control, positioning them as second-line treatments. Weight management remains fundamental in diabetes care, with Diabet nurses providing vital support through dietary guidance, physical activity promotion, and weight loss assistance for diabetic patients. Predicting patient responses to GLP-1A therapy is crucial for optimizing treatment outcomes, streamlining decisions, averting potential complications. Artificial intelligence (AI) and machine learning (ML) offer promising avenues for enhancing healthcare delivery. Our study aimed to forecast fasting blood sugar levels, HbA1C values, and weight loss outcomes in diabetic patients using exenatide, utilizing the random forest algorithm. Analyzing real patient data from the Western-Mediterranean, this study achieved substantial success rates of %99.9, %99.9 and %97.3 in predicting weight loss, fasting blood sugar levels, and HbA1C values, respectively. Our findings underscore the potential of AI-driven approaches in nursing, particularly in prognostic modeling for diabetic patient management. By leveraging ML, nurses can anticipate treatment responses, streamline decision-making, and elevate patient care quality. As AI applications evolve, integrating these technologies into nursing roles promises to advance patient-centered care and optimize health outcomes.

show abstract

“…The study consists of choosing the measurable and easily controllable parameters of the submerged electric arc welding regime, after welding the samples subjected to X-ray control, the images obtained, and the measurements performed will be analysed and interpreted to find a correlation between the various welding defects that will appear and the variable parameters. [2][3][4][5]…”

Section: Aim and Research Tasksmentioning

confidence: 99%

X-ray analysis of welding parameters influence on pressure vessel steel P355 N

Cioroagă,

Cioată

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

The paper presents a study on the influence of welding parameters that is done by radiographic interpretation of the X-ray analysis results collected from welded joins that had been realized by varying the welding current intensity, tension and welding speed. The base material of the experiment is a pressure vessel steel P355 N used for constructing railway tanks for transporting liquids by train. The welding method used is flux-cored arc welding, done by a semi-automatic welding machine using welding wire as joining material. The research aims to find connections between different material welding defects and the welding parameters involved in the process of flux-cored arc welding.

show abstract

Defects recognition on X-ray images for weld inspection using SVM

Cited by 17 publications

References 2 publications

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Recent Advances in Wayside Railway Wheel Flat Detection Techniques: A Review

Nursing Strategies for Diabetic Patient Management: Predicting Parameter Values Post-Exenatide Treatment with Machine Learning Algorithm

X-ray analysis of welding parameters influence on pressure vessel steel P355 N

Contact Info

Product

Resources

About