Title:
CNN-based Labelled Crack Detection for Image Annotation
 
 
Short title:

Machine Learning, Convolutional Neural Networks (CNNs),Image Annotation, Crack Detection
 
Abstract
Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.
 
Field
Mechanical Engineering, Material Engineering, Industrial Engineering, Computer Engineering, Civil Engineering, Aerospace Engineering
Journal
1. Optics and Laser Technology (8.3 CiteScore, 5.0 Impact Factor)
2. Optics and Lasers in Engineering (9.3 CiteScore, 4.6 Impact Factor)
3. The International Journal of Advanced Manufacturing Technology (3.4 CiteScore, 3.226 Impact Factor)
 
با عرض سلام نفرات ١ تا ٤ اين مقاله جهت ارسال به ژورنال خالي مي باشد. دوستاني كه نياز دارند به ايدي بنده پيام بدن.

@Raminmousa

@paper4money

☕️ Latte: Latent Diffusion Transformer for Video Generation


▪Github
▪Project
▪Paper

@Machine_learn

باعرض سلام نفرات ۱ تا ۳ از این مقاله باقی مونده
@paper4money

uilding Skills in Object-Oriented Design,
Step-by-Step Construction of A Complete Application
This is release 4.2003, published Mar 04, 2020.
Link:https://slott56.github.io/building-skills-oo-design-book/build/html/

@Machine_learn

⚡️ DBRX, a groundbreaking open-source Large Language Model (LLM) with a staggering 132 billion parameters.


▪Github: https://github.com/databricks/dbrx
▪HF: https://huggingface.co/databricks/dbrx-base
▪Demo: https://huggingface.co/spaces/databricks/dbrx-instruct
▪Docs: https://docs.databricks.com/en/machine-learning/foundation-models/index.html

@Machine_learn

⚡️ DesignEdit: Multi-Layered Latent Decomposition and Fusion for Unified & Accurate Image Editing

Microsoft представляет DesignEd it!

▪Github: https://github.com/design-edit/DesignEdit.git
▪Paper: https://arxiv.org/abs/2403.14487
▪Project: https://design-edit.github.io/

@Machine_learn

⚡️Map-relative Pose Regression🔥(#CVPR2024 highlight)

▪Paper: https://arxiv.org/abs/2404.09884
▪Page: https://nianticlabs.github.io/marepo

@Machine_learn

A quick introduction to Docker.

📓 book

@Machine_learn

نفرات ۱ تا ۴ مقاله ی زیر خالی می باشد از دوستان اگر کسی خواست در خدمتیم

Title

Solar Energy Production Forecasting: A Comparative Study of LSTM, Bi-LSTM, and XGBoost Models with Activation Function Analysis



Abstract
This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Through the utilization of machine learning algorithms, such as XGBoost regression and neural networks, our predictive model endeavors to discern intricate patterns and correlations within these datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. By utilizing Kaggle's rich dataset and employing advanced machine learning techniques, this study aims to contribute valuable insights that can inform sustainable energy policies and practices, ultimately promoting a more efficient and reliable renewable energy infrastructure.

Related Fields
Business, Marketing, Industrial Engineering, Computer Engineering.

Candidate Journals
1. Sustainability (5.8 CiteScore, 3.9 Impact Factor)
2. Archives of Computational Methods in Engineering (14.1 CiteScore, 9.7 Impact Factor)
3. Journal of Building Engineering (8.3 CiteScore, 6.4 Impact Factor)

@Raminmousa
@paper4money
@Machine_learn

🧬 AlphaFold 3 predicts the structure and interactions of all of life’s molecules


▪Blog: https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/
▪Nature: https://www.nature.com/articles/s41586-024-07487-w
▪Two Minute Papers: https://www.youtube.com/watch?v=Mz7Qp73lj9o

@Machine_learn