Artificial Intelligence

The project of paper "Massive Coordination of Distributed Energy Resource in VPP: A DRL based Multi-stage Optimization Approach"


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Brushless DC (BLDC) motors depend on accurate rotor position detection via Hall sensors for optimal performance. Faults, such as sensor displacement, can disrupt commutation and lead to efficiency losses. This study utilizes deep learning to detect Hall sensor faults, focusing on a meticulously prepared dataset designed for this purpose. The dataset study consists of phase current measurements under various Hall sensor displacement conditions, categorized as No Delay, 0.0001 Delay, 0.005 Delay, and 0.01 Delay.

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The Universal Networking Language (UNL) UDE Dictionary for Indian Cooking is a pioneering framework aimed at facilitating seamless communication and knowledge sharing across diverse languages and cultures, with a special focus on the rich culinary traditions of India. This dictionary provides a comprehensive and structured representation of essential culinary terms, ingredients, cooking techniques, and descriptors in Hindi, paired with their corresponding UNL equivalents. Each entry includes a UNL term, a definition and examples.

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The Universal Networking Language (UNL) UDE Dictionary for French Cooking is a pioneering framework aimed at facilitating seamless communication and knowledge sharing across diverse languages and cultures, with a special focus on the rich culinary traditions of France. This dictionary provides a comprehensive and structured representation of essential culinary terms, ingredients, cooking techniques, and descriptors in French, paired with their corresponding UNL equivalents. Each entry includes a UNL term, a definition and examples.

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The dataset includes five subclasses of white blood cells: eosinophils, basophils, neutrophils, monocytes, and lymphocytes. The types and positions of white blood cells in the images have been manually and accurately annotated using an annotation tool, and the annotation information files are stored in .txt, .xml, and .csv formats to meet the format requirements of different network model training for annotation files.

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The dataset provides a collection of image-based classification and regression problems for artificial intelligence, under extreme visual attention constraints. Spatial shapes with simple geometries and called geometrons are embedded in an intense visual texture stream, with the aim of investigating the limits of artificial visual attention to capture known or unknown, but ghost or buried shapes.

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The Universal Networking Language (UNL) is a pioneering framework designed to facilitate seamless communication and knowledge sharing across different languages and cultures. This UNL French Dictionary focuses specifically on the rich and diverse world of French cuisine, offering a structured representation of culinary terms, ingredients, cooking techniques, and descriptors in French alongside their universal equivalents.

Purpose and Importance:

The UNL French Dictionary serves several key purposes:

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The Universal Networking Language (UNL) serves as a conceptual framework aimed at facilitating communication across different languages and cultures. In the context of culinary arts, the UNL dictionary provides a structured approach to represent Indian culinary terms, ingredients, cooking methods, and descriptors in a universally understandable manner.

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The dataset has undergone format conversion based on URPC2021_Sonar_images_data, enabling it to be trained by YOLO and RT-DETR models.

 The folder 'images' contains image files

The folder 'labels' contains TXT format annotation files.

The annotation file in the folder annotations is in XML format

Data.yaml is the configuration file for YOLO training

Data_deTR is the configuration file for RT-DETR and US-DETR training

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The SINEW (Sensors in Home for Elderly Wellbeing) dataset consists of 15 high-level biomarker features, derived from raw sensor readings collected by in-home sensors used for predictive modeling research: SINEW Weekly Biomarker.

This dataset was collected for a study focused on the early detection of mild cognitive impairment, providing an opportunity for timely intervention before it progresses to Alzheimer's disease.

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