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Automated CNN-based Semantic Segmentation for Thermal Image of Solar Photovoltaic (PV) Panel

Automated CNN-based Semantic Segmentation for Thermal Image ...

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IEEE international conference on Control System, computing and Engineering (ICCSCE)

作者： Nurul Huda Binti Ishak Iza Sazanita Binti Isa Muhammad Khusairi Bin Osman Kamarulazhar Bin Daud Mohd Zulhamdy Bin Ab Hamid Mohd Shawal Bin Jadin Centre for Electrical Engineering Studies Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Pulau Pinang Malaysia Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Driven by the growing worldwide need for sustainable energy sources, the utilization of solar photovoltaic (PV) panels has significantly increased in many applications. However, monitoring solar PV panels using thermal imaging significantly impacts panel efficiency and performance. To address this, thermal imaging offers a promising technique for monitoring and diagnosing these issues using computer vision and intelligence methods. This study proposes an automated semantic segmentation approach based on CNN to segment solar PV panels’ thermal images. The proposed method used U-Net and ResNet as CNN-based models for segmenting two different regions, solar panels and the background of thermal images. This segmentation approach leverages the power of deep learning to analyze thermal images, effectively highlighting precise results that facilitate prompt maintenance and optimization of PV panel performance. The model’s performance and pixel are evaluated using Intersection over Union (IoU) metrics and accuracy. The result, which showcases the potential of the proposed method, reveals that the proposed method using ResNet 50 has achieved superior performance compared to ResNet 18 and augmented UNet, with an IoU score of 0.9383 and an accuracy of 97%. In conclusion, semantic segmentation based on the CNN model significantly impacts thermal image pattern recognition in solar PV pattern recognition.

关键词： Deep learning Renewable energy sources Computer vision Semantic segmentation Computational modeling Imaging Thermal analysis pattern recognition Maintenance Monitoring

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Medicinal Herbs Identification

Medicinal Herbs Identification

引用

international conference on Advanced computing and Communication Systems (ICACCS)

作者： M. Preethi S. Jansi Rani K. S. Pradhiksha J. Ram Kumar T. Vishal Sri Ramakrishna Engineering College Coimbatore India

Many rural communities have a strong belief in plant diversity. They collect useful plants and herbs and use them with indigenous knowledge and customs. One such oldest system that results in the use of medicinal herbs is Ayurveda. Approximately 10,000 plants are used medicinally in India, but not all plants are included in the official Ayurvedic Pharmacopoeia. Before becoming part of Ayurvedic medicine, all plants need to be thoroughly studied. For this reason, identifying herbs is the most important step. Many of these identifications are fully supported by human perception, leaving room for error and misjudgement. Therefore, it is necessary to develop an efficient system using computer vision, pattern recognition, and image processing algorithms alongside the availability of various combinations of feature detection methods with different classifiers that are often utilized in building an automatic identification system for herbal leaves using leaf images and reveal its associated information to realize knowledge.

关键词： Training Image segmentation Machine learning algorithms Image color analysis Neural networks Machine learning pattern recognition

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Hand Sign Language Translator Using Flex Sensors and Gyro Sensors in pattern recognition Method

Hand Sign Language Translator Using Flex Sensors and Gyro Se...

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Biomedical Engineering international conference (BMEiCON)

作者： Nanticha Supmool Pawarit Kositanon Supakorn Chaichalotornkul Udomporn Manupibul Department of Biomedical Engineering Faculty of Engineering Mahidol University Nakhon Phathom Thailand Faculty of Medicine Siriraj Hospital Sirindhorn School of Prosthetics and Orthotics Mahidol University Bangkok Thailand

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

This project presents an innovative language translation glove designed specifically for individuals who are deaf or difficult of hearing and rely on sign language for communication. This glove represents a significant advancement in assistive technology because it employs pattern recognition to facilitate real-time, two-way communication. It is medical equipment that has the unique ability to translate both sign language gestures into audible speech and spoken language into text displayed on a screen. The inside wiring of the glove prioritizes efficiency, durability, and user comfort, ensuring practical application in everyday use. This paper presents the development of the software and hardware, with a focus on achieving high accuracy rates ranging from 93.3 to 100 percents for initial word predictions. The successful realization of this glove and system has the potential to impact assistive technologies for individuals with disabilities.

关键词： Sign language Cloud computing Translation Flexible printed circuits Databases Hardware Sensors Gyroscopes Servers Wearable sensors

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Joint IAPR international Workshop on Structural, Syntactic, and Statistical pattern recognition, S+SSPR 2022

Joint IAPR International Workshop on Structural, Syntactic, ...

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Joint IAPR international Workshop on Structural, Syntactic, and Statistical pattern recognition, S+SSPR 2022

ISBN: (纸本)9783031230271

The proceedings contain 32 papers. The special focus in this conference is on Structural, Syntactic, and Statistical pattern recognition. The topics include: Learning Distances Between Graph Nodes and Edges;Self-supervised Out-of-Distribution Detection with Dynamic Latent Scale GAN;a Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures;discovering Respects for Visual Similarity;graph Regression Based on Graph Autoencoders;distributed Decision Trees;a Capsule Network for Hierarchical Multi-label Image Classification;Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification;one-Against-All Halfplane Dichotomies;maximal Independent Vertex Set Applied to Graph Pooling;fast Distance Transforms in Graphs and in Gmaps;retargeted Regression Methods for Multi-label Learning;transformer with Spatio-Temporal Representation for Video Anomaly Detection;efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings;graph Similarity Using Tree Edit Distance;data Augmentation on Graphs for Table Type Classification;improved Training for 3D Point Cloud Classification;on the Importance of Temporal Features in Domain Adaptation Methods for Action recognition;zero-Error Digitisation and Contextualisation of Piping and Instrumentation Diagrams Using Node Classification and Sub-graph Search;Refining AttnGAN Using Attention on Attention Network;annotation-Free Keyword Spotting in Historical Vietnamese Manuscripts Using Graph Matching;an Autoencoding Method for Detecting Counterfeit Coins;tarragona Graph Database for Machine Learning Based on Graphs;Human Description in the Wild: Description of the Scene with Ensembles of AI Models;interactive Generalized Dirichlet Mixture Allocation Model;classifying Me softly: A Novel Graph Neural Network Based on Features soft-Alignment;review of Handwriting Analysis for Predicting Personality Traits;graph Reduction Neural Networks for Structural pattern recognition;Sentiment Analysis from User Reviews U

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OAM pattern recognition Method Based on ResNet-18 Migration Learning

OAM Pattern Recognition Method Based on ResNet-18 Migration ...

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software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Zhong Li Xiaoxia Yang Zhenning Gao Zehui Cheng North University of China school of software Taiyuan China

Aiming at the application of Orbital Angular Momentum (OAM) in the field of optical communication, this paper proposes an OAM pattern recognition method based on ResNet-18 transfer learning. The OAM mode is a light wave with a spiral phase structure, which has the characteristics of high degree of freedom and large capacity, and has broad application prospects in the field of optical communication. However, detection and classification of OAM modes is difficult due to their complex wavefront structures. To address this issue, we use a pretrained ResNet-18 model to classify OAM patterns. This method uses the pre-trained ResNet-18 model for feature extraction and fine-tuning, which can effectively improve the classification performance of the model. Through experimental verification, the method has high accuracy and stability in the classification task of OAM mode. The research in this paper provides strong support for the application of OAM technology, and has certain reference value for image classification problems in other fields.

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HCMA'22: 3rd international Workshop on Human-centric Multimedia Analysis 22

HCMA'22: 3rd International Workshop on Human-centric Multime...

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30th ACM international conference on Multimedia (MM)

作者： Zhang, Dingwen Fang, Chaowei Liu, Wu Liu, Xinchen Song, Jingkuan Zhu, Hongyuan Huang, Wenbing Smith, John Northwestern Polytech Univ Xian Peoples R China Xidian Univ Xian Peoples R China JD AI Res Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China ASTAR Singapore Singapore Tsinghua Univ Beijing Peoples R China IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9781450392037

The Third international Workshop on Human-Centric Multimedia Analysis concentrates on the tasks of human-centric analysis with multimedia and multimodal information. It involves multiple tasks such as face detection and recognition, human body pattern analysis, person re-identification, human action detection, etc. Today, multiple multimedia sensing technologies and large-scale computing infrastructures are emerging at a rapid velocity a wide variety of big multi-modality data for human-centric analysis, which provides rich knowledge to help tackle these challenges. Researchers have strived to push the limits of human-centric multimedia analysis in a wide variety of applications, such as intelligent surveillance, retailing, fashion design, and services. Therefore, this workshop aims to provide a platform to bridge the gap between the communities of human analysis and multimedia.

关键词： Human Analysis Multi-media and Multi-modal Analysis

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Deep Neural Network Accelerator: Exploring the Potential of 3D Chiplet Integration

Deep Neural Network Accelerator: Exploring the Potential of ...

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pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Shu Wang Tianxing Wang Siying Bi Feng Jiao Xinrong Long Zhong Ma Third Technical Department Xi'an Microelectronics Technology Institute (of CASC) Xi'an China Sencond Technical Department Xi'an Microelectronics Technology Institute (of CASC) Xi'an China First Technical Department Xi'an Microelectronics Technology Institute (of CASC) Xi'an China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

The development of deep neuro networks brings a sharp rise in demands for expeditious convolutional computing operations, but the “memory wall” originated from the intense data transfer, has greatly hindered the performance of deep neural network accelerators. As the integrated circuits have entered the post-Moore's law era, evolving the neural network accelerator by advance semiconductor fabrication technology is no longer available. This paper presents an in-depth analysis of data flow within a fundamental neural processing unit system, examining the impact of 3D integration and chiplet architecture on overcoming the “memory wall” challenge. A versatile neural processing unit architecture, featuring adjustable processing elements and buffer sizes, is developed to evaluate performance improvements induced by 3D chiplet integration. The results prove the feasibility of 3D chiplet integration in performance advancement of neural processing unit from both memory and processing element perspectives, which provides a complete guidance for potential solutions for reducing memory bottlenecks.

关键词： Fabrication Three-dimensional displays Chiplets Computer architecture Artificial neural networks Data transfer System-on-chip pattern recognition Through-silicon vias Three-dimensional integrated circuits

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Resume Parsing and Skill Extraction using Custom pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. This paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. Through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. This enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. This work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation Machine learning algorithms Resumes Optical character recognition Semantics Robustness Data mining pattern matching Recruitment

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Potentialfinder - Fostering Network Innovation by Connecting Data Owners Using Scaled Business-Relevant pattern recognition and Clustering 55

Potentialfinder - Fostering Network Innovation by Connecting...

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55th Annual Hawaii international conference on System Sciences (HICSS)

作者： Riechert, Mathias Bhandari, Roshan Amle, Abhijeet Abhinav, Abhimanyu Hubig, Nina Clemson Univ Clemson SC 29634 USA

ISBN: (纸本)9780998133157

The advancement in collection, computing and storage technologies has led to an exponential growth of available data in multiple disciplines. However, the human capacity of analyzing this data does not grow at the same rate, leaving a vast amount of potential disparate, invisible and unused. We want to enhance the capability of humans to automatically find relevant patterns in data to leverage potential in this increasing sea of data. We present an innovation network creation framework and Python library that detects exponential growth patterns from publicly available tabular data. It works as a magnifying glass to reveal the most relevant parts of the data and the processes represented by it. The extracted exponential patterns can be useful for topic or disease detection as well as for organisations such as venture capital and consulting firms to improve investment decisions. Additionally, startups and innovation units in corporates can leverage these information to base their business models on insights into sectors, markets or customer segments with exponential growth. To foster the innovation based on the revealed patterns, we connect the respective data owners that uploaded similar patterns. This paper proposes a framework for networked innovation creation including a) an algorithm to automatically detect exponential, b) approaches to scale its application to public tabular data in different sizes and formats, c) a similarity network connecting the found patterns to innovation networks, d) a clustering to group the data owners and enable co- and crowd innovation. We run experiments on large scale data for all steps to provide evidence for cost-efficiency, scalability and feasibility of the contributions.

关键词： Python

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pattern recognition and Prediction in Time Series Data Through Retrieval-Augmented Techniques

Pattern Recognition and Prediction in Time Series Data Throu...

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Electrical Electronics and computing Technologies (ICEECT), international conference on

作者： Nitya Singh Aleena Swetapadma School of Computer Engineering Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Time series forecasting is an essential technique used in various domains, with finance being one of the primary users. There are several techniques for time series forecasting, with machine learning being one of the most common practices. Machine learning models rely on numerical input with time encoded in the input for prediction. However, visual pattern-based time series forecasting is also a popular method practiced by professionals in various fields. Traditional numerical input-based models succeed in recognizing patterns but often fail when the patterns are skewed or stretched compared to those in the training data. The aim of the work is to address this issue using a visual approach. In this work, OpenAI’s CLIP model has been used to perform a visual similarity search among visual time series patterns to achieve forecasting using the top k most similar patterns. The performance of the model shows it can be used effectively for time series analysis.

关键词： Visualization Databases Time series analysis Machine learning Metadata Predictive models Market research Vectors Numerical models Forecasting

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