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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using Computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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Federated Learning Based Privacy-Preserving Renewable Energy Consumption for the Local Community 5

Federated Learning Based Privacy-Preserving Renewable Energy...

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5th International Conference on Power and Energy Technology, ICPET 2023

作者： Wei, Renjie Wang, Yishen Wang, Xiaohui An, Ningyu Liang, Xiao Yang, Yuqiang State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Zhejiang Electric Power Co. Ltd. Zhejiang China

ISBN: (纸本)9798350339673

With the growing demand for sustainable energy, there is an increasing need for efficient management and distribution of renewable energy resources. However, collecting and sharing energy consumption data in a centralized manner poses a significant privacy risk for individuals. To address this issue, Federated Learning can be leveraged to train a predictive model on decentralized data sources while preserving data privacy. In this approach, each participant can train a local model using their own energy consumption data and share the model updates with a central server. The central server then aggregates the updates to generate a global model, which can be used to predict energy consumption patterns for the local community. The proposed approach can enable effective management of renewable energy resources while preserving the privacy of individual energy consumption data. It has the potential to provide a sustainable solution to the growing demand for renewable energy consumption in local communities. © 2023 IEEE.

关键词： energy management system federated learning privacy protection renewable energy generation

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Enhancing Data Consistency in Distribution Networks Leveraging Graph Contrastive Representation Learning

Enhancing Data Consistency in Distribution Networks Leveragi...

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International Conference on Power and Energy Technology (ICPET)

作者： Siyan Liu Danni Shi Zhuang Tang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd. Beijing China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

The rapid integration of distributed renewable energy sources and the diversification of loads have precipitated profound changes in the configuration and operation of power distribution networks in the 21st century, underscoring the necessity for accurate and standardized power grid topology model data. This manuscript introduces a novel graph contrastive learning-based approach for the representation of node features in distribution networks, specifically targeting the alignment of identical nodes across different Common Information Model (CIM) documents. By combining self-supervised learning with graph neural networks, a feature extraction framework that integrates topological structural features and attribute features of the distribution network is proposed in the paper. Besides, the study designs a graph view generation strategy considering distribution network characteristics. The proposed method is validated by an 11-feeder distribution network dataset, and the results show the effectivity of the graph contrastive learning.

关键词： Representation learning Renewable energy sources Network topology Power distribution Distribution networks Contrastive learning Feature extraction Power grids Topology Load modeling

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A CBL Estimation Method for Industrial Customers Based on K-Means and LSTM

A CBL Estimation Method for Industrial Customers Based on K-...

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China International Conference on Electricity Distribution, CICED

作者： Wensi Zhang Shuya Lei Mohan Liu Renjie Wei Yishen Wang State Grid Laboratory of Grid Advanced Computing Applications (China Electric Power Research Institute Co. Ltd.) Beijing China

ISBN: (数字)9798350368345

ISBN: (纸本)9798350368352

In pursuit of attaining the objective of “carbon peaking and carbon neutrality”, the structure of energy in China has undergone a great transformation. With the rapid development of renewable energy generation, the power grid has been confronted with new challenges. The reliability and flexibility of the grid have been affected. Demand response (DR), as an effective management method, has become a hotspot. Customer baseline load (CBL) serves as the standard for calculating economic compensation in DR. Hence, accurate estimation of CBL is a crucial aspect in ensuring the effective implementation of DR strategies. This paper proposes a CBL estimation method for industrial customers based on the K-means and long short-term memory (LSTM). First, the K-means method is used to cluster the daily load curves of various industries to yield the typical patterns of electricity consumption behaviors. Second, the LSTM neural network is established to estimate CBL. It is trained with historical load data of different industries and clustering results. Finally, the effectiveness of the novel method is validated via a real data set.

关键词： Industries Renewable energy sources Accuracy Electricity Neural networks Estimation Steel Long short term memory Standards Textiles

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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 IEEE.

关键词： Optical character recognition

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OPTIMIZATION MODEL OF DISTRIBUTED ENERGY SUPPLY SYSTEM BASED ON MULTI-ENERGY COMPLEMENTARITY 12

OPTIMIZATION MODEL OF DISTRIBUTED ENERGY SUPPLY SYSTEM BASED...

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12th International Conference on Renewable Power Generation, RPG 2023

作者： Li, Jie Chai, Bo Liu, Siyan Wang, Yishen Shi, Xinghua State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Grid Zhejiang Eletric Power Co. Ltd. Hangzhou China

The distributed energy supply system based on multi-energy complementarity is currently a research hotspot in the field of energy engineering, and the key issue faced by this system is operation optimization. The reasonable configuration or not directly affects the economic benefits and operational efficiency of the system. This article focuses on this key issue faced by distributed energy supply systems with multiple complementary energy sources. Taking a cultural tourism park as the research object, considering factors such as economic indicators, energy consumption, and carbon emissions, a multi-objective optimization model for distributed energy supply systems with multiple complementary energy sources is established by gradually decomposing and introducing hourly energy constraints. Experiments have shown that compared to traditional systems, this model can achieve hourly operation optimization and has good economic efficiency and energy-saving and emission-reduction benefits. Therefore, it provides a good reference and reference for the energy supply and comprehensive energy planning of multi-energy complementary distributed energy supply systems. © The Institution of Engineering & Technology 2023.

关键词： Parks

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Design and Implementation of System Communication computing Acceleration Algorithm under Cloud Edge Collaboration

Design and Implementation of System Communication Computing ...

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Electronic Engineering and Information Systems (EEISS), International Conference on

作者： Qinghe Ye Yue Wang Chunpeng Wu Min Zheng Long Lin Fei Zhou State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

ISBN: (数字)9798350351033

ISBN: (纸本)9798350351040

The security software platform based on the support body has increasingly complex application functions and also faces massive data transmission, which puts higher requirements on the performance of the system. At present, most secure computing platforms are based on distributed and redundant communication architectures, and lack appropriate failure management mechanisms to effectively utilize the fault tolerance of redundant communication architectures. In response to the problems faced by existing support managed secure computing platforms, this article conducted a study on the fast management mechanism of redundant communication architecture in support managed secure computing platforms. It discussed the implementation of different acceleration algorithms for system communication computing based on cloud edge collaboration, and experiments showed that the accuracy of system communication LSH (Locality Sensitive Hashing) technology acceleration was higher than other algorithms. The accuracy of cloud computing platforms based on LSH technology was 0.92. At the same time, cloud edge collaboration can improve the resource utilization of system communication.

关键词： Cloud computing Accuracy Collaboration Computer architecture Real-time systems Software Telecommunication computing

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Application of Self-Distillation Framework in Transmission Line Defect Recognition

Application of Self-Distillation Framework in Transmission L...

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IEEE advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： He Yang Cuncun Shi Long Lin State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

Drones have become very common in inspections of transmission lines across the country, but the images and videos captured by drones still require manual processing, which is time-consuming and labor-intensive, not intelligent enough to meet real-time requirements, and the response is relatively passive; currently in the power sector artificial intelligence has been used to process drone footage, but it has not yet reached the level of human experts. In recent years, methods to improve model performance have focused on conceiving new network structures and improving the quantity and quality of sample sets. Both methods are costly. This paper proposes a training framework for the model based on self-distillation. Without changing the model structure or changing the data set, you can learn the knowledge of your own network to improve model performance and use smaller models under the same performance requirements. Based on this framework, we implemented a self-distillation system on ResNet and verified the framework by comparing original training and self-distillation training. Of course, this framework is also applicable to other networks. Experiments show that the self-distillation method can significantly improve the accuracy of recognition.

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Vertex Centric Weighted Edge Index to Accelerate Weighted Graph Traversal

Vertex Centric Weighted Edge Index to Accelerate Weighted Gr...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Jianchao Lin Chunhui Ren Siyan Liu State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

Indexing is an important technique to optimize graph database performance. However, indexing in existing graph databases focuses mainly on property-value based query, and has not drawn much attention on graph traversal. We propose a new indexing technique, which maintains sorted indexing of vertex and weight values of the vertex's adjacent edges. With this proposed vertex centric adjacent weighted edge index, graph traversal algorithms based on weight values like Dijkstra's algorithm and Prim's algorithm have significant performance gains.

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Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) Classification and Prediction of Heart Disease

Enhanced Bayesian Optimized Support Vector Machine (BO-SVM) ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： P. Chairmadurai P. Kavitha S. Kamalakkannan Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Medical data analysis is increasingly used to plan, improve research techniques, and explain diagnoses. The allocation of medical resources is based on the prevalence of specific pathologies. Artificial intelligence (AI) offers robust software solutions to analyze existing data and provide optimal predictions of medical outcomes. The dataset for heart disease prediction consists missing values in important features like cholesterol, ECG results and lifestyle factors. This research focuses to solve the issues in the traditional machine learning algorithm and designing a system model incorporating multiple data processing algorithms to classify heart disease. The classification process allows for the generation of a predictive model from training and testing datasets. These datasets are processed using an algorithm for classification to create a new model that can analyze detailed data, potentially with these same classes, by combining computational techniques with mathematical tools. This study uses optimization strategies to improve this model's performance and maximize prediction outcomes. This study aims to provide a framework to forecast cardiac problems based on significant risk using various classifier techniques, including the Bayesian Optimized SVM, to variables. The UCI repository is the source of the dataset utilized in this investigation. Linear Discriminant Analysis is used for feature extraction to decrease dimensionality and enhance the model's capacity to identify relevant patterns in the data. To generate the results as a precision, Recall, f1 score and accuracy.

关键词： Support vector machines Heart Accuracy Feature extraction Prediction algorithms Data models Bayes methods Optimization Medical diagnostic imaging Diseases

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