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检索条件"任意字段=Conference on Visualization and Optimization Techniques"

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SiMoS: Sistema para monitoramento de plataformas de poços de petróleo através de dispositivos móveis

SiMoS: Sistema para monitoramento de plataformas de poços d...

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IEEE/IAS International conference on Industry Applications (INDUSCON)

作者： Clayton Maciel Costa Ivandro Claudino de Sá Cicília Raquel Maia Leite Pedro Fernandes Ribeiro Neto Ricardo A. M. Valentim Ana Maria Guimarães Guerreiro Instituto Federal de Educação Ciência e Tecnologia Rio Grande do Norte Brazil Cix00EA ncia e Tecnologia da Paraíba Instituto Federal de Educação Rio Grande do Norte Brazil Faculdade de Ciências e Tecnologia Mater Christi Universidade do Estado do Rio Grande do Norte Grande do Norte e Faculdade de Ciências e Tecnologia Mater Christi Universidade do Estado do Rio de Janeiro Brazil Universidade Federal do Rio Grande do Norte Ciência e Tecnologia da Paraíba Instituto Federal de Educação Brazil Universidade Federal do Rio de Janeiro Rio Grande do Norte Brazil Universidade do Estado do Rio Grande do Norte

The popularization of the Web and the advent of new information and communication technologies of mobile devices make possible new aspects for management, interaction and information sharing. In petroliferous area, monitoring systems on mobile devices have been used for process optimization, data monitoring, reporting, alarm generation and identification of failures in real-time on oil wells platforms. However, there are few systems that identify efficiently problems due the fact they do not use techniques of real-time systems. This paper proposes the new system, SiMoS, a real-time system for remote monitoring oil wells platforms through mobile devices.

关键词： Monitoring Java visualization Software Internet Mobile handsets Real time systems

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Towards Improving the Design Space Exploration Process Using Generative Design With MBSE

Towards Improving the Design Space Exploration Process Using...

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IEEE conference on Aerospace

作者： Louis Timperley Lucy Berthoud Chris Snider Theo Tryfonas Antonio Prezzavento Kyle Palmer University of Bristol Queen's Building University Walk Bristol Functional Avionics R&D Coordinator Airbus Space and Defence Stevenage Airbus Space and Defence Stevenage

Model Based Systems Engineering (MBSE) is an interesting alternative to traditional systems engineering methods. Instead of using electronic documents to record system information, MBSE uses a unified and coherent system model. Trade-offs are a major element of a space systems engineer's role in early system design. This can be a particularly challenging process in the domain of spacecraft, as the system designs are often very complex and the constraints can be difficult to characterize. There has been little previous research on the use of MBSE as a design space exploration tool or in support of trade-offs. This paper investigates the potential to use MBSE for design exploration and to understand trade-offs, through the creation of a new toolset including a SysML profile. The tool draws on generative design (allowing automatic guided generation of a multitude of design alternatives) and system optimization to rapidly generate and assess new designs using interactive analysis and visualizations. techniques such as surrogate modelling, genetic algorithms and robustness measurements will be available in the toolset. The toolset was applied to a design scenario aiming to improve the trade off and design selection process for LEO Earth observation satellites. The upcoming ESA TRUTHS space mission was used as a case study and the design process was recorded and compared to a manual design exploration approach. The toolset was found to reduce the design exploration time by 38% to 96%, allow exploration of more designs in an equivalent time and provide better quantification of the relationships present in the design space, all without drops in selected design quality. For now, the toolset can only perform parameter variation in the design exploration and future work is expected to extend this to higher levels of variability. The study also discusses how the MBSE toolset could be applied to other missions, offering the same advantages to all early phase spacecraft designers

关键词： Space vehicles visualization Satellites Space missions Low earth orbit satellites Manuals Robustness

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Enhanced Object Detection with Deep Convolutional Neural Networks for Vehicle Detection

Enhanced Object Detection with Deep Convolutional Neural Net...

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International conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Prasoon Bharat Mishra Abdul Malik M. Safa G Saranaya D Arun Department of Networking and Communication SRM Institute of Science and Technology Chennai India VMware

ISBN: (纸本)9781665462761

Recent years have seen a significant increase in scholarly interest in object detection because of its strong connection to video analysis and picture interpretation. Shallow trainable structures and handcrafted features facilitate conventional object recognition techniques. Intricate ensembles that integrate poor visual elements with high-level data from object detectors and scene classifiers reach an effectiveness threshold relatively quickly. In order to help with problems with conventional architectures, more powerful tools that can learn deeper, higher-level, and more semantic characteristics are becoming available as deep learning develops quickly. For instance, these models behave differently when it comes to network architecture, training strategy, and optimization methodology. In this paper, we evaluate studies on object recognition using deep learning. The authors of the study begin with a primer on deep learning and its principal methods, the convolutional neural network (CNN). The subject will then shift to a number of widely used generic object detection methods, as well as various improvements and practical strategies for improving detection generally. The topic of different common genealogical patterns for object recognition will next be covered, along with some tweaks and practical methods for enhancing detection. even more performance It also briefly explores a number of particular detection tasks, such as pedestrian identification, face detection, and recognition of remarkable items because they display a range of properties. Experimental analyses are also available to compare alternate strategies and get some useful results. The recommendations for further research in both the object and object-oriented fields cover a wide range of prospective directions and objectives.

关键词： Deep learning Training visualization Text recognition Vehicle detection Object detection Object recognition

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5th International conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems, SADASC 2024

5th International Conference on Smart Applications and Data ...

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5th International conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems, SADASC 2024

ISBN: (纸本)9783031770395

The proceedings contain 40 papers. The special focus in this conference is on Smart Applications and Data Analysis for Smart Cyber Physical Systems. The topics include: Modeling and Managing Product Unavailability Risk in Inventory Through a Fuzzy Bayesian Network;a Binary Particle Swarm optimization Based Hybrid Feature Selection Method for Accident Severity Prediction;The Integration of NLP and Topic-Modeling-Based Machine Learning Approaches for Arabic Mobile App Review Classification;feature Selection for High-Dimensional Gene Expression Data: A Review;legal Contract Quality and Validity Assessment Through the Bayesian Networks;Optimizing Recommendation Systems in E-Learning: Synergistic Integration of Lang Chain, GPT Models, and Retrieval Augmented Generation (RAG);Revolutionizing Skin Cancer Diagnosis: Unleashing AI Precision Through Deep Learning;fuzzy Bayesian Network Applied to Modeling Vehicles Cooling Systems Failure Risk;Utilization of FMECA to Optimize Predictive Maintenance;comprehensive Study on Sentiment Analysis: Insights Into Data Preprocessing, Feature Extraction, and Deep Learning Model Selection;A Combined AHP-TOPSIS Model for the Selection of Employees for Promotion;towards Explainable Models: Explaining Black-Box Models;Advanced NLP and N-Gram techniques in Financial News Sentiment Analysis: Diverse Machine Learning Approaches;comparative Study of Feature Selection Algorithms for Cardiovascular Disease Prediction with Artificial Neural Networks;ensemble Learning for Malware Detection;Enhancing Security Through Data Analysis and visualization with ELK;Industry 4.0 Efficiency: Predictive Maintenance with TinyML and an Incremental Model;Tiny-ML and IoT Based Early Covid19 Detection Wearable System.

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Real-Time Data Processing and Analytics in IoT Cloud Computing Systems

Real-Time Data Processing and Analytics in IoT Cloud Computi...

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International conference on Computational and Characterization techniques in Engineering & Sciences (CCTES)

作者： T. Manikandan Satheesh Kumar S A. Muruganandham S. Karthikeya J L Mazher Iqbal Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India Department of ECE KPR Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering Rajarajeswari College of Engineering VTU Belagavi Department of Electronics and Communication Engineering Velammal Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

From the cloud computing systems perspective, IoT real-time data analytics plays a major role in innovation boost and the value creation of a number of industries. This paper proposes a method for operations of real-time analytics in the internet of things cloud configurations with the background of data collection, ingestion, processing, analysis, and visualization. Adoption of intelligent technologies like machine learning, edge computing, and cloud-native architectures, the algorithm will easily transform the raw data from IoT streams into real-time actionable insights for the organizations. Having a system in order with a methodical approach, which includes identifying data sources, query processing, and scalability evaluation enables organizations to design and implement robust, resilient and cost-effective real time analytics ones that are tailor-made to suit the unique requirements of the organization. Through the value-based analysis, the key role of an immediate glimpse depicting process improvement, better decision making, and profit generation as well as customer experience perfection are emphasized. While robust uses the word that meshes several key ideas and has a negative connotation privacy and security measures aim for data integrity, confidentiality, compliance, and data protection against possible risks and liabilities (as criticism, protection, or a plan). The creation of an environment where people think innovatively and collaborate with each other and have the ability to review and build on their performance can be done with the help of the real-time data analytics. This can drive the businesses to sustainable growth and success in the digital era.

关键词： Cloud computing Technological innovation Soft sensors Decision making Organizations Machine learning Real-time systems Security Streams optimization

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Advancements in Assistive Technology: A Blind Aid Bot for Enhanced Spatial Awareness and Navigation

Advancements in Assistive Technology: A Blind Aid Bot for En...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International conference on

作者： Tushar Mathur Nandini Jain Utkarsh Jaiswal Rachita Kothiyal Puneet Sharma Rohit Bathla Computer Science and Engineering SET MRIIRS Faridabad India Faculty Computer Science and Engineering SET MRIIRS Faridabad India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Independence of people with vision impairments is necessary as they face many hurdles in their daily lives from locating objects to navigating through obstacles. In the current environment, it is still challenging to provide the visually impaired with appropriate guidance and to find solutions to their difficulties. In order to enhance the awareness and day-to-day activities of visually impaired users, this project introduces a software-centred approach called the visual assistant. The software architecture of the blind assistance robot is based on deep learning and can use patterns for object recognition, intervention and optimization. The system uses convolutional neural networks (CNN) and you only look once (yolov5) and is trained on many different urban and indoor environments to ensure robustness and accuracy in different situations. Integration with natural language processing (NLP) makes it intuitive, allowing users to receive messages and send commands with simple voice. Test in a simulated environment to check the effectiveness of algorithms and user interfaces. Preliminary results show significant improvements in reporting accuracy and user confidence when interacting in an unfamiliar environment. Responses from users in the first test show the potential of the blind program to reduce the daily problems of the visually impaired. This paper not only demonstrates the feasibility of software solutions in helping the visually impaired, but also opens up personal development opportunities. Future work will focus on real-world testing and integration of adaptive learning techniques to further improve user interaction and environment-based performance.

关键词： YOLO visualization Accuracy Navigation Face recognition Visual impairment Chatbots Object recognition Convolutional neural networks Testing

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Robot Traversability Prediction: Towards Third-Person-View Extension of Walk2Map with Photometric and Physical Constraints

Robot Traversability Prediction: Towards Third-Person-View E...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Jonathan Tay Yu Liang Kanji Tanaka Fundamental Engineering for Knowledge-Based Society Graduate School of Engineering University of Fukui Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Walk2Map has emerged as a promising data-driven method to generate indoor traversability maps based solely on pedestrian trajectories, offering great potential for indoor robot navigation. In this study, we investigate a novel approach called Walk2Map++, which involves replacing Walk2Map’s first-person sensor (i.e., IMU) with a human observing third-person view from the robot’s onboard camera. However, human observation from a third-person camera is significantly ill-posed due to visual uncertainties resulting from occlusion, nonlinear perspective, depth ambiguity, and human-to-human interaction. To regularize the ill-posedness, we propose integrating two types of constraints: photometric (i.e., occlusion ordering) and physical (i.e., collision avoidance). We demonstrate that these constraints can be effectively inferred from the interaction between past and present observations, human trackers, and object reconstructions. We depict the seamless integration of asynchronous map optimization events, like loop closure, into the real-time traversability map, facilitating incremental and efficient map refinement. We validate the efficacy of our enhanced methodology through rigorous fusion and comparison with established techniques, demonstrating its capability to advance traversability prediction in complex indoor environments. The code and datasets associated with this study are available for further research and adoption in the field at https://***/jonathantyl97/HO3-SLAM.

关键词： Legged locomotion visualization Pedestrians Uncertainty Robot vision systems Cameras Prediction algorithms Real-time systems Trajectory Collision avoidance

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Enhancing Automatic Image Captioning: A Systematic Analysis of Models, Datasets, and Evaluation Metrics

Enhancing Automatic Image Captioning: A Systematic Analysis ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International conference on

作者： Kothai Ganesan S. Amutha G. Venkata Saketh G. Vishnu Vardhan Reddy G. Harinath Chowdary J. Ajay Sai Department of Computer Science and Engineering KPR institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The fusion of computer vision and natural language processing (NLP) has given rise to the interdisciplinary field of automatic image captioning, which aims to generate descriptive text for images without human intervention. This area of research has attracted considerable interest due to its potential uses in improving accessibility, managing content, and enhancing search engine optimization. Over the past years, there has been a drastic increase in the accuracy and detail of captions thanks to recent advances in deep learning, especially with convolutional neural networks (CNNs), long short-term memory (LSTMs), and recurrent neural networks (RNNs). This research examines cutting-edge approaches to automatic image captioning, including attention mechanisms, models based on transformers, and techniques utilizing reinforcement learning. The effectiveness of these methods was evaluated using benchmark datasets, and their advantages and limitations were assessed. Additionally, the obstacles faced in producing contextually appropriate and linguistically precise captions were explored, such as dealing with intricate scenes, uncommon objects, and abstract ideas. The paper concludes by highlighting potential areas for future research, including learning across multiple modalities, captioning across languages, and incorporating common-sense knowledge to improve caption generation.

关键词： Deep learning visualization Recurrent neural networks Systematics Reinforcement learning Search engines Transformers Ubiquitous computing Natural language processing Convolutional neural networks

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Explainable FL-Based Framework for Diagnosis of Diabetic Retinopathy in Healthcare 4.0 Environment

Explainable FL-Based Framework for Diagnosis of Diabetic Ret...

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International conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Pranshav Patel Naman Jain Hrishita Patni Ramoliya Fenil Rajesh Gupta Sudeep Tanwar Hossein Shahinzadeh Department of Comp. Sci. & Engg. Institute of Technology Nirma University Ahmedabad India Department of Electrical Engg. Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Ensuring interpretability and transparency in AIdriven decision-making is crucial, especially in healthcare. However, the resource-intensive nature of training complex AI models can often be a significant obstacle, particularly for smaller healthcare institutions and remote clinics. This is where Federated Learning (FL) and Explainable AI (XAI) come into play. This paper proposes a novel Explainable Federated Learning (EFL) framework for diagnosing Diabetic Retinopathy in a Healthcare 4.0 environment. Our approach combines the privacy-preserving benefits of FL with the enhanced transparency of XAI techniques, addressing the challenges of data privacy, resource optimization, and interpretability. By training lightweight local models on client devices and aggregating their weights to update a global model, our framework eliminates the need for high-resource centralized training, making it accessible to a wider range of healthcare providers. Furthermore, we integrate XAI methods, such as Local Interpretable Model-Agnostic Explanations (LIME), to provide visual explanations of the model's decision-making process. This enhances trust and transparency for medical professionals and patients while offering insights into the model's learning process. Our performance evaluation demonstrates the effectiveness of this EFL approach, with local models achieving high accuracy and low loss over multiple communication rounds and the global model exhibiting robust performance metrics, including accuracy, precision, recall, and F1-score. This framework can be readily extended to other healthcare applications where interpretability and privacy are paramount.

关键词： Training Diabetic retinopathy Privacy visualization Accuracy Federated learning Decision making Medical services Signal processing Security

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Hyper Spectral Satellite Image Classification Based Land And Water Bodies Area Detection Using Machine Learning And Opencv techniques

Hyper Spectral Satellite Image Classification Based Land And...

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Green Industrial Electronics and Sustainable Technologies (GIEST), IEEE International conference on

作者： Menaka. S Harsh Jain Prithvi Charan B Hemanth P Jeffrey Cris Amanjeet Singh Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram India Department of CSE SRM Institute of Science and Technology Ramapuram India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

Remote sensing is one of the important applications for monitoring the ecological conditions in the world. Over the years of climate change and urbanization produced a greater impact on land utilization as compared to its original form. With the help of satellite images like LIDAR, Senitel and Hyperspectral images, the remote sensing process were performed. Among these images, the Hyper-Spectral image have a greater advantage and impact in analyzing the land utilization. Because this HS images has both spectral and spatial properties of the region and it helps to improve the land classification. Several research works also stated that both spatial and spectral properties of HS image is required for analysis and these features were collected using multi-models or individual network. Hence, in this, the land classification using HS images were performed using Attention based modified VGN network. To perform this, the images undergo two main processes namely channel transformation from RGB to LAB and LBP image formation for an efficient feature extraction and classification in A-M-VGN. This proposed method was tested on multi-class like agricultural, barren, green and water bodies region in HS images and evaluated using Accuracy, computation time, Kurtosis, Standard Deviation and mean. The Kurtosis, mean and SD helps to analyze the pixel variations in the image. The results showed that the proposed approach is efficient for land classification using HS images.

关键词： visualization Image color analysis Urban areas Kurtosis Vectors Satellite images Standards optimization Monitoring Hyperspectral imaging Climate change

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