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Comparing Deep Reinforcement Learning Methods for HVAC System Optimization with Forecast Parameters Over Various Time Periods

Comparing Deep Reinforcement Learning Methods for HVAC Syste...

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Interdisciplinary Approaches in Technology and management for Social Innovation (IATMSI), IEEE Conference on

作者： Prema Arokia Mary G Hema M S S Bama Nageswara Guptha Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru India Dept. of Computer Science and Engineering RV Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Buildings are significant contributors to global energy consumption. Maintaining comfortable indoor temperatures while reducing energy consumption are conflicting objectives. Deep Reinforcement Learning (DRL) is a promising area of research for building Heating, Ventilation and Air Conditioning (HVAC) system optimization. In this study an open-source framework Building Optimization Testing Framework (BOPTEST), which is a virtual testbed that help comparison different control strategies for evaluation of DRL control methods is used. A Proportional-Integral (PI) controller is used to benchmark the DRL methods. A single zone residential building of 192 m 2 with a radial heating system and a heat pump in a climate zone with high heating requirement with dynamic electricity prices with prices varying every 15 min based on demand is chosen for implementing different control strategies. On comparing Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Twin Delay DDPG (TD3) based DRL controllers and the baseline controller, the DDPG based controller reduced energy consumption by 97.3 % and operating cost by 17.7 % during the peak heating period with reference to baseline method. Then on analyzing the impact of inclusion of forecast parameters occupancy, solar irradiance, and electricity prices over the period 3, 6 and 12 hours in DDPG based controller. The prediction for 3 hours gave the greatest reduction in thermal discomfort of 99.7 % and prediction for 12 hours gave maximum reduction in cost by 30.4 % but resulted in only 82% reduction in thermal comfort when compared with baseline method indicating that longer prediction horizon is not necessarily results in better performance.

关键词： Energy consumption HVAC Costs PI control Heat pumps Electricity Buildings Deep reinforcement learning Optimization Resistance heating

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Classification of Foods based on Bijective Soft Set

Classification of Foods based on Bijective Soft Set

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Priyanka Das Rupam Bhattacharya Dept. of Computer Application and Science Institute of Engineering & Management Kolkata

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers.

关键词： Accuracy Neural networks Set theory Expert systems Recommender systems Guidelines

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Biomass Fuel Classification from Agricultural Residues Using Machine Learning 12

Biomass Fuel Classification from Agricultural Residues Using...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Pachauri, Nikhil Gupta, Himanshu Verma, Om Prakash Kumar Singh, Sushil Verma, Deepak Rn, Ravikumar Manipal Academy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Karnataka Manipal India Dr B R Ambedkar National Institute of Technology Jalandhar Department of Instrumentation and Control Engineering Jalandhar India Marwadi University Department of Computer Engineering of Computer Engineering Gujarat Rajkot India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

This work incorporates an adaptive learning-based boosting (ADboost) ML classifier to classify four types of fuel: agricultural residue, coals, wood, and produced biomass. Further, the ADboost's hyperparameters, such as learning rate, maximum number of splits, and minimum leaf size are adjusted using teaching learning-based optimization (TLBO), resulting in TADboost. The performance of TADboost is compared against various popular ML (NN, BAG, NB, and SVM) models. Simulated result reveals that the suggested classifiers outperform other compared ML classifiers for fuel classification with the classification accuracy, precision, recall, F1-score, and kappa as 0.9659, 0.9671, 0.9449, 0.9558, and 0.9482, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Advanced Machine Learning Techniques for Optimizing Renewable Energy Systems in Healthcare Facilities 4

Advanced Machine Learning Techniques for Optimizing Renewabl...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Pradeep, S. Kalpana, P. Sreeram, G. Adgaonkar, Amarja Roy, Devika Rani Namdev, Arpit Dept. of Cse Telangana Secunderabad India Christ University Dept. of Computer Science Karnataka Bangalore India Vignana Bharathi Institute of Technology Dept. of Cse Telangana Hyderabad India K.C. College of Engineering & Management Studies & Research Thane India University Institute of Technology Rgpv Dept. of It MP Bhopal India

ISBN: (纸本)9798350387490

Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a potential solution in case of optimizing energy consumption along with management, which is particularly relevant for healthcare institutions that are looking to decrease their operating expenses and carbon footprint by using renewable energy sources. Optimal deployment of renewable ES like solar along with wind may be achieved by the analysis of consumption patterns, prediction of future energy demands, and the investigation of different ML methods, such as neural networks, reinforcement learning, and predictive analytics. Research in this area aims to increase hospital renewable energy systems' efficiency and dependability by creating models that take use of environmental factors, real-time energy consumption indicators, and historical data. The results imply that healthcare facilities may save money, have less of an effect on the environment, and be more resilient if they use sophisticated machine learning methods to perk upcompetence of their renewable energy systems. The study emphasizes the importance of hyperparameter configuration in improving machine learning model accuracy, with a proposed approach achieving 99 % training and 96 % testing accuracy, outperforming conventional methods and outperforming other techniques. © 2024 IEEE.

关键词： Advanced Machine Learning Energy Optimization Healthcare Facilities Neural Networks Reinforcement Learning Renewable Energy Systems

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MINOS: A Tool To Analyze HTTP Requests for compliance to GDPR

MINOS: A Tool To Analyze HTTP Requests for compliance to GDP...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Lorenzo Laudadio Riccardo Coppola Antonio Vetrò Marco Torchiano Juan Carlos De Martin Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Many of the currently available services and commodities extensively share personal data and digital identities, raising privacy, security and ethical *** this paper, we present a novel software, MINOS, designed to enhance users’ privacy awareness while browsing the web. Our approach combines a user-friendly browser with a backend tailored to record and analyze HTTP requests directed to countries outside the European Economic Area (EEA).As opposed to other available tools, our solution uniquely addresses the issue of data transfers to third *** simulations have yielded promising results and clear directions for further research and development of the tool.

关键词： Economics Data privacy Ethics Europe Data transfer HTTP Software Information and communication technology Security Research and development

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PDF to EPUB Transformation: An Overview of Editing and Conversion Tools

PDF to EPUB Transformation: An Overview of Editing and Conve...

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Interdisciplinary Approaches in Technology and management for Social Innovation (IATMSI), IEEE Conference on

作者： Suyog Jagdale Jaydeep Jagdale Aniruddha Jadhav Pushpi Rani Dept. of Computer Engineering G H Raisoni College of Engineering & Management Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The Portable Document Format (PDF) is a well-established format for sharing documents while maintaining consistent layout and structure across various devices. However, converting PDFs into more flexible formats like EPUB (Electronic Publication) presents significant challenges, especially when dealing with complex document structures such as tables, images, and metadata. This paper aims to explore and compare comprehensive tools that enable PDF editing and conversion into EPUB format. The primary focus is on evaluating the efficiency and accuracy of these tools in recognizing document structures and maintaining content integrity. In addition, we discuss various methodologies and algorithms that enhance the conversion process, such as table recognition, hidden structure extraction, and metadata organization, while emphasizing the importance of these tools in e-publishing.

关键词： Electronic publishing Technological innovation Accuracy Layout Organizations Manuals Machine learning Metadata Portable document format Reliability

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Talk to your data: Enhancing Business Intelligence and Inventory management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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Deep Image Inpainting to Support Endoscopic Procedures

Deep Image Inpainting to Support Endoscopic Procedures

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Filippo Betello Francesco Delli Priscoli Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome “La Sapienza” Rome Italy

Deep image inpainting is a computer vision task that uses Deep Neural Networks to generate plausible content to complete an image, for example for the restoration of a damaged image or the removal of unwanted elements captured in the picture. This paper uses deep image inpainting to restore endoscopic images that are affected by various types of artifacts. To this end, we developed a transfer learning-based procedure that uses the CSA inpainting model, which was originally proposed for unrelated tasks including the restoration of images from the Paris StreetView Dataset. The proposed system is trained and validated on the EndoCV2020 dataset, consisting of images from real endoscopies, highlighting how deep image inpainting may be a promising technology for frame restoration during medical procedures.

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Vertically-Advised Federated Learning for Multi-Strategic Stock Predictions through Stochastic Attention-based LSTM

Vertically-Advised Federated Learning for Multi-Strategic St...

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Mediterranean Conference on control and Automation (MED)

作者： Danilo Menegatti Emanuele Ciccarelli Michele Viscione Alessandro Giuseppi Dept. of Computer Control and Management Engineering (DIAG) University of Rome ”La Sapienza” Rome Italy

In recent years, stock price forecasting has become a challenging task commonly used to evaluate the performance of various machine learning solutions. This work explores a Federated Learning (FL) framework within a competitive collaboration scenario with the aim of training a centralised model advised by non-recoverable decentralised strategies so that no exchange of private data is required. The proposed Vertically-Advised Federated Learning (VAFL) framework combines elements from both horizontal and vertical FL, as each client trains two independent models. Furthermore, a novel forecasting architecture, based on a stochastic variant of an Attention-based Long Short Term Memory (LSTM) network, is proposed and validated on a simulated scenario based on real data from the stock market.

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WebTrek Learner : AI Integrated Cloud Based Learning Platform

WebTrek Learner : AI Integrated Cloud Based Learning Platfor...

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Arjun Bhandari Harisha A Kumar Rishav Mariyam Shifana Jazil Sameer Dept. of Computer Science and Engineering Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

In an era defined by the rapid proliferation of digital technology and connectivity, the need for virtual education has never been more critical. This article, referred to hereon as “WebTrek Learner,” is a forward-thinking initiative aimed at catering quality virtual education through a dynamic and engaging approach of gaming. To further enhance the learning experience, WebTrek Learner incorporates cutting-edge LLM based AI technology to curate internal insights and content based on glossary documents. Cloud based a gaming platform enables lightweight experience to all potential learners.

关键词： Cloud computing Ethics Mechatronics Education Force Dynamics Systems architecture Reliability engineering Usability Intelligent systems

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