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A machine learning approach to predict the performance of refrigerator and air conditioning using gaussian process regression and support vector methods

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Recent Patents on Mechanical engineering 2021年第4期14卷 550-557页

作者： Sharma, Harinarayan Kumari, Sonam Dutt, Aniket K. Kumar, Pawan Makhatha, Mamookho E. Department of Mechanical Engineering Netaji Subhas Institute of Technology Bihta Bihar India Department of Computer Science and Engineering Netaji Subhas Institute of Technology Bihta Bihar India Faculty of Engineering and the Built Environment Department of Engineering Metallurgy University of Johannesburg John Orr Building DFC 25 Louisa St Doornfontein Johannesburg2028 South Africa

Aim: The aim of this study is to develop machine learning models for the performance of refrigerator and air-conditioning system. Background: The Coefficient Of Performance (COP) of Refrigerator and Air-Conditioning (RAC) is a complex function of evaporative temperature and concentration of nanoparticles in lubricants. In recent years, researchers have focused on experimental studies for the improvement of COP. Further, few researchers have applied simulation techniques such as fuzzy system, Artificial Neural Network (ANN), simulated annealing, etc. to Vapour Compression Refrigeration (VCR) cycle. Though, there is a scarcity of modeling research work for the performance of RAC system. Objective: The study aims to develop the machine learning predictive models for the performance of refrigerator and air-conditioning system using experimental data. Methods: The experiment was performed on VCR system to determine COP. Three different concentrations of lubricants (added 0.5, 1.0 and 1.5g nano-TiO2 particle on 1 liter of Polyolester (POE) oil) were used. The experimentally calculated COP was used to train and test the machine learning models. Gaussian Process Regression (GPR) and Support Vector Regression (SVR) methods were applied to develop the models. Results: The experimental result reveals that the COP increases with increasing the concentration (of nano particles) at a given temperature. The addition of 0.5 and 1.0g TiO2 in the POE oil shows better rate of increment in the COP in comparison to addition of 1.5g TiO2 in the POE oil. Machine learning models using GPR and SVR with RBF kernel function is the most appropriate machine learning model for the nonlinear relationship between the output parameter (COP) and the input parameter (evaporative temperature and concentration of TiO2). Conclusion: The present study was conducted to investigate the machine learning approaches for the performance of RAC system using experimental data sets. The experimental result shows

关键词： Air conditioning

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Intelligent Task Offloading in IoT Devices Using Deep Reinforcement Learning Approaches

Intelligent Task Offloading in IoT Devices Using Deep Reinfo...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Megha Sharma Abhinav Tomar Abhishek Hazra Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

The proliferation of smart devices has led to a rapid expansion of Internet of Things (IoT) applications. Consequently, a substantial volume of unprocessed data is produced, necessitating processing and storage. Individual IoT devices alone are insufficient to manage significant volumes of data. Fog computing is an essential technology that enables the proximity of cloud services to the end-user. Efficient task offloading techniques are necessary to balance the demand due to the unpredictable nature of tasks with user's Quality of Service (QoS) needs. To address the issue effectively, two different model-free off-policy i.e. Deep Q-Network (DQN) and on-policy i.e. Asynchronous Advantage Actor-Critic (A3C) Deep Reinforcement Learning (DRL) techniques are described as optimising resource utilisation by maximising the reward. Ultimately, extensive experiments are carried out to verify and assess the efficiency and effectiveness of the proposed methods.

关键词： Performance evaluation Privacy Quality of service Deep reinforcement learning Internet of Things Security Resource management

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Multi-agent Reinforcement Learning Based Approach for Vehicle Routing Problem 1

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2nd International Conference on Pan-African Intelligence and Smart Systems, PAAISS 2022

作者： Singh, Jagdeep Dhurandher, Sanjay Kumar Woungang, Isaac Ngatched, Telex Magloire N. Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal India Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science Toronto Metropolitan University Toronto Canada Faculty of Engineering and Applied Science Grenfell Campus Memorial University St. John’s Canada

ISBN: (数字)9783031252716

ISBN: (纸本)9783031252709

Multi-Vehicle routing to service consumers in dynamic and unpredictable surroundings such as congested urban areas is a difficult operation that needs robust and flexible planning. Value iteration networks hold promise for planning vehicle routing problems. Conventional approaches aren’t usually constructed for real-life settings, and they are too slow to be useful in real time. In comparison, the Vehicle Routing Problem with Value Iteration Network (VRP-VIN) offers a neural network model based on graphs that can execute multi-agent routing in a highly dispersed but connected graph with constantly fluctuating traffic conditions using learned value iteration. Furthermore, the model’s communication module allows vehicles to work better in a cooperative manner online and can easily adapt to changes. A virtual environment is constructed to simulate real-world mapping by self-driving vehicles with uncertain traffic circumstances and minimal edge coverage. This method beats standard solutions based on overall cost and run time. Experiments show that the model achieves a total cost difference of 3% when compared with a state-of-art solver having global information. Also, after being trained with only 2 agents on networks with 25 nodes, can easily generalize to a scenario having additional agents (or nodes). © 2023, ICST institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Neural network models

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Comparison of LSCP and Cluster Coverage Model for Emergency Train's Assets Management: Use Case;North Eastern Railway

Comparison of LSCP and Cluster Coverage Model for Emergency ...

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2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022

作者： Mishra, Kshitij Mishra, Swastika Abhyuday International Institute of Information Technology Computer Engineering Hyderabad India Netaji Subhas University of Technology Mechanical Engineering New Delhi India Indian Institute of Technology Indian Railways Service of Mechanical Engineers Kharagpur India

ISBN: (纸本)9781665461795

Deployment of Accident Relief Medical Vans (ARMV) used for accident relief and rescue operations is generally done heuristically. ARMV is an ambulance-train equipped with various tools like jacks, heavy cutting tools, generators, operation theatre, artificial breathing equipment etc. to ease the rescue process. Taking North Eastern Railway (NER) as a case study, Location Set Covering Problem (LSCP) model will be used to find the minimum number of ARMVs required for the given network and their locations so that all demand points can be "covered"within a maximum of one hour @ 100 kmph for each of the nearest facility. This paper proposes a new mathematical model based on the Cluster Coverage Model (CCM) to provide a better coverage of ARMV's demands. We have used Girvan-Newman (GN) algorithm, a weight-based graph for clustering of NER network where nodes are taken as depots with ARMV parking facilities & crew availability and distance between these nodes is taken as edge weight of the graph. Finally, we present a comparison of results between the proposed cluster coverage models with LSCP and suggest relocations of the existing ARMVs and show that our proposed model is economical and provides a better coverage of ARMVs demands. © 2022 IEEE.

关键词： Location

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Edge Device Selection For Industrial IoT Task Offloading In Mobile Edge Computing

Edge Device Selection For Industrial IoT Task Offloading In ...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Megha Sharma Abhinav Tomar Abhishek Hazra Zaid Akhter Daksh Dhangar Rahul Kumar Singh Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

In the realm of Mobile Edge Computing (MEC), mobile devices have the option to transfer their tasks to edge servers for processing, thereby significantly diminishing task completion duration and reducing the energy consumption of mobile devices. This offloading mechanism optimizes resource utilisation and enhances overall efficiency by leveraging the computational capabilities of nearby edge servers. This paper addresses the issue of efficient task offloading from mobile devices to edge servers in resource-constrained Industrial Internet of Things (IIoT) heterogenous networks, focusing on minimising energy consumption and heterogenous network delay. The methodology involves checking the matching feasibility using Hall's Marriage Theorem and employing a server selection algorithm for task assignment. The results show that the proposed approach minimizes energy consumption and overall time delay compared to standard algorithms. The implications include the potential for improving various QoS factors in future research.

关键词： Energy consumption Multi-access edge computing Quality of service Mobile handsets Servers Resource management Task analysis

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A Novel Computational Model for Hardware-Level Security of Cloud Databases in Public Clouds 6

A Novel Computational Model for Hardware-Level Security of C...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Yadav, Arun Kumar Bharti, Rajendra Kumar Raw, Ram Shringar VMSB-Uttarakhand Technical University Uttarakhand Dehradun India Itm University Department of Csa MP Gwalior India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science and Engineering Uttarakhand Dwarahat India Netaji Subhas University of Technology East Campus Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350346961

Today, all cloud-based social media applications are data-intensive, and the large amounts of data they generate are managed on the public cloud, which compromises user data security and privacy. Many government and nongovernment organizations that manage users' sensitive and personal data do not choose to use the public cloud to administer their databases because of the numerous security risks it has. However, these organizations are jeopardizing the performance of the application by handling the user's data on the private cloud. Without jeopardizing user data security and privacy or application performance, a security solution will encourage these organizations to manage their databases on the public cloud. This paper presents an L-PNR computational model for the hardware-level security of cloud databases in a multi-tenant public cloud environment. The L-PNR model's performance has been simulated and evaluated using VMware vCenter Simulator. According to experimental findings, the proposed model outperforms the current models THR-RS, LR-MU, DDPA, and DSRBACA in terms of performance and security. The suggested L-PNR model may be used by cloud service providers (CSPs) to offer data security over the public cloud, where organizations can affordably manage their sensitive data.. © 2023 IEEE.

关键词： Sensitive data

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Effect of temperature and strain rate on mechanical properties and potential energy of nanocrystalline tantalum

Effect of temperature and strain rate on mechanical properti...

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2022 International Conference on Future Trends in Materials and Mechanical engineering, ICFTMME 2022

作者： Kumar Gupta, Mahesh Panwar, Vinay Prasad Mahapatra, Rajendra Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Modinagar U P Ghaziabad India Mechanical Engineering Department Netaji Subhas University of Technology Dwarka New Delhi110078 India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Modinagar U P Ghaziabad India

In this work, molecular dynamics simulations have been conducted on nanocrystalline tantalum. Uniaxial tensile tests have been conducted and the effects of temperature and strain rate on mechanical properties and potential energy of nanocrystalline tantalum have been observed. The response of nanocrystalline tantalum with temperature variation from 300 K to 1500 K and step-size of 300 K at a constant strain rate has been analyzed. The response at different strain-rates i.e., 5 × 108sec -1, 1 × 109sec -1, 5 × 109sec -1, 1 × 1010sec -1at constant temperature has also been studied. It has been observed that the yield strength and young's modulus show a drop of 14.4 % and 12.3 % respectively when the temperature is increased from 300 K to 1500 K, but increase by 105.8 % and 4.08 % respectively, when strain rate is increased from 5 × 108sec-1to 1 × 1010sec -1. Moreover, it has been observed that the potential energy of nanocrystalline tantalum first increases in magnitude with an increase in strain, and then attains saturation at a constant value. The magnitude of this saturation potential energy has an inverse relationship with both temperature and strain rate. © 2023 Elsevier Ltd. All rights reserved.

关键词： Strain rate

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BB-PATCH: BLACKBOX ADVERSARIAL PATCH-ATTACK USING ZEROTH-ORDER OPTIMIZATION

arXiv

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arXiv 2024年

作者： Kumar, Satyadwyoom Gupta, Saurabh Buduru, Arun Balaji Dept. of Electronics and Communication Engineering Netaji Subhas Institute of Technology Delhi India Dept. of Computer Science Engineering Indraprastha Institute of Information Technology Delhi India

Deep Learning has become popular due to its vast applications in almost all domains. However, models trained using deep learning are prone to failure for adversarial samples and carry a considerable risk in sensitive applications. Most of these adversarial attack strategies assume that the adversary has access to the training data, the model parameters, and the input during deployment, hence, focus on perturbing the pixel level information present in the input image. Adversarial Patches were introduced to the community which helped in bringing out the vulnerability of deep learning models in a much more pragmatic manner but here the attacker has a white-box access to the model parameters. Recently, there has been an attempt to develop these adversarial attacks using black-box techniques. However, certain assumptions such as availability large training data is not valid for a real-life scenarios. In a real-life scenario, the attacker can only assume the type of model architecture used from a select list of state-of-the-art architectures while having access to only a subset of input dataset. Hence, we propose an black-box adversarial attack strategy that produces adversarial patches which can be applied anywhere in the input image to perform an adversarial attack. © 2024, CC BY.

关键词： Deep learning

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Multi-class Regret Detection in Hindi Devanagari Script

arXiv

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arXiv 2024年

作者： Sharma, Renuka Nagpal, Sushama Sabharwal, Sangeeta Butt, Sabur Department of Computer Science and Engineering Netaji Subhas University of Technology India Institute for the Future of Education Tecnológico de Monterrey 64849 Mexico

The number of Hindi speakers on social media has increased dramatically in recent years. Regret is a common emotional experience in our everyday life. Many speakers on social media, share their regretful experiences and opinions regularly. It might cause a re-evaluation of one’s choices and a desire to make a different option if given the chance. As a result, knowing the source of regret is critical for investigating its impact on behavior and decision-making. This study focuses on regret and how it is expressed, specifically in Hindi, on various social media platforms. In our study, we present a novel dataset from three different sources, where each sentence has been manually classified into one of three classes "Regret by action", "Regret by inaction", and "No regret". Next, we use this dataset to investigate the linguistic expressions of regret in Hindi text and also identify the textual domains that are most frequently associated with regret. Our findings indicate that individuals on social media platforms frequently express regret for both past inactions and actions, particularly within the domain of interpersonal relationships. We use a pre-trained BERT model to generate word embeddings for the Hindi dataset and also compare deep learning models with conventional machine learning models in order to demonstrate accuracy. Our results show that BERT embedding with CNN consistently surpassed other models. This described the effectiveness of BERT for conveying the context and meaning of words in the regret domain. © 2024, CC BY-NC-SA.

关键词： Embeddings

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Sentiment analysis using RNN model with LSTM

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International Journal of Intelligent Systems Technologies and Applications 2023年第3期21卷 229-249页

作者： Zhou, Liang Sharma, Arpit Kumar Kanhaiya, Kishan Nandal, Amita Dhaka, Arvind Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences Shanghai China Center for Medicine Intelligent and Development China Hospital Development Institute Shanghai Jiao Tong University Shanghai China Department of Computer and Communication Engineering Manipal University Jaipur India Netaji Subhas University of Technology Dwarka Sector-3 Dwarka Delhi110078 India

In today’s digital world with a rapid increase in e-commerce portals, the consumers are more oriented towards seeking out online reviews, feedback, or ratings over a product during the online buying process. In this research work, we tried to investigate the relationship between the review ratings and the sentiment of reviews in the form of their polarity. We have tried to predict the sentiments over the given reviews by implementing various machine learning techniques, i.e., logistic regression, support vector machine (SVM), k-nearest neighbours (KNN), and recurrent neural network (RNN). The machine learning techniques predict the sentiments of provided reviews in two scenarios, i.e., scenario 1 – negative (–) and positive (+) and scenario 2 – negative (–), neutral (0) and positive (+). In this paper, we have proposed the architecture for predicting the sentiments with better accuracy over other techniques. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Sentiment analysis

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