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检索条件"任意字段=International Conference on Automation and Computing"

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Impact of Cloud computing on Agricultural Advancement using Data Mining Algorithms

Impact of Cloud Computing on Agricultural Advancement using ...

引用

automation, computing and Renewable Systems (ICACRS), international conference on

作者： S. K. Saravanan F. Nisha V. Robin Rohit J. Lenin P D Selvam M. Rajmohan Department of BigData and Network Security Saveetha School of Engineering SIMATS Chennai Tamil Nadu India School of Engineering and Technology Computer Science & Engineering CMR University Bengaluru Karnataka India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Bengaluru Karnataka India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Incorporating Internet of Things (IoT) algorithms, Ensemble Learning, and Explainable AI (XAI) into the Reinforcement Learning framework provides a comprehensive solution. The framework’s dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement Learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble Learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. Data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates.

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Digital-Twins-as-a-Service in Construction Engineering

Digital-Twins-as-a-Service in Construction Engineering

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： Philipp Zech Claudio Nardin Sashko Ristov Matthias Flora Ruth Breu Department of Computer Science University of Innsbruck Tyrol Austria Unit of Construction Management and Tunneling University of Innsbruck Tyrol Austria

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Digital twins are powerful tools for real-time monitoring and control, and simulation and optimization of cyber-physical systems and processes, (cf. the physical twin). The Architecture, Engineering, Construction and Operation (AECO) domain is highly interested in exploiting digital twins, yet struggles with easily establishing these due to a lack of open, publicly accessible twinning platforms. Yet, with the introduction of the Industry Foundation Classes, an open standard for model and data exchange, and the broad availability of public cloud infrastructures, the provisioning of a twinning platform for the AECO domain for the fully automated generation of digital twins of buildings (cf. the physical twin) is feasible. We report on a novel cloud-based platform for automated instantiation of digital twins. Our results demonstrate both the feasibility and relevance of our proposal to reduce the time and effort to implement and the cost to operate a digital twin atop managed cloud services.

关键词： Industries Cloud computing Costs Process control Real-time systems Data models Digital twins Proposals Standards Optimization

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Design & Development of a Smart Blind System Using Fuzzy Logic

Design & Development of a Smart Blind System Using Fuzzy Log...

引用

IEEE/ASME international conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Jordon Lowe Kaya Kuru School of Engineering and Computing University of Central Lancashire Preston UK

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Thanks to cyber-physical systems (CPSs) and enhanced AI techniques, the "everyday things" in our environment have become increasingly intelligent in recent years in the aspects of automation of Everything (AoE). An IoT application for a smart blind system (SBS) using fuzzy logic to intelligently adjust to an optimal position based on data from local sensors, Wisdom-as-a-Service (WaaS) and Insight-as-a-Service (InaaS) from smart domains and customer preferences has been developed. Testing of this system as a proof-of-concept yields promising results, as the fuzzy logic can effectively control the blind to a position that is desirable under the given environmental conditions. Further, all the input data is utilised productively, ensuring that decisions made by the system are well-informed. Compared to competitors’ offerings, the proposed system offers superior performance due to its extensive input consideration and the precise design of the fuzzy logic for automated decision-making.

关键词： Fuzzy logic Fuzzy sets automation Decision making Cyber-physical systems User interfaces Sensor systems and applications Cognition Intelligent sensors Testing

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Exploring Low-Power IoT Solutions for Monitoring Natural Gas Systems Using CH4 MOS Detection

Exploring Low-Power IoT Solutions for Monitoring Natural Gas...

引用

international conference on Emerging Technologies and Factory automation (ETFA)

作者： Sofia Alves Sofia Paiva Paulo Santos Jorge Cabral Department of Industrial Electronics Algoritmi Center University of Minho Braga Portugal Embedded and Edge Computing Group DTx - Digital Transformation Colab Guimarães Portugal

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

In light of the increased concern about environmen-tal sustainability and the urgent need to reduce greenhouse gas emissions, methane (CH 4 ) detection plays a critical role regarding air quality and climate change. As the major component of natural gas, methane significantly contributes to global warming and serves as an ancestor to the creation of harmful atmospheric pollutants. Therefore, effective detection and mitigation of methane leaks are essential for promoting sustainable practices. The first step to achieve this relies on the creation of systems capable of reliably and rapidly detecting methane leaks into the environment. This paper explores solutions for methane detection leaks in IoT natural gas monitoring systems, with a focus on the integration of low-power sensors, particularly using Metal-Oxide-Semiconductor (MOS) technology. The research encompasses various aspects, including the development of a test plan, to ensure validation of sensor performance across diverse scenarios, aiming to simulate sensor behaviour under conditions closely resembling the environments in which they will be deployed.

关键词： Methane Prevention and mitigation Sensor systems Sensors Reliability Sustainable development Greenhouse gases Monitoring Gas detectors Manufacturing automation

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MGRU-M1dCNN: A Hybrid Model for Stock Price Prediction

MGRU-M1dCNN: A Hybrid Model for Stock Price Prediction

引用

international conference on Computer and automation Engineering, ICCAE

作者： Anika Kanwal Siva Chandrasekaran Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia

Predicting the stocks price is both intriguing and difficult due to the nonlinear and noisy stock’s data. Stock investors’ investment risk can reduce and their return on investment can be significantly increased by properly predicting the change in stock prices. It is challenging for researchers to forecast stock prices using an individual stock forecasting model. Therefore, in this research we proposes MGRU-M1dCNN, a hybrid model to predict the stock opening price of the next day. This model is consisted of multiple gated recurrent unit (MGRU) and Multiple single dimensional convolutional neural networks (M1dCNN). MGRU employs to extract the valuable feature patterns from opening price of stocks. M1dCNN is used to extract the spatial features of the inputs received from MGRU to predict the stock prices. The outcomes demonstrate that this technique performs best, with the least Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) which are 0.854 and 0.456. When compared to other GRU based models (GRU-CNN, GRU-DNN, and GRU), the MGRU-M1dCNN model is better suited for stock price forecasting and for providing investors a trustworthy means to choose which stocks to buy.

关键词： Training Computational modeling Time series analysis Predictive models Feature extraction Convolutional neural networks Noise measurement

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Conditional Generative Adversarial Network for Intrusion Detection System Based on Deep Learning

Conditional Generative Adversarial Network for Intrusion Det...

引用

international conference on Computer and automation Engineering, ICCAE

作者： Zhen Huang Yong Xiang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

With the development of big data and artificial intelligence, the tremendous amount of data have led to an increase in the number of cyberattacks. Convolutional neural network combined with intrusion detection system has been widely used to improve the performance of the traditional intrusion detection system. And KDD CPU99 is a frequently used dataset with old traffic data and unbalanced data distribution. In this paper, we proposed an intrusion detection system combined with generative adversarial network and deep residual network. The model is trained with NSL-KDD dataset which is an improved version of KDD CPU99. We also use CTGAN to synthesize data to provide more training samples for unknown cyberattacks. All the data is converted into gray scale images for ResNet-18. As a result, the proposed system perfoms well in experiment and shows great generalizaion performance.

关键词： Training Measurement Deep learning Computational modeling Intrusion detection Generative adversarial networks Convolutional neural networks

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Trajectory Tracking for an Adaptive Cruise Control System Using Differential Flatness and Gaussian Processes

Trajectory Tracking for an Adaptive Cruise Control System Us...

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international conference on Methods and Models in automation and Robotics (MMAR)

作者： Bhavini Jeloka Florentina Nicolau Adnane Saoud Ravi N. Banavar Georgia Tech Atlanta United States Quartz EA7393 Laboratory ENSEA Cergy France College of Computing University Mohammed VI Polytechnic Benguerir Morocco Systems and Control Engineering IIT Bombay Bombay India

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In this paper, we explain how differential flatness combined with Gaussian Processes can be applied for the adaptive cruise control system under external, unknown dynamics, to attain accurate trajectory tracking. The proposed approach employs differential flatness for controller design, along with Gaussian Processes to estimate the unknown components in the dynamics. In order to ensure robustness to this data-driven technique, a robust control law is also introduced, that further exploits the Gaussian Process framework. In the recent publication [12], we showed the effectiveness of the proposed control methodology for set-point regulation. In the current paper, we generalize it to trajectory tracking and show numerically that the error dynamics converge to zero.

关键词： Robust control Adaptive systems Trajectory tracking Process control Gaussian processes Robustness Regulation

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Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms

Proactive Algorithms for Stabilizing the State of Technologi...

引用

international Russian automation conference (RusAutoCon)

作者： A. Musaev A. Makshanov D. Grigoriev St. Petersburg State Institute of Technology (Technical University) Saint-Petersburg Russia Department of Computing Systems and Computer Science Admiral Makarov State University of Maritime and Inland Shipping Saint-Petersburg Russia Center for econometrics and business analytics (CEBA) St. Petersburg State University (SPBU) Saint-Petersburg Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given.

关键词： Measurement Machine learning algorithms Statistical analysis Heuristic algorithms Process control Training data Predictive models Random processes Forecasting Monitoring

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computing Midcurve with Multi-Layer and Convolutional Neural Networks

Computing Midcurve with Multi-Layer and Convolutional Neural...

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Computational, Communication and Information Technology (ICCCIT), international conference on

作者： Prashanth Sreenivasan Yogesh H. Kulkarni

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Midcurve computation: the dimension reduction of 2D thin polygon shapes to 1D curves is important for several Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) applications, particularly in finite element analysis and robot path planning. We advance the state-of-the-art by building on previous work on MidcurveNN, through two new neural architectures: a multi-layer dense network and a CNN-based architecture with skip connections. Through comparative analysis, we demonstrate that while the dense network shows limited improvements, the CNN-based architecture with skip connections achieves a tenfold reduction in average loss compared to existing methods. The proposed approach improves the quality of the generated Midcurves.

关键词： Dimensionality reduction Solid modeling Design automation Shape Architecture Buildings Noise Path planning Convolutional neural networks Robots

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RoboKube: Establishing a New Foundation for the Cloud Native Evolution in Robotics

RoboKube: Establishing a New Foundation for the Cloud Native...

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international conference on Autonomous Robots and Agents, ICARA

作者： Yu Liu Aitor Hernandez Herranz Roberto C. Sundin Ericsson Research Stockholm Sweden

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

Cloud native technologies have been observed to expand into the realm of Internet of Things (IoT) and Cyber-physical Systems, of which an important application domain is robotics. In this paper, we review the cloudification practice in the robotics domain from both literature and industrial perspectives. We propose RoboKube, an adaptive framework that is based on the Kubernetes (K8s) ecosystem to set up a common platform across the device-cloud continuum for the deployment of cloudified Robotic Operating System (ROS) powered applications, to facilitate the cloud native evolution in robotics. We examine the process of modernizing ROS applications using cloud-native technologies, focusing on both the platform and application perspectives. In addition, we address the challenges of networking setups for heterogeneous environments. This paper intends to serves as a guide for developers and researchers, offering insights into containerization strategies, ROS node distribution and clustering, and deployment options. To demonstrate the feasibility of our approach, we present a case study involving the cloudification of a teleoperation testbed.

关键词： Cloud computing automation Adaptive systems Service robots Reviews Operating systems Ecosystems

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