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Air Quality Prediction using Deep Learning models

Air Quality Prediction using Deep Learning models

引用

2024 international conference on Advancements in Power, communication and Intelligent Systems, APCI 2024

作者： Fathima, M. Dhilsath Donavalli, Sashank Kambham, Harshitha School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chennai Kattankulathur India

ISBN: (纸本)9798350363289

In today's world, air pollution is an important concern for both the general public and the environment. Monitoring and forecasting pollutant concentrations in the environment is critical to effective pollution control and management. Using advanced deep-learning techniques, this study focuses on the development of a time series air quality prediction (AQP) model for numerous pollutants in the air, including small particles of PM2.5, PM10, ground-level ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2), and nitrogen dioxide (NO2). Time series data are used in the study to capture temporal relationships and seasonal trends inherent in air quality measurements. This proposed AQP model evaluates many deep learning architectures, such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (Bi- LSTM). This study demonstrates how effective these models are at capturing temporal dependencies and seasonal patterns in air quality datasets, pointing towards novel approaches for precise and timely air quality monitoring. The results of this research provide a substantial contribution to the advancement of air quality prediction systems, allowing for better decision- making for public health and environmental protection. © 2024 IEEE.

关键词： Sulfur dioxide

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Joint Optimization of Transmission and computing Resource in Mobile Edge computing Systems with Multiple Base Stations 5

Joint Optimization of Transmission and Computing Resource in...

引用

5th IEEE Advanced Information Management, Communicates, Electronic and automation control conference, IMCEC 2022

作者： Ding, Zhonglin Lv, Chao Huang, Zhongming Zhang, Ming Chang, Ming Liu, Rui State Grid Electric Power Research Institute Co. Ltd. China Nanjing Nari Information Communication Technology Co. Ltd. China

ISBN: (纸本)9781665479677

In the power grid networks, mobile edge computing (MEC) is a critical technology to tackle the insufficient processing capacity and limited resources of smart devices. This paper considers a smart grid system, where MEC servers are used to collect and process the information obtained by the power terminals. To guarantee energy conservation, we aim to minimize total energy consumption by jointly optimizing base stations association, transmission power, bandwidth allocation and com-pression rate allocation. By the problem decomposition, four sub-problems are derived and solved iteratively. The simulation results show that the proposed MEC system can significantly reduce the total energy consumption. © 2022 IEEE.

关键词： Energy utilization

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Three-class Severity Detection and Classification of Brain Tumor ROI Using YOLO-v9 4

Three-class Severity Detection and Classification of Brain T...

引用

4th international conference on Computer, communication, control and Information Technology, C3IT 2024

作者： Jain, Garima Jain, Sanat Vishwakarma, Harshlata Jayanthi, J. Devi, M. Anousouya Bramhe, Vijendra Singh School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India School of Computer Science and Engineering Vit Bhopal University Sehore India Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350389128

A vital part of diagnosing and treating medical issues is the use of medical imaging for the detection and categorization of brain tumors. For this research, we looked into how well the YOLOv9 deep learning architecture identified ROIs and classified brain tumors using a dataset consisting of nine thousand annotated images. One of the main aim of our work was to find the region of interest (ROI) in brain scans as precisely as possible so that cancers could be located. The second goal was to use the ROIs to categorize the tumors as meningioma, pituitary, or glioma. Mean average precision (mAP) for all tumor classes is 0.85, and for meningioma brain tumors particularly it is 0.949, indicating competitive accuracy metrics for the model. In this trained the network with weights from a generic object identification task before initializing it with YOLOv9 architecture-based transfer learning techniques. After that, adjusted the model based on the unique features of medical imaging by fine-tuning it using our dataset of brain tumors. The significant mAP scores, especially for meningioma tumors, highlight the potential of this deep learning approach to improve clinical workflows in radiology and neurology. This study contributes to the growing area of deep learning applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Digitization in Production: a Use Case on a Cloud-based Manufacturing Execution System 22

Digitization in Production: a Use Case on a Cloud-based Manu...

引用

8th international conference on Computer Technology Applications, ICCTA 2022

作者： Pessl, Ernst Rabel, Birgit Institute Industrial Management Fh JOANNEUM-University of Applied Sciences Austria

ISBN: (纸本)9781450396226

All types of companies, including manufacturing companies, are facing a major shift due to the influence of digitization. To fulfill the requirements of digital production, the classic hierarchical architecture of the automation pyramid will change to decentralized distributed communication participants in the cloud. This will lead to a flattening of the pyramid towards service-oriented network architectures in the cloud. The paper focuses on the necessary theoretical aspects concerning dissolution of the automation pyramid in relation to Manufacturing Execution Systems and cloud computing. The second purpose of this paper is to show, how dissolution in an industrial environment can be realized by means of a use case. This demonstrates how manufacturing companies can participate in cloud solutions for Manufacturing Execution Systems. © 2022 Owner/Author.

关键词： automation

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OPTIMISED OPERATIONAL MANAGEMENT OF DISTRIBUTION GRIDS BY UTILISING FLEXIBILITIES THROUGH automation OF ELECTRICAL ASSETS USING A MULTI-AGENT-SYSTEM APPROACH 27

OPTIMISED OPERATIONAL MANAGEMENT OF DISTRIBUTION GRIDS BY UT...

引用

27th international conference on Electricity Distribution, CIRED 2023

作者： Riebesel, David Schramm, Simon Mehlmann, Gert Luther, Matthias Friedrich-Alexander-Universität Erlangen-Nürnberg Institute of Electrical Energy Systems Germany

ISBN: (纸本)9781839539596

By reducing inertia and increasing the number of distributed generation and load units, the energy transition places additional stress on the distribution grid. However, there is an opportunity to take advantage of the new grid structure. This can be achieved with a certain degree of automation through information and communication technology (ICT). A multi-agent system (MAS) control approach was used to address this problem and take advantage of the decentralized topology. Through the MAS, each generation/load unit gains a degree of autonomy and intelligence while being able to communicate with others, in this case the immediate neighborhood. Under the assumption of existing load forecasting, a local control algorithm for DERs is derived. This improves the operational management of distribution networks by reducing the residual load through a decentralized approach. The proof of concept is validated in a co-simulation using a shared memory (SHM) interface to connect the electrical network model with the MAS logical model. © The Institution of Engineering and Technology 2023.

关键词： Multi agent systems

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Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor 23

Implementing and Benchmarking the Locally Competitive Algori...

引用

2023 international conference on Neuromorphic Systems, ICONS 2023

作者： Parpart, Gavin Risbud, Sumedh Kenyon, Garrett Watkins, Yijing Pacific Northwest National Laboratory RichlandWA United States Intel Labs Neuromorphic Computing Lab Santa ClaraCA United States Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798400701757

Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing. The Locally Competitive Algorithm (LCA) has been utilized for power efficient sparse coding on neuromorphic processors, including the first Loihi processor [4, 5]. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible [9]. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is orders of magnitude more efficient and faster for large sparsity penalties, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent, autonomous, real-time processing on small robots, satellites where there are strict SWaP (small, lightweight, and low power) requirements. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices. © 2023 ACM.

关键词： Energy efficiency

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Tree of Thought Prompt in Robotic Arm control 18th

Tree of Thought Prompt in Robotic Arm Control

引用

18th international conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Liang, Fan Ge, Lingqiang Sam Houston State University HuntsvilleTX United States Columbus State University ColumbusGA United States

ISBN: (纸本)9783031714696

The integration of Artificial Intelligence (AI) into robotic arms, particularly through the use of Large Language Models (LLMs), marks a significant advancement in automation, enabling these devices to perform complex tasks with unparalleled precision. Despite the potential of LLMs to revolutionize robotic functionality by understanding and processing human language, their application faces challenges, notably in generating precise, actionable commands for robotic operations. To address these issues, we introduce the "Tree of Thoughts" (ToT) methodology, enhanced by a Depth-First Search (DFS) strategy, designed to optimize LLM performance in robotic control. The ToT approach bridges the gap between LLMs’ abstract language comprehension and the generation of specific instructions, streamlining the command generation process and enabling robotic arms to execute tasks with increased precision and adaptability. This methodology not only demonstrates the feasibility of adapting LLMs as feedback controllers for complex tasks but also significantly improves the performance and adaptability of robotic arms, paving the way for more intelligent, responsive robotic systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic arms

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LoRa-Powered Water Management System for Real Time Monitoring and control in Gated Communities

LoRa-Powered Water Management System for Real Time Monitorin...

引用

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

作者： B. Padmavathi L. Sathya G.K. Sathya Narayanan B. Sanjana Ashiq Rasool Department of ECE SRM Institute of Science and Technology Ramapuram Campus Chennai

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In today’s world, effective water resource management is more crucial than ever. This work unveils a forward-thinking, affordable, and reliable solution for real-time water level and quality monitoring across multiple tanks using the powerful ESP32 microcontroller. By seamlessly integrating the ESP32 module, the solution empowers individuals and communities, including gated communities, to remotely monitor and control their water resources across these multiple tanks. Incorporating LoRa technology enables long-range, low-power communication, making it especially beneficial for tracking tanks spread over large areas without the need for complex wiring infrastructure. The system also tracks water quality, providing users with a comprehensive view of their water resources to ensure both efficiency and safety. This forward-thinking approach to water management is set to redefine how we manage this essential resource, contributing to a more sustainable future.

关键词： Wiring Technological innovation automation LoRa Water quality Logic gates Real-time systems Reliability Water resources Monitoring

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Evaluation of control over the Edge of a Configurable Mid-band 5G Base Station 6

Evaluation of Control over the Edge of a Configurable Mid-ba...

引用

6th IEEE international conference on Fog and Edge computing (ICFEC)

作者： Peng, Haorui Tarneberg, William Fitzgerald, Emma Tufvesson, Fredrik Kihl, Maria Lund Univ Dept Elect & Informat Technol Lund Sweden Warsaw Univ Technol Inst Telecommun Warsaw Poland

ISBN: (纸本)9781665495240

Mission-critical applications such as industrial control processes are evolving towards a new development paradigm by offloading their heavy computations to the edge of the emerging Fifth Generation Wireless Specifications (5G) network. In this manner, the applications can gain the economical and efficiency benefits of cloud computing, as well as reliable communication from the 5G network. However, the limited access to a configurable infrastructure of the 5G network and its edge computing infrastructure has restrained academic researchers from experimenting and validating their mission-critical application design under reasonable communication and computation scenarios. In this paper, we present a configurable mid-band 5G Stand-Alone (SA) deployment and demonstrate a control process that is running over the edge of the 5G network. We show in this paper a complete system setup for control over the Edge (CoE) of the 5G network, and validate the feasibility of deploying similar mission-critical applications over the edge of 5G network.

关键词： control over edge 5G Mission-critical application Mobile edge computing

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Identify the objective parameters of direct communication between PLC and MS SQL server 24

Identify the objective parameters of direct communication be...

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24th international Carpathian control conference, ICCC 2023

作者： Flaga, Stanislaw Majcher, Maciej AGH University of Science and Technology Faculty of Mechanical Engineering and Robotics Department of Process Control Krakow Poland

ISBN: (纸本)9798350310221

When building digital twins of existing technical solutions, we need a large amount of data on the parameters of their operation under various conditions. The source of data on the values of process variables at the lowest level is control devices - most often PLCs. There are many ways to acquire the qualities of process variables available from the PLC level. Increasingly, OPC UA is replacing the OPC model, and the OPC UA server is increasingly a functionality of the PLC CPU. The Industrial Internet of Things IIoT is used in companies moving toward Factory 4.0. Data from controllers can be acquired using high-level languages such as C++, C# or Python. Libraries often made available under an open-source license are used for this. For example, you can find commercial libraries for communication between PLCs of selected manufacturers and MySQL servers. Siemens provides a library for communication between S7-1200/1500 controllers and MS SQL server. The author has yet to find an exhaustive analysis of such a solution. In the literature, there need to be more studies of experiments describing the performance of query generation and execution and the impact of the library on PLC performance. The article presents the results of an experimental study of this issue. © 2023 IEEE.

关键词： Libraries

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