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Natural Lighting System Using Fiber Optics for Energy Efficiency 8

Natural Lighting System Using Fiber Optics for Energy Effici...

引用

8th IEEE International Conference on engineering Technologies and Applied sciences, ICETAS 2023

作者： Ibrahim, Babul Salam Soomro, Dur Muhammad Sundarajoo, Sharman Nordin, Zulhelmi Gulf University for Science & Technology Electrical and Computer Engineering Department Kuwait Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia

ISBN: (纸本)9798350327090

The sunlight's utilization as an illumination supply for lighting inside the building has expanded significantly since the 1900s. Lighting holds an integral part of the total electricity usage in a building. It is also one of the scopes with numerous ways to increase energy effectiveness by lowering energy usage. Hence, a natural lighting system is recommended to curtail electrical energy consumption for illumination throughout the daytime. This work aims to create a natural lighting system model to direct and address natural illuminance into challenging and complicated buildings. In this work, solar energy from the sun is conducted towards the fiber optics package's edge by utilizing a convex lens. This system aims to lower electrical-based lighting consumption during daytime illumination by transmitting the solar light over fiber optics. According to the sun's position, the system is sustained by an optic directional controller to guarantee the utmost connected illuminance. The control system would assure the highest illuminance accumulation also automatically turn on the electricity-based illumination if the acquired natural light did not satisfy the sufficient lux. The PIC16F877A microcontroller was used as a processor to power the tracking device engine. The percentage of the reduction in energy usage for daytime lighting was obtained. The obtained results show that this system will save energy during the day by 57%. Therefore, this paper represents a notable improvement in optical fiber natural lighting systems' commercialization in energy-saving and cost reduction. © 2023 IEEE.

关键词： Fiber optics

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Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning 20

Intent-driven Intelligent Control and Orchestration in O-RAN...

引用

20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Habib, Md Arafat Zhou, Hao Iturria-Rivera, Pedro Enrique Elsayed, Medhat Bavand, Majid Gaigalas, Raimundas Ozcan, Yigit Erol-Kantarci, Melike University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada Ericsson Inc. Ottawa Canada

ISBN: (纸本)9798350324334

rApps and xApps need to be controlled and orchestrated well in the open radio access network (O-RAN) so that they can deliver a guaranteed network performance in a complex multi-vendor environment. This paper proposes a novel intent-driven intelligent control and orchestration scheme based on hierarchical reinforcement learning (HRL). The proposed scheme can orchestrate multiple rApps or xApps according to the operator's intent of optimizing certain key performance indicators (KPIs), such as throughput, energy efficiency, and latency. Specifically, we propose a bi-level architecture with a meta-controller and a controller. The meta-controller provides the target performance in terms of KPIs, while the controller performs xApp orchestration at the lower level. Our simulation results show that the proposed HRL-based intent-driven xApp orchestration mechanism achieves 7.5% and 21.4% increase in average system throughput with respect to two baselines, i.e. a single xApp baseline and a non-machine learning-based algorithm, respectively. Similarly, 17.3% and 37.9% increase in energy efficiency is observed in comparison to the same baselines. © 2023 IEEE.

关键词： Controllers

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Intelligent Energy Management Energy Storage Systems Using Machine Learning

Intelligent Energy Management Energy Storage Systems Using M...

引用

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Govindasamy, Chinnaraj Dayana, T.R Nisha Adkar, Siddhika Dilip Prakash, D. Shyam Gupta, A. Shilpa Trivedi, Sanjeev Kumar Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamilnadu Chennai602105 India Vels Institute of Science Technology and Advance Studies Department of Computer Science Chennai India School of Computer Science Engineering and Applications D y Patil International University Akurdi India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Electrical and Electronics Engineering Aurora's Technological Research Institute Uppal Hyderabad India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Department of Electronics Lucknow India

ISBN: (纸本)9798350375237

A nevertheless-emerging generation called cloud computing permits customers to pay for services on a usage-based foundation. Internet-primarily based IT offerings are supplied through cloud computing, at the same time as virtualization enables the availability of PC sources. The muse of cloud computing is the information center, which is made up of networked computers, cables, electricity components, and different components that host and shop corporate records. With cloud facts centers, high overall performance has continually been the most critical concern, yet it compromises strength utilization. The key hassle is to lessen power consumption at the same time as preserving provider nice and performance a good way to stability device performance and strength intake. A detailed grasp of strength use styles within the cloud environment is needed for our suggested technique. We look at power consumption tendencies and reveal how, through the use of the right optimization standards based on our strength intake models, we can keep more strength in cloud records facilities. All through the prediction section, tablet optimization, which has a 97 percent accuracy rate, permits this era to provide extra correct future price forecasts. © 2024 IEEE.

关键词： Machine learning

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Using IOT and WSNs Technology for Monitoring an Aquaponic Greenhouse System 4

Using IOT and WSNs Technology for Monitoring an Aquaponic Gr...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Touhami, Achouak Bourouis, Amina Benahmed, Tariq Touhami, Sana Touhami, Nawal Benahmed, Khelifa Ali Kafi university center Dept. Mathematics and Computer Science Tindouf Algeria Tahri Mohammed university Dept. Mathematics and Computer Science Bechar Algeria Ahmed Draria university Dept. Electrical Engineering Adrar Algeria

ISBN: (纸本)9798331519872

The increasing population, climate fluctuations, soil erosion, limited water resources, and concerns about food security highlight the need for effective and sustainable approaches to food production. Aquaponics represents a sustainable method that conserves land and water while minimizing its environmental impact. This technique operates as a closed-loop, soil-less farming system, requiring meticulous monitoring, control, and management. Monitoring an aquaponic greenhouse system involves tracking various parameters to maintain the health and productivity of both plants and fish. Our research introduces a design and algorithms for monitoring an aquaponic greenhouse, utilizing wireless sensor networks (WSN), aquaponic systems, and Internet of Things (IoT) technology. © 2024 IEEE.

关键词： Aquaponic greenhouse Closed-loop Fish Internet of things Plant Wireless sensor networks

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Hardware-Limited Non-Uniform Task-Based Quantizers 48

Hardware-Limited Non-Uniform Task-Based Quantizers

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bernardo, Neil Irwin Zhu, Jingge Eldar, Yonina C. Evans, Jamie University of Melbourne Department of Electrical and Electronic Engineering Australia University of the Philippines Diliman Electrical and Electronics Engineering Institute Philippines Weizmann Institute of Science Faculty of Math and Computer Science Israel

ISBN: (纸本)9781728163277

Hardware-limited task-based quantization is a new design paradigm for data acquisition systems equipped with scalar analog-to-digital converters using a small number of bits. By taking into account the system task, task-based quantizers can efficiently recover the desired parameters from the low-bit quantized observation. Current design and analysis frameworks for hardware-limited task-based quantization are only applicable to inputs with bounded support and uniform quantizers with non-subtractive dithering. In this paper, we propose a new framework based on generalized Bussgang decomposition that enables the design and analysis of hardware-limited task-based quantizers equipped with non-uniform scalar quantizers or have inputs with unbounded support. We consider the scenario in which the task is linear. Under this scenario, we derive new pre-quantization and post-quantization mappings for task-based quantizers with mean squared error (MSE) that closely matches the theoretical MSE. © 2023 IEEE.

关键词： Analog-to-digital conversion Quantization

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Digital Rubber Duck: Leveraging Large Language Models for Extreme Programming

Digital Rubber Duck: Leveraging Large Language Models for Ex...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Elvira, Timothy Procko, Tyler Thomas Couder, Juan Ortiz Ochoa, Omar Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL United States

ISBN: (纸本)9798350327595

The recent prevalence of Large Language models (LLMs), e.g., GPT-3.5 and GPT-4, has brought about a new age of man-computer symbiosis, where LLMs are employed for a litany of creative, constructive, scientific, or otherwise content-generative tasks, e.g., as general chatbot assistants, writing editors, digital subject matter experts, programming consultants, and so on. Of interest to software engineers is the concept of 'rubber duck debugging', which is the act of expressing code, line-by-line, in natural language, to an inanimate object, e.g., a rubber duck, for the purpose of elucidating potential issues that can then be corrected. In this paper, we detail a workflow process that leverages the concept of rubber duck debugging, replacing the duck with a capable LLM, e.g., GPT-4. We call it Digital Rubber Duck Programming. Furthermore, the Extreme Programming (XP) method, an implementation of the Agile paradigm, is considered as easily integrated with the proposed workflow, as XP is performed in pairs (much like the modern software engineer works in pairwise fashion with an LLM) and because XP places emphasis on performing extensive code reviews and unit testing all code, which capable LLMs like GPT-4 can facilitate. © 2023 IEEE.

关键词： ChatGPT Paper Type - 'Regular research Paper' Code Refactoring Extreme Programmaing GPT-4 Software engineering Walkthroughs

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Distributed Attacks over Federated Reinforcement Learning-Enabled Cell Sleep Control

Distributed Attacks over Federated Reinforcement Learning-En...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Zhang, Han Zhou, Hao Elsayed, Medhat Bavand, Majid Gaigalas, Raimundas Ozcan, Yigit Erol-Kantarci, Melike School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Ericsson Inc. Ottawa Canada

ISBN: (纸本)9798350370218

Federated learning (FL) is particularly useful in wireless networks due to its distributed implementation and privacy-preserving features. However, as a distributed learning system, FL can be vulnerable to malicious attacks from both internal and external sources. Our work aims to investigate the attack models in a FL-enabled wireless networks. Specifically, we consider a cell sleep control scenario, and apply federated reinforcement learning to improve energy-efficiency. We design three attacks, namely free rider attacks, Byzantine data poisoning attacks and backdoor attacks. The simulation results show that the designed attacks can degrade the network performance and lead to lower energy-efficiency. Moreover, we also explore possible ways to mitigate the above attacks. We design a defense model called refined-Krum to defend against attacks by enabling a secure aggregation on the global server. The proposed refined-Krum scheme outperforms the existing Krum scheme and can effectively prevent wireless networks from malicious attacks, improving the system energy-efficiency performance. © 2023 IEEE.

关键词： attacks deep reinforcement learning defense Federated learning radio access networks security

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Texlivernet: Leveraging Medical Knowledge and Spatial-Frequency Perception for Enhanced Liver Tumor Segmentation 22

Texlivernet: Leveraging Medical Knowledge and Spatial-Freque...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Jiang, Xiaoyan Zhou, Zhi Wang, Hailing Wang, Guozhong Fang, Zhijun School of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai China School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9798331520526

Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. How-ever, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracting nuanced features, especially for fine-grained segmentation of tumor boundaries and small lesions. To address these limitations, we developed datasets with lesion-specific text annotations for liver tumors and introduced the TexLiverNet model. TexLiverNet employs an agent-based cross-attention module that integrates text features efficiently with visual features, significantly reducing computational costs. Additionally, enhanced spatial and adaptive frequency domain perception is proposed to precisely delineate lesion boundaries, reduce background interference, and recover fine details in small lesions. Comprehensive evaluations on public and private datasets demonstrate that TexLiverNet achieves superior performance compared to current state-of-the-art methods. Code is available at https://***/sky-visionX/TexLiverNet. © 2025 IEEE.

关键词： Image segmentation

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A Strategy for Boundary Adherence and Exploration in Black-Box Testing of Autonomous Vehicles 1

A Strategy for Boundary Adherence and Exploration in Black-B...

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Thompson, John M. Goss, Quentin Akbas, Mustafa Ilhan Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL United States

ISBN: (纸本)9798350319958

The validation of artificial intelligence (AI) controlled vehicles is a vexing challenge. Black box testing of decision making in these vehicles has been used to abstract out the inner complexity of the AI. In scenario-based black box testing, the AI is placed within a scenario, and the input space for that scenario is explored. The fundamental metric of 'how well tested the system is for that scenario' is based on the input state space coverage. Since most of these spaces have a high number of dimensions, it is critical to sample the state space efficiently and identify performance boundaries for the vehicle under test. In this paper, we propose a boundary adherence approach for autonomous vehicle validation that can explore the boundary between targeted and non-targeted behavior. This paper significantly improves and extends our previous approach that focused on generic black-box testing of AI systems by optimizing the algorithm itself, adding new tools for exploration, and applying the strategy to scenario-based AV testing. We provide an example regression of a scenario which illustrates the ability to model boundaries after they have been explored. Further results on higher dimensions show differing adherence strategies can improve exploration efficiency and how boundary exploration focuses on more 'interesting' scenarios. Upon exploring the boundary, we found that predictions can be made about whether or not the system will result in targeted behavior. © 2023 IEEE.

关键词： Decision making

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Machine Learning and MANET-Driven 5G Technology for Smart Healthcare: Enhanced Patient Diagnosis and Treatment 4

Machine Learning and MANET-Driven 5G Technology for Smart He...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Bhaskaranaik, S. Sreedevi, M. Rafick, S. Ravivarman, G. Rajendiran, M. Maranan, Ramya SVU College commerce management & Computer Science Department of Computer Science Andara Pradesh Tirupati India RVS College of Engineering and Technology Department of Mechanical Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Poonamallee Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This article presents the design, development, implementation, and evaluation of a system for patient health monitoring by synergistically combining machine learning models, mobile ad hoc networks (MANET), and Internet of Things (IoT). In this patient health monitoring system, data are gathered from numerous sensors installed on IoT devices in real-time to support dynamic and continuous health monitoring of patients within predefined constraints. The data are sent over MANET, which has nodes that send data to cloud storage for a centralized management. The data collected from the patients are used to train various Machine Learning (ML) models to learn when the patient is likely to experience a health problem. The analysis carried out in this study reveal that the Support Vector Machine (SVM) is found to be the most accurate model for predicting patient health problems, with an accuracy of 91.23%. The Artificial Neural Network (ANN), Random Forest (RF), and Naive Bayes (NB) rank next, respectively, with accuracy of 88.7%, 86.5%, and 84.4% respectively, following SVM. Recall, accuracy, and F1 score are given to provide comparative information on the relative merits of these models. This integrated healthcare monitoring system, with its timely alert system, real-time monitoring, and powerful predictive analytics, provide patients with personalized and proactive care. It enhances healthcare practices by showing that the data-driven strategic approach and its associated technologies are transforming patient outcomes in the emerging field of healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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