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Fuzzy-Deep Learning-Based Artificial Intelligence for Edge Computing and Real-Time Decision-Making in Uncertain IoT Environments

Fuzzy-Deep Learning-Based Artificial Intelligence for Edge C...

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Advances in computer science, electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： B.P. Joshi Akhilesh Singh Nagendra Kumar Siddharth Rautela Department of Mathematics Department of Computer Science & Engineering Graphic Era Hill University India Department of Electrical Engineering VMSB UTU Campus Pithoragarh India Department of Electrical & Electronics Engineering GL Bajaj Institute of Technology & Management Greater Noida India Department of Mathematics Central Board of Secondary Education Bhimtal Uttarakhand India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Owing to the Internet of Vehicles (IoV) quick growth, both academics and the sector have paid close emphasis to vehicular edge computation (VEC). Nevertheless, because of the unbalanced congestion and the strict delay requirements, task offloading in various junction situations continues to struggle from inefficient resource allocated and poor operation implementation standards. This study proposes a task-offloading technique using a fuzzy decision-making method to deal with ambiguity and uncertainties to solve these problems. Roadside Utilities (RSUs) placed alongside remote roadways typically have limited energy resources, thus they must offer energy-effective planning assistance with the distribution of duties to VEC. However, planning decisions for regional task execution incur computational costs, and assigning duties to edge automobiles incurs transmission costs, making energy usage management difficult. Task data transmission to edge automobiles results in increased RSU power usage even while task offloading lowers response delay. To meet task schedule and supply restrictions, this study proposes an energy-effective automobile planning issue for offloading functions to mobile edge units. This study develops a planning technique depending on on-policy deep reinforcement learning (DRL) and a fuzzy-based DRL to address the extremely complex problem brought on by a rise in the number of automobiles under RSU service. When contrasted to the Q-learning method, this FRL not only speeds up the learning procedure but also enhances long-term payoff.

关键词： Schedules Uncertainty Q-learning Decision making Deep reinforcement learning Real-time systems Delays Automobiles Standards Edge computing

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A Deep Learning Approach For Faults Recognition of Dissolved Oxygen Sensor in Wastewater Treatment Plants

A Deep Learning Approach For Faults Recognition of Dissolved...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Liliana Maria Ghinea Mihaela Miron Harsha Ratnaweera Department of Automatic Control and Electrical Engineering Department Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Department of Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics Galați Romania Faculty of Science and Technology Norwegian University of Life Sciences Oslo Norway

Fault diagnosis in wastewater treatment plants (WWTPs) is important to protect communities and ecosystems from toxic elements discharged into water. In this sense, fault identification of sensors plays an important role as they are the key components of the water plants control, especially because environmental legislation is very strict when referring to failures or anomalies in WWTPs. This paper analyzes the performances of two Deep Learning models, a Feedforward Neural Network (FFNN) and a 1D Convolution Neural Network (1DCNN) for identifying five operating states of the dissolved oxygen (DO) sensor: normal and faulty (bias, stuck, spike and precision degradation faults). The experiments were conducted on the Benchmark Simulator Model No 2 (BSM2) developed by the IWA Task Group. The performance of the Deep Learning (DL) classifiers was evaluated via accuracy, precision, recall, and F1-score metrics. The best overall classification accuracy was obtained by FFNN, 98.32% for training and 98.30% for testing.

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Implicit Neural Representation for Mesh-Free Inverse Obstacle Scattering

Implicit Neural Representation for Mesh-Free Inverse Obstacl...

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Asilomar Conference on Signals, Systems & computers

作者： Tin Vlašić Hieu Nguyen AmirEhsan Khorashadizadeh Ivan Dokmanić Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Mathematics and Computer Science University of Basel Basel Switzerland

Implicit representation of shapes as level sets of multilayer perceptrons has recently flourished in different shape analysis, compression, and reconstruction tasks. In this paper, we introduce an implicit neural representation-based framework for solving the inverse obstacle scattering problem in a mesh-free fashion. We express the obstacle shape as the zero-level set of a signed distance function which is implicitly determined by network parameters. To solve the direct scattering problem, we implement the implicit boundary integral method. It uses projections of the grid points in the tubular neighborhood onto the boundary to compute the PDE solution directly in the level-set framework. The proposed implicit representation conveniently handles the shape perturbation in the optimization process. To update the shape, we use PyTorch's automatic differentiation to backpropagate the loss function w.r.t. the network parameters, allowing us to avoid complex and error-prone manual derivation of the shape derivative. Additionally, we propose a deep generative model of implicit neural shape representations that can fit into the framework. The deep generative model effectively regularizes the inverse obstacle scattering problem, making it more tractable and robust, while yielding high-quality reconstruction results even in noise-corrupted setups.

关键词： Shape Perturbation methods Integral equations Scattering Manuals Programming Multilayer perceptrons

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Using Z3 to Verify Inferences in Fragments of Linear Logic

arXiv

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arXiv 2023年

作者： Docef, Alen Negulescu, Radu Prunescu, Mihai Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Research Center for Logic Optimization and Security Faculty of Mathematics and Computer Science University of Bucharest Bucharest Romania Simion Stoilow Institute of Mathematics Romanian Academy Bucharest Romania

Linear logic is a substructural logic proposed as a refinement of classical and intuitionistic logics, with applications in programming languages, game semantics, and quantum physics. We present a template for Gentzen-style linear logic sequents that supports verification of logic inference rules using automatic theorem proving. Specifically, we use the Z3 Theorem Prover [8] to check targeted inference rules based on a set of inference rules that are presumed to be valid. To demonstrate the approach, we apply it to validate several derived inference rules for two different fragments of linear logic: MLL+Mix (Multiplicative Linear Logic extended with a Mix rule) and MILL (Multiplicative Intuitionistic Linear Logic). © 2023, CC BY.

关键词： Linear algebra

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Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems 1

Delay Tolerant and Energy Reduced Task Allocation in Interne...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Raghunath Patil, Dipak Borkar, Bharat Markad, Ashok Kadlag, Sunil Kumbhkar, Makhan Jamal, Ahmad Amrutvahini College of Engineering Dept. of Computer Engineering Pune Sangamner India Amrutvahini College of Engineering Dept. of Information Technology Pune Sangamner India Amrutvahini College of Engineering Department of Electrical Engineering Pune Sangamner India Christian Eminent College Indore & Visiting Faculty School of Data Science and Forecasting Devi Ahilya University Department of Computer Science & Electronics Indore India Tulas Institute Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665456876

Through the utilization of the Internet, cloud computing supplies storage and computation resources to deliver services for many sectors. The speed of such systems suffers, though, since delay-sensitive systems, such as smart city and health applications, increasingly need for processing over massive volumes of data transmitted to centralised datacenters. When compared to cloud services, the paradigms of fog as well as edge computing provide innovative solutions by affecting the financial closer to the customer and by having low energy consumption and latency effectiveness. To optimise expense and resource efficiency, enhance QoS, and increase security and privacy, it is crucial to discover the best locations for services and assets inside the 3 IoT. The bipartite graph task scheduling method we provide in this research enhances cost effectiveness in real world applications with strict deadlines. The proposed method's performance in terms of delay, network congestion, as well as cost is deployed and tested to use the iFogSim simulator, an expanded version of CloudSim. The evaluation results demonstrate that the suggested algorithm outperforms Round-Robin and Minimum Response Time methods in terms of both cost as well as throughput. © 2022 IEEE.

关键词： Internet of things

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Optimization of Uniform Amplitude Periodic Linear Phased Arrays for Grating Lobe Reduction

Optimization of Uniform Amplitude Periodic Linear Phased Arr...

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European Conference on Antennas and Propagation, EuCAP

作者： Yanki Aslan Nehir Berk Onat Faculty of Electrical Engineering Mathematics and Computer Science Microwave Sensing Signals and Systems Group Delft University of Technology Delft The Netherlands

The optimization of mode excitation coefficients in linear periodic arrays of multi-mode antenna elements is studied for grating lobe reduction. A novel beamforming architecture is proposed with a new optimization problem based on equi-amplitude element excitations for optimal power efficiency. The capabilities of the proposed synthesis approach on suppressing the grating lobe for wide scan angles, and on maintaining the peak gain at the steering angle are analyzed. A 16-element 0.7-wavelength spaced array of dual-mode circular patch antenna elements is used for demonstration purposes. It is shown that a good performance trade-off is achieved when the excitation amplitude of the high order mode is restricted to a sufficiently large value. The ratio of the peak gain outside the main lobe to the gain at the angle of steering is reduced up to about −15 dB and −14 dB for scanning towards 30 and 45 degrees off-broadside, respectively.

关键词： Phased arrays Array signal processing Patch antennas Insertion loss Propagation losses Linear antenna arrays Gratings

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Channel Estimation using LS and MMSE Channel Estimation Techniques for MIMO-OFDM Systems 4

Channel Estimation using LS and MMSE Channel Estimation Tech...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Ahmed, Ali Saadoon Hamdi, Mustafa Maad Abood, Mohammed Salah Khaleel, Arshad Mohammed Fathy, Maha Khaleefah, Shihab Hamad Al-Maarif University College Department of Computer Science Al-Anbar Iraq Al-Maarif University College Department of Computer Engineering Techniques Al-Anbar Iraq Beijing Institute of Technology School of Information and Electronics Engineering Beijing China Altinbas Universiti Department of Electrical and Computer Engineering Istanbul Turkey Al-Maarif University College Faculty of Computer Science Anbar31001 Iraq

ISBN: (纸本)9781665468350

The conventional strategies utilized for channel assessment don't exploit the multipath lack. In MIMO-OFDM frameworks, channel assessment is critical for computing framework execution. There are pre-codes and books higher than obscure channel boundaries using channel status data (CSI). Multiple Input Multiple Output (MIMO) systems are practical innovations that can be used to meet the growing demand for remote communication and fast data transfers. To predict the transmission and gathering of data between radio wires, various procedures for channel assessment can be used. The article focuses on a channel estimation technique that employs LS and MMSE algorithms and is based on pilot-assisted block type training symbols. The results for the BER and SNR scenarios were presented using the LS (least squares) and MMSE assessment techniques. The bit blunder rate (BER) is tested for various computerized tweak plans with various sign to-commotion (SNR) values in the OFDM framework. This resulted in fantastic results. Because of this, it can be concluded that the least-squares assessment strategy is a valuable method for assessing channels. © 2022 IEEE.

关键词： Orthogonal frequency division multiplexing

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AN ARCHITECTURAL DESIGN DECISION MODEL FOR RESILIENT IOT APPLICATION

arXiv

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arXiv 2023年

作者： Iglesias, Cristovao Freitas Miceli, Claudio Bolic, Miodrag School of Electrical Engineering and Computer Science Faculty of Engineering University of Ottawa Canada PESC Federal University of Rio de Janeiro Brazil

The Internet of Things is a paradigm that refers to the ubiquitous presence around us of physical objects equipped with sensing, networking, and processing capabilities that allow them to cooperate with their environment to reach common goals. However, any threat affecting the availability of IoT applications can be crucial financially and for the safety of the physical integrity of users. This feature calls for IoT applications that remain operational and efficiently handle possible threats. However, designing an IoT application that can handle threats is challenging for stakeholders due to the high susceptibility to threats of IoT applications and the lack of modeling mechanisms that contemplate resilience as a first-class representation. In this paper, an architectural Design Decision Model for Resilient IoT applications is presented to reduce the difficulty of stakeholders in designing resilient IoT applications. Our approach is illustrated and demonstrates the value through the modeling of a case. © 2023, CC BY.

关键词： Internet of things

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Solutions for Digital Interaction of a Resilient Energy Community in a Service-oriented Framework 12

Solutions for Digital Interaction of a Resilient Energy Comm...

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12th International Conference and Exposition on electrical and Power engineering, EPE 2022

作者： Sanduleac, Mihai Ionescu, Constantin Mandis, Alexandru Gropa, Victor Efremov, Cristina Sanduleac, Vlad Department of Power Systems Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Dep. of Energy Generation and Use Faculty of Energy Engineering University Politehnica of Bucharest Bucharest Romania Faculty of Energetics and Electrical Engineering Technical University of Moldova Chisinau Moldova Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Romania

ISBN: (数字)9781665489942

ISBN: (纸本)9781665489942

Energy communities are emerging entities which need their own Information and Communication System. Resilience is a key metric of such communities, and it has to be implemented for both energy supply versus public network outages and for its information system versus cyber-attacks, acting as cyber-citadels which have to resist external malicious attacks while having also digital interactions with external entities. The paper presents solutions for a resilient energy community with appropriate implementations of digital interaction for data exchange, in order to acquire external energy service for specialized companies and to deliver also information and communication-related services for external users. Principles of a Contractual Data Protection Regulation are presented, as an adaptation of data protection tailored for automated energy services and based on secure data exchange. The principles are verified in a demonstration project which has also external entities with digital interaction. © 20XX IEEE.

关键词： Cybersecurity

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A Case Study on ChatGPT Question Generation

A Case Study on ChatGPT Question Generation

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IEEE International Conference on Big Data

作者： Winston Chan Aijun An Heidar Davoudi Dept. of Electrical Engineering and Computer Science York University Toronto Canada Faculty of Science Ontario Tech University Oshawa Ontario

The advent of transformers and the subsequent development of Large Language Models (LLMs) based on these technologies has revolutionized the field of Natural Language Processing (NLP). These models are able to understand and generate coherent natural language and hold conversations with humans continuously. Meanwhile, ChatGPT has become famous among many LLMs for its general-purpose characteristics and versatility. With that in mind, we investigate the capabilities of ChatGPT, which is very successful in many downstream NLP tasks on the task of Question Generation (QG). In particular, our experiments show that appropriate context through our designed prompts makes ChatGPT an appropriate tool for accurately performing the QG task. We compare ChatGPT’s question generation results with the state-of-the-art models, particularly on the SQuAD and car manual datasets. The results show that ChatGPT is able to compete with or even outperform some of the baseline models. Furthermore, we illustrate that we may improve ChatGPT through additional fine-tuning of the prompts. Finally, we also investigate the use of ChatGPT to evaluate QG models. While the use of ChatGPT for such purposes is still in its early stages, our results demonstrate that ChatGPT can potentially be a strong QG accuracy evaluator comparable to human evaluators.

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