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arXiv 2022年

作者： van Hulst, Jilles Poot, Maurice Kostić, Dragan Yan, Kai Wa Portegies, Jim Oomen, Tom Control Systems Technology Section Dept. of Mechanical Engineering Eindhoven University of Technology Netherlands Center of Competency ASM Pacific Technology Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands Delft Center for Systems and Control Delft University of Technology Netherlands

Advanced feedforward control methods enable mechatronic systems to perform varying motion tasks with extreme accuracy and throughput. The aim of this paper is to develop a data-driven feedforward controller that addresses input nonlinearities, which are common in typical applications such as semiconductor back-end equipment. The developed method consists of parametric inverse-model feedforward that is optimized for tracking error reduction by exploiting ideas from iterative learning control. Results on a simulated set-up indicate improved performance over existing identification methods for systems with nonlinearities at the input. © 2022, CC BY-NC-ND.

关键词： Feedforward control

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Excitation allocation for generic identifiability of linear dynamic networks with fixed modules

arXiv

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arXiv 2022年

作者： Dreef, Mannes H.J. Shi, Shengling Cheng, Xiaodong Donkers, Tijs M.C.F. Van den Hof, Paul M.J. The Dept. of Electrical Engineering Eindhoven University of Technology Netherlands The Delft Center for Systems and Control Delft University of Technology Netherlands The Department of Engineering University of Cambridge United Kingdom

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability in the full measurement case has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

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A Dementia Diagnosis Technique Based on AI and Hardware Acceleration

A Dementia Diagnosis Technique Based on AI and Hardware Acce...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Kasem Khalil Mohammad M. R. Khan Mamun Ahmed Sherif Mohamed Elsersy Ahmad Abdel-Aliem Imam Kamal Abouzaid Electrical and Computer Engineering Department University of Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Computer and Information Systems Dept. Higher Colleges of Technology AlAin Abu Dhabi UAE College of Osteopathic Medicine William Carey University Mississippi MS USA

The effects of Alzheimer’s disease (AD) are devastating, both personally and within the patient’s family, as the disease progresses slowly over many years. It could significantly affect illness consequences and outcomes if detected early and accurately. Non-invasive medical testing often uses blood bio-samples because they are easy to collect and relatively cheap to analyze. This paper proposes a federated learning (FL)-based diagnostic model for AD using blood bio-samples. We have used blood bio-sample data sets downloaded from the ADNI website to test and compare the efficacy of our models. According to the enormous data collected for early AD detection, we used a hardware acceleration scheme to implement our FL model to fasten the training and testing operations. The hardware-accelerator method is implemented using VHDL and Altera 10 GX FPGA. The simulation results show the proposed algorithms reach an accuracy and sensitivity of 89% and 87%, respectively, for early detection while taking less time to train than other state-of-the-art algorithms. The proposed algorithms also consume 52-172 mW, which makes them suitable for constrained devices.

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Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing

Task Reverse Offloading with Deep Reinforcement Learning in ...

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International Conference on computer and Communication engineering, ICCCE

作者： Mamoon M. Saeed Rashid A. Saeed Rania A. Mokhtar Othman O. Khalifa Zeinab E. Ahmed Mohammed Barakat Areeg Ali Elnaim Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif Saudi Arabia Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya Electrical and Computer Engineering Dept. International Islamic University Malaysia Malaysia Computer Engineering Dept. University of Gezira Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan College of Engineering Alneelain University Khartoum Sudan

The Multi-access Edge Computing (MEC) technology’s quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques.

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Probabilistic Decomposed Linear Dynamical systems for Robust Discovery of Latent Neural Dynamics

arXiv

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arXiv 2024年

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model’s ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024, CC BY-SA.

关键词： State space methods

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Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids

TechRxiv

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TechRxiv 2022年

作者： Carli, Raffaele Cavone, Graziana Pippia, Tomás De Schutter, Bart Dotoli, Mariagrazia The Dept. of Electrical and Information Engineering Polytechnic of Bari Bari Italy The Delft Center for Systems and Control Delft University of Technology Delft Netherlands

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. © 2022, CC BY-NC-SA.

关键词： Demand side management

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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A Multi-Carrier Energy Method for Self-Consumption Enhancement in Smart Residential Buildings 11

A Multi-Carrier Energy Method for Self-Consumption Enhanceme...

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11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022

作者： Zhanbolatov, Arshyn Zhakiyeva, Svetlana Azibek, Balzhan Zhakiyev, Nurkhat Kayisli, Korhan Tursyngul, Dushayeva Astana It University Dept. of Science and Innovation Astana Kazakhstan L.N. Gumilyov Eurasian National University Mathematical and Computer Modeling Astana Kazakhstan Astana It University Department of Intellectual Systems and Cybersecurity Astana Kazakhstan Gazi University Electrical-Electronic Engineering Ankara Turkey Zhangir Khan West Kazakhstan Agrarian Technical University Dept. of Information Technology Uralsk Kazakhstan

ISBN: (数字)9781665471404

ISBN: (纸本)9781665471404

For the most time nowadays people stay indoors, at their homes, in buildings, which leads to increased energy demand and resource consumption in the residential sector. In this context, PV deployment has gained traction among residences as there is no cost for the energy and no emissions when the energy is produced. With the growing deployment of PV, the cost per PV panel has comparably decreased and the feed-in tariffs are starting to be gradually lowered and, in some cases, eliminated entirely. This created a new market incentive, known as self-consumption. The problem with self-consumption is a misalignment between supply and demand scheduling due to the intermittent nature of renewable energy sources. The goal of this paper is to apply a comprehensive multi-carrier energy approach for numerical assessment of PV self-consumption enhancement ways in residential buildings. The obtained results can be used for different cases of renewable integration for Smart Homes. © 2022 IEEE.

关键词： Renewable energy resources

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Design of receiver RF front end for mm-Wave 5G applications

Design of receiver RF front end for mm-Wave 5G applications

引用

Learning and technology Conference (L&T)

作者： Aziza I. Hussein Nora Alamoudi Mirna Ali Ahmed Zakaria M. Mourad Mabrook College of Engineering Energy and Technology Research Center Effat University Saudi Arabia Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia Telecommunication Engineer Telecom Egypt Asyut Egypt Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Egypt

Recently, the research emphasis has shifted towards 5G due to its potential to accommodate the increasing demand for data traffic, extensive interconnectivity of devices, and the emergence of numerous novel applications. Given the inadequacy of speed provided by spectrum resources in the lower frequency bands of 5G, a novel sub-generation utilizing millimeter wave frequencies has been introduced. The new sub-generation is called mm-wave 5G and provides higher bandwidth for faster speed and higher capacity. RF system architecture, circuits, and antenna innovation will be required to provide the requisite speed and capacity. In this paper the design challenges and trade-offs in RF front-end circuits and receiver sub-systems are discussed. Moreover, the massive multiple-input multiple-output (MIMO) techniques are examined. In addition, design, and simulation of key components in 5G mm-wave receiver, including the design of linear phased array antenna receiver and an analog-to-digital converter (ADC) is presented. Sigma-Delta (ΣΔM) Converters are a type of ADC with a technique that involves oversampling and shaping quantization noise to achieve a higher resolution which make it a best choice to be used in the design of the mm wave 5G communication applications. The proposed ΣΔM uses multistage architecture to provide high-order noise shaping and high resolution. The simulation results shows that the designed Cascade 2-2 MASH ΣΔ achieve a signal to noise ratio of 110(resolution of 16 bits).

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Team Precoding Towards Scalable Cell-free Massive MIMO Networks

arXiv

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arXiv 2022年

作者： Miretti, Lorenzo Björnson, Emil Gesbert, David Communication Systems Dept. EURECOM Sophia Antipolis France Dept. of Computer Science KTH Royal Institute of Technology Stockholm Sweden Dept. of Electrical Engineering Linköping University Linköping Sweden

In a recent work, we studied a novel precoding design for cell-free networks called team minimum mean-square error (TMMSE) precoding, which rigorously generalizes centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSI). Despite its flexibility in handling different cooperation regimes at the CSI sharing level, TMMSE precoding assumes network-wide sharing of the data bearing signals, and hence it is inherently not scalable. In this work, inspired by recent advances on scalable cell-free architectures based on user-centric network clustering techniques, we address this issue by proposing a novel version of the TMMSE precoding design covering partial message sharing. The obtained framework is then successfully applied to derive a variety of novel, optimal, and efficient precoding schemes for a user-centric cell-free network deployed using multiple radio stripes. Numerical simulations of a typical industrial internet-of-things scenario corroborate the gains of TMMSE precoding over competing schemes in terms of spectral efficiency under different power constraints. Although presented in the context of downlink precoding, the results of this paper may be applied also on the uplink. © 2022, CC BY.

关键词： Mean square error

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