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NCFS:new chaotic fuzzy system as a general function approximator

Journal of Control and Decision 2023年第4期10卷 514-528页

作者： Hamid Abbasi Mahdi Yaghoobi Arash Sharifi Mohammad Teshnehlab Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehranIran Department of Electrical and Computer Engineering Mashhad BranchIslamic Azad UniversityMashhadIran Department of Electrical and Computer Engineering K.N.T University of TechnologyTehranIran

Conventional fuzzy systems(type-1 and type 2)are universal *** goal of this paper is to design and implement a new chaotic fuzzy system(NCFS)based on the Lee oscil-lator for function approximation and chaotic *** incorporates fuzzy reasoning of the fuzzy systems,self-adaptation of the neural networks,and chaotic signal generation in a unique *** features enable the structure to handle uncertainties by generating new information or by chaotic search among prior *** fusion of chaotic structure into the neurons of the membership layer of a conventional fuzzy system makes the NCFS more capable of confronting nonlinear *** on the GFA and Stone-Weierstrass theorems,we show that the proposed model has the function approximation *** NCFS perfor-mance is investigated by applying it to the problem of chaotic *** results are demonstrated to ilustrate the concept of function approximation.

关键词： Chaotic fuzzy system function approximation chaotic neural network oscillatory neuron

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Incomplete and Asymmetric Information in General Lotto Games

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IEEE Transactions on Automatic Control 2024年第6期70卷 3617-3632页

作者： Paarporn, Keith Chandan, Rahul Alizadeh, Mahnoosh Marden, Jason R. University of Colorado Department of Computer Science Colorado Springs United States Amazon Robotics United States University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States

In this paper, we consider incomplete and asymmetric information formulations of the General Lotto game, which describes two opposing players that strategically allocate limited resources over multiple contests. In particular, we consider scenarios where one of the player's resource budget is common knowledge while the other player's is private. Our main contribution provides complete equilibrium characterizations in the scenario where the private resource budget is drawn from a Bernoulli distribution. We then leverage these characterizations to analyze a resource assignment problem where a commander must decide how to assign resources to sub-colonels that compete against opponents in separate General Lotto games. While optimal deterministic policies have previously been characterized in the literature, we broaden the context by deriving optimal randomized policies, which induce asymmetric information General Lotto games. Leveraging our equilibrium characterizations, we demonstrate that randomization can offer a four-fold improvement in the commander's performance over the optimal deterministic assignments. © 1963-2012 IEEE.

关键词： Budget control

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Novel Design of Blockchain based IIoT Framework for Smart Factory 26

Novel Design of Blockchain based IIoT Framework for Smart Fa...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Song, Ahyun Seo, Euiseong Kim, Heeyoul Sungkyunkwan University Department of Computer Science and Engineering Korea Republic of Kyonggi University Division of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9791188428120

A smart factory is an advanced manufacturing system that utilizes various cutting-edge technologies such as IIoT (Industrial Internet of Things), big data, AI(Artificial Intelligence), blockchain to automate and optimize production processes. While there is a growing demand for smart factories in recent times, the adoption and proliferation of these facilities have been delayed due to concerns about security, the reliability of collected information, and challenges in management and control. Moreover, traditional centralized smart factory systems pose a risk of operational downtime in the event of failures or attacks since a central server controls the entire system. Therefore, we propose the design of a secure, transparent, and reliable blockchain-based IIoT framework for smart factories. The framework consists of three layers: the blockchain core layer, the blockchain operation layer, and the IIoT service layer. The IIoT service layer plays a crucial role in providing various services essential for the advancement of smart factories, utilizing blockchain technology. Our proposed framework combines IIoT and blockchain technologies to leverage the advantages of decentralization, trust, security, transparency, data management, and traceability. © 2024 Global IT Research Institute - GIRI.

关键词： Blockchain

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An intelligent impulsive noise mitigation with deep learning method

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International Journal of Mechanical System Dynamics 2024年第3期4卷 346-360页

作者： Guo Yang Yuwen Qian Zikun Wang Xiangwei Zhou Wen Wu The School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjingChina The Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLouisianaUSA

To enable message transmission among sensors and equipment,power line communication(PLC)is a widely adopted smart ***,due to the occurrence of impulsive noise(IN),reliable transmissions over PLC channels in the smart grid are ***,in this paper,we propose an adaptive noise mitigation scheme to clip the IN with the sliding window-based method,where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time *** detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme,we develop an intelligent algorithm based on the long short-term memory *** prevent the useful signals from being eliminated as IN signals,we propose the accelerated proximal gradient method(APGM)based on tone reservation to reduce the peak-to-average power ratio(PAPR)for the transmitting signals with low computational *** addition,the closed-form expression of the bit error rate(BER)is derived for the proposed sliding window-based IN mitigation scheme according to the probability density function of the *** results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation *** addition,the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

关键词： impulsive noise deep learning smart grid

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Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

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China Communications 2022年第10期19卷 12-20页

作者： Yunus Dursun Fang Fang Zhiguo Ding School of Electrical and Electronic Engineering University of ManchesterManchesterUK Department of Electrical and Computer Engineering and the Department of Computer Science Western UniversityLondonCanada

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

关键词： NOMA MEC MIMO Generalized sin-gular value decomposition sixth generation networks(6G) delay minimization

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Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading

IEEE Networking Letters

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IEEE Networking Letters 2024年第4期6卷 232-236页

作者： Zhou, Changshi Liu, Weiqi Han, Tao Ansari, Nirwan New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States

From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage. © 2019 IEEE.

关键词： Reinforcement learning

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An Efficient Federated Learning Framework for IoT Intrusion Detection 100

An Efficient Federated Learning Framework for IoT Intrusion ...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Chen, Yushen Fang, Fang Wang, Boyu Zhang, Lan Western University Department of Computer Science London Canada Western University Department of Electrical and Computer Engineering London Canada Clemson University Department of Electrical and Computer Engineering Clemson United States

ISBN: (纸本)9798331517786

The exponential growth of the Internet of Things (IoT) ecosystems has raised significant cybersecurity concerns. Deep learning (DL)-based methods have shown promising performance in detecting potential cyber threats in IoT networks. However, as these methods often involve data centralization, they can pose serious data privacy issues for IoT users and increase the communication burden of local networks. Federated learning (FL), as a distributed learning paradigm, enables privacy-preserving training of IoT intrusion detection models by requiring only model updates from IoT devices. However, the resource-constrained nature of IoT devices can significantly decrease FL training efficiencies, such as increased training latency and delayed convergence speed. Moreover, the data heterogeneous issues of IoT devices can also impact the accuracy and robustness of the trained model. To address these challenges, we propose an efficient FL framework, FedKD-Prox, based on federated proximal (FedProx) and knowledge distillation (KD). To improve the prediction accuracy within a limited time budget, the proposed framework aims to efficiently exploit the computation capability of the IoT trainers, reduce the communication overhead of FL, and alleviate the impact of heterogeneous data issues. The simulation results show that FedKD-Prox achieves higher accuracy and improves the robustness of the trained intrusion detection model. © 2024 IEEE.

关键词： Federated learning

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A 28.8-mW Accelerator IC for Dark Channel Prior-Based Blind Image Deblurring

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IEEE Journal of Solid-State Circuits 2024年第6期59卷 1899-1911页

作者： Chen, Po-Shao Chen, Yen-Lung Lee, Yu-Chi Fu, Zih-Sing Yang, Chia-Hsiang Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI48109 United States Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science CambridgeMA02139 United States National Taiwan University Department of Electrical Engineering Graduate Institute of Electronics Engineering Taipei10617 Taiwan

This work presents an accelerator that performs blind deblurring based on the dark channel prior. The alternating minimization algorithm is leveraged for latent image and blur kernel estimation. A 2-D Laplace equation solver is embedded to reduce the latency by 56% for boundary wrapping. For latent image estimation, gradient data locality is employed to reduce the latency by 57%. A sorting engine is designed to reduce the latency in data access by 96% for calculating the dark channel. A pipelined mixed-radix 1-D fast Fourier transform (FFT) engine is used for efficient latent image estimation and blur kernel estimation. By employing image size approximation, 85% of additions and 97% of multiplications for FFT can further be saved. In the blur kernel estimator, a 2-D convolution engine with a parallel architecture is implemented, reducing the latency by 79%. The accelerator supports blur kernels of 25times 25 and 49times 49 pixels for blurred images of 129times 129 and 257times 257 pixels, respectively. Fabricated in 40-nm CMOS, the accelerator's core area is 3.98 mm2. The chip dissipates 28.8 mW at 65 MHz from a 0.65-V supply. It can estimate a blur kernel of 25times 25 pixels for an image patch with 129times 129 pixels for deblurring a full-HD image in 1.7 s, achieving a 2562times shorter latency than a high-end CPU. Compared with the state-of-the-art design, the chip achieves a four times higher normalized area efficiency and a 7.5times higher normalized energy efficiency. © 1966-2012 IEEE.

关键词： CMOS integrated circuits

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PIDSNeRF: pose interpolation depth supervision neural radiance fields for view synthesis from challenging input

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Jianxin Shao, Haijian Deng, Xing Jiang, Yingtao School of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China Department of Electrical and Computer Engineering University of Nevada Las VegasNV89154 United States

Recently, Neural Radiance Fields(NeRF) have shown remarkable performance in the task of novel view synthesis through multi-view. The present study introduces an advanced optimization framework, termed Pose Interpolation Depth Supervision Neural Radiance Fields (PIDSNeRF), designed to address the challenges encountered by NeRF in novel view synthesis. These challenges manifest as artifacts, texture details loss, and geometric inconsistencies, particularly in scenes characterized by different lighting conditions, Textureless regions, and sparse images as inputs. The principle of PIDSNeRF involves interpolating virtual camera positions in the 3D space domain based on known cameras, Subsequently, the sparse point clouds formed during camera pose estimation is re-projected onto these virtual poses, employing an inverse angle weighting strategy, thereby generating depth-supervised rays. Furthermore, we propose a depth diffusion method that spreads depth-supervised rays along the pixel plane, forming Gaussian functions with different variances based on the diffusion distances. Finally, depth supervision during volume rendering is achieved by optimizing the Kullback-Leibler(KL) divergence between the weight distribution of sampled points along each ray and the probability density function of the corresponding Gaussian function along that ray. The above process enables a more comprehensive optimization of multi-resolution grid features that align with the sampled points along the rays. The experiments on the DTU, LLFF, and DL3DV-10K datasets demonstrate, PIDSNeRF can synthesize complete novel views within minutes, and the various metrics of the synthesized images achieve the best performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Probability density function

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A Half-Bridge/Dual Stacked-Switches Structured AC/DC Electrolytic Capacitor-Less Bidirectional Converter with Soft-Switching for High-Voltage EV Charger

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IEEE Transactions on Power Electronics 2025年第8期40卷 10912-10930页

作者： Derakhshan, Siamak Lam, John York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada

This paper presents a Half-Bridge (HB)/dual stacked switches, fully soft-switched bidirectional electrolytic capacitor-less AC/DC resonant converter, tailored for High-Voltage (HV) Electric Vehicle (EV) systems. The proposed converter integrates a closed-loop Power Factor Correction (PFC) mechanism based on the $dq$ model of the input LCL filter, ensuring high-quality sinusoidal input current and reduced Total Harmonic Distortion (THD) in both AC/DC conversion and DC/AC inversion modes. The front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) with soft-switching operation, which eliminates the high peak current issue that happens in the switches in Discontinuous Conduction Mode (DCM). Moreover, the dual stacked switches configuration of the proposed converter reduces the voltage stress on the switches to half of the DC-link and output voltage. A dedicated duty ratio-based closed-loop control system in both primary and secondary sides of the dual-stacked-switches resonant converter drastically reduces the double-line frequency voltage ripple, thereby eliminating the need for bulky electrolytic storage capacitors. Output voltage regulation in AC/DC conversion mode is achieved through Variable Frequency Modulation (VFM), and soft-switching is maintained for all switches for all the operating condition in both modes. The performance of the proposed bidirectional converter is validated through a 1kW, 120Vrms/750Vdc proof-of-concept prototype. © 1986-2012 IEEE.

关键词： Capacitor bank

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