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An intelligent impulsive noise mitigation with deep learning method

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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Improvement of reconstruction accuracy for hardware systems with Finite Rate of Innovation 2

Improvement of reconstruction accuracy for hardware systems ...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Wang, Jingyu Ding, Li Zhou, Tao University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798331515669

In classical Finite Rate of Innovation (FRI) theory, signal reconstruction is initially achieved with precision using the zeroing filter algorithm on noiseless pulse signals. However, with the introduction of noise, the zeroing filter algorithm exhibits reduced robustness when handling signals with low signal-to-noise ratios (SNR). This paper introduces the TLS-ESPRIT algorithm, which enhances robustness against noise to some extent. Additionally, building on the development of an FRI reconstruction system based on C-TEM sampling, this work innovatively proposes a cascaded filter phase compensation method. This method mitigates the nonlinear phase effects of analog filters, thereby improving reconstruction accuracy. Ultimately achieving a reduction in sampling rate while improving reconstruction accuracy. © 2024 IEEE.

关键词： Signal to noise ratio

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FPMT: Enhanced Semi-supervised Model for Traffic Incident Detection 27th

FPMT: Enhanced Semi-supervised Model for Traffic Incident De...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Lu, Xinying Xiao, Jianli School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9783031781063

For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Effect of granular shape on radial segregation in a two-dimensional drum

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Chinese Physics B 2024年第4期33卷 509-514页

作者：徐悦李然迟志鹏修文正孙其诚杨晖 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Electrical and Electronic Engineering Shandong University of TechnologyZibo 255049China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing 100084China College of Medical Instruments Shanghai University of Medicine and Health SciencesShanghai 201318China

Granular segregation is widely observed in nature and *** research has focused on segregation caused by differences in the size and density of spherical ***,due to the fact that grains typically have different shapes,the focus is shifting towards shape *** this study,experiments are conducted by mixing cubic and spherical *** results indicate that spherical grains gather at the center and cubic grains are distributed around them,and the degree of segregation is *** experiments,a structured analysis of local regions is conducted to explain the inability to form stable segregation patterns with obviously different geometric ***,through simulations,the reasons for the central and peripheral distributions are explained by comparing velocities and the number of collisions of the grains in the flow layer.

关键词： granular materials cubic grains shape segregation segregation mechanism

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Bridging efficacy and efficiency: Innovations in Shapley value estimation for model-agnostic data valuation in machine learning 4

Bridging efficacy and efficiency: Innovations in Shapley val...

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4th International Conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Yang, Yilu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781510674721

The escalating advancement of generative AI models amplifies the imperative for adept data valuation techniques. Amidst a myriad of methodologies, various Shapley value estimation techniques, such as Data Shapley, have garnered attention for their proficient data valuation capabilities, despite computational challenges when grappling with large datasets. This paper introduces an innovative, empirically-driven batch method, aiming to expedite data valuation while preserving precision. This method strategically optimizes training batch sizes and testing subsets, effectively striking a balance between computational efficiency and valuation accuracy, a critical step forward given the substantial volume of data processed in contemporary machine learning tasks. A thorough evaluation of different Shapley value estimation techniques is conducted, underscoring TMC-Shapley for its notable efficacy. Furthermore, the exploration delves into the model-agnostic nature of Shapley value estimations, utilizing diverse machine learning models across distinct training phases. This practice not only demonstrates the versatility of Shapley value methods but also highlights their adaptability and generalizability across varied model architectures, reaffirming the significance of this approach in the broader context of machine learning research. The holistic approach and findings presented herein serve as a robust foundation for future explorations and optimizations in the realm of data valuation, paving the way for more nuanced and efficient methodologies. © 2024 SPIE.

关键词： Machine learning

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An automatic control method for semi-active suspension of driverless vehicle based on multi-sensor information fusion in complex environment 9

An automatic control method for semi-active suspension of dr...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Zhou, Zheng Jing, Yunsen Lyu, Tairan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

ISBN: (纸本)9781510672802

A semi-active suspension control method based on multi-sensor information fusion was proposed to improve the safety and smoothness of driverless A quarter suspension vibration model considering multi-sensor information fusion was established, revealing the relationship between road roughness information and vehicle vibration. A quarter suspension vibration model considering multi-sensor information fusion was established, revealing the relationship between road roughness information and vehicle vibration. The camera and radar wave were used to scan and identify the uneven road conditions, and a mathematical model of road roughness was created. The information fusion and matching of uneven road surface were carried out by The information fusion and matching of uneven road surface were carried out by detecting the edge intersection ratio and Graph Neural Network(GNN) algorithm, obtaining a more reliable mathematical model of uneven road surface in complex environment. It is proposed to calculate the optimal damping ratio of semi-active suspension using the information of vehicle speed and road roughness, and to adjust the suspension to the optimal damping ratio of semi-active suspension. It is proposed to calculate the optimal damping ratio of semi-active suspension using the information of vehicle speed and road roughness, and to adjust the suspension to this damping ratio to adapt to different road conditions in real time. The vehicle ride comfort test under typical road input conditions was carried out. The vehicle ride comfort test under typical road input conditions was carried out, and the vibration acceleration time domain response signals of different suspensions were compared and analysed. The results show that the maximum peak vibration acceleration of the unmanned suspension controlled by multiple information fusion is reduced by 43 % comparing with that of the unmanned suspension. reduced by 43 % comparing with that in the passive suspension under the same

关键词： driverless vehicle information fusion multi-sensor information semi-active suspension

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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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Light(Science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

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Conversion and Superposition of optical Stokes Skyrmion Topologies

Conversion and Superposition of Optical Stokes Skyrmion Topo...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Teng, Houan Zhong, Jinzhan Chen, Jian Lei, Xinrui Zhan, Qiwen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

ISBN: (纸本)9798350375909

optical skyrmions are topological quasiparticles with non-trivial textures. In this study, we successfully achieve the conversion of optical skyrmions among Néel, Bloch, intermediate skyrmions, and bimerons using polarization devices. Furthermore, our investigation into the superposition of different skyrmions reveals fusion and annihilation of topological structures. These results enhance our comprehension of optical topological quasiparticles, which could have a significant impact on the transfer, storage, and communication of optical information. © 2024 IEEE.

关键词： Topology

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Global Color-Aware Arbitrary Style Transfer with Discrete Wavelet Transform 20th

Global Color-Aware Arbitrary Style Transfer with Discrete W...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Chen, Bochao Tan, Junhao Li, Xiaoqiang Pei, Songwen School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819628292

The purpose of image arbitrary style transfer is to apply a given artistic or photorealistic style to a target content image. While existing methods can effectively transfer style information, the variability in color brightness features between the style and content images can significantly impact the quality of the style transfer. Specifically, traditional arbitrary style transfer relies on an encoder-decoder architecture of spatial-aware neural networks operating in the RGB domain, which typically preserves only local color brightness information and lacks a global color perception compared to the wavelet frequency domain. In this paper, we address the issue of insufficient global color perception in images from the perspective of image enhancement. We propose Global Color-aware Arbitrary Style Transfer with Discrete Wavelet Transform (GCAST), which achieves a balance between the global color characteristics of style images and the local details of content images. Our approach involves an encoder that integrates preliminary feature encoding through cross-layer attention fusion, while the decoder modifies frequency features using a wavelet-based adjustment block. To maintain a balance between content and style in the stylized images, we design a comprehensive loss function that incorporates multiple restoration loss, double style loss, and noise regularization loss. Extensive experiments comparing our method with state-of-the-art techniques demonstrate that our proposed model generates stylized images with superior results in terms of global color perception and human perceptual studies. © IFIP International Federation for Information Processing 2025.

关键词： Decoding

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SVTNet: Dual Branch of Swin Transformer and Vision Transformer for Monocular Depth Estimation

SVTNet: Dual Branch of Swin Transformer and Vision Transform...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Shuwen Wang, Yongxiong Chen, Han Huang, Shuai School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350368741

In monocular depth estimation, effective acquisition of global and local information is the key to improving accuracy. We introduce a novel dual branch network called Swin Vision Transformer Net (SVTNet), where the Swin Transformer and Vision Transformer are combined to learn features with global and local information. In Swin Transformer branch, we remove the Feature Pyramid Networks which reduces the noise introduced by interpolation and maintains the original characteristics of the hierarchical features extracted from each layer of Swin Transformer. In Vision Transformer Branch, we remove the multi-scale convolutional structure and keep the same-scale feature maps extracted by Vision Transformer for reducing the consumption of memory and extracting more global features. The experimental results on the NYU-Depth v2, KITTI, and CityScapes datasets show that compared with existing advanced methods, the SVTNet has achieved significant performance improvements in multiple evaluation metrics. © 2025 IEEE.

关键词： dual branch Feature Pyramid Networks monocular depth estimation Same-scale feature Swin Transformer Vision Transformer

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