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A workload-based nonlinear approach for predicting available computing resources

Journal of Systems Engineering and Electronics 2020年第1期31卷 224-230页

作者： JIA Yunfei ZHOU Zhiquan WU Renbiao Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of ChinaTianjin 300300China School of Computing and Information Technology University of WollongongWollongong 2522Australia

Performance degradation or system resource exhaustion can be attributed to inadequate computing resources as a result of software *** the real world,the workload of a web server varies with time,which will cause a nonlinear aging *** nonlinear property often makes analysis and modelling *** is one of the important factors influencing the speed of *** paper quantitatively analyzes the workload-aging relation and proposes a framework for aging control under varying *** addition,this paper proposes an approach that employs prior information of workloads to accurately forecast incoming system *** workload data are used as a threshold to divide the system resource usage data into multiple sections,while in each section the workload data can be treated as a *** section is described by an individual autoregression(AR)*** with other AR models,the proposed approach can forecast the aging process with a higher accuracy.

关键词： software aging nonlinear phenomenon fault forecasting

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3D LOD-FDTD for the Multi-Pole Debye Dispersive Media Using Z-Transform

3D LOD-FDTD for the Multi-Pole Debye Dispersive Media Using ...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Hou, Guilin Song, Ziheng Xie, Guoda Pan, Pan Huang, Zhixiang Anhui University School of Electronic and Information Engineering Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9781665452366

This paper introduces the application of unconditionally stable locally one-dimensional finite-difference time-domain(LOD-FDTD) method to 3D multi-pole Debye dispersive media model. Compared with other methods for dispersive media, the Z-transform method does not need to retain the electromagnetic values of the previous moments, has less memory footprint, and its numerical implementation is more straightforward. Through numerical examples, ZT-LOD-FDTD shows the advantages of accuracy and high computational efficiency. © 2022 IEEE.

关键词： Poles

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High order SF-SFDTD method for solving the time-dependent Schrödinger equation

High order SF-SFDTD method for solving the time-dependent Sc...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Pan, Pan Xie, Guoda Huang, Zhixiang Hou, Guilin Song, Ziheng Anhui University School of Electronic and Information Engineering Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9781665452366

A high-order spatial filtering-symplectic finite difference time domain (SF-SFDTD) scheme with controllable stability condition is proposed to solve the time-dependent Schrödinger equation (TDSE). Firstly, the higher-order symplectic framework for the discretization of Schrödinger equation is described, and then the spatial filtering method is carried out to extend the stability of the standard SFDTD method. Finally, a numerical example is given to evaluate the correctness of the SF-SFDTD method in numerical solving Schrödinger equation. © 2022 IEEE.

关键词： Finite difference time domain method

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A Communication Protocol Identification Method Based on Time-Frequency Domain Combination for Internet of Things

A Communication Protocol Identification Method Based on Time...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Zhiwei Zheng Hui Zhi Kui Tie Xianpeng Wang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Zhongdian Kesiyi Technology (Anhui) Co. Ltd Bengbu China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

At present, Internet of Things (IoT) testing instruments and meters are unable to identify the communication protocol of communication data when all characteristic parameters of communication data are unknown. Regarding this problem, this paper utilizes the K-Nearest Neighbors (KNN) model in machine learning, designs communication protocol identification methods and proposes a communication protocol identification method based on time-frequency domain combination (CPI- TFC). First, we generate time-domain waveforms and frequency-domain waveforms from sampling communication data sequences, and attempt only time-domain identification method and only frequency-domain identification method based on KNN model. Then, we compare the results, design a communication protocol identification method based on the combination of time-domain identification and frequency domain identification, and propose CPI-TFC. Experimental results show that CPI-TFC can achieve higher identification accuracy than only time-domain identification method and only frequency-domain identification method, so it can effectively identify the communication protocol for a IoT communication data. Moreover, CPI- TFC can provide prior information for subsequent configuring parameters in IoT testing instruments, improve the parameter configuration efficiency.

关键词： Meters Time-frequency analysis Protocols Accuracy Simulation Machine learning Nearest neighbor methods Internet of Things Time-domain analysis Testing

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Anisotropic Infrared Absorption in Monolayer Black Phosphorus Metasurface With/Without Local Oxidative Effect

SSRN

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SSRN 2023年

作者： Feng, Naixing Yin, Jianxiang Wang, Huan Wang, Xuan Zhang, Yuxian Huang, Zhixiang Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China

As one kind of anisotropic two-dimensional (2-D) layered material, the monolayer black phosphorus (BP) has ability to excite the surface plasmon resonance in the infrared spectrum, thus providing sufficient conditions for the strong light-matter interaction, so that some potential applications can be shown in the field of optoelectronics. Based on those physical properties, we design the patterned monolayer BP to achieve extremely high infrared absorption, and meanwhile use the finite element method to model the effects of factors such as n-type doping concentration, dielectric thickness, and angle of incidence light. Moreover, due to the strong anisotropic in-plane properties of monolayer BP, we analyze detailedly the absorption spectra in both the armchair and zigzag directions separately, and then understand that low-angle narrow-band absorption in the zigzag direction could be captured in that structure. The structure could achieve perfect absorption in the zigzag direction at an incidence angle of 89.3° with n-type doping concentration n=3×1013 cm-2 and medium thickness d = 4.6 μm, and the maximum absorption can reach 99.98% with the full width at half peak (FWHM) of only 0.57 μm. Considering the case on surface oxidation, it may occur some possible effects for the perfect absorption, but with our structure, the proposed narrow-band absorption still has wide applications in the sensing and light body monitoring. © 2023, The Authors. All rights reserved.

关键词： Monolayers

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A Multimodal Knowledge Representation Method for Fake News Detection

A Multimodal Knowledge Representation Method for Fake News D...

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Computer, Control and Robotics (ICCCR), International Conference on

作者： Fanhao Zeng Jiaxin Yao Yijie Xu Yanhua Liu Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Engineering Research Center of Big Data Intelligence Ministry of Education (Fuzhou University) Fuzhou China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

To address the key challenge encountered in fake news detection, i.e., multimodal data is difficult to be effectively semantically represented due to its intrinsic heterogeneity, this paper proposes a multimodal knowledge representation method for fake news detection. First, visual feature extraction is performed for fake news image data, the relevant images are sliced into multiple blocks, and then visual modal features are obtained by linear projection layer mapping. This simplifies the feature extraction process and reduces the computational cost, which helps to improve the fake news recognition performance. Second, to meet the actual fake news detection needs, a long text representation method based on topic words is investigated for the text data in fake news. Finally, the multimodal representation of the same fake news data is optimized by establishing a connection between two different modalities, visual and text, and inputting it into a BiLSTM-Attention based network to achieve the fusion of multimodal features. The experiment selects the same fake news data of EANN model and uses four classical classification methods to verify the effect of knowledge representation and compare it with the fusion model ViLT which is not optimized for long text. The experiment proves that the accuracy rate of fake news detection using the multimodal representation proposed in this paper is improved by 7.4% compared to the EANN model, and by 9.3% compared to the ViLT representation.

关键词： Digital control Visualization Accuracy Knowledge representation Machine learning Feature extraction Data models

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NetPrompt: Neural Network Prompting Enhances Event Extraction in Large Language Models

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IEEE Transactions on Big Data 2025年

作者： Mu, Lin Cheng, Yide Shen, Jun Zhang, Yiwen Zhong, Hong School of Computer Science and Technology Anhui University Anhui Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education the Anhui Engineering Laboratory of IoT Security Technologies the School of Computer Science and Technology the Institute of Physical Science and Information Technology Anhui University Hefei230039 China

Event Extraction involves extracting event-related information such as event types and event arguments from context, which has long been tackled through well-designed neural networks or fine-tuned pre-trained language models. These approaches require substantial annotated data for tuning parameters and are resource-intensive. Recently, Prompting strategies with frozen parameters, such as Chain-of-Thought and Self-Consistency, have delivered success in NLP using LLMs by generating intermediate thought steps. However, they suffer from the challenge of error propagation and lack of interaction between different thoughts. In this paper, we propose Neural Network-based Prompting (NetPrompt), a novel network-structured prompting strategy for event extraction. The core idea behind NetPrompt is to imitate the excellent information integration capabilities of neural network structures. Specifically, we first decompose the event extraction problem into diverse intermediate subtasks, and each subtask is represented as a node in different layers of the network, the output of the nodes in the preceding layer is fed into the subsequent layer. Secondly, we propose pruning strategies to adapt the reasoning overhead to different problems. Finally, we have conducted extensive experiments on two widely used event extraction benchmarks to evaluate NetPrompt. The results demonstrated that NetPrompt significantly improved the event extraction performance compared to previous methods. ©2015 IEEE.

关键词： Neural network models

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A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI-SCALE AGGREGATION AND BOUNDARY-AWARE FOR LATERAL VENTRICLE SEGMENTATION ON MR IMAGES 47

A NOVEL CONVOLUTIONAL NEURAL NETWORK BASED ON ADAPTIVE MULTI...

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2022 IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2022

作者： Ye, Fei Wang, Zhiqiang Zhu, Sheng Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Department of Radiology Affiliated Hospital of Xiangnan University Chenzhou423000 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781665405409

In this paper, we propose a novel convolutional neural network based on adaptive multi-scale feature aggregation and boundary-aware for lateral ventricle segmentation (MB-Net), which mainly includes three parts, i.e., an adaptive multi-scale feature aggregation module (AMSFM), an embedded boundary refinement module (EBRM), and a local feature extraction module (LFM). Specifically, the AMSFM is used to extract multi-scale features through the different receptive fields to effectively solve the problem of distinct target regions on magnetic resonance (MR) images. The EBRM is intended to extract boundary information to effectively solve blurred boundary problems. The LFM can make the extraction of local information based on spatial and channel attention mechanisms to solve the problem of irregular shapes. Finally, extensive experiments are conducted from different perspectives to evaluate the performance of the proposed MB-Net. Furthermore, we also verify the robustness of the model on other public datasets, i.e., COVID-SemiSeg and CHASE DB1. The results show that our MB-Net can achieve competitive results when compared with state-of-the-art methods. © 2022 IEEE

关键词： Magnetic resonance

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Sparsity-aware Subband Adaptive Filtering in Impulsive Interferences

Sparsity-aware Subband Adaptive Filtering in Impulsive Inter...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Zhikai Gong Yingsong Li Baohe Pang Xueyan Li Wudang Xiao Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In this paper, we propose two new subband adaptive filtering (SAF) algorithms based on the cost function of the logistic distance metric cost function and use the proportionate proximal gradient algorithm to exploit the sparsity of the system fully. Compared with prior works, the proposed algorithms can handle many different situations better. Through computer simulations, we prove that the algorithm performs excellently in sparse systems disturbed by impulse noises. The algorithm can be used in areas like system identifications.

关键词： Measurement Adaptive systems Computer simulation Noise Filtering algorithms Cost function Information filters

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Hexagonal Convolutional Neural Network for Spatial Transcriptomics Classification

Hexagonal Convolutional Neural Network for Spatial Transcrip...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Gao, Jing Zhang, Fa Hu, Kai Cui, Xuefeng Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan China Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou China Shandong University School of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665468190

Recent advances in spatial transcriptomics have enabled the comprehensive measurement of transcriptional profiles while retaining the spatial contextual information. Identifying spatial domains is a critical step in the analysis of spatially resolved transcriptomics. Existing unsupervised methods perform poorly on this task owing to the large amount of noise and dropout events in the transcriptomic profiles. To address this problem, we first extend an unsupervised algorithm to a supervised learning method that can identify useful features and reduce noise hindrance. Second, inspired by the classical convolution in convolutional neural networks (CNNs), we designed a regular hexagonal convolution to compensate for the missing gene expression patterns from adjacent nodes. Compared with the graph convolution in graph neural networks (GNNs), our hexagonal convolution can preserve the relative spatial location information of different nodes in graph-structured data. Third, based on the hexagonal convolution, a novel hexagonal Convolutional Neural Network (hexCNN) is proposed for spatial transcriptomics classification. Finally, we compared the proposed hexCNN with existing methods on the DLPFC dataset. The results show that hexCNN achieves a classification accuracy of 87.2% and an average Rand index (ARI) of 78.2% (1.9% and 3.3% higher than those of GNNs). © 2022 IEEE.

关键词： Convolution

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