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Revealing the Fluttering Motions of Mid-Water Trawl Codend Through Sea Trials:Case Study of Antarctic Krill Trawl Codend

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Journal of Ocean University of China 2023年第2期22卷 555-564页

作者： LIU Wei TANG Hao NYATCHOUBA NSANGUE Bruno Thierry ZHANG Feng YIN Liqiang XU Liuxiong HU Fuxiang LIU Pengfei College of Marine Sciences Shanghai Ocean UniversityShanghai 201306China National Engineering Research Center for Oceanic Fisheries Shanghai 201306China Key Laboratory of Oceanic Fisheries Exploration Ministry of Agriculture and Rural AffairsShanghai 201306China The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources Shanghai Ocean UniversityMinistry of EducationShanghai 201306China Scientific Observing and Experimental Station of Oceanic Fishery Resources Ministry of Agriculture and Rural AffairsShanghai 201306China Faculty of Marine Science Tokyo University of Marine Science and TechnologyMinatoTokyo 108-8477Japan Tendering Office Shanghai Ocean UniversityShanghai 201306China

The dynamic coupling between the fluttering motions and hydrodynamic characteristics of codend is essential in understanding the trawl selectivity through fish response and the drag force acting on the whole *** study investigated the effect of towing speed,warp length,warp tension,and catch size on the fluttering motions of Antarctic krill trawl codend during net shooting,towing,and hauling by using sea trial *** time-periodicity of codend oscillation was analyzed by the Morlet wavelet transform *** indicated that the period of codend oscillation was between 50 s and 90 s and showed an increasing trend with the warp tension but a decreased value at the towing *** coefficient amplitude of codend oscillation was between 0 and 4 at the net shooting and hauling stages,and between 0.2 and 0.6 at the towing *** amplitude of codend oscillation increased with the warp tension,towing speed,and catch size,but decreased with the increase of the warp *** addition,the period of codend oscillation increased with the towing speed at the net shooting and hauling stages,but decreased at the towing *** results from codend fluttering motions can improve the understanding of fish behavior and gear shape that modify the hydrodynamic force on the codend instantaneously.

关键词： codend fluttering motions sea trials Morlet wavelet periodicity

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High-Performance Sensing: Application of FPGA-based Winograd CNN Accelerator in Electronic Nose Systems 5

High-Performance Sensing: Application of FPGA-based Winograd...

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5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Tan, Aolong Duan, Shukai Chen, Mingzhe Ding, Ke Li, Changqing Wang, Lidan State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China College of Artificial Intelligence Southwest University Chongqing400715 China National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology 400715 China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing400715 China Ministry of Education Southwest University Chongqing400715 China

ISBN: (纸本)9798331528911

Electronic nose technology has been widely used in various scenarios such as industrial, military, food and environmental, and its recognition accelerator is responsible for completing the detection and identification of gases. Therefore, the application of advanced artificial intelligence algorithms to recognition accelerators is a common practice among researchers nowadays. However, neural networks with high recognition rates tend to have complex structures and a large number of parameters, which exacerbates the difficulty of deployment. To address this challenge, this paper first analyses and processes the detected gas data and proposes a hardware-friendly lightweight gas classification network, and then, based on this network, designs an FPGA-based Winograd CNN accelerator through the study of hardware implementation of Convolutional Neural Networks (CNN). The Winograd algorithm is used to reduce the number of multiplications used in the convolutional operation so as to reduce the consumption of hardware computing resources by the accelerator. Finally, the designed gas classification network is deployed on FPGA to complete the implementation of the recognition acceleration engine in the electronic nose system. The detection and recognition experiments on the laboratory gas dataset demonstrated good performance in terms of 99.5% recognition rate and only 2.94W power consumption of the accelerator, respectively. © 2024 IEEE.

关键词： Electronic nose

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Deep Learning-based AP Selection Method in Software-defined Networks 5

Deep Learning-based AP Selection Method in Software-defined ...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Duan, Miaomiao Zhi, Hui Yang, Lixia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

In the Software Defined Network (SDN), the terminal has been connected to the network in the process when an access point failure or damage, if the access point is not properly selected, it will lead to a decline in the quality of service applications. To address this problem, most traditional AP selection methods use access point selection based on received signal strength without take into account the channel and channel capacity of the access point access, while this paper uses Deep Neural Networks (DNN)-based access point (AP) selection method and considers the channel and channel capacity as part of the parameters. In the SDN controller, the received signal strength, throughput, number of connected devices, channel, and the channel capacity and access point performance quality are used as the input and label of the DNN, respectively. The analysis and simulation results show that the algorithm can select AP with better performance quality compared with the traditional AP selection method based on received signal strength, and the correct rate is significantly improved by 5% when compared with the AP selection method based on feedforward neural network. Meanwhile, the DNN-based access point selection method in SDN achieves flexible control of network traffic and balances the load of access points in the network. © 2022 IEEE.

关键词： Deep neural networks

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Releasing Inequality Phenomenon in ∞-norm Adversarial Training via Input Gradient Distillation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5632-5646页

作者： Chen, Junxi Dong, Junhao Xie, Xiaohua Lai, Jianhuang Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou510006 China

Adversarial training (AT) is considered the most effective defense against adversarial attacks. However, a recent study revealed that ∞-norm adversarial training ( ∞-AT) will also induce unevenly distributed input gradients, which is called the inequality phenomenon. This phenomenon makes the ∞-norm adversarially trained model more vulnerable than the standard-trained model when high-attribution or randomly selected pixels are perturbed, enabling robust and practical black-box attacks against ∞-adversarially trained models. In this paper, we propose a simple yet effective method called Input Gradient Distillation (IGD) to release the inequality phenomenon in ∞-AT. IGD distills the standard-trained teacher model’s equal decision pattern into the ∞-adversarially trained student model by aligning input gradients of the student model and the standard-trained model with the Cosine Similarity. Experiments show that IGD can mitigate the inequality phenomenon and its threats while preserving adversarial robustness. Compared to vanilla ∞-AT, IGD reduces error rates against inductive noise, inductive occlusion, random noise, and noisy images in ImageNet-C by up to 60%,16%,50%, and 21%, respectively. Other than empirical experiments, we also conduct a theoretical analysis to explain why releasing the inequality phenomenon can improve such robustness and discuss why the severity of the inequality phenomenon varies according to the dataset’s image resolution. Our code is available at https://***/ fhdnskfbeuv/Inuput-Gradient-Distillation. 2005-2012 IEEE.

关键词： Deep neural networks

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation 17

Penta-Encoder with Medical Transformer for Incomplete Multim...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Yu, Guohui Wang, Lihui Chen, Qijian Wang, Li Tang, Kun Cheng, Xinyu Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France

ISBN: (纸本)9798350387384

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets. © 2024 IEEE.

关键词： Semantic Segmentation

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Revisiting Adversarial Training under Long-Tailed Distributions

arXiv

引用

arXiv 2024年

作者： Yue, Xinli Mou, Ningping Wang, Qian Zhao, Lingchen Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China

Deep neural networks are vulnerable to adversarial attacks, often leading to erroneous outputs. Adversarial training has been recognized as one of the most effective methods to counter such attacks. However, existing adversarial training techniques have predominantly been tested on balanced datasets, whereas real-world data often exhibit a long-tailed distribution, casting doubt on the efficacy of these methods in practical scenarios. In this paper, we delve into adversarial training under long-tailed distributions. Through an analysis of the previous work "RoBal", we discover that utilizing Balanced Softmax Loss alone can achieve performance comparable to the complete RoBal approach while significantly reducing training overheads. Additionally, we reveal that, similar to uniform distributions, adversarial training under long-tailed distributions also suffers from robust overfitting. To address this, we explore data augmentation as a solution and unexpectedly discover that, unlike results obtained with balanced data, data augmentation not only effectively alleviates robust overfitting but also significantly improves robustness. We further investigate the reasons behind the improvement of robustness through data augmentation and identify that it is attributable to the increased diversity of examples. Extensive experiments further corroborate that data augmentation alone can significantly improve robustness. Finally, building on these findings, we demonstrate that compared to RoBal, the combination of BSL and data augmentation leads to a +6.66% improvement in model robustness under AutoAttack on CIFAR-10-LT. Our code is available at: https://***/NISPLab/AT-BSL. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN

arXiv

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arXiv 2024年

作者： Liu, Ziyi Ye, Dengpan Tang, Long Zhang, Yunming Deng, Jiacheng The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China

With the development of artificial intelligence, neural networks play a key role in network intrusion detection systems (NIDS). Despite the tremendous advantages, neural networks are susceptible to adversarial attacks. To improve the reliability of NIDS, many research has been conducted and plenty of solutions have been proposed. However, the existing solutions rarely consider the adversarial attacks against recurrent neural networks (RNN) with time steps, which would greatly affect the application of NIDS in real world. Therefore, we first propose a novel RNN adversarial attack model based on feature reconstruction called Temporal adversarial Examples Attack Model (TEAM), which applied to time series data and reveals the potential connection between adversarial and time steps in RNN. That is, the past adversarial examples within the same time steps can trigger further attacks on current or future original examples. Moreover, TEAM leverages Time Dilation (TD) to effectively mitigates the effect of temporal among adversarial examples within the same time steps. Experimental results show that in most attack categories, TEAM improves the misjudgment rate of NIDS on both black and white boxes, making the misjudgment rate reach more than 96.68%. Meanwhile, the maximum increase in the misjudgment rate of the NIDS for subsequent original samples exceeds 95.57%. Copyright © 2024, The Authors. All rights reserved.

关键词： Network intrusion

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Elimination of Interference Analysis for RIS Element Demage

Elimination of Interference Analysis for RIS Element Demage

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

作者： Sipei Min Canping Yu Liping Li Yingsong Li Nikola Zlatanov 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

This paper aims to investigate the ability of reconfigurable intelligent surfaces (RIS) damaged in multi-user environments to eliminate interference. Our research found that even if the RIS is damaged, interference can still be eliminated in the absence of a direct path and when the number of RIS is sufficient. In order to solve interference cancellation, we adopted the alternating projection algorithm, which ensures local convergence and good convergence speed.

关键词： Base stations Interference cancellation Reconfigurable intelligent surfaces Information and communication technology Projection algorithms Convergence

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Transient Electro-thermal Analysis of the Bipolar Transistor

Transient Electro-thermal Analysis of the Bipolar Transistor

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Xiao, Hui Ren, Xingang Yan, Yeqiang Si, Junjie Cai, Xueyuan Wu, Bo Huang, Zhixiang Wu, Xianliang Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan23700 China Engineering Technology Research Center of Specific Power Devices with Novel Semiconductor Materials Anhui Province Chizhou247100 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9781733509633

This paper describes the transient electro-thermal properties of the bipolar transistor by a two-dimensional transient electro-thermal simulation, in which model the coupled heat conduction equation and drift-diffusion equations are solved. The backward difference scheme with the second-order accuracy was used to discretize the time derivative. The increment of bipolar transistor temperature will affect the drift and diffusion of carriers and thus its electrical characteristics. Through the transient study of electro-thermal characteristics of the bipolar transistor, the influence of the self-heating effect on bipolar transistor characteristic has considered in amplification mode. The developed model and results obtained in this work can be used to optimize the thermal design of bipolar transistor. © 2023 ACES.

关键词： Thermoanalysis

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