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Propagation and Dispersion Characteristics of Scholte Wave in VTI Media with the Presence of Irregular Seabed

Journal of Earth Science 2024年第2期35卷 722-725页

作者： Xiaobo Liu Yun Wang Jianlei Zhang Haifeng Chen Hua Zhang School of Ocean Sciences China University of GeosciencesBeijing 100083China Key Laboratory of Polar Geology and Marine Mineral Resources China University of GeosciencesBeijing 100083China School of Geophysics and Information Technology China University of GeosciencesBeijing 100083China Research&Development Center BGP IncCNPCZhuozhou 072751China National Engineering Research Center for Oil and Gas Exploration Computer Software Beijing 100080China

INTRODUCTION Surface waves propagating along the seafloor are general ly called Scholte waves,and were first discovered and studied in the early 1950s(Kugler et al.,2005;Buchen and Ben-Hador,1996).Scholte waves exhibit a dispersion phenomenon,which implies that their velocity varies with ***-frequency Scholte waves can propagate over long distances on the seabed with little attenuation(Bohlen et al.,2004).The particle mo tion of Scholte waves in a solid medium changes from a retro grade to a prograde ellipse(Klein et al.,2005;McMechan and Yedlin,1981).

关键词： seabed Dispersion implies

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Towards Label-Efficient Deep Learning-based Aging-related Bug Prediction with Spiking Convolutional Neural Networks

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IEEE Transactions on Emerging Topics in Computing 2025年第02期13卷 314-329页

作者： Tian, Yunzhe Li, Yike Chen, Kang Zhang, Zhenguo Tong, Endong Liu, Jiqiang Qin, Fangyun Zheng, Zheng Niu, Wenjia Hebei Boshilin Technology Development Co. Ltd. Shijiazhuang050051 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tangshan Research Institute of Beijing Jiaotong University Tangshan063000 China Capital Normal University College of Information Engineering Beijing100048 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210093 China Beihang University Laboratory of Dependable Intelligent Systems School of Automation Science and Electrical Engineering Beijing100191 China

Recent advances in Deep Learning (DL) have enhanced Aging-Related Bug (ARB) prediction for mitigating software aging. However, DL-based ARB prediction models face a dual challenge: overcoming overfitting to enhance generalization and managing the high labeling costs associated with extensive data requirements. To address the first issue, we utilize the sparse and binary nature of spiking communication in Spiking Neural Networks (SNNs), which inherently provides brain-inspired regularization to effectively alleviate overfitting. Therefore, we propose a Spiking Convolutional Neural Network (SCNN)-based ARB prediction model along with a training framework that handles the model's spatial-temporal dynamics and non-differentiable nature. To reduce labeling costs, we introduce a Bio-inspired and Diversity-aware Active Learning framework (BiDAL), which prioritizes highly informative and diverse samples, enabling more efficient usage of the limited labeling budget. This framework incorporates bio-inspired uncertainty to enhance informativeness measurement along with using a diversity-aware selection strategy based on clustering to prevent redundant labeling. Experiments on three ARB datasets show that ARB-SCNN effectively reduces overfitting, improving generalization performance by 6.65% over other DL-based classifiers. Additionally, BiDAL boosts label efficiency for ARB-SCNN training, outperforming four state-of-the-art active learning methods by 4.77% within limited labeling budgets. © 2013 IEEE.

关键词： Active learning

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Diving into Continual Ultra-fine-grained Visual Categorization

Diving into Continual Ultra-fine-grained Visual Categorizati...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin Wu, Xiaoyu Gao, Yongsheng Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beijing China Griffith University Institute for Integrated and Intelligent Systems Nathan4111 Australia

ISBN: (纸本)9798350382204

Recent advance in ultra-fine-grained visual categorization (ultra-FGVC) has significantly boosted the capability of deep neural networks for ultra-FGVC tasks. However, building models for continually learning to recognize increasing ultra-fine-grained categories is still under-explored. This limits the application of ultra-FGVC techniques in real-world production. To this, we take the first attempt for continual ultra-FGVC. By evaluating existing continual learning methods on the constructed continual ultra-FGVC benchmark, we observe that the main bottleneck lies in the limited model plasticity for incrementally adapting to new tasks. This can be caused by excessive anti-forgetting constraints as the difficult ultra-FGVC task requires substantial update of parameters, and over-fitting on early tasks given that the ultra-fine-grained categories are with very few training samples. To tackle these problems, we propose a joint self-supervised learning and prompting model. The prompt-based continual learning framework offers proper anti-forgetting operation by fixed pretrained vision transformer and adaptive prompt selection. By jointly optimizing the learnable prompts with an adversarial self-supervised loss, the over-fitting on each continual learning task is mitigated. Extensive experiments demonstrate that the proposed method outperforms existing continual learning methods on the challenging continual ultra-FGVC problem. © 2023 IEEE.

关键词： Deep neural networks

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and systems for Video Technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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TRU GENERATION SUPPRESSION BY HALEU FUEL WITH FIXED DISCHARGE BURNUP USING BURNABLE POISON 30

TRU GENERATION SUPPRESSION BY HALEU FUEL WITH FIXED DISCHARG...

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30th International Conference on Nuclear engineering, ICONE 2023

作者： Hiraiwa, Kouji Yoshioka, Kenichi Kimura, Rei Takeda, Satoshi Wada, Satoshi Kitada, Takanori Sugita, Tsukasa Isogo Engineering Center Toshiba Energy Systems & Solutions Corporation Yokohama Japan Nuclear Engineering Laboratory Toshiba Energy Systems & Solutions Corporation Kawasaki Japan Energy Systems Research and Development Center Toshiba Energy Systems & Solutions Corporation Kawasaki Japan Graduate School of Engineering Division of Sustainable Energy and Environmental Engineering Quantum and Energy Engineering Area Osaka University Osaka Japan

ISBN: (纸本)9784888982566

Effects of burnable poison (BP) addition on radiotoxicity by TRU were investigated for FORSETI fuel, which is assumed to be loaded into a BWR plant. Since the purpose of this study was to confirm the relative change in radiotoxicity due to TRU, the weight ratio of TRU was used as an index of the relative magnitude of radiotoxicity. The weight fraction of TRU nuclides with 235U enrichment from 3.8 wt% to 20 wt% was examined with and without BP. The weight fraction of TRU nuclides in the spent fuel decreased by about 60% for 238Pu, 240Pu, and 241Pu. The results of these nuclides are almost the same between with and without BP. On the other hand, 239Pu, which is not dominant in the toxicity of TRU, is slightly increased by the addition of BP. The relative difference in TRU weight fraction between with-BP and without-BP by increase of 235U enrichment does not exceed 20wt% except for *** is desirable to consider the error in the amount of TRU generation when evaluating without BP for analytical simplicity. © 2023 by JSME.

关键词： Isotopes

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Query-Based External Information Leakage Attacks on Face Recognition Models

Query-Based External Information Leakage Attacks on Face Rec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Edita Grolman Amit Giloni Ryuta Kremer Hiroo Saito Tomoyuki Shibata Tsukasa Omino Misaki Komatsu Yoshikazu Hanatani Asaf Shabtai Yuval Elovici Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Corporate Research & Development Center Toshiba Corporation

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Recent studies have demonstrated external information leakage (EIL) attacks which allow an attacker to infer various sensitive implicit properties related to a machine learning (ML) model’s training data. Most of those attacks assumed 1) a white-box scenario in which the attacker has complete access to the ML model, its structure, and its parameters, or 2) a black-box (alternatively gray-box) scenario with non-realistic requirements such as a high query budget or high computational resources for the attacker. In this paper, we propose two practical query-based (i.e., black-box) EIL attacks that target face recognition ML models and allow an attacker to infer sensitive implicit properties, such as the facial characteristics, gender, ethnicity, income level, and average age of the individuals in the training data, with a limited number of queries. The first proposed attack, referred to as the random noise injection (RNI) attack, exploits the effect of injecting random noise into input samples on the target model’s predictions. The second proposed attack, referred to as the property substitute model (PSM) attack, creates a substitute model for each property value examined, whose predictions are compared to the target model’s predictions. Our comprehensive evaluation (a total of 730 experiments) performed on the CelebA dataset shows that the proposed attacks outperform existing EIL attacks and successfully infer private information, posing a threat to the privacy and security of the face recognition models.

关键词： Privacy Face recognition Computational modeling Noise Training data Closed box Predictive models

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Intelligent Water Quality Management in Maturation Tanks for Penaeus Monodon Using Fuzzy Logic 8

Intelligent Water Quality Management in Maturation Tanks for...

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8th International Conference on Business and Industrial research, ICBIR 2023

作者： Chua, Abraham Imperial, Eian David Mababangloob, Giolo Rei Tinhay, Jose Benito Concepcion, Ronnie De la Salle University Department of Manufacturing Engineering and Management Manila Philippines De la Salle University Center for Engineering and Sustainability Development Research Intelligent Systems Laboratory Department of Manufacturing Engineering and Management Manila Philippines

ISBN: (纸本)9798350399646

With the global population expected to grow considerably in the upcoming years, global food production should also keep up with the growing demand. Rapid and uncontrolled food production can potentially affect aquaculture methods. Such impact leads to the destruction of habitats and neglect of maintaining tanks, ponds, water, and feed. Thus, the health status of stocks has been reported as causing bacteria and viral infections to exploit poor water maintenance. This study developed and examined the efficacy of using Mamdani fuzzy logic controllers on water quality management for aquaculture, specifically in Penaeus monodon cultivation. MATLAB was used to model the Fuzzy Inference systems (FIS) consisting of x rules based on the combinations of highly significant water parameters, particularly temperature, dissolved oxygen, salinity, and pH level. The FIS was then simulated in Simulink, wherein the fuzzy controllers were capable of sending a signal to different pumps that would optimize the said water parameters. Overall, this study provides strong support for the effectiveness of fuzzy logic to automatically adjust water parameters and reducing the need for human inputs. © 2023 IEEE.

关键词： Aquaculture

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Autonomous Environmental Exploration and Target Tracking Control for Unmanned Surface Vehicles in Uncertain Environment

Autonomous Environmental Exploration and Target Tracking Con...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Zhu, Kaiwei Kong, Shihan Wu, Fang Sun, Jinan Yu, Junzhi College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China SPIC Nuclear Energy Co. Ltd. China Peking University National Engineering Research Center for Software Engineering Beijing100871 China

ISBN: (纸本)9798350372601

In this paper, a set of methods of autonomous obstacle exploration, avoiding, and target-tracking control in the uncertain environment is proposed. First, a secure and reliable obstacle exploration framework via multi-sensors information fusion is developed for USV to autonomously perceive obstacles in unknown environments. Second, a modified dynamic artificial potential field-based path planning method is proposed to dynamically generate a safe and feasible path even without complete prior obstacle position information. Third, a robust sliding mode control law with extended state observer is designed to track expectational target in the dynamical and uncertain environment. Finally, the target-tracking control law has been validated through the simulation experiments. Besides, the proposed methods of obstacle exploration, path planning, and target-tracking control are comprehensively evaluated through the simulation experiments in WEBOTS software, which is closer to the real world. © 2024 IEEE.

关键词： Target tracking

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Optimal Strategies for the Game with Two Faster 3D Pursuers and One Slower 2D Evader 41

Optimal Strategies for the Game with Two Faster 3D Pursuers ...

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第41届中国控制会议

作者： Runqi Zhang Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LabCollege of EngineeringPeking University National Engineering Research Center of Software Engineering Peking University Center for Multi-Agent Research Institute for Artificial IntelligencePeking University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A pursuit-evasion differential game with two faster pursuers in 3 D space and one slower evader in a 2 D plane is *** pursuers,which have different speeds,attempt to cooperate to capture the evader while the evader struggles to avoid or delay the *** differential games and the concept of isochron,we present a complete solution to this game and develop the optimal strategies for both the pursuers and the *** order to guarantee the optimality of the proposed strategies,the value function of the game is given and is further proved to be the solution of the corresponding Hamilton-Jacobi-Isaacs ***,simulations are carried out with comparisons between the proposed strategies and other typical strategies,the results of which show the optimality of the proposed strategies.

关键词： Pursuit-evasion differential games multi-agent system optimal strategy

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