检索结果-内蒙古大学图书馆

SSRN 2022年

作者： Jia, Yang Zhang, Xinmeng Kuang, Yaxi Chen, Yanping Wang, Gang Zhang, Qixing School of Computer Science Xi'an University of Posts and Telecommunications 710121 China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications 710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications 710121 China School of Information Science and Technology University of Science and Technology of China 230026 China XNIF262 710061 China State Key Laboratory of Fire Science University of Science and Technology of China 230026 China

Video-based flame detection (VFD) aims to recognize fire events by the image features. Flame segmentation is an essential task in VFD, which provides suspected regions for feature analysis and object recognition. The development of deep learning has promoted VFD performances to a higher level. However, due to the rarity of recorded fire events, it is challenging to get tremendous positive flame samples for model training. Existing deep learning-based VFD methods are limited to the lack of flame samples under different scenarios. In this paper, we propose an assumption that we can train a segmentation model with virtual flame images and design experiments to prove it. We take many virtual flame videos to extend existing flame datasets, which provide adequate flame samples for deep learning-based VFD methods. We also take a random background-pasting method to distribute the flame images among different scenarios. The proposed method trains a flame segmentation model with zero real flame image. Moreover, we perform segmentation testing with real flame images, which the model has never touched, to see if the model trained with ‘fake’ images can segment real objects. We trained four segmentation models based on FCN, U-Net, Deeplabv3, and Mask-RCNN with synthetic flame video frames, and obtained the highest mPA of 0.783 and mIoU of 0.515. The experimental results on FIRE-SMOKE-DATASET and Fire-Detection-Image-Dataset demonstrate that the ‘fake’ flame samples generated by the proposed random background-pasting method can improve the performances of existing state-of-the-art flame segmentation methods obviously on cross-dataset evaluation settings. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Artificial Neural Network-Based Approach to Modeling Energy Bands of GaN-Based Heterojunction Materials

Artificial Neural Network-Based Approach to Modeling Energy ...

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High Performance Big Data and intelligent Systems (HPBD&IS)

作者： Meilan Hao Shu Wei Lina Yu Weijun Li Min Wu Jingyi Liu Wenqiang Li Yanjie Li AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing China School of Information and Electrical Engineering Hebei University of Engineering Handan China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing China School of Integrated Circuits University of Chinese Academy of Sciences Beijing China

This work reports a preliminary investigation of energy bands of AlxGal-xN/GaN heterojunction based on the use of artificial neural networks (ANN). Numerical energy bands simulations were used to generate training and testing dataset for ANN model. The input parameters of the ANN are the Al content, the thicknesses of AlxGal-xN barrier layer and position description of two-Layer Materials, respectively. The outputs of the ANN are the conduction band energy profile of the AlxGal_xN/GaN heterojunction and the channel Two-dimensional electron gas (2DEG) concentration distributions. The results show that trained ANN model can effectively predict the energy bands structure. The model achieved a mean squared error of 7.94×10 6 on the testing dataset, with an average relative L2 error of 4.44×10 3 for electron concentration prediction and 2.57×10 3 for conduction band energy prediction.

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Gaussian-Hermite Moment Invariants of General Multi-Channel Functions

arXiv

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arXiv 2022年

作者： Mo, Hanlin Li, Hua Zhao, Guoying The Center for Machine Vision and Signal Analysis University of Oulu OuluFI-90014 Finland The Key lab of Intelligent Information Processing The Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China The School of Information and Technology Northwest University Xi'An710069 China

With the development of data acquisition technology, large amounts of multi-channel data are collected and widely used in many fields. Most of them, such as RGB images and vector fields, can be expressed as different types of multi-channel functions. Feature extraction of multi-channel data for identifying interest patterns is a critical but challenging task. This paper focuses on constructing moment-based features of general multi-channel functions. Specifically, we define two transform models, rotation-affine transform and total rotation transform, to describe real deformations of multi-channel data. Then, we design a structural framework to generate Gaussian-Hermite moment invariants for these two transform models systematically. It is the first time that a unified framework has been proposed in the literature to construct orthogonal moment invariants of general multi-channel functions. Given a specific type of multi-channel data, we demonstrate how to utilize the new method to derive all possible invariants and eliminate dependences among them. We obtain independent sets of invariants with low orders and low degrees for RGB images, 2D vector fields and color volume data. Based on synthetic and real multi-channel data, we conduct extensive experiments to evaluate the stability and discriminability of these invariants and their robustness to noise. The results show that new moment invariants significantly outperform previous moment invariants of multi-channel data in RGB image classification and vortex detection in 2D vector fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Image classification

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A Model-free False Data Injection Attack Strategy in Networked Control Systems

A Model-free False Data Injection Attack Strategy in Network...

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IEEE Conference on Decision and Control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Jianping He The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology and Institute of Cyberspace Research Zhejiang University China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The data-driven attack strategies recently have received much attention when the full knowledge of the system model is unknown or difficult to be obtained for the adversary. Note that despite the critical parameters of the system model being unavailable for the adversary, the existing data-driven attack methods still depend on the linearity of the unknown system model. In this paper, we design a completely model-free attack strategy where the adversary with limited capability aims to compromise state variables such that the output value follows the expected trajectory. Specifically, we first construct a zeroth-order feedback optimization framework and uninterruptedly use probing signals for real-time measurements. Then, we iteratively update the attack signals along the composite direction of the gradient estimates of the objective function evaluations and the projected gradients. These objective function evaluations can be obtained only by real-time measurements. Furthermore, we characterize the optimality of these solutions via the optimality gap, which is affected by the dimensions of the attack signal, the iterations of solutions, and the convergence rate of the system. Extensive simulations are conducted to show the effectiveness of the proposed attack strategy.

关键词： Networked control systems Computational modeling Current measurement Linear programming Real-time systems Data models Time measurement

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CRRNet: Channel Relation Reasoning Network for Salient Object Detection 3rd

CRRNet: Channel Relation Reasoning Network for Salient Obje...

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3rd China intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Gao, Shuyong Xing, Haozhe Zhang, Chenglong Zhang, Wenqiang Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China School of Computer Science Fudan University Shanghai200433 China Academy for Engineering and Technology Fudan University Shanghai200433 China Yiwu Research Institute of Fudan University Chengbei Road Zhejiang Yiwu City322000 China

ISBN: (纸本)9789819903009

Channel map matters for salient object detection. Effectively exploring the relationship between channels can enhance the relevant channel maps and infer a better saliency map. The existing channel map enhancement methods focus on learning the weights to re-weight the channel maps or using the similarity between the channels to combine the channel maps and enhance them. We propose a Channel Relation Reasoning Module (CRRM) based on graph convolution, inferring the semantic relations between channels and enhancing the channel maps according to the mutual relations. First, we carefully designed a multi-level complementary feature extraction module, which is used to extract multi-level edge and salient object internal features simultaneously. Then, the rich multi-level edge and salient features are fed to CRRM which projects the features into the hidden space and uses the graph convolution to establish the semantic relationship between the channels to enhance the feature expression. Finally, two stacked convolution layers can produce high-quality saliency maps. Extensive experiments on multiple datasets show the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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The Database and Benchmark For the Source Speaker Tracing Challenge 2024

The Database and Benchmark For the Source Speaker Tracing Ch...

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IEEE Spoken Language technology Workshop

作者： Ze Li Yuke Lin Tian Yao Hongbin Suo Pengyuan Zhang Yanzhen Ren Zexin Cai Hiromitsu Nishizaki Ming Li School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China AI Center OPPO Beijing China Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics CAS China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Center for Language and Speech Processing Johns Hopkins University USA Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi 4-4-37 Takeda Kofu Yamanashi Japan

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Voice conversion (VC) systems can transform audio to mimic another speaker’s voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found here 1 . 1 https://***/

关键词： Databases Conferences Transforms Contrastive learning Benchmark testing Multitasking Calibration

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Adaptive Local-Global Relational Network for Facial Action Units Recognition and Facial Paralysis Estimation

arXiv

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arXiv 2022年

作者： Ge, Xuri Jose, Joemon M. Wang, Pengcheng Iyer, Arunachalam Liu, Xiao Han, Hu The School of Computing Science University of Glasgow United Kingdom Beijing100080 China The Department of Otolaryngology and Head and Neck Surgery University Hospital Monklands Airdrie United Kingdom The Online Media Business Unit Tencent Beijing100080 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China

Facial action units (AUs) refer to a unique set of facial muscle movements at certain facial locations defined by the Facial Action Coding System (FACS), which can be used for describing nearly any anatomically possible facial expression. Many existing facial action units (AUs) recognition approaches often enhance the AU representation by combining local features from multiple independent branches, each corresponding to a different AU, which usually neglect potential mutual assistance and exclusion relationship between AU branches or simply employ a predefined and fixed knowledge-graph as a prior. In addition, extracting features from pre-defined AU regions of regular shapes limits the representation ability. In this paper, we propose a novel Adaptive Local-Global Relational Network (ALGRNet) for facial AU recognition and apply it to facial paralysis estimation. ALGRNet mainly consists of three novel structures, i.e., an adaptive region learning module which learns the adaptive muscle regions based on the detected landmarks, a skip-BiLSTM module which models the latent mutual assistance and exclusion relationship among local AU features, and a feature fusion&refining module which explores the complementarity between local AUs and the whole face for the local AU refinement. In order to evaluate our proposed method, we migrated ALGRNet to a facial paralysis dataset which is collected and annotated by medical professionals. Experiments on the BP4D and DISFA AU datasets show that the proposed approach outperforms the state-of-the-art methods by a large margin. Additionally, we also demonstrated the effectiveness of the proposed ALGRNet in applications to facial paralysis estimation. Copyright © 2022, The Authors. All rights reserved.

关键词： Refining

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3D Visual Illusion Depth Estimation

arXiv

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arXiv 2025年

作者： Yao, Chengtang Liu, Zhidan Zeng, Jiaxi Yu, Lidong Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China NVIDIA United States

3D visual illusion is a perceptual phenomenon where a two-dimensional plane is manipulated to simulate three-dimensional spatial relationships, making a flat artwork or object look three-dimensional in the human visual system. In this paper, we reveal that the machine visual system is also seriously fooled by 3D visual illusion, including monocular and binocular depth estimation. In order to explore and analyze the impact of 3D visual illusion on depth estimation, we collect a large dataset containing almost 3k scenes and 200k images to train and evaluate SOTA monocular and binocular depth estimation methods. We also propose a robust depth estimation framework that uses common sense from a vision-language model to adaptively select reliable depth from binocular disparity and monocular depth. Experiments show that SOTA monocular, binocular, and multi-view depth estimation approaches are all fooled by various 3D visual illusions, while our method achieves SOTA performance. https://***/YaoChengTang/3DVisual-Illusion-Depth-Estimation © 2025, CC BY.

关键词： Three dimensional computer graphics

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Unauthorized UAV Countermeasure for Low-Altitude Economy: Joint Communications and Jamming based on MIMO Cellular Systems

arXiv

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arXiv 2024年

作者： Li, Zhuoran Gao, Zhen Wang, Kuiyu Mei, Yikun Zhu, Chunli Chen, Lei Wu, Xiaomei Niyato, Dusit Beijing100081 China BIT Zhuhai519088 China The MIT Key Laboratory of Complex-Field Intelligent Sensing BIT Beijing100081 China Jinan250307 China Jiaxing314019 China The State Key Laboratory of CNS ATM & State Key Laboratory of Explosion Science and Safety Protection Beijing Institute of Technology Beijing100081 China China Tower Corporation Limited No.9 Don-gran North Street Haidian District Beijing100089 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Research Institue of Multidisciplinary Sciences Beijing Institute of Technology Beijing100081 China

To ensure the thriving development of low-altitude economy, countering unauthorized unmanned aerial vehicles (UAVs) is an essential task. The existing widely deployed base stations hold great potential for joint communication and jamming. In light of this, this paper investigates the joint design of beamforming to simultaneously support communication with legitimate users and countermeasure against unauthorized UAVs based on dual-functional multiple-input multiple-output (MIMO) cellular systems. We first formulate a joint communication and jamming (JCJ) problem, relaxing it through semi-definite relaxation (SDR) to obtain a tractable semi-definite programming (SDP) problem, with SDR providing an essential step toward simplifying the complex JCJ design. Although the solution to the relaxed SDP problem cannot directly solve the original problem, it offers valuable insights for further refinement. Therefore, we design a novel constraint specifically tailored to the structure of the SDP problem, ensuring that the solution adheres to the rank-1 constraint of the original problem. Finally, we validate effectiveness of the proposed JCJ scheme through extensive simulations. Simulation codes are provided to reproduce the results in this paper: https://***/LiZhuoRan0. The results confirm that the proposed JCJ scheme can operate effectively when the total number of legitimate users and unauthorized UAVs exceeds the number of antennas. Copyright © 2024, The Authors. All rights reserved.

关键词： Jamming

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SVLR: Genome Structure Variant Detection Using Long Read Sequencing Data 1

引用

16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Gu, Wenyan Zhou, Aizhong Wang, Lusheng Sun, Shiwei Cui, Xuefeng Zhu, Daming School of Computer Science and Technology Shandong University Qingdao China Department of Computer Science City University of Hong Kong Kowloon Hong Kong Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

Genome structural variants have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long read sequencing technologies arise to make it possible to find structural variants of as long as ten thousand nucleotides. Thus, long read based structural variant detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect structural variants recently. In this article, we present a new method, called SVLR, to detect Structural Variants based on Long Read sequencing data. Comparing to existing methods, SVLR can detect three new kinds of structural variants: block replacements, block interchanges and translocations. Although these new structural variants are structurally more complicated, SVLR achieves accuracies that are comparable to those of the classic structural variants. Moreover, for the classic structural variants that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up-to without harming the precisions (i.e., above). We also point out three directions to further improve structural variant detection in the future. Source codes: https://***/GWYSDU/SVLR. © 2020, Springer Nature Switzerland AG.

关键词： Genes

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