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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Zhao, Ying Xu, Dantong Wang, Tie Ren, Yanchun Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing School of Information Engineering Inner Mongolia University of Science and Technology Baotou Inner Mongolia

ISBN: (纸本)9781728189543

Purpose. To assess the recognition pattern of dynamic action by visual prosthesis' wearers. Methods. Twenty volunteers (classified by gender and experience) were recruited to carried out action recognition test in simulated prosthetic vision. Twenty-three common dynamic actions video clips usually see in daily life were selected and formed the experimental action set which was divided into three parts including combined actions, simple actions and difficult actions in turn. The action animations made by 3D Studio Max were recorded by Banicam to convert to MP4 video format and processed to phosphene video clips by MATLAB in different resolutions (16 × 16, 24 × 24, 32 × 32, 48 × 48, 64 × 64, 128 × 128). Result. Comparing the recognition results, male group and female group had no significant difference. 48 × 48 resolution was the considerable latent capacity which subjects had stable performance. After a period of study, the performance of prosthesis wearers could be improved significantly. © 2020 IEEE.

关键词： MATLAB

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A study on soft x-ray spectra from pulsed 1 μm Nd:YAG laser-induced ytterbium plasmas

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Plasma Science and Technology 2020年第10期22卷 119-125页

作者： Tao WU Jun HE Li YANG Peixiang LU Hubei Key Laboratory Optical Information and Pattern Recognition School of Optical Information and Energy EngineeringWuhan Institute of TechnologyWuhan 430205People's Republic of China Guangdong Intelligent Robotics Institute Dongguan 523808People's Republic of China

The Nd:YAG laser with a wavelength of 1.064 μm was used to generate plasmas on a high-purity solid ytterbium(70 Yb) target in a vacuum chamber. The soft x-ray time-and space-integration spectra from the Yb plasmas were measured in the wavelength range of 1.0–8.5 nm under different power densities. The atomic spectral data of the unresolved transition arrays from highly charged Yb ions were calculated based on Cowan's suite of codes, including configuration interaction. The calculated Gaussian envelope of the emission determined by the weighted spontaneous transition rates was compared with the experimental spectra, and a good agreement between them was obtained. The spatial-temporal evolutions of the plasmas under the experimental conditions are simulated based on the collisional-radiative model, enabling the understanding of the mechanism of the plasma conditions for optimal water window waveband emission output.

关键词： laser produced plasma water window sources unresolved transition arrays ytterbium

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Sequence-level training for non-autoregressive neural machine translation

arXiv

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arXiv 2021年

作者： Shao, Chenze Feng, Yang Zhang, Jinchao Meng, Fandong Zhou, Jie Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Pattern Recognition Center WeChat AI Tencent Inc

In recent years, Neural Machine Translation (NMT) has achieved notable results in various translation tasks. However, the word-by-word generation manner determined by the autoregressive mechanism leads to high translation latency of the NMT and restricts its low-latency applications. Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously. Nevertheless, NAT still takes the word-level cross-entropy loss as the training objective, which is not optimal because the output of NAT cannot be properly evaluated due to the multimodality problem. In this article, we propose using sequence-level training objectives to train NAT models, which evaluate the NAT outputs as a whole and correlates well with the real translation quality. Firstly, we propose training NAT models to optimize sequence-level evaluation metrics (e.g., BLEU) based on several novel reinforcement algorithms customized for NAT, which outperforms the conventional method by reducing the variance of gradient estimation. Secondly, we introduce a novel training objective for NAT models, which aims to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. The BoN training objective is differentiable and can be calculated efficiently without doing any approximations. Finally, we apply a three-stage training strategy to combine these two methods to train the NAT model. We validate our approach on four translation tasks (WMT14 En↔De, WMT16 En↔Ro), which shows that our approach largely outperforms NAT baselines and achieves remarkable performance on all translation tasks. The source code is available at https://***/ictnlp/Seq-NAT. Copyright © 2021, The Authors. All rights reserved.

关键词： Neural machine translation

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Neural network structure design based on N-gauss activation function1

arXiv

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arXiv 2021年

作者： Lu, Xiangri Ma, Hongbin Zhang, Jingcheng State Key Laboratory of Intelligent Decision and Control for Complex Systems Beijing Institute ofTechnology Beijing China Institute of Pattern Recognition and Intelligent Systems School of Automation Beijing Institute ofTechnology China Institute of Navigation Guidance and Control School of Automation Beijing Institute of

Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to the scale of the data set, and the data set can be trained more deeply, more accurately and more effectively. In this article, we have accepted the experimental results and introduced the core block N − Gauss, N − Gauss, and Swish (Conv1, Conv2, FC1) neural network structure design to train MNIST, CIFAR10, and CIFAR100 respectively. Experiments show that N − Gauss gives full play to the main role of nonlinear modeling of activation functions, so that deep convolutional neural networks have hierarchical nonlinear mapping learning capabilities. At the same time, the training ability of N − Gauss on simple one-dimensional channel small data sets is equivalent to the performance of ReLU and Swish. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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Sub-syllable Representation-based Hmong Language Text-to-Speech Method

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Science Technology and Engineering 2024年第19期24卷 8176-8185页

作者： Cai, Shan Wang, Lin Tan, Mian Guo, Sheng Wu, Lei Wang, Fei College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guiyang550025 China College of Humanities & Sciences of Guizhou Minzu University Guiyang550025 China

Speech synthesis of minority languages contributes to the preservation, protection and development of national culture, while the research results in this field are currently limited. To address the problem of speech synthesis errors where words with different tones sound similar, a sub-syllable representation-based text-to-speech method for the Hmong language was proposed. The method utilized sub-syllables as training primitives to accurately represent the pronunciation information of the Hmong language, enabling distinctive learning of similar sounds across different syllables. According to the monotonicity of alignment between text sequence and Mel-spectrogram, a monotonic alignment loss was introduced to guide the attention module to learn alignment more accurately, thereby reducing synthesis phenomena such as word skipping and repetition inherent in the autoregressive attention mechanism. To verify the effectiveness of the proposed method, a self-built Hmong language speech synthesis corpus, HmongSpeech (download link: http: / / sxjxsf. gzmu. edu. cn / info / 1728 / 1214. htm), was utilized as the benchmark dataset. Comparative experiments were conducted with typical speech synthesis methods. The experimental results show that the proposed method successfully reduces the synthetic error rate caused by the similar pronunciation of words with different tones. Notably, the word error rate is only 0. 96%, outperforming the baseline method by 6. 25% . © 2024 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Context sensitive grammars

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Analysis Model of Terrorist Attacks Based on Big Data 32

Analysis Model of Terrorist Attacks Based on Big Data

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第32届中国控制与决策会议

作者： LIN Zhenkai DOU Yimin LI Jinping School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing(University of Jinan) Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Institute of Pattern Recognition and Intelligent Systems University of Jinan

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

With the continuous deepening of international anti-terrorism movement,the anti-terrorism has entered a new stage,and it is facing new *** of the new challenges is to extract useful and valuable information from massive data *** anti-terrorism system model based on local shallow information is imperfect,which is not conducive to obtaining accurate prediction *** shortcomings of existing research are the lack of comprehensive analysis and deeper mining of *** order to improve the efficiency and accuracy of the present anti-terrorism system,we propose an effective method for risk assessment and prediction based on machine learning by using Global Terrorism Database(GTD).There are four basic steps:first,we reduce the data dimension through correlation calculation and Singular Value Decomposition(SVD),then,the function is established to rank the harmfulness of terrorist attacks;second,the cascaded network with attention mechanism is used to predict suspects;third,k-means is used to cluster the regions of terrorist attacks,and then we establish a generalized linear regression model to predict the situation of terrorist *** verify the feasibility of the model by comparing with the real *** experimental results show that the proposed method can analyze and predict the information related to terrorist attacks comprehensively and accurately.

关键词： anti-terrorism warning data modeling SVD cascade network with attention mechanism K-means

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Unsupervised Difference Learning for Noisy Rigid Image Alignment

arXiv

引用

arXiv 2022年

作者： Chen, Yu-Xuan Feng, Dagan Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Computer Science University of Sydney Sydney2006 Australia

Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spatial transformer networks show an improved performance on clean images but will not achieve satisfactory performance on noisy images due to its heavy reliance on pixel value comparations. To handle such challenging applications, we report a new unsupervised difference learning (UDL) strategy and apply it to rigid image alignment. UDL exploits the quantitative properties of regression tasks and converts the original unsupervised problem to pseudo supervised problem. Under the new UDL-based image alignment pipeline, rotation can be accurately estimated on both clean and noisy images and translations can then be easily solved. Experimental results on both nature and cryo-EM images demonstrate the efficacy of our UDL-based unsupervised rigid image alignment method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Research on the identification of obstacle image based on convolutional neural network

Research on the identification of obstacle image based on co...

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2021 International Conference on Signal Image Processing and Communication, ICSIPC 2021

作者： Zhongyu, Li Huajun, Wang Yuhang, Cao Yuting, Dai College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China College of Computer Science Chengdu Normal University Chengdu611130 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu Sichuan611130 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

To effectively realize the reasonable obstacle avoidance of the detection robot, VGG based obstacle discrimination method is proposed. Above all, the image captured by the robot is input into the multi-layer convolutional neural network to obtain the high-level image features, which are used to construct the more accurate neural network model parameters and to train the softmax classifier with these parameters. Then the distance between the imported images and the data images is calculated by using the softmax classifier, and the similarity between the obstacles and non-obstacles is estimated. The experimental results show that the discrimination accuracy increase to above 94%. And the proposed method is more effectively compared with traditional ultrasonic and radar methods. © 2021 SPIE.

关键词： Convolution

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Large second-harmonic vortex beam generation with quasi-nonlinear spin–orbit interaction

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Science Bulletin 2021年第5期66卷 449-456,M0004页

作者： Wenchao Zhao Kai Wang Xuanmiao Hong Bingxia Wang Xiaobo Han Kun Wang Weiwei Liu Hua Long Bing Wang Peixiang Lu Wuhan National Laboratory for Optoelectronics and School of Physics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205China Guangdong Intelligent Robotics Institute Dongguan 523808China CAS Center for Excellence in Ultra-intense Laser Science Shanghai 201800China

A harmonic vortex beam is a typical vector beam with a helical wavefront at harmonic frequencies(e.g.,second and third harmonics). It provides an additional degree of freedom beyond spin-and orbitalangular momentum, which may greatly increase the capacity for communicating and encoding information. However, conventional harmonic vortex beam generators suffer from complex designs and a low nonlinear conversion efficiency. Here, we propose and experimentally demonstrate the generation of a large second-harmonic(SH) vortex beam with quasi-nonlinear spin–orbit interaction(SOI). Highquality SH vortex beams with large topological charges up to 28 are realized experimentally. This indicated that the quasi-angular-momentum of a plasmonic spiral phase plate at the excitation wavelength(topological charge, q) could be imprinted on the harmonic signals from the attached WS2 monolayer. The generated harmonic vortex beam has a topological charge of l_(n)= 2 nq(n is the harmonic order). The results may open new avenues for generating harmonic optical vortices for optical communications and enables novel multi-functional hybrid metasurface devices to manipulate harmonic beams.

关键词： Plasmonic metasurface Orbital-angular momentum Monolayer WS_(2) Second-harmonic generation Metalenses

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CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection

CDFI: Cross Domain Feature Interaction for Robust Bronchi Lu...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiasheng Xu Tianyi Zhang Yangqian Wu Jie Yang Guang–Zhong Yang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai CHINA Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai CHINA MOE Key Laboratory of System Control and Information Processing Shanghai P.R. China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex airway networks, robust lumen detection is essential for intraoperative guidance. However, these methods are sensitive to visual artifacts which are inevitable during the surgery. In this work, a cross domain feature interaction (CDFI) network is proposed to extract the structural features of lumens, as well as to provide artifact cues to characterize the visual features. To effectively extract the structural and artifact features, the Quadruple Feature Constraints (QFC) module is designed to constrain the intrinsic connections of samples with various imaging-quality. Furthermore, we design a Guided Feature Fusion (GFF) module to supervise the model for adaptive feature fusion based on different types of artifacts. Results show that the features extracted by the proposed method can preserve the structural information of lumen in the presence of large visual variations, bringing much-improved lumen detection accuracy.

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