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Flexible micro-perforated panels prepared by CNC machining

IOP Conference Series: Earth and Environmental Science 2019年第4期358卷

作者： Dongyue Xie Xiaoning Tang Ming Hu Deyi Kong Yujie Qian Hefei University of Technology Hefei 230601 China State Key Laboratory Cultivation Base for New Textile Materials and Advanced Processing Technology School of Textile Science and Engineering Wuhan Textile University Wuhan 430200 China State Key Laboratory of Transducer Technology Key Laboratory of Biomimetic Sensing and Advanced Robot Technology of Anhui Province Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei 230031 China IOT Engineering College Hohai University Changzhou 213022 China

With the advantages of light weight, thin thickness and environment friendly, the micro-perforated panel (MPP) has been widely studied for noise reduction. Maa's model pointed out that the MPP could obtain higher sound absorption over broader frequency band when the perforations were reduced to less than 100 μm. However, it is challenging to manufacture MPPs with the aperture of approximate 100 μm, thus its potential application has been restricted. In this study, we used a computer numerical control (CNC) milling machine to process MPP. Four different kinds of raw materials including paperboard, polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polycarbonate (PC) were taken to prepare micro-perforated panels (MPPs). It has been indicated that MPPs with good sound absorption properties were successfully prepared by this facile method.

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Quality assessment of dibr-synthesized views: An overview

arXiv

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arXiv 2019年

作者： Tian, Shishun Zhang, Lu Zou, Wenbin Li, Xia Ting, Su Luce, Morin Olivier, Déforges College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Rennes France UMR CNRS 6164 Rennes France Research Center for Medical Articial Intelligence Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong518055 China

The Depth-Image-Based-Rendering (DIBR) is one of the main fundamental technique to generate new views in 3D video applications, such as Multi-View Videos (MVV), Free-Viewpoint Videos (FVV) and Virtual Reality (VR). However, the quality assessment of DIBR-synthesized views is quite different from the traditional 2D images/videos. In recent years, several efforts have been made towards this topic, but there is a lack of detailed survey in the literature. In this paper, we provide a comprehensive survey on various current approaches for DIBR-synthesized views. The current accessible datasets of DIBR-synthesized views are firstly reviewed, followed by a summary analysis of the representative state-of-the-art objective metrics. Then, the performances of different objective metrics are evaluated and discussed on all available datasets. Finally, we discuss the potential challenges and suggest possible directions for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Surveys

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Weighted Feature Pyramid Networks for Object Detection

Weighted Feature Pyramid Networks for Object Detection

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiaohan Li Taotao Lai Shuaiyu Wang Quan Chen Changcai Yang Riqing Chen Jinxun Lin Fu Zheng Digital Fujian Institute of Big Data for Agriculture and Forestry College of Computer and information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Fu Jian Shu Bo Xun Information Technology Co. Ltd

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Feature Pyramid Networks (FPN) is a popular feature extraction. However, FPN and its variants do not investigate the influence of resolution information and semantic information in the object detection. Thus, FPN and its variants cannot detect some objects on challenging images. In this paper, based on FPN, we propose to use gaussian kernel function to assign different weight values to semantic information and resolution information for different images in the object detection. The proposed method, is called a Weighted Feature Pyramid Network (WFPN), and shows significant improvement over the traditional feature pyramids in several applications. Using WFPN in Faster R-CNN system, the proposed method achieves better performance on the PASCAL detection benchmark.

关键词： Feature extraction Kernel Object detection Agriculture Forestry Semantics Big Data

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A nonlinear impulsive control system with impulse time windows and un-fixed coefficient of impulsive intensity

A nonlinear impulsive control system with impulse time windo...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Yuming Feng Zitao Wang Xiaoyu Liu Wei Zhang Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9781728158570

ISBN: (纸本)9781728158587

Based on the fact that there always exists error in the impulsive intensity, which leads to the fact that the coefficient is un-fixed, and in the occurrence of the impulses, we propose a new model, which is called nonlinear impulsive control system with impulse time windows and un-fixed coefficient of impulsive intensity. We find sufficient conditions for ensuring its stability by using Lypunov's method. We choose chaotic Lorenz system and Chua's system as numerical examples to show the effectiveness of the results, by employing such method, the systems are controlled.

关键词： Impulsive control system impulse time windows impulsive intensity stability control systems Impulsiveness Pulses Time windows

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An Image Denoising Method Based on Deep Residual GAN

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Journal of Physics: Conference Series 2020年第3期1550卷

作者： Ziyuan Wang Lidan Wang Shukai Duan Yunfei Li Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing 400715 China Schoolof Electronic and information engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing 400715 China Braininspired Computing & Intelligent Control of Chongqing Key Lab Chongqing 400715 China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects.

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Probabilistic Adaptive Genetic Algorithm for Pre-treatment Radiomics Features Predict Patient Survival

Probabilistic Adaptive Genetic Algorithm for Pre-treatment R...

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2018 International Conference on Computer, Communications and Mechatronics Engineering（CCME 2018）

作者： Xiao-ying PAN Ting ZHANG Qing-ping YANG Liu CHEN School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and Telecommunications

In order to provide personalized treatment for patients with tongue carcinoma, a probabilistic adaptive genetic algorithm neural network(PAGA-BP) model is proposed in this paper. The PAGA-BP model ameliorates selection sorting operator, adaptive crossover operator and u-adaptive mutation operator to optimize the initial weight of BP neural network. By comparing with traditional GA-BP neural network and BP neural network survival prediction model, the results show that PAGA-BP prediction model has the highest approximation accuracy and better survival period prediction.

关键词： PGA-BP neural network Radiomics features Survival Prediction

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Multi-View broad learning system for primate oculomotor decision decoding

arXiv

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arXiv 2019年

作者： Shi, Zhenhua Chen, Xiaomo Zhao, Changming He, He Stuphorn, Veit Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Zanvyl Krieger Mind and Brain Institute Johns Hopkins University School of Medicine Department of Neurobiology Stanford University StanfordCA United States Department of Neuroscience Johns Hopkins University BaltimoreMD United States

Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source. This approach is suitable for primate brain state decoding using cortical neural signals. This is because the complementary components of simultaneously recorded neural signals, local field potentials (LFPs) and action potentials (spikes), can be treated as two views. In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in monkey oculomotor decision decoding from medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in non-human primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approach to classify the oculomotor decision, than several classical and state-of-the-art single-view and multi-view learning approaches. Copyright © 2019, The Authors. All rights reserved.

关键词： Electrophysiology

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A Novel Multiple-traces Electric Field/Combined Field Integral Equation for Electromagnetic Modeling of Microstrip Objects

A Novel Multiple-traces Electric Field/Combined Field Integr...

引用

International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yv Long Wang Ran Zhao Zhi Xiang Huang Jun Hu Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Electronic Engineering University of Electronic Science and Technology of China (UESTC)

In this paper, a new multiple-traces electric field/combined field integral equation (MT-EF/CFIE) is proposed for the electromagnetic modeling of microstrip objects. Different from traditional EFIE-PMCHWT method, this new multiple-traces method decomposes the original microstrip object into two independent domains i.e., the exterior region (free space) and interior region (microstrip), and enforces the Robin transmission conditions (TCs) on the interface between exterior and interior regions to ensure the continuity of fields. In comparison to the traditional EFIE-PMCHWT, this new MT-EF/CFIE has a better convergence property. Because the continuity of fields is ensured by TCs, the exterior and interior regions can be discretized by non-conformal meshes, which improves the flexibility and efficiency of the proposed methods substantially.

关键词： Microstrip Microstrip antenna arrays Dielectrics Integral equations Electromagnetic scattering Convergence

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Affect estimation in 3d space using multi-task active learning for regression

arXiv

引用

arXiv 2018年

作者： Wu, Dongrui Huang, Jian Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

—Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries. Copyright © 2018, The Authors. All rights reserved.

关键词： Regression analysis

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Kernelized convex hull for visual tracking 5

Kernelized convex hull for visual tracking

引用

2017 International Conference on Progress in Informatics and Computing(PIC)

作者： Jun Wang Yuanyun Wang Chengzhi Deng Shengqian Wang Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology School of Information Engineering Nanchang Institute of Technology

ISBN: (纸本)9781538619797;9781538619780

In visual tracking, developing a robust appearance model is a challenging task due to variations of object appearances such as background clutter, illumination variation and partial occlusion. In existing tracking algorithms, a target candidate is represented by linear combinations of target ***, the relationship between a target candidate and the corresponding target templates is nonlinear because of appearance variations. In this paper, we propose a kernelized convex hull based target representation for visual tracking. Namely, a target is represented by a nonlinear combination of target templates in a mapped higher dimensional feature space. The convex hull model can covers the target appearances that do not appear in the target templates. Experimental results demonstrate the robustness and effectiveness of the proposed tracking algorithm against several state-of-the-art tracking algorithms.

关键词： Visual tracking Kernel Convex hull Appearance model

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