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Infer light diffuseness on light probes with different kinds of mesoreliefs

Infer light diffuseness on light probes with different kinds...

16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors (第十六届中国国际半导体照明论坛暨2019国际第三代半导体论坛)

作者： Yudi Wang Ling Xia Jinfeng Huang Ruipeng Xu Xiaofeng Liu College of IoT Engineering Hohai UniversityJiangsu Key Laboratory of Robotics and Intelligent Technology ChangzhouChina

ISBN: (数字)9781728157566

ISBN: (纸本)9781728157573

The appearance of objects varies enormously depending on light ***,the appearance of objects might be a cue to the light *** would provide a solution for lighting designers to guess-estimate the light diffuseness levels based on the appearance of a light ***,the question remains whether rough probe provides additional cues about light diffuseness comparing with the smooth *** the answer to the last question is yes,whether different kinds of roughness performs the ***,how the estimation of light diffuseness might be influenced by the illumination *** answer these questions,an appearance-matching experiment was performed,within which white spherical probes with four kinds of surface mesoreliefs were adopted and 5 illumination directions were *** results confirmed that observers' abilities to match the light diffuseness were improved by the use of probes with 3D texture,which means that observers can derive light diffuseness from the additional cues provided by the texture over the rough ***,the results showed that the roughness has no obvious effect on diffuseness perception at illumination direction of 90o or 120o owing to the much useful information provided by the terminator of the body shadow.

关键词： Probes Lighting Observers Rough surfaces Surface roughness Three-dimensional displays

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The Effect of Lighting on Stereo Vision Test with Random-Dot Stereogram

The Effect of Lighting on Stereo Vision Test with Random-Dot...

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16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors (第十六届中国国际半导体照明论坛暨2019国际第三代半导体论坛)

作者： Jinfeng Huang Tingting Zhang Yudi Wang Jinwei Xie Xiaofeng Liu Jiangsu Key Laboratory of Robotics and Intelligent Technology College of IoT EngineeringHohai UniversityChangzhouChina

ISBN: (数字)9781728157566

ISBN: (纸本)9781728157573

Depth perception is the ability of people to evaluate the relative and absolute *** practitioners such as pilots,car drivers,athletes,tailors,and seafarers,always require better stereo vision and good depth perception ability to achieve high efficiency and good ***,the lighting environment is another important factor that influence their working *** paper investigated the influence of ambient luminance and screen brightness on depth perception based on Random-Dot *** the experiment,we set three ambient illumination and three screen brightness to *** found depth perception of the participants was influenced by screen brightness(16 Lux,8 Lux and 1.5 Lux),but not influenced by ambient illumination(124 Lux,20 Lux,2 Lux).Hence,we could conclude that the ambient illumination of 124 Lux,20 Lux,2 Lux is a safe lighting that does not influence depth perception and it is not suitable for operators to work with the screen brightness of 16 Lux,8 Lux and 1.5 Lux.

关键词： Brightness Visualization Fluorescent lamps Stereo image processing Correlation

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Boosting the Efficiency and Mechanical Stability of Organic Solar Cells Through a Polymer Acceptor by Reducing the Elastic Modulus

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Advanced Energy Materials 2025年

作者： Wang, Yan Yu, Han Zhao, Dan Liu, Wei Liu, Baoze Wu, Xin Gao, Danpeng Zhang, Dong Zhang, Shoufeng Sun, Xianglang Zhang, Chunlei Zhao, Chaoyue Fu, Yuchen Song, Wei Gong, Shaokuan Fu, Yuang Kwok, Chung Hang Ge, Ziyi Lu, Xinhui Chen, Xihan Xiao, Shuang Wong, Wai-Yeung Chai, Yu Yan, He Zhu, Zonglong Department of Chemistry City University of Hong Kong Kowloon999077 Hong Kong Department of Chemistry Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials Energy Institute and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Hong Kong University of Science and Technology-Shenzhen Research Institute No. 9 Yuexing 1st RD Hi-tech Park Nanshan Guangdong Shenzhen518057 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Physics City University of Hong Kong Kowloon999077 Hong Kong Zhejiang Engineering Research Center for Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Physics The Chinese University of Hong Kong Shatin999077 Hong Kong Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China Shenzhen Research Institute City University of Hong Kong Shenzhen518057 China

Organic solar cells (OSCs) are regarded as one of the most promising flexible power sources due to their lightweight and flexible properties, with the improvement of photovoltaic and mechanical performance. To improve the current density and power conversion efficiency (PCE), mPh4F-TS (TS) and PYSe2F-T (PA) are introduced into the binary host, PM6/mPh4F-TT (PM6/TT) as third components. It is demonstrated that the corresponding ternary devices, in both rigid and flexible devices, achieved superior efficiencies (19.6%/17.7% for PM6/TT+TS, and 19.2%/17.4% for PM6/TT+PA) outperform the binary counterparts (18.3%/16.4%). However, distinct differences in mechanical performance are observed between the polymer acceptor (PA) and small-molecular acceptor (TS). The PM6/TT+PA significantly improved the mechanical stability of flexible devices with a lower elastic modulus of 3.6 GPa, while the PM6/TT+TS resulted in the opposite effect with a higher elastic modulus of 5.5 GPa. Through in-depth investigation, a clear correlation between the elastic modulus, crack density, and mechanical stability of the active layer blends is successfully established, revealing the key role of reducing the elastic modulus in enhancing the mechanical stability of flexible OSCs. This study provides important guidance for the development of flexible photovoltaic devices with both high efficiency and mechanical robustness. © 2025 The Author(s). Advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Organic solar cells

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Towards intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

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IEEE Network 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Xiangjiang Laboratory Changsha410205 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China University of Concordia Department of Electrical and Computer Engineering Montreal Canada Nanyang Technological University College of Computing and Data Science Singapore Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

In intelligent transportation systems (ITSs), integrating pedestrians and vehicles into traffic management models is essential for developing realistic and safe solutions. However, current systems often fail to simulate complex, real-world scenarios due to the absence of a comprehensive digital twin framework across diverse traffic environments and effective modeling of pedestrian-vehicle interactions. In this article, we propose a surveillance video-assisted federated digital twin (SV-FDT) framework to enhance ITSs by incorporating pedestrians and vehicles into the control loop. SV-FDT improves computational efficiency and communication performance by transmitting only semantic data and agent parameters, rather than raw video streams. The proposed framework adopts three-layer architecture and constructs detailed pedestrian-vehicle interaction models using multi-source traffic surveillance videos. The three-layer architecture includes: (i) an end layer that collects surveillance videos from multiple sources;(ii) an edge layer that performs self-supervised semantic segmentation to extract interactions, converts them into executable traffic codes, and generates local digital twin systems (LDTSs) for regional traffic modeling;and (iii) a cloud layer that integrates LDTSs into a real-time global digital twin model. key design considerations, challenges, and practical implementation guidelines are discussed for SV-FDT, and a testbed evaluation is used to show that SV-FDT improves traffic flow, reduces mirroring delay, and enhances recognition accuracy and system efficiency compared to traditional terminal-server frameworks. Finally, we outline open challenges and potential directions for future research in digital twin-enabled ITS. © 1986-2012 IEEE.

关键词： Air traffic control

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Research on PMSM Vector Control System Based on Fuzzy PI Parameter Self-Tuning 10

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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10th International Conference on Power and Energy Systems, ICPES 2020

作者： Ding, Jianhua Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability. © 2020 IEEE.

关键词： Permanent magnets

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Short-Term Photovoltaic Power Prediction Modeling Based on AdaBoost Algorithm and Elman 10

Short-Term Photovoltaic Power Prediction Modeling Based on A...

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10th International Conference on Power and Energy Systems, ICPES 2020

作者： Sun, Wenxuan Zhang, Tao Tao, Ran Wang, Anna Northeastern University College of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9781665404945

In recent years, with the rapid expansion of the installed capacity of renewable energy systems, the availability, stability and quality of smart grids have become increasingly important[1]. The application of renewable energy production forecasting has also been rapidly developed, especially in the field of solar photovoltaic (PV)[2][3]. In the example of solar PV output prediction, machine learning and hybrid technologies have been implemented for many applications. In this paper, a high-precision PV system output power prediction model based on improved AdaBoost and Elman is proposed. Multiple model using integrated AdaBoost algorithm fusion with the bat algorithm for the parameters of optimized combination of weak Elman neural network predictor to become a higher prediction precision, the method of strong predictor of the model can according to the weather information, such as temperature, solar radiation and the history of the output of the PV system data, the probability of photovoltaic power generation for 12 hours and deterministic prediction. The prediction accuracy of the model is determined by Root Mean Squared Error (RMSE). Experimental results show that the prediction accuracy of this algorithm is better than that of other benchmark models, and the algorithm can effectively predict the volatility and irregularity of complex time series. © 2020 IEEE.

关键词： Adaptive boosting

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Group-wise inhibition based feature regularization for robust classification

arXiv

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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fNIRS Feature Extraction and Classification in Grip-Force Tasks

fNIRS Feature Extraction and Classification in Grip-Force Ta...

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IEEE International Conference on robotics and Biomimetics

作者： Jinrui Liu Ting Song Zhilin Shu Jianda Han Ningbo Yu College of Artificial Intelligence Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (纸本)9781665405362

It has been of increasing interest to decode the movements based on the changes in hemoglobin concentration in the cerebral cortex that can be measured by functional near-infrared spectroscopy (fNIRS). Existing work were mainly focused on motor imagery tasks for the advantages of fNIRS on cognitive activation detection, and motor tasks were not well studied. This paper investigated fNIRS feature extraction and classification methods for grip-force tasks. Two methods were implemented and compared. The first method took the classic machine learning approach. Features were extracted with respect to the physiological characteristics of fNIRS signals, including the mean, area and slope features. Then, support vector machine was used for classification of grip-force tasks with the extracted features. The second method took the deep learning approach without extracting features. An LSTM-based network was constructed to take in the fNIRS data and output the classified task type. The two methods were tested and compared with an grip-force task fNIRS dataset. The classic machine learning method with extracted features achieved the accuracy of 71.70%, and the LSTM-based deep learning method achieved 82.86%. The results demonstrate the feasibility and potential of fNIRS-based decoding of motor tasks.

关键词： Deep learning Support vector machines Cerebral cortex Stroke (medical condition) Feature extraction Decoding Task analysis

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Line Drawing Guided Progressive Inpainting of Mural Damage

arXiv

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arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

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Joint Coronary Centerline Extraction And Lumen Segmentation From Ccta Using Cnntracker And Vascular Graph Convolutional Network

Joint Coronary Centerline Extraction And Lumen Segmentation ...

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IEEE International Symposium on Biomedical Imaging

作者： Ruochen Gao Zhihui Hou Jun Li Hu Han Bin Lu S. Kevin Zhou Medical Imaging Robotics Analytic Computing Laboratory/Engineering (MIRACLE) Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Fuwai Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Peng Cheng Laboratory Shenzhen China

Automatic analysis of coronary artery in coronary computed tomography angiography (CCTA) is important for clinicians to diagnose and evaluate coronary artery disease (CAD). Often there are two analysis tasks involved: centerline extraction and lumen segmentation, which are related yet often treated separately in the literature. In this work, we leverage their mutual relationship and propose an automatic approach for joint centerline extraction and lumen segmentation from CCTA using a hybrid of deep learning models. Our approach features the following designs, including: (i) the use of CNNTracker that traces out the centerline from seeds to detected ostia or the end of vessels, which fixes the breakage issue commonly found in previous pixel-based segmentation methods; (ii) a vascular Graph Convolutional Network (GCN) that leverages the geometric shape prior for accurate mesh-based lumen segmentation; (iii) the alternation between CNNTracker and GCN that refines the centerline and reduces the drift in tracking. We experiment the proposed method on CCTA data from 101 patients and achieve state-of-the-art performance in both centerline extraction and lumen segmentation of the right coronary artery (RCA), left anterior descending (LAD), and left circumflex (LCX).

关键词： Deep learning Solid modeling Image segmentation Shape Predictive models Feature extraction Data mining

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