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2021 China Automation Congress, CAC 2021

作者： Chang, Ruijie Li, Wenzhuo Zhao, Na Li, Shanglai Li, Ang Ji, Xin Beijing Key Laboratory of Research and System Evaluation of Dispatching Automation Technology China Electric Power Research Institute Beijing China China Electric Power Research Institute Co. Ltd Beijing China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426473

Power dispatching automation software system is a software-intensive system. The safe and stable operation of the software system is related to the security and stability of the whole power system. The degree and scope of the damage caused by malicious attacks are also constantly deepening and expanding, so the evaluation and prediction of the reliability of the software is particularly important. In this paper, the dual-process model is used to evaluate and predict the reliability of the software of power grid dispatching automation system, and the two models are weighted and mixed to improve the accuracy and robustness of the software reliability model. © 2021 IEEE

关键词： Software reliability

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Polarised Full-Waveform Warning Lidar with Dust Backscattering Suppression

SSRN

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SSRN 2022年

作者： Liu, Jing Jin, Weiqi Que, Kailiang MoE Key Lab of Photoelectronic Imaging Technology and System Beijing Institute of Technology Beijing100081 China Baoding Galaxy Electronic Technology Co. Ltd. Baoding071025 China College of Electronic and Information Engineering Hebei University Baoding071002 China

LIDAR, when used under adverse weather conditions such as fog and dust, suffers from attenuation and backscattering. In particular, backscattering creates spurious echo peaks in the received waveforms at incorrected ranges and leads to false alarms. As a solution, this paper proposes polarised full-waveform warning LIDAR for dust backscattering echo suppression, intended for applications under dusty environments. To this end, first, a theoretical model of the polarised LIDAR is developed based on the LIDAR equation and depolarisation characteristics of dust backscattering and target reflection. Thereafter, the depolarisation characteristics are experimentally evaluated under an artificially generated dusty environment;the results reveal that the target reflection features a strong depolarisation characteristic, whereas dust backscattering has a weak one. When the LIDAR emits linearly polarised light, a significant orthogonal-polarised component exists owing to the target reflection, and dust backscattering induces a mainly parallel-polarised component. On interposing an orthogonal-polarisation analyser in the receiving optical path, the parallel-polarised component from the dust backscattering and target reflection is blocked, whereas the orthogonal-polarised component is allowed to pass;this helps improve the signal-to-noise ratio (SNR) of the target reflection to dust backscattering. Finally, based on the theoretical model of the polarised LIDAR and experimental results, an orthogonal-polarised LIDAR prototype is designed and fabricated. Using this prototype, target detection experiments in a dusty environment are performed;the corresponding experimental results indicate that the orthogonal-polarised LIDAR effectively suppresses dust backscattering echoes and that the SNR of the target reflection to dust backscattering is improved by 4.78–7.12 dB. Utilization of the polarised full-waveform warning LIDAR can significantly reduce the false alarm rate and improves th

关键词： Backscattering

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Online adaptive resistance control of an arm exercise exoskeleton 23rd

Online adaptive resistance control of an arm exercise exoske...

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23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2020

作者： Xiong, Xiaofeng Manoonpong, Poramate Odense5230 Denmark Bio-Inspired Robotics and Neural Engineering Lab The School of Information Science and Technology Vidyasirimedhi Institute of Science and Technology Rayong21210 Thailand

ISBN: (纸本)9781916449046

Resistance training is an effective means of strengthening musculoskeletal capabilities;thereby preventing musculoskeletal disorders (MSDs). However, most existing exoskeletons fail to provide active and affordable resistance training. To address this problem, we develop a resistance-as-needed (RAN) controller and an arm exercise (RISE) exoskeleton for resistance training. The exoskeleton is designed and manufactured in a portable, lightweight (0.425 kg), and 3D print manner. Such control adaptability and design portability are facilitated by the RAN controller whose implementation relies only on internal (e.g., joint position) sensing, rather than external (i.e., EMG, electromyography) sensing and physical compliant mechanisms (e.g., springs). The active resistance torque to the RISE exoskeleton is generated by the RAN controller, in which impedance gains Kp and Kd are online tuned. As a result, the RAN controller enables the RISE exoskeleton to produce more adaptive and effective resistance motions that lead to higher training efficiency, compared to conventional constant impedance controllers. The proposed RAN controller and designed RISE exoskeleton pave a way for providing portable and affordable resistance training. © CLAWAR Association Ltd.

关键词： Controllers

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Objective Portrait: A practice-based inquiry to explore Al as a reflective design partner 23

Objective Portrait: A practice-based inquiry to explore Al a...

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Proceedings of the 2023 ACM Designing Interactive systems Conference

作者： Vera van der Burg Gijs de Boer Alkim Almila Akdag Salah Senthil Chandrasegaran Peter Lloyd Designing Intelligence Lab Technical University Delft Netherlands MA Critical Inquiry Lab Design Academy Eindhoven Netherlands Faculty of Science Information and computing sciences Utrecht University Netherlands Faculty of Industrial Design Engineering TU Delft Netherlands Delft University of Technology Netherlands

ISBN: (纸本)9781450398930

Artificial intelligence (AI) is increasingly being viewed as a creative partner rather than as a tool. How to design such collaborations is still a subject of speculation. In this pictorial, we propose a collaborative role for AI to prompt self-reflection. We explore this through a practice-based inquiry of whether and how AI could help a designer reflect on and relate to their own work. Three designers annotate a collection of images representing their fascinations, with subjective labels, indicating different dimensions of their visual concepts. These labels are used to teach an object detection model the designers’ perspectives. Then, they used this trained model on their own design work to evaluate the AI's potential to prompt self-reflection. By describing this process of AI-training we explore how an AI can help us become aware of our own implicit perspectives.

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT systems

Intra-Guided Dual-Energy Material Decomposition Method for D...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Xinyun Zhong Tianling Lyu Zhan Wu Wei Gao Yan Xi Wei Zhao Wentao Zhu Yang Chen Laboratory of Image Science and Technology School of Computer Science and Engineering Southeast University Nanjing China Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps.

关键词： Geometry Head Costs Imaging Phantoms Filtering algorithms Matrix decomposition engineering in medicine and biology Image reconstruction X-ray imaging

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Super-resolution Reconstruction of Single Image for Latent features

arXiv

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arXiv 2022年

作者： Wang, Xin Yan, Jing-Ke Cai, Jing-Ye Deng, Jian-Hua Qin, Qin Cheng, Yao School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu610000 China School of Marine Engineering Guilin University of Electronic Technology Guilin541000 China School of Computer and Information Security Guilin University of Electronic Technology Guilin541000 China

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks. © 2022, CC0.

关键词： Image reconstruction

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Denoising of RDTS Based on One-Dimensional Convolutional Neural Networks 27

Denoising of RDTS Based on One-Dimensional Convolutional Neu...

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27th International Conference on Optical Fiber Sensors, OFS 2020

作者： Zhang, Zhongshu Zhao, Can Wu, Hao Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

One-dimensional convolutional neural network is proposed to denoise the signal of Raman distributed temperature sensor. The uncertainty of a 10-km stand-single-mode fiber is reduced from 4 °C to 1 °C. ©... 详细信息

ISBN: (纸本)9781557523075

关键词： Single mode fibers

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Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization

arXiv

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arXiv 2023年

作者： Yang, Siyuan Liu, Jun Lu, Shijian Hwa, Er Meng Hu, Yongjian Kot, Alex C. The Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The School of Computer Science & Engineering Nanyang Technological University Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore The South China University of Technology Guangzhou China

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition. We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data. We represent a skeleton action sequence as a 3D skeleton cloud and colorize each point in the cloud according to its temporal and spatial orders in the original (unannotated) skeleton sequence. Leveraging the colorized skeleton point cloud, we design an auto-encoder framework that can learn spatial-temporal features from the artificial color labels of skeleton joints effectively. Specifically, we design a two-steam pretraining network that leverages fine-grained and coarse-grained colorization to learn multi-scale spatial-temporal features. In addition, we design a Masked Skeleton Cloud Repainting task that can pretrain the designed auto-encoder framework to learn informative representations. We evaluate our skeleton cloud colorization approach with linear classifiers trained under different configurations, including unsupervised, semi-supervised, fully-supervised, and transfer learning settings. Extensive experiments on NTU RGB+D, NTU RGB+D 120, PKU-MMD, NW-UCLA, and UWA3D datasets show that the proposed method outperforms existing unsupervised and semi-supervised 3D action recognition methods by large margins and achieves competitive performance in supervised 3D action recognition as well. Copyright © 2023, The Authors. All rights reserved.

关键词： Musculoskeletal system

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基于肖特基势垒调控的低能耗高识别精度的有机突触晶体管

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science China Materials 2023年第11期66卷 4453-4463页

作者：陈天健俞衽坚高昌松陈振家陈惠鹏郭太良陈伟 Institute of Optoelectronic Display National&Local United Engineering Lab of Flat Panel Display TechnologyFuzhou UniversityFuzhou 350002China Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou 350100China Joint School of National University of Singapore and Tianjin University International Campus of Tianjin UniversityFuzhou 350207China Department of Chemistry National University of SingaporeSingapore 117543Singapore Department of Physics National University of SingaporeSingapore 117543Singapore

为了构建类脑神经形态计算网络,单一的人工突触器件应该表现出极低的能量消耗,达到飞焦耳级别.然而,大多数现有的基于欧姆接触的低能耗突触器件实施方案,要么结构复杂,要么需要特定材料,这些因素都阻碍了人工神经网络的进一步发展.本文... 详细信息

为了构建类脑神经形态计算网络,单一的人工突触器件应该表现出极低的能量消耗,达到飞焦耳级别.然而,大多数现有的基于欧姆接触的低能耗突触器件实施方案,要么结构复杂,要么需要特定材料,这些因素都阻碍了人工神经网络的进一步发展.本文报告了一种肖特基势垒调控的有机突触晶体管(SBROST).通过在源电极和半导体之间的接触界面引入肖特基势垒,显著降低了单个突触事件的能耗,与使用欧姆接触的传统有机突触晶体管相比,SBROST的性能得到了改善.SBROST不仅可在低工作电压和电流下运行,还具有可适用于不同有机突触器件的简单结构.此外,SBROST可以实现低能耗下的高识别精度.经过100个周期,基于SBROST的手写人工神经网络表现出卓越的识别精度(93.53%),接近理想精度(95.62%).将肖特基势垒引入突触晶体管的方案为构建类脑神经计算网络提供了新的视角.

关键词： synaptic plasticity low energy consumption Schottky barrier high recognition accuracy artificial neural network

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Fading-free polarization-sensitive optical fiber sensing 27

Fading-free polarization-sensitive optical fiber sensing

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27th International Conference on Optical Fiber Sensors, OFS 2020

作者： Wang, Xuefeng Zhao, Can Wu, Hao Tang, Ming National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

A novel polarization-based forward transmission system without signal fading is proposed for vibration detection. This system promises potential application in perimeter security and physical parameters measurement. &... 详细信息

ISBN: (纸本)9781557523075

关键词： Polarization

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