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In-field early disease recognition of potato late blight based on deep learning and proximal hyperspectral imaging

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arXiv 2021年

作者： Qi, Chao Sandroni, Murilo Westergaard, Jesper Cairo Sundmark, Ea Høegh Riis Bagge, Merethe Alexandersson, Erik Gao, Junfeng Lincoln Agri-Robotics Lincoln Institute for Agri-Food Technology University of Lincoln Lincoln United Kingdom College of Engineering Nanjing Agricultural University Nanjing210031 China Department of Plant Protection Biology Swedish University of Agricultural Sciences Alnarp Sweden Department of Plant and Environmental Sciences University of Copenhagen Taastrup Denmark Danespo Breeding Company Give Denmark Lincoln Centre for Autonomous System University of Lincoln Lincoln United Kingdom

Effective early detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight at an early stage in fields with conventional imaging approaches because of the lack of visual cues displayed at the canopy level. Hyperspectral imaging can, capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. In this context, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492nm, 519nm, 560nm, 592nm, 717nm and 765nm). This study shows an encouraging result for early detection of PLB with deep learning and proximal hyperspectral imaging. Copyright © 2021, The Authors. All rights reserved.

关键词： Hyperspectral imaging

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review

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National Science Review 2023年第5期 38-58页

作者：江宁陈晨何家源孟建军潘立志苏世勇朱向阳 National Clinical Research Center for Geriatrics West China Hospitaland Med-X Center for ManufacturingSichuan University State Key Laboratory of Mechanical System and Vibration and Institute of RoboticsShanghai Jiao Tong University Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education School of Mechanical EngineeringTianjin University Institute of Neuroscience Université Catholique Louvain

A decade ago,a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control,a widely used bio-robotics *** proposed that four key technical challenges,if addressed,could bridge this gap and translate academic research into clinically and commercially viable *** challenges are unintuitive control schemes,lack of sensory feedback,poor robustness and single sensor ***,we provide a perspective review on the research effort that occurred in the last decade,aiming at addressing these *** addition,we discuss three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review 10 years ago:deep learning methods,surface electromyogram decomposition and open-source *** conclude the review,we provide an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.

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The consistent behavior of negative Poisson's ratio with interlayer interactions

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arXiv 2022年

作者： Wang, Yancong Yu, Linfeng Zhang, Fa Chen, Qiang Zhan, Yuqi Zheng, Xiong Wang, Huimin Qin, Zhenzhen Qin, Guangzhao State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Hunan Key Laboratory for Micro-Nano Energy Materials & Device School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China School of Physics and Microelectronics Zhengzhou University Zhengzhou450001 China

Negative Poisson's ratio (NPR) is of great interest due to the novel applications in lots of fields. Films are the most commonly used form in practical applications, which involves multiple layers. However, the effect of interlayer interactions on the NPR is still unclear. In this study, based on first principles calculations, we systematically investigate the effect of interlayer interactions on the NPR by comparably studying single-layer graphene, few-layer graphene, h-BN, and graphene-BN heterostructure. It is found that they almost have the same geometry-strain response. Consequently, the NPR in bilayer graphene, triple-layer graphene, and graphene-BN heterostructure are consistent with that in single-layer graphene and h-BN. The fundamental mechanism lies in that the response to strain of the orbital coupling are consistent under the effect of interlayer interactions. The deep understanding of the NPR with the effect of interlayer interactions as achieved in this study is beneficial for the future design and development of micro-/nanoscale electromechanical devices with novel functions based on nanostructures. © 2022, CC BY-NC-SA.

关键词： Graphene

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification

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arXiv 2024年

作者： Peng, Ruimin Du, Zhenbang Zhao, Changming Luo, Jingwei Liu, Wenzhong Chen, Xinxing Wu, Dongrui Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China AI Platform Software Engineering Research Center Dongfeng Corporation Research & Development Institute Wuhan430072 China China Electronic System Technology Co. Ltd. Beijing100089 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification. © 2024, CC BY.

关键词： Electroencephalography

Interaction Analysis Investigations of Multiple Supply Duct Split-type Air-conditioning units in an Open Space

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IOP Conference Series: Materials Science and engineering 2019年第1期501卷

作者： W Widsonkun D Woradechjumroen S Vongbunyong Institute of FIeld robotics (FIBO) King Mongkut's University of Technology Thonburi Bangkok Thailand Department of Mechanical System Engineering and Industrial Innovation School of Engineering Sripatum University Bangkok Thailand (+66)948503366

Oversizing has been seldom concerning in Thailand. However, oversizing causes short-term operations of an air-conditioning (AC) unit and excessive power consumptions. In addition, multiple operations of oversized AC units can be faulty operations when they are used to serve in an open space area because interaction among AC units are not considered before installations. This paper collects data operations of the multiple supply duct split-type air-conditioning (SAC) units used in an open space from additional sensors installed in zone and return temperature. Significant wireless sensors are added for measuring temperature effect among a interaction area between two SAC units. To measure the system variation caused by system interaction, runtime fraction (RTF) is used to quantify the compressor runtime ratio which equals to a stage-on time operation over the cycle time at an AC design condition. The results show that one of the four SAC units performs under-sizing instead of oversizing performance because it runs continuously without off cycling. To diagnose this issue, interactions between zone and return temperature are quantified in terms of correlation coefficient, which demonstrates that this SAC results in higher oversizing of the rest SAC units. This interaction analysis can be further used to design a soft-repair algorithm to reduce oversizing effect of the multiple SAC operations.

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Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots

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arXiv 2023年

作者： Arm, Philip Waibel, Gabriel Preisig, Jan Tuna, Turcan Zhou, Ruyi Bickel, Valentin Ligeza, Gabriela Miki, Takahiro Kehl, Florian Kolvenbach, Hendrik Hutter, Marco Robotic Systems Lab ETH Zurich Zurich Switzerland State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China Laboratory of Hydraulics Hydrology and Glaciology ETH Zurich Zurich Switzerland Center for Space and Habitability University of Bern Bern Switzerland Department of Environmental Sciences University of Basel Basel Switzerland University of Zurich Dübendorf Switzerland Center for Theoretical Astrophysics and Cosmology University of Zurich Zurich Switzerland Institute of Medical Engineering Space Biology Group Lucerne University of Applied Sciences and Art Hergiswil Switzerland

The interest in exploring planetary bodies for scientific investigation and in-situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots’ inability to traverse steep slopes, unstructured terrain, and loose soil. Additionally, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and post-mission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in-situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25 degrees, loose soil, and unstructured terrain, highlighting their advantages compared to wheeled rover systems. We successfully verified the approach in analog deployments at the BeyondGravity ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach. Copyright © 2023, The Authors. All rights reserved.

关键词： Robots

[email protected] , an ecosystem of virtual environments and tools for realistic indoor robotic simulation

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Expert systems with Applications 2022年 208卷

作者： David Fernandez-Chaves Jose-Raul Ruiz-Sarmiento Alberto Jaenal Nicolai Petkov Javier Gonzalez-Jimenez Machine Perception and Intelligent Robotics group (MAPIR) Department of System Engineering and Automation Biomedical Research Institute of Malaga (IBIMA) University of Malaga Spain Johann Bernoulli Institute of Mathematics and Computing Science University of Groningen The Netherlands

Simulations and synthetic datasets have historically empower the research in different service robotics-related problems, being revamped nowadays with the utilization of rich virtual environments. However, with their use, special attention must be paid so the resulting algorithms are not biased by the synthetic data and can generalize to real world conditions. These aspects are usually compromised when the virtual environments are manually designed. This article presents [email protected] , an ecosystem of virtual environments and tools that allows for the management of realistic virtual environments where robotic simulations can be performed. Here “realistic” means that those environments have been designed by mimicking the rooms’ layout and objects appearing in 30 real houses, hence not being influenced by the designer’s knowledge. The provided virtual environments are highly customizable (lighting conditions, textures, objects’ models, etc.), accommodate meta-information about the elements appearing therein (objects’ types, room categories and layouts, etc.), and support the inclusion of virtual service robots and sensors. To illustrate the possibilities of [email protected] we show how it has been used to collect a synthetic dataset, and also exemplify how to exploit it to successfully face two service robotics-related problems: semantic mapping and appearance-based localization.

关键词： Virtual environments Robotic simulation Service robots Semantic maps Robotic dataset Unity

Research progress on the interconnection of carbon nanotubes

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Carbon 2016年 100卷 710-710页

作者： Li-jun Yang Jian-lei Cui Yang Wang Xue-song Mei Wen-jun Wang Hui Xie State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin 150001 China State Key Laboratory for Manufacturing System Engineering Xi’an Jiaotong University Xi'an 710049 China