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Abnormal area identification of corn ear based on semi-supervised learning

IET IMAGE PROCESSING 2022年第9期16卷 2351-2360页

作者： Wei, Jian Ma, Qin Wang, Weitao Guo, Hao Liu, Zhe Zhang, Jiajing China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr & Rural Affairs Key Lab Agr Machinery Monitoring & Big Data Appli Beijing Peoples R China China Agr Univ Coll Land Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Screening corn ear is the key link in the breeding process of new varieties. But manual testing is difficult to measure the proportion of abnormal area. Meanwhile, the abnormal areas are mainly caused by mildew, moth and mechanical collision. A new refined semantic segmentation model was proposed based on the semi-supervised learning method of generating antagonistic networks (GAN). Besides k-means algorithm was used to remove a large amount of background information for data preprocessing. By using feature fusion and weighted loss function the model performance was improved. The introduction of transfer learning accelerated model convergence. Through the high-throughput corn ear collection system, 1448 ear images (including abnormal conditions such as mildew, moth and mechanical damage) were collected and labelled. The proposed method was tested on real corn ear images with an accuracy of 0.950, mean precision of 0.933, mean IoU of 0.884, and FwIoU of 0.908. Experimental results show that the proposed method has better performance than general networks.

关键词： transfer learning accelerated model convergence weighted loss refined semantic segmentation model antagonistic networks convolutional neural nets geophysical image processing 1448 ear images breeding process learning (artificial intelligence) manual testing mechanical collision corn ear collection system feature extraction abnormal area identification deep learning (artificial intelligence) ear image segmentation feature fusion key link corn ear images mechanical damage abnormal conditions semisupervised learning method image classification moth

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VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent systems for Transport Automation

arXiv

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arXiv 2024年

作者： Chang, Cheng Zhang, Jiawei Ge, Jingwei Zhang, Zuo Wei, Junqing Li, Li Wang, Fei-Yue DiDi Autonomous Driving Company Beijing100094 China The Department of Automation BNRist Tsinghua University Beijing100084 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent systems

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Nonfungible Tokens: Constructing Value systems in Parallel Societies

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第5期8卷 1062-1067页

作者： Wang, Fei-Yue Qin, Rui Yuan, Yong Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China

Welcome to the fifth issue of Ieee Transactions on Computational Social systems (TCSS) of 2021. As usual, we would like to share some great news first. Since April 2021, IEEE TCSS has been added to the Science Citation Index Expanded (SCIE) database in Clarivate Web of Science. We are excited to report that all TCSS articles published since 2018 have been backtracked and indexed by SCIE.

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FIT: Frequency-based Image Translation for Domain Adaptive Object Detection

arXiv

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arXiv 2023年

作者： Zhang, Siqi Zhang, Lu Liu, Zhiyong Feng, Hangtao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Planning-inspired Hierarchical Trajectory Prediction for Autonomous Driving

arXiv

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arXiv 2023年

作者： Li, Ding Zhang, Qichao Xia, Zhongpu Zhang, Kuan Yi, Menglong Jin, Wenda Zhao, Dongbin School of Artificial Intelligence University of Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Baidu Inc. China

Recently, anchor-based trajectory prediction methods have shown promising performance, which directly selects a final set of anchors as future intents in the spatio-temporal coupled space. However, such methods typically neglect a deeper semantic interpretation of path intents and suffer from inferior performance under the imperfect High-Definition (HD) map. To address this challenge, we propose a novel Planning-inspired Hierarchical (PiH) trajectory prediction framework that selects path and speed intents through a hierarchical lateral and longitudinal decomposition. Especially, a hybrid lateral predictor is presented to select a set of fixed-distance lateral paths from map-based road-following and cluster-based free-move path candidates. Then, the subsequent longitudinal predictor selects plausible goals sampled from a set of lateral paths as speed intents. Finally, a trajectory decoder is given to generate future trajectories conditioned on a categorical distribution over lateral-longitudinal intents. Experiments demonstrate that PiH achieves competitive and more balanced results against state-of-the-art methods on the Argoverse motion forecasting benchmark and has the strongest robustness under the imperfect HD map. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

Deep Domain Adaptation Regression for Force Calibration of O...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Zhuo Chen Ni Ou Jiaqi Jiang Shan Luo Department of Engineering Robot Perception Lab Centre for Robotics Research King’s College London London United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors.

关键词： Accuracy Force Tactile sensors Lighting Elastomers Data collection Optical imaging Adaptive optics Calibration Optical sensors

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ACP based Large-Scale Coordinated Route Planning: From Perspective of Cyber-Physical-Social systems 25

ACP based Large-Scale Coordinated Route Planning: From Persp...

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IEEE 25th International Conference on Intelligent Transportation systems (ITSC)

作者： Luo, Guiyang Zhang, Hui Wang, Xiao Yuan, Quan Li, Jinglin Wang, Fei-Yue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230093 Anhui Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Traffic congestion is deteriorating the sustainability and livability of the metropolis. However, most existing solutions are expected utility-based, i.e., making routing decisions based on statistical expectation, which would lead to restricted efficiency in alleviating the congestion problem. Building upon cyber-physical-social systems (CPSS), this paper proposes ParallelRoute that exploits computational experiments in ACP theory to coordinate the large-scale routes considering the human use in ITS, efficiently alleviating the congestion problem. ParallelRoute adopts a congestion game theory for coordinating the route decisions to bridge user fairness and system efficiency and further supports the simulation over the future to capture the traffic dynamics, leading to coordinated, effective, and informed route decisions. Meanwhile, an ACPbased tree search algorithm is proposed to intertwine evaluations and prune, effectively handling the exponential number of states during the simulation. Comprehensive experiments on a real-world road network are conducted to evaluate the performance, and results show that ParallelRoute achieves the lowest travel time, and is effective for coordinating large-scale routes and alleviating the traffic congestion problem.

关键词： Roads Heuristic algorithms Computational modeling Routing Planning Computational efficiency Sustainable development

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Perception for Coupler Rod of Freight Train Based on Image and Point Clouds

Perception for Coupler Rod of Freight Train Based on Image a...

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2022 Chinese Automation Congress, CAC 2022

作者： Guo, Renjie Feng, Jun Yang, Huachang Luan, Dejie Yang, Guodong Li, En Institute of Automation CAS State Key Laboratory of Management and Control for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China China Academy of Railway Sciences Corporation Limited Signal & Communication Research Institute Beijing100081 China

ISBN: (纸本)9781665465335

In the construction of automated marshaling yards, the automatic uncoupling operation of freight trains has always been the research and development direction. The coupler rod is the operation object of the uncoupling operation. The identification of coupler rod type and estimating position and posture are the prerequisites to realizing automatic uncoupling operation. In this paper, we propose a perception method of coupler rod by RGB image combined with the point cloud. We use an RGBD camera to synchronously collect frame-aligned RGB and depth images and generate point cloud data with colors according to the mapping relationship. Then, we use the YOLOv5 neural network to detect the collected RGB images and recognize the type of coupler rod in the image. Clear clutter in the point cloud and the bounding box is mapped to a spatial coordinate system for straight-through filtering of the point cloud. We use RANSAC for coarse registration and ICP for fine registration. The result of registration is carried out with the lower sampling point cloud model to achieve the registration of the coupler rod. Experiments show that this method can effectively segment the point cloud of the region of interest with high robustness. © 2022 IEEE.

关键词： Data fusion

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A New Perspective for Computational Social systems: Fuzzy Modeling and Reasoning for Social Computing in CPSS

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2024年第1期11卷 101-116页

作者： Wang, Tan Zhu, Yifan Ye, Peijun Gong, Weichao Lu, Hao Mo, Hong Wang, Fei-Yue Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Macau Peoples R China Chinese Acad Sci Inst Automat Chinese Assoc Automat Beijing 100190 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 10086 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha 410114 Peoples R China

The evolution of modern mobile terminals, social networks, and other intelligent services makes everyone become a ubiquitous information perceiver, producer, and propagator. Also known as "social sensor" and "social IoT", these individuals and communities generate a huge volume of social signals, which has shown prominent value for mining. These unstructured social signals provide a new perspective in the research of complex systems, which makes the traditional cyber-physical system (CPS)-oriented information computing sublimate to the cyber-physical-social system (CPSS)-oriented knowledge computing. However, there still exist great uncertainties, ambiguities, and complexities in modeling behaviors of social individuals or groups. Especially when we apply big-data-driven learning-based models in specific fields and scenarios, the lack of domain expert knowledge and characteristics of system uncertainty severely limits the performance and accuracy of these models. The introduction of fuzzy system modeling integrates data and knowledge in the social computing area, which has shown its unique advantages in solving the above issues and has drawn more attention to this topic. In this article, we conduct a review of recent advances in social computing with fuzzy technologies in CPSS. First, we briefly review the development of social computing, and analyze the characteristics and advantages of social computing through fuzzy methods. Second, we refine core fuzzy system methods for social computing and elaborate on existing fuzzy-technology-empowered social computing methodologies. As in a range of social spaces, we also review and analyze related advances in human-in-the-loop systems. We also reveal the trend of decentralized, autonomous, and organized computing in cyber-physical-social space with fuzzy-based methods and proposed a framework to categorize related studies in CPSS. Finally, we conclude the research trends and hotspots based on current studies, and discuss th

关键词： Social computing Computational modeling Fuzzy systems Uncertainty complex systems Analytical models Human in the loop Computational systems cyber-physical-social systems (CPSSs) fuzzy paradigm parallel society social computing

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A hidden demethylation pathway removes mercury from rice plants and mitigates mercury flux to food chains

NATURE FOOD

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NATURE FOOD 2024年第1期5卷 72-82页

作者： Tang, Wenli Bai, Xu Zhou, Yang Sonne, Christian Wu, Mengjie Lam, Su Shiung Hintelmann, Holger Mitchell, Carl P. J. Johs, Alexander Gu, Baohua Nunes, Luis Liu, Cun Feng, Naixian Yang, Sihai Rinklebe, Joerg Lin, Yan Chen, Long Zhang, Yanxu Yang, Yanan Wang, Jiaqi Li, Shouying Wu, Qingru Ok, Yong Sik Xu, Diandou Li, Hong Zhang, Xu-Xiang Ren, Hongqiang Jiang, Guibin Chai, Zhifang Gao, Yuxi Zhao, Jiating Zhong, Huan Nanjing Univ Sch Environm State Key Lab Pollut Control & Resource Reuse Nanjing Peoples R China Chinese Acad Sci IHEP Key Lab Biomed Effects Nanomat & Nanosafety Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Aarhus Univ Arctic Res Ctr Dept Ecosci Roskilde Denmark Univ Petr & Energy Studies Sch Engn Sustainabil Cluster Dehra Dun Uttar Pradesh India Univ Malaysia Terengganu Higher Inst Ctr Excellence HICoE Inst Trop Aquaculture & Fisheries AKUATROP Kuala Nerus Malaysia Saveetha Univ Saveetha Inst Med & Tech Sci SIMATS CGHR Chennai Tamil Nadu India Trent Univ Dept Chem Peterborough ON Canada Trent Univ Sch Environm Peterborough ON Canada Univ Toronto Scarborough Dept Phys & Environm Sci Scarborough ON Canada Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Univ Algarve Fac Sci & Technol Civil Engn Res & Innovat Sustainabil Ctr Faro Portugal Chinese Acad Sci Inst Soil Sci Key Lab Soil Environm & Pollut Remediat Nanjing Peoples R China Jinan Univ Coll Life Sci & Technol Guangzhou Peoples R China Nanjing Univ Sch Life Sci State Key Lab Pharmaceut Biotechnol Nanjing Peoples R China Univ Wuppertal Inst Fdn Engn Water & Waste Management Sch Architecture & Civil Engn Lab Soil & Groundwater Management Wuppertal Germany Nankai Univ Coll Environm Sci & Engn Tianjin Key Lab Environm Technol Complex Trans Me Tianjin Peoples R China East China Normal Univ Sch Geog Sci Key Lab Geog Informat Sci Minist Educ Shanghai Peoples R China Nanjing Univ Sch Atmospher Sci Nanjing Peoples R China Tsinghua Univ Sch Environm State Key Joint Lab Environm Simulat & Pollut Con Beijing Peoples R China State Environm Protect Key Lab Sources & Control Beijing Peoples R China Korea Univ Korea Biochar Res Ctr APRU Sustainable Waste Management Program Seoul South Korea Korea Univ Div Environm Sci & Ecol Engn Seoul South Korea Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Environm Chem & Ecotoxicol Beijing Peoples R China Sooc

Dietary exposure to methylmercury (MeHg) causes irreversible damage to human cognition and is mitigated by photolysis and microbial demethylation of MeHg. Rice (Oryza sativa L.) has been identified as a major dietary source of MeHg. However, it remains unknown what drives the process within plants for MeHg to make its way from soils to rice and the subsequent human dietary exposure to Hg. Here we report a hidden pathway of MeHg demethylation independent of light and microorganisms in rice plants. This natural pathway is driven by reactive oxygen species generated in vivo, rapidly transforming MeHg to inorganic Hg and then eliminating Hg from plants as gaseous Hg degrees. MeHg concentrations in rice grains would increase by 2.4- to 4.7-fold without this pathway, which equates to intelligence quotient losses of 0.01-0.51 points per newborn in major rice-consuming countries, corresponding to annual economic losses of US$30.7-84.2 billion globally. This discovered pathway effectively removes Hg from human food webs, playing an important role in exposure mitigation and global Hg cycling. A natural pathway in rice and in seven other major crop species degrades 57-89% of accumulated methylmercury and releases Hg into the air. The MeHg demethylation within rice mitigates Hg flux into human food webs, diminishing human cognitive damage and generating global economic benefits of US$30.7-84.2 billion.

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