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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gil Gonçalves Umberto Andriolo Lúcio Paiva Cristiano Premebida António Ferraz Department of Mathematics University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering INESC Coimbra Coimbra Portugal Research Centre for Natural Resources Environment and Society Polytechnic Institute of Coimbra Coimbra Agrarian Technical School Coimbra Portugal Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal NASA Jet Propulsion Laboratory Pasadena United States

Multispectral images and leaf-clip measurements were used for evaluating the Chlorophyll (Chl) content on grapevine leaves through the use of vegetation indices. The multispectral leaf images were taken by a sensor onboard an UAS, which was placed over a table at a height of 70 cm. Images were radiometrically and geometrically processed to obtain accurate coregistered five band image stacks. Using a Kmeans segmentation, each leaf imaged in a multispectral image was automatically detected and the mean leaf reflectance values were used to compute three vegetation indices: NDVI, NDRE, and GLI. When compared to leaf-clip measurements, the results indicated that the NDRE index was the best predictor of Chl leaf content (R2=0.81). The obtained NDRE regression model can be used in UAS-based multispectral orthomosaics to generate canopy Chl maps at vine-row scale, which can assist vine growers in monitoring the spatial and temporal variability of grapevine vigor.

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Conflict-Based Search and Improvement Strategies for Solving a New Lexicographic Bi-Objective Multi-Agent Path Finding Problem

Conflict-Based Search and Improvement Strategies for Solving...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Li, Siyi Li, Xingyang Zhou, Mengchu Zhao, Ziyan Liu, Shixin Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University Foshan Graduate School of Innovation Foshan528311 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Macao Inst. of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology 999078 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Multi-Agent Path Finding (MAPF) is an important problem with a variety of applications. Its aim is to find collision-free paths for agents having separate start and goal positions. This work proposes a new lexicographic bi-objective MAPF considering different task types, where agents are divided into two kinds to perform critical and acritical tasks. This is common in practical intelligent warehousing scenarios where a critical/acritical-task-performing agent (called c-agent and a-agent, respectively) may represent a full-load/no-load one or the one conducting urgent/non-urgent tasks. The primary objective is to minimize the sum-of-costs of c-agents, while the secondary objective is to minimize the sum-of-costs of a-agents. Two MAPF algorithms are modified to fit and solve the concerned problem for the first time. Moreover, four improvement strategies are embedded to the proposed algorithms and proved to be effective in solving MAPF problems with different task types. © 2022 IEEE.

关键词： Multi agent systems

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Multi-Robot System for Autonomous Cooperative Counter-UAS Missions: Design, Integration, and Field Testing

Multi-Robot System for Autonomous Cooperative Counter-UAS Mi...

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IEEE International Workshop on Safety, Security, and Rescue robotics (SSRR)

作者： Antonella Barisic Marlan Ball Noah Jackson Riley McCarthy Nasib Naimi Luca Strässle Jonathan Becker Maurice Brunner Julius Fricke Lovro Markovic Isaac Seslar David Novick Jonathan Salton Roland Siegwart Stjepan Bogdan Rafael Fierro Laboratory for Robotics and Intelligent Control Systems (LAR-ICS) Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA ETH Zurich Autonomous Systems Lab (ASL) Zurich Switzerland Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (纸本)9781665456814

With the rapid development of technology and the proliferation of uncrewed aerial systems (UAS), there is an immediate need for security solutions. Toward this end, we propose the use of a multi-robot system for autonomous and cooperative counter-UAS missions. In this paper, we present the design of the hardware and software components of different complementary robotic platforms: a mobile uncrewed ground vehicle (UGV) equipped with a LiDAR sensor, an uncrewed aerial vehicle (UAV) with a gimbal-mounted stereo camera for air-to-air inspections, and a UAV with a capture mechanism equipped with radars and camera. Our proposed system features 1) scalability to larger areas due to the distributed approach and online processing, 2) long-term cooperative missions, and 3) complementary multimodal perception for the detection of multirotor UAVs. In field experiments, we demonstrate the integration of all subsystems in accomplishing a counter-UAS task within an unstructured environment. The obtained results confirm the promising direction of using multi-robot and multi-modal systems for C-UAS.

关键词： Scalability Robot vision systems Autonomous aerial vehicles Cameras Software Safety Security

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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IEEE International Conference on robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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Few-shot learning based histopathological image classification of colorectal cancer

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intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

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Human-in-the-loop Optimization for Adaptive Assist-as-Needed Rehabilitation 12

Human-in-the-loop Optimization for Adaptive Assist-as-Needed...

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12th International Conference on CYBER Technology in Automation, Control, and intelligent systems, CYBER 2022

作者： Li, Ning Yang, Yang Yang, Tie Chen, Wenyuan Wang, Yihan Yu, Peng Wang, Wenxue Xi, Ning Liu, Lianqing Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of the Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang10016 China School of Electrical and Electronics Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Emerging Technologies Institute Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Pokfulam999077 Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Active voluntary participation in robot-assisted rehabilitation promote the recovery of stroke hemiplegia. However, different patients have personalized recovery states, which needs the adaptive control strategy to improve the patient's participation. Therefore, in this study, a Mirror Adaptive Assist-As-Needed (MAAN) scheme is proposed to encourage subjects to participate actively and avoid the slack of the subjects including two modules: Functional Capability Estimation (FCE) and Mirror Adaptation (MAD). Specifically, the first MSFE module can obtain joint functional capability based on fused biological and motional signals. The second MAD module acquires the needed assistance via the estimated torques of FCE based on healthy limb. Through the iteration and collaboration of these two modules, a human-in-the-loop adaptive training is achieved to promote the patient participation. The results demonstrate that the MAAN strategy can generate suitable assistance for the patients to correct abnormal muscle activation patterns and promote active voluntary participation. Compared with other methods, the proposed strategy improves motion stability and consistency with the natural physiological state of healthy limbs. This research can be expected to greatly enhance the rehabilitation and power assistance outcome of exoskeleton robot in more practical applications. © 2022 IEEE.

关键词： Mirrors

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A Novel Long Short-Term Memory (LSTM)-Based Degradation Detection in Photovoltaic systems using Performance Ratio

A Novel Long Short-Term Memory (LSTM)-Based Degradation Dete...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Amirhossein Dadashpour Aref Eskandari Amir Nedaei Sherko Salehpour Parviz Parvin Andreas Livera George Makrides Mohammadreza Aghaei Department of Electrical Engineering Amirkabir University of Technology (AUT) Tehran Iran Department of Electrical Engineering Iran University of Science and Technology (IUST) Tehran Iran Department of Physics and Energy Engineering Amirkabir University of Technology (AUT) Tehran Iran PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus (UCY) PHAENTON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Department of Ocean Operations and Civil Engineering Norwegian University of Science and Technology (NTNU) Alesund Norway Professor Department of Sustainable Systems Engineering (INATECH) University of Freiburg Freiburg Germany

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Photovoltaic (PV) systems deteriorate over time as a result of prolonged exposure to UV light and harsh weather conditions. This degradation can have a major impact on the performance of the PV systems and increase their levelized cost of energy (LCOE). To deal with this problem, in this paper, a method has been proposed based on predicting The PV systems' performance ratio, which is a measure of the actual output compared to the expected output. To estimate the performance ratio, a dataset including a PV array of normal and degraded states under different environmental conditions has been created. The main model uses an architecture with long short-term memory (LSTM). The model results show high accuracy in predicting the performance ratio of systems. The model achieved the outstanding $\mathbf{R}^{2}$ scores of $\mathbf{9 9. 4 7 \%}$ and $\mathbf{9 9. 4 6 \%}$ and the root mean squared error (RMSE) values of $\mathbf{0. 0 1 6 3}$ and 0.0169 for the training and testing processes, respectively.

关键词： Degradation Photovoltaic systems Training Accuracy System performance Predictive models Long short term memory Testing Meteorology

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OpenVNA: A Framework for Analyzing the Behavior of Multimodal Language Understanding System under Noisy Scenarios

arXiv

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arXiv 2024年

作者： Yuan, Ziqi Zhang, Baozheng Xu, Hua Liang, Zhiyun Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China China School of Information Science and Engineering Hebei University of Science and Technology China College of Information and Electrical Engineering China Agricultural University China Technology Development Co. Ltd. China

We present OpenVNA, an open-source framework designed for analyzing the behavior of multimodal language understanding systems under noisy conditions. OpenVNA serves as an intuitive toolkit tailored for researchers, facilitating convenience batch-level robustness evaluation and on-the-fly instance-level demonstration. It primarily features a benchmark Python library for assessing global model robustness, offering high flexibility and extensibility, thereby enabling customization with user-defined noise types and models. Additionally, a GUI-based interface has been developed to intuitively analyze local model behavior. In this paper, we delineate the design principles and utilization of the created library and GUI-based web platform. Currently, OpenVNA is publicly accessible at https://***/thuiar/OpenVNA, with a demonstration video available at https://***/0Z9cW7RGct4. © 2024, CC BY-NC-SA.

关键词： Graphical user interfaces

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese Automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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Arbitrage equilibria in active matter systems

arXiv

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arXiv 2024年

作者： Venkatasubramanian, Venkat Sivaram, Abhishek Sanjeevrajan, N. Sankar, Arun Complex Resilient Intelligent Systems Laboratory Department of Chemical Engineering Columbia University New YorkNY10027 United States Department of Chemical and Biochemical Engineering Technical University of Denmark Kongens Lyngby2800 Denmark Department of Materials Engineering Indian Institute of Technology-Madras Chennai600036 India School of Electrical Computer and Energy Engineering Arizona State University TempeAZ United States

The motility-induced phase separation (MIPS) phenomenon in active matter has been of great interest for the past decade or so. A central conceptual puzzle is that this behavior, which is generally characterized as a nonequilibrium phenomenon, can yet be explained using simple equilibrium models of thermodynamics. Here, we address this problem using a new theory, statistical teleodynamics, which is a conceptual synthesis of game theory and statistical mechanics. In this framework, active agents compete in their pursuit of maximum effective utility, and this self-organizing dynamics results in an arbitrage equilibrium in which all agents have the same effective utility. We show that MIPS is an example of arbitrage equilibrium and that it is mathematically equivalent to other phase-separation phenomena in entirely different domains, such as sociology and economics. As examples, we present the behavior of Janus particles in a potential trap and the effect of chemotaxis on MIPS. © 2024, CC BY.

关键词： Phase separation

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