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What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?

Journal of Integrative Agriculture 2020年第7期19卷 1789-1801页

作者： WANG Xiu-juan KANG Meng-zhen FAN Xing-rong YANG Li-li ZHANG Bao-gui HUANG San-wen Philippe DE REFFYE WANG Fei-yue The State Key Laboratory of Management and Control for Cormplex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China Bejjing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of SciencesBejing 100190P.R.China Innovation Center for Parallel Agriculture Qingdao Academy of Intelligent IndustriesQingdao 266109P.R.China School of Computer Science and Information Engineering Chongqing Technology and Business UniversityChongqing 400067P.R.China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083P.R.China College of Land Science and Technology China Agricultural UniversityBeijing 100193P.R.China Agricultural Genomes Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhen 518124P.R.China AMAP University MontpellierCIRADCNRSINRAIRDMontpellier 34000France The School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049P.R.China

To elucidate the mechanisms underlying the differences in yield formation among two parents(P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model(FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm(PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organlevel model may provide a computational approach to define the target of breeding by combination with a genetic model.

关键词： cucumber biomass production functional-structural plant model source-sink ratio fruit-setting PSO heterosis

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Twisting Sliding Mode control of an Electrostatic MEMS Micromirror for a Laser Scanning System

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 1060-1067页

作者： Hui Chen Zhendong Sun Weijie Sun John Tze Wei Yeow the School of Computer and Information Engineering Henan UniversityKaifeng 475000China the Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190 China the College of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China the Systems Design Engineering University of WaterlooWaterloo N2L3G1Canada IEEE

In this paper,we present a twisting control scheme with proportional-integral-derivative(PID)sliding surface for a two-axis electrostatic torsional micromirror,and the utilization of the proposed scheme in a laser scanning *** experimental results of set-point regulation verify that the proposed scheme provides enhanced transient response and positioning performance as compared to traditional sliding mode *** evaluate the tracking performance of the closed-loop system,triangular waves with different frequencies are used as desired *** the proposed scheme the experimental results verified that the closed-loop controlled micromirror follows the given triangular trajectories precisely.A micromirror-based laser scanning system is developed to obtain *** compared with open-loop control,the experimental results demonstrated that the proposed scheme is able to reduce the distortion of the raster scan,and improve the imaging performance in the presence of cross-coupling effect.

关键词： Electrostatic micromirror imaging microelectromechanical system(MEMS) twisting algorithm

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Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2025年第01期 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnology and Materials Engineering for Resources Graduate School of International Resource Sciences Akita University Department of Mining Engineering Aksum University School of Computer Science and Engineering VIT-AP University Department of Systems Control and Information Engineering National Institute of Technology Asahikawa College Department of Mining Engineering Indian Institute of Technology (Indian School of Mines) Faculty of Engineering Hokkaido University

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities, causing harmful effects on the surrounding environment as well as the nearby residents. Both the local ecology and human communities suffer from the consequences of these vibrations. Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity（PPV） and frequency, which are essential parameters for measuring vibration velocity. Accurate prediction of vibration occurrence is critically important. Therefore, this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models（XGBoost, Catboost, Bagging, Gradient Boosting, and Random Forest Regression） to choose the most efficient performance model. The performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase, there was a strong correlation observed between the actual and the predicted ones. The analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R2= 0.983, MSE = 0.000078, RMSE = 0.008, NRMSE = 0.019, MAD = 0.004, MAPE =35.197 in the PPV prediction, and R2= 0.975, MSE = 0.000243, RMSE = 0.015, NRMSE = 0.031, MAD = 0.008,MAPE = 37.281 for the frequency prediction. This study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration, and frequency. In the context of the mining and civil industry, the application of this study offers significant potential for enhancing safety protocols and optimizing operational efficiency. By employing machine learning models, this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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Boosting occluded image classification via subspace decomposition based estimation of deep features

arXiv

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arXiv 2020年

作者： Cen, Feng Wang, Guanghui Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS66045 United States

Classification of partially occluded images is a highly challenging computer vision problem even for the cutting edge deep learning technologies. To achieve a robust image classification for occluded images, this paper proposes a novel scheme using subspace decomposition based estimation (SDBE). The proposed SDBE-based classification scheme first employs a base convolutional neural network to extract the deep feature vector (DFV) and then utilizes the SDBE to compute the DFV of the original occlusion-free image for classification. The SDBE is performed by projecting the DFV of the occluded image onto the linear span of a class dictionary (CD) along the linear span of an occlusion error dictionary (OED). The CD and OED are constructed respectively by concatenating the DFVs of a training set and the occlusion error vectors of an extra set of image pairs. Two implementations of the SDBE are studied in this paper: The l1-norm and the squared l2-norm regularized least-squares estimates. By employing the ResNet-152, pre-trained on the ILSVRC2012 training set, as the base network, the proposed SBDE-based classification scheme is extensively evaluated on the Caltech-101 and ILSVRC2012 datasets. Extensive experimental results demonstrate that the proposed SDBE-based scheme dramatically boosts the classification accuracy for occluded images, and achieves around 22:25% increase in classification accuracy under 20% occlusion on the ILSVRC2012 dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Deep learning of Hash Method for Fast Image Retrieval

Deep learning of Hash Method for Fast Image Retrieval

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Chinese Automation Congress (CAC)

作者： Taiying Peng Tian Wang Kexin Liu Mengyi Zhang Peng Shi Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426480

Most of the image retrieval technology are complex and advanced methods to improve the accuracy of retrieval. They also have deep net layers, which costs much time to caculate the result. When we need deal with complex image task, it will be hard to complete the fast image *** paper presents a convolutional neural network combining with the fast encoding technology under the hash algorithm to improve fast retrieval time and accuracy. In order to keep the existing accuracy and retrieval speed of the network, we present a Hash method path of the network combining witn the dual networks. By clustering analysis and AlexNet,we get lots of high dimensional feature. Then these feature is put into Hash-coding algorithm. The algorithm will output binary feature to match the target image. Through learning similar graph pairs, Hash net work shorten the distance between the similar images, and achieve fast clustering. Then it realizes image retrieval and outputs the most similar picture. Complete the search task.

关键词： Training Deep learning Image coding Costs Image retrieval Clustering algorithms Feature extraction

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TDNCIB: Target-Driven Navigation with Convolutional Information Branch Based on Deep Reinforcement Learning

TDNCIB: Target-Driven Navigation with Convolutional Informat...

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Chinese Automation Congress (CAC)

作者： Xinzhou Zhao Tian Wang Kexin Liu Mengyi Zhang Peng Shi Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence Beihang University Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China School of Mathematics and Computer Science Fujian Normal University Fujian China Institute Charles Delaunay-LM2S University of Technology of Troyes Troyes France

ISBN: (纸本)9781665426480

Target-driven navigation has been a significant application field of reinforcement learning. However, sparse reward and lack of useful environment information are still existing problems. Therefore, in this paper, we propose a novel design to the target-driven visual navigation based on deep reinforcement learning: Target-Driven Navigation with Convolutional Information Branch (TDNCIB), which enriches the high-dimensional information learned by the network from the environment. It is an improvement of A2C algorithm. CIB can help make full use of environmental information and serve as a complement to the baseline. Besides, a novel reward design using similarity is introduced to relieve sparse reward. The ablation study is implemented in 4 different scenes of AI2-THOR framework comparing the baseline and our TDNCIB. According to our evaluation, TDNCIB converges faster than the baseline and achieves better performance in 5 common metrics with shorter trajectory length, less collision and higher success rate.

关键词： Measurement Visualization Automation Navigation Reinforcement learning Trajectory

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Composite Nonlinear Bilateral control for Teleoperation Systems With External Disturbances

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1220-1229页

作者： Zhenhua Zhao Jun Yang Shihua Li Wen-Hua Chen the College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China the School of Automation Southeast UniversityKey Laboratory of Measurement and Control of School of Computer Science and Engineering (CSE)Ministry of EducationNanjing 210096China the Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLE11 3TUUK

This paper presents a new composite nonlinear bilateral control method based on the nonlinear disturbance observer(NDOB) for teleoperation systems with external disturbances. By introducing the estimations of NDOB and systems' nominal nonlinear dynamics into controller design, a NDOB based composite nonlinear bilateral controller is constructed to attenuate the influence of disturbance and uncertain nonlinearities. As compared with the existing bilateral control methods which usually achieve force haptic(i.e., contact force tracking)through a passive way, the newly proposed method has two major merits: 1) asymptotical convergence of both position and force tracking errors is guaranteed;2) disturbance influence on force tracking error dynamics is rejected through the direct feedforward compensation of disturbance estimation. Simulations on a nonlinear teleoperation system are carried out and the results validate the effectiveness of the proposed controller.

关键词： Bilateral control disturbance observer force haptic teleoperation

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Retraction notice to "An intrusion identification and prevention for cloud computing: From the perspective of deep learning" [Optik 270 (2022) 170044] (Optik (2022) 270, (S003040262201302X), (10.1016/***.2022.170044))

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Optik 2025年 335卷

作者： Kumar, Anil Umurzoqovich, Rajabov Sherzod Duong, Nguyen Duc Kanani, Pratik Kuppusamy, Arulmani Praneesh, M. Hieu, Minh Ngyen Data Science Research Group School of Computing DIT University Dehradun India The Head of the Department of Quality Control of Education Tashkent Institute of Finance Tashkent Uzbekistan East Asia University of Technology Hanoi Viet Nam Department of Computer Engineering D. J. Sanghvi College of Engineering Mumbai400056 India SASTRA Deemed to be University Thanjavur India PG & Research Department of Computer Science Sri Ramakrishna College of Arts & Science Tamil Nadu Coimbatore India Pusa Research Lab Viet Nam

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Post-publication, an investigation conducted on behalf of the journal by Elsevier's Research Integrity & Publishing Ethics team discovered suspicious changes in authorship between the original submission and the revised version of this paper. During revision, the authors Anil Kumar, Rajabov Sherzod Umurzoqovich, Nguyen Duc Duong, Pratik Kanani, Arulmani Kuppusamy, and Praneesh M were added to the revised paper;and three authors were removed;without explanation and without exceptional approval by the journal editor, which is contrary to the journal policy on changes to authorship. None of the Authors responded to three emails that were sent related to this investigation and requesting further information. As such and due to the nature of the irregularities uncovered, the Editor has lost confidence in the validity/integrity and findings of the article and decided to retract the article. The scientific community takes a very strong view on this matter and apologies are offered to readers. © 2025 Elsevier GmbH

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Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

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Chaos, Solitons and Fractals 2025年 198卷

作者： Wang, Xiangrong Lu, Dan Wu, Zongze Xu, Weina Hou, Hongru Hu, Yanqing Moreno, Yamir College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Hangzhou International Innovation Institute Beihang University Hangzhou China School of Computer Science and Engineering Beihang University Beijing China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China University of Zaragoza Zaragoza50018 Spain Department of Theoretical Physics University of Zaragoza Zaragoza50009 Spain

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive. © 2025

关键词： Deep neural networks

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