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A Fast Optimal Power Flow Algorithm Using Powerball Method

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第11期16卷 6993-7003页

作者： Zhang, Hai-Tao Sun, Weigao Li, Yuanzheng Fu, Dongfei Yuan, Ye Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

The complexity and randomness of the power system with distributed energy resources have led to the difficulties for fast optimal power flow (OPF) analysis. As a remedy, in this paper, we develop an interior point Powerball algorithm to accelerate the OPF solution process. To achieve better convergence characteristics, the proposed IPPB algorithm which is based on the Powerball optimization method, improves the search directions during iterative optimization by a nonlinear transformation. Also, a Newton-Raphson Powerball algorithm is derived for a faster power flow calculation, which is a basic yet critical part of the OPF problem. Numerical case studies are conducted on benchmark power systems with different scales to validate the proposed algorithms. Performances of the proposed algorithms to address improper initial points are studied by randomly picking the initial bus voltages. Numerical study results verify the feasibility and superiority of the proposed algorithms.

关键词： Optimization Convergence Acceleration Voltage control Generators Power transmission lines Convergence speed interior point method Newton-Raphson (NR) method optimal power flow (OPF) optimization methods

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Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo Scott Chapman Yufeng Ge Benoit de Solan Yanfeng Ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia Department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

The number of leaves at a given time is important to characterize plant growth and *** this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB *** digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning *** results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation ***,the spatial resolution should be better than 0.6 mm per pixel to identify leaf *** method is claimed to be self-supervised since no manual labeling is required for model *** self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping *** trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

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Representative Graph Neural Network 16th

Representative Graph Neural Network

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16th European Conference on Computer Vision, ECCV 2020

作者： Yu, Changqian Liu, Yifan Gao, Changxin Shen, Chunhua Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China The University of Adelaide Adelaide Australia

ISBN: (纸本)9783030585709

Non-local operation is widely explored to model the long-range dependencies. However, the redundant computation in this operation leads to a prohibitive complexity. In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy. Instead of propagating the messages from all positions, our RepGraph layer computes the response of one node merely with a few representative nodes. The locations of representative nodes come from a learned spatial offset matrix. The RepGraph layer is flexible to integrate into many visual architectures and combine with other operations. With the application of semantic segmentation, without any bells and whistles, our RepGraph network can compete or perform favourably against the state-of-the-art methods on three challenging benchmarks: ADE20K, Cityscapes, and PASCAL-Context datasets. In the task of object detection, our RepGraph layer can also improve the performance on the COCO dataset compared to the non-local operation. Code is available at https://***/RepGraph. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Decentralized Consensus Decision-Making for Cybersecurity Protection in Multimicrogrid Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第4期51卷 2187-2198页

作者： Hu, Bowen Zhou, Chunjie Tian, Yu-Chu Hu, Xiaoya Junping, Xinjue Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia Huazhong Univ Sci & Technol Sch China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

Multimicrogrid (MMG) systems play an increasingly important role in the smart grid. They come with various potential cyberattacks, which may cause power supply interruption or even human casualties. Therefore, decision-making for timely mitigation of cyberattack risks is highly desirable in the security protection of power systems. However, there is a lack of effective decentralized decision-making strategies that are able to deal with MMG scenarios through distributed consensus. To address this issue, a decentralized consensus decision-making (DCDM) approach is proposed in this article for the security of MMG systems. It achieves decentralized consensus without the need of a trusted authority or central server, making it distinct from existing consensus methods. Meanwhile, it guarantees the consistency and nonrepudiability of consensus results, which are stored on the blockchain in sequence. In each of the distributed agents, the approach consists of a fuzzy static Bayesian game model (FSB-GM) to determine the optimal security strategy and a hybrid consensus algorithm to achieve consensus. The FSB-GM considers the fuzzy preferences of different types of attackers and defenders. The hybrid consensus algorithm is implemented by the fusion improvement of two consensus mechanisms in the blockchain. The effectiveness of the presented approach is demonstrated through a case study on an MMG system.

关键词： Games Decision making Bayes methods Computer crime Blockchain cybersecurity Decentralized consensus decision-making (DCDM) fuzzy static Bayesian game model (FSB-GM) multimicrogrid (MMG)

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A novel no-equilibrium HR neuron model with hidden homogeneous extreme multistability

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CHAOS SOLITONS & FRACTALS 2021年 145卷 110761-110761页

作者： Zhang, Sen Zheng, Jiahao Wang, Xiaoping Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

In this paper, a novel no-equilibrium Hindmarsh-Rose (HR) neuron model with memristive electromagnetic induction is proposed. This memristive HR neuron model exhibits complex memristor initial offset boosting dynamics, from which infinitely many coexisting hidden attractors sharing the same shape but with different positions can be generated, therefore breeding the interesting phenomenon of hidden homogeneous extreme multistability. The complicated dynamical behaviors are detailedly investigated via bifurcation diagrams, Lyapunov exponents, time series, attraction basins and spectral entropy (SE) complexity. Moreover, PSIM circuit simulations and DSP hardware experiments are carried out to demonstrate the theoretical analyses and numerical simulations. Finally, a pseudorandom number generator is also designed by using the memristor initial-controlled chaotic sequences extracted from the memristive HR neuron model. The performance analysis results show that these chaotic sequences can yield pseudorandom numbers with excellent randomness, which are more suitable for chaos-based engineering applications. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Memristive HR neuron model initial offset boosting homogeneous multistability initial-controlled chaotic sequence pseudorandom number generator

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From photon momentum transfer to acceleration sensing

arXiv

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arXiv 2024年

作者： Yang, Jianyu Li, Nan Pan, Yuyao Yang, Jing Chen, Zhiming Cai, Han Wang, Yuliang Han, Chuankun Chen, Xingfan Liu, Cheng Hu, Huizhu State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou310027 China Research Center for Advanced Computational Sensing and Intelligent Processing Zhejiang Lab Hangzhou310000 China Beijing Aerospace Control Instrument Research Institute Beijing100854 China

As a typical application of photon momentum transfer, optical levitation systems are known for their ideal isolation from mechanical dissipation and thermal noise. These characters offer extraordinary potential for acceleration precision sensing and have attracted extensive attention in both fundamental and applied physics. Although considerable improvements of optical levitation accelerometers has been reported, the dynamic testing of the sensing performance remains a crucial challenge before the utilization in practical application scenarios. In this work, we present a dual-beam optical levitation accelerometer and demonstrate the test with dynamic inputs for the first time. An acceleration sensing sensitivity of 0.1µg and a measurement range of 1g are achieved. These advancements solidify the potential of optical levitation accelerometer for deployment in practical domains, including navigation, intelligent driving, and industrial automation, building a bridge between the laboratory systems and real-world applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Momentum transfer

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Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification

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NATURE COMMUNICATIONS 2025年第1期16卷 1-12页

作者： Liu, Tao Liu, Jiahao Li, Dong Tan, Shan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ China Wuhan 430074 Peoples R China Tsinghua Univ IDG McGovern Inst Brain Res Tsinghua Peking Ctr Life Sci Beijing Frontier Res Ctr Biol StructSch Life Sci Beijing 100084 Peoples R China

The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell super-resolution imaging. Although recent deep learning techniques have substantially advanced SIM, their transparency and reliability remain uncertain and under-explored, often resulting in unreliable results and biological misinterpretation. Here, we develop Bayesian deep learning (BayesDL) for SIM, which enhances the reconstruction of densely labeled structures while enabling the quantification of super-resolution uncertainty. With the uncertainty, BayesDL-SIM achieves high-fidelity distribution-informed SIM imaging, allowing for the communication of credibility estimates to users regarding the model outcomes. We also demonstrate that BayesDL-SIM boosts SIM reliability by identifying and preventing erroneous generalizations in various model misuse scenarios. Moreover, the BayesDL uncertainty shows versatile utilities for daily super-resolution imaging, such as error estimation, data acquisition evaluation, etc. Furthermore, we demonstrate the effectiveness and superiority of BayesDL-SIM in live-cell imaging, which reliably reveals F-actin dynamics and the reorganization of the cell cytoskeleton. This work lays the foundation for the reliable implementation of deep learning-based SIM methods in practical applications.

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Positive consensus of fractional-order multi-agent systems

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NEURAL COMPUTING & APPLICATIONS 2021年第23期33卷 16139-16148页

作者： Chen, Siyu An, Qing Ye, Yanyan Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Educ Minist China Key Lab Image Proc & Intelligent Control Luoyu Rd 1037 Wuhan 430074 Peoples R China Wuchang Univ Technol Artificial Intelligence Sch Wuhan 430223 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

This paper considers the positive consensus of fractional-order multi-agent systems described by general linear dynamics with the fractional order belonging to (0, 2) via state feedback. Firstly, the distributed positive consensus protocols are given, and then, the necessary and sufficient conditions are gained for the consensus with positive constraint based on the fractional-order stability theory as well as the features of Metzler matrix. Furthermore, some sufficient positive consensus conditions are provided and are reduced to the conditions without the global networked information for the system with a (0, 1). Finally, illustration examples are provided to indicate not only the validity but also the superiority of the presented approach, compared with the highly related methods.

关键词： Fractional-order system General linear model Multi-agent system Positive consensus

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MMTrans-MT: A Framework for Multimodal Emotion Recognition Using Multitask Learning 13

MMTrans-MT: A Framework for Multimodal Emotion Recognition U...

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13th International Conference on Advanced Computational Intelligence (ICACI)

作者： Shen, Jinrui Zheng, Jiahao Wang, Xiaoping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan Peoples R China

ISBN: (纸本)9781665412544

With the development of deep learning, emotion recognition tasks are more inclined to use multimodal data and adequate supervised information to improve accuracy. In this work, MMTrans-MT (Multimodal Transformer-Multitask), the framework for multimodal emotion recognition using multitask learning is proposed. It has three modules: modalities representation module, multimodal fusion module, and multitask output module. Three modalities, i.e, words, audio and video, are comprehensively utilized to carry out emotion recognition by a simple but efficient fusion model based on Transformer. As for multitask learning, the two tasks are defined as categorical emotion classification and dimensional emotion regression. Considering a potential mapping relationship between two kinds of emotion model, multitask learning is adopted to make the two tasks promote each other and improve recognition accuracy. We conduct experiments on CMU-MOSEI and IEMOCAP datasets. Comprehensive experiments show that the accuracy of recognition using multimodal information is higher than that using unimodal information. Adopting multitask learning promotes the performance of emotion recognition.

关键词： Multimodal fusion Emotion recognition Emotion model Multitask learning Transformer

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Brain Model Based on the Canonical Ensemble with Functional MRI: A Thermodynamic Exploration of the Neural System

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COMPLEXITY 2021年第1期2021卷

作者： Zhou, Chenxi Yang, Bin Fan, Wenliang Li, Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Minist China Image Proc & Intelligent Control Key Lab Educ Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Union Hosp Dept Radiol Tongji Med Coll Wuhan 430074 Hubei Peoples R China

Objective. System modeling is an important method to study the working mechanisms of the brain. This study attempted to build a model of the brain from the perspective of thermodynamics at the system level, which brought a new perspective to brain modeling. Approach. Regarding brain regions as systems, voxels as particles, and intensity of signals as energy of particles, the thermodynamic model of the brain was built based on the canonical ensemble theory. Two pairs of activated regions and two pairs of inactivated brain regions were selected for comparison in this study, and the thermodynamic properties based on the proposed model were analyzed. In addition, the thermodynamic properties were extracted as input features for the detection of Alzheimer's disease. Main Results. The experimental results verified the assumption that the brain follows thermodynamic laws. This demonstrated the feasibility and rationality of the proposed brain thermodynamic modeling method, indicating that thermodynamic parameters drawn from our model can be applied to describe the state of the neural system. Meanwhile, the brain thermodynamic model achieved good accuracy in the detection of Alzheimer's disease, suggesting the potential application of thermodynamic models in auxiliary diagnosis. Significance. (1) In the previous studies, only some thermodynamic parameters in physics were analogized and applied to brain image analysis, while, in this study, a complete system model of the brain was proposed through the principles of thermodynamics. And, based on the neural system models proposed, thermodynamic parameters were obtained to describe the observation and evolution of the neural system. (2) Based on the proposed thermodynamic models, we found and confirmed that the neural system also follows the laws of thermodynamics: the activation of system always leads to increased internal energy, increased free energy, and decreased entropy as what is discovered in many other systems besides

关键词： Brain

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