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Robust multi-objective optimization for islanded data center microgrid operations

APPLIED ENERGY 2023年第PartB期330卷

作者： Lian, Yicheng Li, Yuanzheng Zhao, Yong Yu, Chaofan Zhao, Tianyang Wu, Lei Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Minist China Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China Univ Bath Dept Elect & Elect Engn Bath BA2 England Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

Electricity cost has become a critical concern of data center operations with the rapid increasing of information processing demand. Data center microgrid (DCMG) is a promising way to reduce electric energy consumption from traditional fossil fuel generators and the billing cost, by effectively utilizing local renewable energy, e.g., wind power. However, uncertainties of wind power generation and real-time workload of data center would have significant impacts on the operational efficiency of DCMG, especially when it is in the island mode. For this reason, a novel affinely adjustable policy based robust multi-objective optimization model under flexible uncertainty set is proposed in this paper, which simultaneously optimizes wind power curtailment, the operation cost, and the over-plus level of computation resource, while considering uncertainties of both the wind power and real-time workload. Through numerical simulation studies, the validity of robust multi -objective optimization model for the island operation of DCMG is verified. Besides, the effectiveness of the proposed methods, i.e., the novel affinely adjustable policy and the flexible uncertainty set, in handling uncertainties are evaluated. Compared to the conventional robust multi-objective optimization model, the proposed approach reduces the operating costs of about 10% in average while maintaining similar reliability in numerical simulations. Moreover, the complex quantitative relationship among these multiple objectives is further investigated. Simulation results indicate the minimization of wind power curtailment and over-plus level of computation resource increases about 25% of the operation cost. These quantitative relationships can well support the decision making of DCMG operation management.

关键词： Data center microgrid Flexible uncertainty set Affinely adjustable policy Robust multi-objective optimization

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Global stability of delayed genetic regulatory networks with wider hill functions: A mixing monotone semiflows approach

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NEUROCOMPUTING 2023年第1期526卷 39-47页

作者： Chen, Jiejie Jiang, Ping Chen, Boshan Zeng, Zhigang Hubei Normal Univ Coll Comp Sci & Informat Engn Huangshi 435002 Peoples R China Hubei Normal Univ Coll Math & Stat Huangshi 435002 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Minist China Key Lab Image Proc & Intelligent Control Educ Wuhan 430074 Peoples R China Hubei Polytech Univ Comp Sch Huangshi 435002 Peoples R China

In this paper, we study the global stability of delayed genetic regulatory networks (DGRNs) with Hill-type activation (or inhibition) functions based on the mixing monotone semiflows approach, where Hill coef-ficients can be arbitrary positive real number. A new result on the global stability of DGRN is given in the case that all Hill coefficients are less than or equal to 1. In addition, for all Hill coefficients greater than 1, a new sufficient condition for global convergence of DGRN is given, which is less conservative than the con-dition of the existing correlation results. Finally, two numerical examples and simulations are given to explain the effect of obtained results.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Global stability Genetic regulatory Networks Time -delay Mixed monotone semiflows

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Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach

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PLANT PHENOMICS 2023年 5卷 0064页

作者： Gao, Yangmingrui Li, Yinglun Jiang, Ruibo Zhan, Xiaohai Lu, Hao Guo, Wei Yang, Wanneng Ding, Yanfeng Liu, Shouyang Nanjing Agr Univ Acad Adv Interdisciplinary Studies Jiangsu Collaborat Innovat Ctr Modern Crop Prod Plant Phen Res Ctr Nanjing Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Univ Tokyo Grad Sch Agr & Life Sci 1-1-1 Midori Cho Nishitokyo Tokyo Japan Huazhong Agr Univ Natl Ctr Plant Gene Res Natl Key Lab Crop Genet Improvement Wuhan 430070 Peoples R China Huazhong Agr Univ Hubei Key Lab Agr Bioinformat Wuhan 430070 Peoples R China

The green fraction (GF), which is the fraction of green vegetation in a given viewing direction, is closely related to the light interception ability of the crop canopy. Monitoring the dynamics of GF is therefore of great interest for breeders to identify genotypes with high radiation use efficiency. The accuracy of GF estimation depends heavily on the quality of the segmentation dataset and the accuracy of the image segmentation method. To enhance segmentation accuracy while reducing annotation costs, we developed a self-supervised strategy for deep learning semantic segmentation of rice and wheat field images with very contrasting field backgrounds. First, the Digital Plant Phenotyping Platform was used to generate large, perfectly labeled simulated field images for wheat and rice crops, considering diverse canopy structures and a wide range of environmental conditions (sim dataset). We then used the domain adaptation model cycle-consistent generative adversarial network (CycleGAN) to bridge the reality gap between the simulated and real images (real dataset), producing simulation-to-reality images (sim2real dataset). Finally, 3 different semantic segmentation models (U-Net, DeeplabV3+, and SegFormer) were trained using 3 datasets (real, sim, and sim2real datasets). The performance of the 9 training strategies was assessed using real images captured from various sites. The results showed that SegFormer trained using the sim2real dataset achieved the best segmentation performance for both rice and wheat crops (rice: Accuracy = 0.940, F1-score = 0.937;wheat: Accuracy = 0.952, F1-score = 0.935). Likewise, favorable GF estimation results were obtained using the above strategy (rice: R2 = 0.967, RMSE = 0.048;wheat: R2 = 0.984, RMSE = 0.028). Compared with SegFormer trained using a real dataset, the optimal strategy demonstrated greater superiority for wheat images than for rice images. This discrepancy can be partially attributed to the differences in the backgrounds o

关键词： Uncertainty analysis

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Disentangling Spatial and Temporal Learning for Efficient image-to-Video Transfer Learning

arXiv

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arXiv 2023年

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Zhang, Yingya Gao, Changxin Zhao, Deli Sang, Nong Key Laboratory of Image Processing Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China ARC National University of Singapore Singapore

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to video recognition still suffers from unsatisfactory temporal modeling capabilities. Existing methods insert tunable structures into or in parallel with the pre-trained model, which either requires back-propagation through the whole pre-trained model and is thus resource-demanding, or is limited by the temporal reasoning capability of the pre-trained structure. In this work, we present DiST, which disentangles the learning of spatial and temporal aspects of videos. Specifically, DiST uses a dual-encoder structure, where a pre-trained foundation model acts as the spatial encoder, and a lightweight network is introduced as the temporal encoder. An integration branch is inserted between the encoders to fuse spatio-temporal information. The disentangled spatial and temporal learning in DiST is highly efficient because it avoids the back-propagation of massive pre-trained parameters. Meanwhile, we empirically show that disentangled learning with an extra network for integration benefits both spatial and temporal understanding. Extensive experiments on five benchmarks show that DiST delivers better performance than existing state-of-the-art methods by convincing gaps. When pretraining on the large-scale Kinetics-710, we achieve 89.7% on Kinetics-400 with a frozen ViT-L model, which verifies the scalability of DiST. Codes and models can be found in https://***/alibaba-mmai-research/DiST. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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A SINGLE UPPER LIMB POSE ESTIMATION METHOD BASED ON THE IMPROVED STACKED HOURGLASS NETWORK

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2021年第1期31卷 123-133页

作者： Peng, Gang Zheng, Yuezhi Li, Jianfeng Yang, Jin Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat 1037 Luoyu Rd Wuhan 430074 Peoples R China

At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human-machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.

关键词： convolutional neural network stacked hourglass network skeleton key point single upper limb pose estimation human-machine coordination

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A sliding mode based foot-end trajectory consensus control method with variable topology for legged motion of heavy-duty robot

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ROBOTICS AND AUTONOMOUS SYSTEMS 2024年 181卷

作者： Xue, Junfeng Chen, Zhihua Wang, Liang Wang, Ruoxing Wang, Junzheng Wang, Shoukun Beijing Inst Technol Sch Automat State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China Nanchang Hangkong Univ Key Lab Jiangxi Prov Image Proc & Pattern Recognit Nanchang 330063 Peoples R China Nanchang Hangkong Univ MOE Key Lab Nondestruct Testing Technol Nanchang Peoples R China

Rational foot-end trajectory planning and control are of great significance for stable-legged walking of heavyduty multi-legged robots. To achieve a fast, active, and compliant response of the leg actuator to disturbances for improvement of the stability and flexibility of the heavy-duty legged robot system during continuous walking on rough roads, a legged consensus control method (LCC) is proposed. Firstly, the LCC includes a foot-end trajectory planner model for designing the trajectory during the swing phase to ensure that the robot's feet are always in a safe workspace during legged motion with continuously variable direction. Secondly, LCC constructs a consensus control method for encoding foot-end position and velocity consensus error based on variable topology networks. Six legs are treated as six intelligent agents and divided into two fully connected networks: the swing phase and stance phase, to achieve smooth and consistent motion that satisfies the geometric constraints of the robot. The foot-end agent can switch between swing and stance groups according to the state of the contact with the environment accompanied by the amendment topology, to enhance the robustness of the robot system through fast compliance control of the foot-end kinematics state. Then, the sliding mode control method based on consensus velocity and position error is deduced in LCC. The sliding mode surface is designed to make the three control variables realize stable movement with a consistent state of foot-end in three X, Y, Z-axis respectively, thereby enhancing the stability of foot-end state and fuselage posture. Finally, simulation and experiments have verified that the proposed LCC can assist legged-robot perform relatively steady legged motion with continuously variable direction on various rugged roads. The body attitude Root Mean Square Error (RMSE) is quickly reduced by 81.0% compared with independent PI control. The LCC algorithm code is publicly available at https://git

关键词： Wheel-legged robot Consensus control Sliding mode control Variable topology

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Adaptive Dynamic Event-Triggered Fault-Tolerant Consensus for Nonlinear Multiagent Systems With Directed/Undirected Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第6期53卷 3901-3912页

作者： Chen, Jiejie Chen, Boshan Zeng, Zhigang Hubei Normal Univ Coll Comp Sci & Informat Engn Huangshi 435002 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Automat Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

The fault-tolerant consensus of nonlinear multiagent systems (MASs) is studied by using the dynamic event-triggered control and the adaptive control techniques. First, a general dynamic adaptive event-triggered mechanism (DAETM) is proposed, which promotes and improves many existing dynamic event-triggered mechanism and static event-triggered mechanism. On this basis, a new distributed dynamic adaptive event-triggered fault-tolerant controller (DDAETFTC) is designed. Then, two simple and clear criteria are derived, respectively, to ensure consensus can be reached asymptotically for nonlinear MASs with directed networks and with undirected networks under the new DDAETFTC. The obtained results also can apply to linear MASs. Furthermore, it is proven that there is no agent to show the Zeno behavior in MASs under the new DAETM. Finally, an example is given to simulate the obtained results.

关键词： Fault tolerant systems Fault tolerance Nonlinear dynamical systems Synchronization Switches Multi-agent systems Electronic mail Dynamic adaptive event-triggered fault-tolerant consensus singular multiagent systems (MASs)

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Instance Consistency Regularization for Semi-Supervised 3D Instance Segmentation

arXiv

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arXiv 2024年

作者： Wu, Yizheng Pan, Zhiyu Wang, Kewei Li, Xingyi Cui, Jiahao Xiao, Liwen Lin, Guosheng Cao, Zhiguo The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China S-Lab Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore

Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3D instance segmentation approaches have explored self-training frameworks, which rely on high-quality pseudo labels for consistency regularization. They intuitively utilize both instance and semantic pseudo labels in a joint learning manner. However, semantic pseudo labels contain numerous noise derived from the imbalanced category distribution and natural confusion of similar but distinct categories, which leads to severe collapses in self-training. Motivated by the observation that 3D instances are non-overlapping and spatially separable, we ask whether we can solely rely on instance consistency regularization for improved semi-supervised segmentation. To this end, we propose a novel self-training network InsTeacher3D to explore and exploit pure instance knowledge from unlabeled data. We first build a parallel base 3D instance segmentation model DKNet, which distinguishes each instance from the others via discriminative instance kernels without reliance on semantic segmentation. Based on DKNet, we further design a novel instance consistency regularization framework to generate and leverage high-quality instance pseudo labels. Experimental results on multiple large-scale datasets show that the InsTeacher3D significantly outperforms prior state-of-the-art semi-supervised approaches. Code is available: https://***/W1zheng/InsTeacher3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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VideoLCM: Video Latent Consistency Model

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Zhang, Han Liu, Yu Zhang, Yingya Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Shanghai Jiao Tong University China

Consistency models have demonstrated powerful capability in efficient image generation and allowed synthesis within a few sampling steps, alleviating the high computational cost in diffusion models. However, the consi...

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Container terminal competition and cooperation considering emission tax

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MARITIME POLICY & MANAGEMENT 2022年第7期49卷 930-953页

作者： Wang, Jian Huang, Qi Lin, Xudong Yu, Mingzhu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Shenzhen Univ Coll Management Inst Big Data Intelligent Management & Decis Shenzhen Peoples R China

This paper studies the competition and cooperation problem in a regional port system with two container terminals. Two game models are proposed to analyze the decisions of container terminal operators and the local government, respectively. We consider the emission tax, container terminal's service level and service price and derive the optimal decisions of the players. From the numerical analysis results, we find that in the regional port system, (1) a relatively developed container terminal is a good choice for consumers who prefer high-quality and efficient service, while those whose main concern is price can choose a developing container terminal;(2) for the benefit of all players, cleaner fuels should be used to decrease the proportion of unit pollution emission;(3) if social welfare is more emphasized than environmental protection, competition between the two container terminals is preferred, otherwise, cooperation is more beneficial.

关键词： Container terminal competition and cooperation hotelling model service level emission tax container terminal pricing

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