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4th International Conference on Data-Driven Optimization of Complex systems, DOCS 2022

作者： Li, Dong Zhang, Qichao Zhao, Dongbin Noah's Ark Lab Huawei Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex System China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Visual navigation in complex environments is inefficient with traditional reactive policy or general-purposed recurrent policy due to the long-term memory problem. To address this issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module, and control module. The memory construction module builds the topological graph based on supervised learning by taking the exploration prior. Then, guided attention features to reach the goal are extracted with the graph attention module. Finally, the deep reinforcement learning based control module makes decisions based on visual observations and guided attention features. In addition, the convergence of the proposed GAM module for recurrent attention operation is analyzed in this paper. We evaluate GAM-based navigation system in two complex 3D ViZDoom environments. Experimental results show that the GAM-based navigation system outperforms all baselines in both success rate and navigation efficiency, and significantly improves the generalization. © 2022 IEEE.

关键词： Deep learning

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Energy Management Based on Safe Multi-Agent Reinforcement Learning for Smart Multi-Energy Buildings in Distribution Networks

SSRN

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SSRN 2024年

作者： Sun, Yiyun Zhang, Senlin Liu, Meiqin Zheng, Ronghao Dong, Shanling College of Electrical Engineering Zhejiang University Hangzhou310027 China National Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an710049 China

The rapid urbanization and increasing use of distributed renewable energy resources have imposed a significant burden on power networks. Smart multi-energy buildings with various energy storage systems and photovoltaic (PV) systems can be used to improve renewable energy integration and voltage quality. However, ensuring voltage stability in large multi-energy buildings presents challenges due to the coexistence of diverse energy sources and the fluctuating nature of renewable energy. This paper proposes a safe multi-energy management framework achieved by online decentralized execution and centralized training for large scale smart multi-energy buildings in distribution networks. The energy management problem is formulated as a safe constraint augmented Markov decision process, which is intractable by dynamic programming due to the large continuous state space. To solve this issue and improve the convergence speed and training process stability, a novel safe constrained multi-agent deep reinforcement learning algorithm based on reward extrapolation is proposed. In this algorithm, hazard values are introduced to enhance cutting-edge non-safe multi-agent reinforcement learning algorithms and meet safety constraints. A novel reward network is designed by imitating expert underlying intentions to ensure the rationality of the reward function for multi-objective tasks. Additionally, the loss function for estimating the Q-network is redesigned during training process to guarantee effective convergence. Theoretical analysis is conducted to provide convergence guarantee. Numerical case studies based on actual data are performed to validate the effectiveness and scalability of our approach, showing that smart multi-energy buildings can achieve superior energy management performance while ensuring voltage safety for distributed grid nodes. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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DenseKD: Dense Knowledge Distillation by Exploiting Region and Sample Importance

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IEEE Transactions on Neural Networks and Learning systems 2025年第6期36卷 11243-11257页

作者： Haonan Zhang Longjun Liu Yi Zhang Xinyu Lei Fei Hui Bihan Wen National Key Laboratory of Human-Machine Hybrid Augmented Intelligence the National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China School of Electronics and Control Engineering Chang’an University Xi’an China School of Electrical and Electronic Engineering Nanyang Technological University Jurong West Singapore

Knowledge distillation (KD) can compress deep neural networks (DNNs) by transferring the knowledge of the redundant teacher model to the resource-friendly student model, where cross-layer KD (CKD) conducts KD between each stage of students and the multiple stages of teachers. However, previous CKD schemes select the coarse-grained stagewise features of teachers to teach students, leading to improper channel alignment. Also, most of these methods conduct uniform distillation for all the knowledge, limiting students to focus more on important knowledge. To address these problems, we propose a dense KD (DenseKD) in this article, dubbed as DenseKD. First, to achieve more accurate feature alignment in CKD, we construct the learnable dense architecture to make each channel of student flexibly capture more diverse channelwise features from teacher. Moreover, we introduce region importance to investigate the region’s guiding potential, it distinguishes the influence of different regions by the variation of representations of teacher models. In addition, to make students pay more attention to useful samples in KD, we calculate sample importance by the loss of teacher models. Consistent improvements over state-of-the-art approaches are observed in experiments on multiple vision tasks. For example, in the classification task, DenseKD achieves 72.30% accuracy of ResNet-20 on CIFAR-100, which is higher than the results of previous CKD methods. In addition, in the object detection task, DenseKD gains 2.84% mean average precision (mAP) improvements of Faster R-CNN with ResNet-18 against vanilla KD.

关键词： Knowledge engineering Cross layer design Chronic kidney disease Knowledge transfer Feature extraction Accuracy Object detection control engineering Visualization Training

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DPF-SLAM: Dense semantic SLAM based on dynamic probability fusion in dynamic environments

DPF-SLAM: Dense semantic SLAM based on dynamic probability f...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Liu, Xin Wen, Shuhuan Yuan, Mingxing Li, Pengjiang Zhao, Yongjie Manfredi, Luigi Yanshan University Eng. Res. Ctr. of the Ministry of Education for Intelligent Control System and Intelligent Equipment Key Laboratory of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China Nankai University School of Artificial Intelligence China University of Dundee United Kingdom

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the development of simultaneous localization and mapping (SLAM) technology, Dynamic SLAM has been a challenging research topic. This paper presents a dynamic probability fusion SLAM (DPF-SLAM) algorithm, which adds a semantic segmentation thread and a dense reconstruction thread to ORB-SLAM2. We integrate dynamic prior probability obtained by semantic segmentation with dynamic probability obtained by dynamic point detection, which can decrease the effect of dynamic objects on the localization accuracy and meanwhile achieve the dense reconstruction of a static background. We evaluate DPF-SLAM system on the public TUM RGBD dataset. The experimental results show that the proposed algorithm has better localization accuracy than DS-SLAM and ORB-SLAM2, which are two relatively new algorithms in this area, and can obtain a good dense reconstruction effect. Moreover, through the performance comparison with our previous work, it is found that the algorithm speed and positioning accuracy are improved. © 2022 IEEE.

关键词： Semantics

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Optimization of a Piezoelectric Bending Beam-based Human Knee Energy Harvester

Optimization of a Piezoelectric Bending Beam-based Human Kne...

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IEEE International Conference on Mechatronics and Automation

作者： Haisu Liao Yidi Wang Qiuyu Shi Ruisen Huang Fei Gao Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy System

The concept of extracting energy from the human body to recharge depleted energy sources has gained significant attention. Recently, scientists have focused on harnessing biomechanical energy from the human body while walking. The aim of this study is to optimize a piezoelectric bending beam-based human knee energy harvester to enhance performance. Specifically, the shape of the bending beam (the substrate: a carbon fiber plate) is optimized to modulate the deformation, such that the average strain of the piezoelectric macro fiber composites (MFC) attached to the bending beam is improved to generate more electricity. In addition, the effects of the substrate’s thickness and the hinge base’s height for the bending beam on the output power are also analyzed. Experimental testing on a subject is conducted to validate our design and test the performance. The harvester generates an average power output of 24.55 mW when walking at 4 km/h on a treadmill. The harvester’s total weight is 278g.

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On Networked Dynamical systems with Heterogeneous Constraints:Equilibrium Points and Stability 41

On Networked Dynamical Systems with Heterogeneous Constraint...

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第41届中国控制会议

作者： Qichao Ma Jiahu Qin Yu Kang Huijun Gao Department of Automation University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Institute of Advanced Technology University of Science and Technology of China Research Institute of Intelligent Control and Systems and the State Key Laboratory of Robotics and System Harbin Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,we investigate multi-agent systems with heterogeneous constraints,which can represent the heuristic beliefs of the agents towards an issue or the physical constraints of the *** typical example is a group of interacting unmanned aerial vehicles in a complex environment with multiple physical *** a result of the heterogeneity of the constraints,an equilibrium point may not exist and is presumably to be a dissensus point when it *** investigate the existence of equilibrium points from the perspective of Kakutani's fixed point theory for a set-valued *** also prove the local/global stability of certain equilibrium ***,a special case that the constraints are homogeneous is taken into *** constrains are assumed to take interval forms with nonempty *** is proved that consensus can be achieved globally and asymptotically for this *** examples are designed to illustrate our theoretical findings.

关键词： Multi-agent systems heterogeneous constraints equilibrium point and its stability constrained consensus

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ViT-based Terrain Recognition system for wearable soft exosuit

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Biomimetic intelligence & Robotics 2023年第1期3卷 57-64页

作者： Fangliang Yang Chunjie Chen Zhuo Wang Hui Chen Yao Liu Gang Li Xinyu Wua Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhen 518055China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055China SIASUN Robot and Automation CO. LtdShenyang 110169China

Terrain classification and force assistance strategies in complex environments have always piqued the interest of many *** wearable soft exosuits,inaccurate terrain recognition can easily introduce undesired assist forces that can easily injure the *** of these problems,we introduced a depth camera into the exosuit system,perform classification of terrain based on a Vision Transformer(ViT),and optimized the control algorithm,which is known as a ViT-Based Terrain Recognition system(TTRS).First,we used the Transformer algorithm to achieve a considerable classification effect in terrain *** also introduced terrain recognition as prior knowledge into the force assistance strategy of the exosuit,providing different force assistance to the exosuit in different ***,we performed human experiments with seven able-bodied people(six males and one female).The promising results demonstrate that our classification accuracy can reach 99.2%under six different terrains and that it can smoothly switch the force–assist curve in different terrains to better adapt to the complex terrain and improve the walking *** aforementioned terrain recognition algorithms and force–assist strategies may positively influence the study of soft exosuit,powered prostheses,and orthotics.

关键词： Vision Transformer(ViT) Depth camera Wearable soft exosuit Force assistance strategy Terrain recognition

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Statistical Feasibility Robust Optimization for Hydrogen-Data Center Microgrid Operation

SSRN

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SSRN 2024年

作者： Duan, Juntao Li, Yuanzheng Li, Yang Liu, Zhiwei Zhao, Yong School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

The increasing processing demands of information technology (IT) workloads cause the booming of data centers (DCs) in recent years. The servers in the DC emit much heat when processing the IT workloads and thus the DC temperature rises. The power consumption when controlling the DC temperature using the air conditioning (A/C) of the DC is high, which leads to expensive power bills. To reduce the power cost, using renewable energy (RE) to power DC has raised much attention in recent years. However, the intermittency of RE and the uncertainties of outside temperature pose operational challenges. Therefore, in this paper, a data-driven statistical-feasibility-based robust rolling optimization framework for the operation of hydrogen-DC microgrid (H2-DCMG) is proposed to control the DC temperature considering these uncertainties and the intermittency of RE. In the framework, the hydrogen energy storage system (HESS) is introduced to deal with the intermittency of RE, and robust optimization (RO) is adopted to deal with the uncertainty of outside temperature. The statistical feasibility is introduced into the framework to decrease the conservatism of RO, through relaxing the DC temperature constraints without harming normal DC operations. Moreover, we propose the Statistical-Guarantee-based Vertex Link (SGVL) algorithm to construct a polyhedron uncertain set of outside temperatures that can ensure the results of dispatch are economical and satisfy statistical feasibility. The constructed polyhedron uncertain set is linear, which reduces computational complexity in solving. A case study based on the real-world temperature dataset verifies the superiority of the SGVL algorithm on the aspect of the economic cost. It further validates the effectiveness of the proposed robust optimization framework for DC temperature control. © 2024, The Authors. All rights reserved.

关键词： Air conditioning

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Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset

arXiv

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arXiv 2024年

作者： Lian, Shijie Zhang, Ziyi Li, Hua Li, Wenjie Yang, Laurence Tianruo Kwong, Sam Cong, Runmin Hainan University China The Hong Kong University of Science and Technology Guangzhou China Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications Southeast University Ministry of Education China Huazhong University of Science and Technology China St. Francis Xavier University Canada LIngnan University School of Data Science Hong Kong Shandong University School of Control Science and Engineering China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. Datasets and codes are released on Github. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Statistical Feasibility Robust Optimization for Hydrogen-Data Center Microgrid Operation

SSRN

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SSRN 2024年

关键词： Air conditioning

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