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arXiv 2025年

作者： Schmidt, Adam Karaoglu, Mert Asim Sinha, Soham Jang, Mingang Ha, Ho-Gun Jung, Kyungmin Gu, Kyeongmo Ullah, Ihsan Lee, Hyunki Šerých, Jonáš Neoral, Michal Matas, Jiří Zhou, Rulin He, Wenlong Wang, An Ren, Hongliang Silva, Bruno Queirós, Sandro Lima, Estêvão Vilaça, João L. Kikuchi, Shunsuke Kouno, Atsushi Matsuzaki, Hiroki Li, Tongtong Chen, Yulu Li, Ling Ma, Xiang Li, Xiaojian Zeinoddin, Mona Sheikh Wang, Xu Tandogdu, Zafer Shaw, Greg Mazomenos, Evangelos Stoyanov, Danail Chen, Yuxin Wu, Zijian Ladikos, Alexander DiMaio, Simon Salcudean, Septimiu E. Mohareri, Omid Intuitive Surgical Inc. Sunnyvale United States ImFusion GmbH Munich Germany Technical University of Munich Munich Germany NVIDIA Santa Clara United States Korea Republic of CMP Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Shenzhen Research Institute China The Chinese University of Hong Kong China Shenzhen University China School of Medicine University of Minho Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga Guimarães Portugal 2Ai –School of Technology IPCA Barcelos Portugal LASI – Associate Laboratory of Intelligent Systems Guimarães Portugal Jmees Inc Japan United States School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei China Hawkes Institute University College London London United Kingdom Dept of Urology University College London Hospitals United Kingdom University of British Columbia Canada

Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are essential to enabling algorithms in these downstream tasks since they allow us to quantify and train algorithms. This paper introduces a point tracking challenge to address this, wherein participants can submit their algorithms for quantification. The submitted algorithms are evaluated using a dataset named surgical tattoos in infrared (STIR), with the challenge aptly named the STIR Challenge 2024. The STIR Challenge 2024 comprises two quantitative components: accuracy and efficiency. The accuracy component tests the accuracy of algorithms on in vivo and ex vivo sequences. The efficiency component tests the latency of algorithm inference. The challenge was conducted as a part of MICCAI EndoVis 2024. In this challenge, we had 8 total teams, with 4 teams submitting before and 4 submitting after challenge day. This paper details the STIR Challenge 2024, which serves to move the field towards more accurate and efficient algorithms for spatial understanding in surgery. In this paper we summarize the design, submissions, and results from the challenge. The challenge dataset is available here: https://***/records/14803158, and the code for baseline models and metric calculation is available here: https://***/athaddius/STIRMetrics Copyright © 2025, The Authors. All rights reserved.

关键词： Endoscopy

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A stochastic optimisation framework for integrating photovoltaic systems, heat pumps, and energy storage in buildings

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Applied Thermal engineering 2025年

作者： Andreas V. Olympios Matthias Mersch Fanourios Kourougianni Christos N. Markides Antonio M. Pantaleo Andreas Kyprianou George E. Georghiou PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Clean Energy Processes (CEP) Laboratory and Sargent Centre for Process Systems Engineering Department of Chemical Engineering Imperial College London UK Centre for Environmental Policy Imperial College London London UK Department of Mechanical and Manufacturing Engineering University of Cyprus Nicosia Cyprus Department of Agro-environmental Sciences University of Bari Italy

In this study, we present a novel stochastic optimisation framework for the selection, sizing, and operation of photovoltaic systems combined with heating, cooling, and energy storage technologies in buildings. The framework integrates building energy modelling, technology cost and performance analysis, and energy system optimisation while addressing future uncertainties in technology and electricity prices. A bi-objective optimisation problem is developed to minimise both the expected total system cost and the variability of costs under uncertain inputs. The tool encompasses various photovoltaic technologies (polycrystalline, monocrystalline and monocrystalline with one-axis tracking), electric heat pumps (air-to-water, ground-to-water, and reversible air-to-air), and energy storage systems (battery and hot-water cylinder). Electricity, heating, and cooling requirements are obtained from a physics-based building model of a typical office and a residential building in Nicosia, Cyprus. Results of cost-effective technology portfolios show that replacing traditional systems with photovoltaic technologies, lithium-ion batteries, reversible air-to-air heat pumps, and air-to-water heat pumps can significantly reduce costs and emissions. Incorporating cost-variability minimisation into the objective function of the optimisation problem leads to a more diverse technology mix, enhancing energy independence and robustness at the expense of higher expected costs. A well-balanced bi–objective approach where both objectives are simultaneously optimised increases expected costs by 2–3% depending on the building type, while reducing the worst–case costs by 5% and narrowing the range of cost variability by up to 29%. The results demonstrate the significance of incorporating uncertainty into building energy system design optimisation.

关键词： Buildings Energy storage Heat pump Photovoltaic Stochastic optimisation

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Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market

arXiv

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arXiv 2020年

作者： Rokhforoz, Pegah Gjorgiev, Blazhe Sansavini, Giovanni Fink, Olga Intelligent Maintenance Systems ETH Zurich Switzerland School of Electrical and Computer Engineering University of Tehran Tehran Iran Reliability and Risk Engineering Laboratory Institute of Energy Technology Department of Mechanical and Process Engineering ETH Zurich Switzerland

Condition-based and predictive maintenance enable early detection of critical system conditions and thereby enable decision makers to forestall faults and mitigate them. However, decision makers also need to take the operational and production needs into consideration for optimal decision-making when scheduling maintenance activities. Particularly in network systems, such as power grids, decisions on the maintenance of single assets can affect the entire network and are, therefore, more complex. This paper proposes a two-level multi-agent decision support systems for the generation maintenance decision (GMS) of power grids in an electricity markets. The GMS plays an important role in increasing the reliability at the network level. The aim of the GMS is to minimize the generation cost while maximizing the system reliability. The proposed framework integrates a central coordination system, i.e. the transmission system operator (TSO), and distributed agents representing power generation units that act to maximize their profit and decide about the optimal maintenance time slots while ensuring the fulfilment of the energy demand. The objective function of agents (power generation companies) is based on the reward and the penalty that they obtain from the interplay between power production and loss of production due to failure, respectively. The optimal strategy of agents is then derived using a distributed algorithm, where agents choose their optimal maintenance decision and send their decisions to the central coordinating system. The TSO decides whether to accept the agents’ decisions by considering the market reliability aspects and power supply constraints. To solve this coordination problem, we propose a negotiation algorithm using an incentive signal to coordinate the agents’ and central system’s decisions such that all the agents’ decisions can be accepted by the central system. We demonstrate the efficiency of our proposed algorithm using a IEEE 39 bus system. Co

关键词： Decision making

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Regret and cumulative constraint violation analysis for online convex optimization with long term constraints

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study Tongji University Shanghai China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore

This paper considers online convex optimization with long term constraints, where constraints can be violated in intermediate rounds, but need to be satisfied in the long run. The cumulative constraint violation is used as the metric to measure constraint violations, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. A novel algorithm is first proposed and it achieves an O(Tmax{c,1-c}) bound for static regret and an O(T (1-c)/2) bound for cumulative constraint violation, where c ϵ (0, 1) is a user-defined trade-off parameter, and thus has improved performance compared with existing results. Both static regret and cumulative constraint violation bounds are reduced to O(log(T )) when the loss functions are strongly convex, which also improves existing results. In order to achieve the optimal regret with respect to any comparator sequence, another algorithm is then proposed and it achieves the optimal O( p T (1 + PT )) regret and an O(√T) cumulative constraint violation, where PT is the path-length of the comparator sequence. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Comparators (optical)

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Distributed Model-Free Adaptive Learning Control of Discrete-Time Nonlinear Multiagent systems

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IEEE transactions on neural networks and learning systems 2025年 PP卷 PP页

作者： Yong-Sheng Ma Wei-Wei Che Shi-Xu Xu Chao Deng Zheng-Guang Wu Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing 100081 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang 110819 China Qingdao University Shandong Key Laboratory of Industrial Control Technology Department of Automation Qingdao 266071 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing 210000 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou 310027 China

This article investigates the distributed control problem for nonlinear multiagent systems (MASs) with unknown system models. A novel distributed model-free adaptive learning algorithm is developed to learn a controller from the online system data. Notably, a significant advancement over conventional methods is that the proposed algorithm requires only local interaction data from neighboring agents, eliminating dependencies on both a priori system structural knowledge and global topology information. Comprehensive simulations validate the theoretical results and demonstrate the superior efficacy of the devised algorithm.

关键词： Distributed control model-free adaptive control (MFAC) multiagent systems (MASs)

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Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

arXiv

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arXiv 2021年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Chai, Tianyou Johansson, Karl H. The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden The Digital Futures Stockholm10044 Sweden The Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

This paper considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve O(T max{κ,1-κ}) static network regret and O(T 1-κ/2) network cumulative constraint violation, where T is the time horizon and κ ∈ (0, 1) is a user-defined trade-off parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to O(T κ). Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2021, The Authors. All rights reserved.

关键词： Economic and social effects

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CSstep: Step-by-step exploration of the chemical space of drug molecules via multi-agent and multi-stage reinforcement learning

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Chemical engineering Science 2025年 317卷

作者： Xinhao Che Yujing Zhao Qilei Liu Fang Yu Hanyu Gao Lei Zhang Frontier Science Center for Smart Materials Oriented Chemical Engineering Institute of Chemical Process Systems Engineering School of Chemical Engineering Dalian University of Technology Dalian 116024 China MOE Key Laboratory of Bio-Intelligent Manufacturing School of Bioengineering Dalian University of Technology Dalian 116024 China School of Pharmaceutical Engineering and Biotechnology School of Cosmetics Zhejiang Pharmaceutical University No 666 Siming Road Ningbo 315599 China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology HongKong 999077 China

Deep generative models have been widely used to explore the chemical space of drug molecules. Although the direct generation of molecules with desired properties has been achieved, the exploration process of most models in chemical space is still unknown, preventing a scientific view of the molecular generation process from a chemical perspective. In response to this issue, CSstep, a drug molecule generation/optimization framework based on the Markov decision process and reinforcement learning is proposed. By combining a multi-agent joint decision strategy and a multi-stage fine-tuning strategy in this framework, the trade-offs among multiple objective properties during the molecular optimization process and the entire exploration process can be visualized, which is expected to provide more chemical insights for drug discovery and inspire solution ideas for general multi-objective optimization problems. The feasibility and effectiveness of CSstep have been validated by molecular dynamics simulations in two drug design cases involving COVID-19-related targets.

关键词： Chemical space COVID-19 Drug design Multi-objective optimization Reinforcement learning

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Preface——Special Issue on New Directions in Nonlinear and Distributed Control

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Journal of systems Science & Complexity 2018年第1期31卷 1-3页

作者： LIU Tengfei XIN Bin JIANG Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing 100081 China Tandon School of Engineering New York University Brooklyn NY 11201 USA

Nonlinearity is ubiquitous in engineering and natural *** development of nonlinear control can be traced back to decades *** date,the research has reached the stage that emphasizes developing methodologies that can ha... 详细信息

关键词：控制问题非线性分布式工程应用程序电子物理自然系统生产线相互作用

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Removal of hydrophobic volatile organic compounds with sodium hypochlorite and surfactant in a co-current rotating packed bed

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Journal of Environmental Sciences 2018年第2期30卷 190-196页

作者： Wenhui Li Haidi Liu Shuangde Li Weiman Li Yunfa Chen Jiajia Gao Yaqun Cao State Key Laboratory of Multi-phase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Healthy &Intelligent Kitchen System Integration Zhejiang Ningbo 315336 China University of Chinese Academy of Sciences Beijing 100049 China

A co-current flow rotating packed bed was applied to remove volatile organic compounds（VOCs） by sodium hypochlorite（Na Cl O） and surfactant（sodium dodecyl benzene sulfonate,SDBS） from air stream. Xylene was used as a model VOC herein. The effect of p H,concentration of Na Cl O and SDBS solution, liquid flow rate, gas flow rate and rotational speed on xylene removal efficiency and overall mass transfer coefficient（KGa） were discussed. Then, a correlation for KGa of the co-current rotating packed bed was proposed by fitting the experimental data of KGa and independent variables of liquid/gas ratio,rotational speed, p H, Na Cl O concentration and treatment time, which was in good agreement with the experimental data（the deviation ≤ ± 30%）.

关键词： Co-current flow rotating packed bed VOCs Removal efficiency Overall mass transfer coefficient

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Review of seru production

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Frontiers of engineering Management 2019年第2期6卷 183-192页

作者： Yang YU Jiafu TANG State Key Laboratory of Synthetic Automation for Process Industries Department of Intelligent Systems Engineering Northeastern University Shenyang 110819 China College of Management Science and Engineering Dongbei University of Finance and Economics Dalian 116023 China

Seru production is regarded as a new production mode and derived from the production site of Japanese electronics industry. This production mode is proposed to overcome the low flexibility of the assembly line. Seru production has been successfully implemented in Japanese electronics industry, such as Canon and Sony. Benefits from Seru production include rapid response, good flexibility, and high productivity. Seru production has received extensive attention in academic research and production practice. This study reviews the background, characteristics, types, and operation of seru production. The advantages and applicable scenes of seru production are summarized from the perspective of business practice. We compare seru production and famous production modes, i.e., assembly line, cellular manufacturing, and Toyota Production System. The literature on seru production is surveyed and classified. Furthermore, future research directions are provided.

关键词： seru production production mode flexibility literature review

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