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arXiv 2024年

作者： Munafò, Riccardo Saitta, Simone Vicentini, Luca Tondi, Davide Ruozzi, Veronica Sturla, Francesco Ingallina, Giacomo Guidotti, Andrea Agricola, Eustachio Votta, Emiliano Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam Netherlands Informatics Institute University of Amsterdam Amsterdam Netherlands Simulands Zurich Switzerland 3D and Computer Simulation Laboratory Milan Italy Unit of Cardiovascular Imaging IRCCS San Raffaele Hospital Milan Italy Vita-Salute San Raffaele University Milan Italy

The MitraClip is the most widely percutaneous treatment for mitral regurgitation, typically performed under the real-time guidance of 3D transesophagel echocardiography (TEE). However, artifacts and low image contrast in echocardiography hinder accurate clip visualization. This study presents an automated pipeline for clip detection from 3D TEE images. An Attention UNet was employed to segment the device, while a DenseNet classifier predicted its configuration among ten possible states, ranging from fully closed to fully open. Based on the predicted configuration, a template model derived from computer-aided design (CAD) was automatically registered to refine the segmentation and enable quantitative characterization of the device. The pipeline was trained and validated on 196 3D TEE images acquired using a heart simulator, with ground-truth annotations refined through CAD-based templates. The Attention UNet achieved an average surface distance of 0.76 mm and 95% Hausdorff distance of 2.44 mm for segmentation, while the DenseNet achieved an average weighted F1-score of 0.75 for classification. Post-refinement, segmentation accuracy improved, with average surface distance and 95% Hausdorff distance reduced to 0.75 mm and 2.05 mm, respectively. This pipeline enhanced clip visualization, providing fast and accurate detection with quantitative feedback, potentially improving procedural efficiency and reducing adverse outcomes. © 2024, CC BY.

关键词： Image segmentation

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Allocation of Hybrid Distributed Generations and Energy Management in Radial Electrical Systems

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Smart Grid and Renewable Energy 2022年第11期13卷 249-267页

作者： Ngoussandou Bello Pierre Nicodem Nisso Benjamin Diboma Jean De Dieu Nguimfack Ndongmo Sadam Alphonse Department of Renewable Energy National Advanced School of Engineering of Maroua University of Maroua Maroua Cameroon Department of Computer Science and Telecommunications National Advanced School of Engineering University of Maroua Maroua Cameroon Department of Electrical Engineering Advanced Technical Training School University of Douala Douala Cameroon Department of Electrical and Power Engineering Higher Technical Teacher Training College University of Bamenda Bamenda Cameroon UFD PAI Laboratoire d’Analyse de Simulation et Essais (LASE) IUT de Ngaoundéré Ngaoundéré Cameroun

This paper presents a method for optimal sizing of a Micro grid connected to a hybrid source to ensure the continuity and quality of energy in a locality with a stochastically changing population. The hybrid system is composed of a solar photovoltaic system, a wind turbine, and an energy storage system. The reliability of the system is evaluated based on the voltage level regulation on IEEE 33-bus and IEEE 69-bus standards. Power factor correction is performed, despite some reliability and robustness constraints. This work focuses on energy management in a hybrid system considering climatic disturbances on the one hand, and on the other hand, this work evaluates the energy quality and the cost of energy. A combination of genetic algorithms of particle swarm optimization (CGAPSO) shows high convergence speed, which illustrates the robustness of the proposed system. The study of this system shows its feasibility and compliance with standards. The results obtained show a significant reduction in the total cost of production of this proposed system.

关键词： Power Losses Hybrid System Distributed Generations Cost of Energy

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Lock-Free simulation Algorithm to Enhance the Performance of Sequential and Parallel Devs Simulators in Shared-Memory Architectures

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SSRN 2024年

作者： Cárdenas, Román Arroba García, Patricia Risco-Martín, José Luis Department of Electronics Engineering Universidad Politécnica de Madrid Avenida Complutense 30 Madrid Madrid28040 Spain Center for Computational Simulation Universidad Politécnica de Madrid Campus de Montegancedo Boadilla del Monte Madrid28660 Spain Department of Computer Architecture and Automation Universidad Complutense de Madrid Calle Profesor José García Santesmases 9 Madrid Madrid28040 Spain

This paper presents a new algorithm for the Discrete EVent System Specification (DEVS) formalism that improves the performance of simulating complex systems by reducing the number of iterations through the model components in each simulation step. It also minimizes unnecessary visits to model components by propagating simulation routines only when necessary. Additionally, we provide two parallel versions of this new simulation algorithm that use work-stealing scheduling and avoid locking mechanisms without compromising the validity of the execution in shared-memory architectures. We implemented the proposed algorithms in the xDEVS simulator and evaluated their performance using the DEVStone synthetic benchmark. The results show that the proposed algorithms outperform state-of-the-art alternatives. For computationally intensive models, parallel implementations achieve high parallelism efficiency. Furthermore, they are more resilient to model complexity than the sequential algorithm, showing better performance for complex models even without computational overhead in state transition functions. © 2024, The Authors. All rights reserved.

关键词： Discrete event simulation

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Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements

Generating Realistic Arm Movements in Reinforcement Learning...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Jhon P.F. Charaja Isabell Wochner Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Georg Martius Daniel F.B. Haeufle Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience University of Tübingen Germany Institute of Computer Engineering (ZITI) Heidelberg University Germany Max Planck Institute for Intelligent Systems Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Mimicking of human-like arm movement characteristics involves considering three factors during control policy synthesis: (a) task requirements, (b) noise during movement execution, and (c) optimality principles. Previous studies showed that when these factors (a-c) are considered individually, it is possible to synthesize arm movements that either kinematically match experimental data or reproduce the stereotypical triphasic muscle activation pattern. However, no quantitative comparison has assessed the realism of arm movements generated by each factor, nor has it been determined whether combining these factors results in movements with human-like kinematic characteristics and the triphasic muscle pattern. To investigate this, we used reinforcement learning to learn a control policy for a musculoskeletal arm model, aiming to discern which combination of factors (a-c) results in realistic arm movements according to four frequently reported stereotypical characteristics. Our findings indicate that incorporating velocity and acceleration requirements into the reaching task, employing reward terms that minimize mechanical work, hand jerk, and control effort, along with the inclusion of noise during movement, leads to realistic human arm movements by reinforcement learning. We expect that the gained insights will help in the future to better predict desired arm movements and corrective forces in wearable assistive devices.

关键词： Noise Reinforcement learning Kinematics Biomechatronics Muscles Manipulators Assistive devices Pattern matching

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DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading

arXiv

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arXiv 2022年

作者： Tusfiqur, Hasan Md Nguyen, Duy M.H. Truong, Mai T.N. Nguyen, Triet A. Nguyen, Binh T. Barz, Michael Profitlich, Hans-Jürgen Than, Ngoc T.T. Le, Ngan Xie, Pengtao Sonntag, Daniel Germany Machine Learning and Simulation Lab University of Stuttgart Germany Department of Multimedia Engineering Dongguk University Korea Republic of Department of Mathematics University of Architecture Ho Chi Minh City Viet Nam AISIA Lab University of Science VNUHCM Viet Nam Byers Eye Institute Stanford University United States Department of Computer Science and Computer Engineering University of Arkansas United States Department of Electrical and Computer Engineering University of California San Diego United States Applied Artificial Intelligence Oldenburg University Germany

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas and provide visual explanation and (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system’s predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner. © 2022, CC BY.

关键词： Grading

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Stochastic processes in renewable power systems: From frequency domain to time domain

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Science China(Technological Sciences) 2019年第12期62卷 2093-2103页

作者： SONG YongHua CHEN XiaoShuang LIN Jin LIU Feng QIU YiWei Department of Electrical and Computer Engineering University of MacaoMacaoChina State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical EngineeringTsinghua UniversityBeijing 100084China

With the increasing penetration of renewable energy resources(RESs), the uncertainties of volatile renewable generations significantly affect the power system operation. Such uncertainties are usually modeled as stochastic variables obeying specific distributions by neglecting the temporal correlations. Conventional approaches to hedge the negative effects caused by such uncertainties are thus hard to pursue a trade-off between computation efficiency and optimality. As an alternative, the theory of stochastic process can naturally model temporal correlation in closed forms. Attracted by this feature, our research group has been conducting thorough researches in the past decade to introduce stochastic processes within renewable power systems. This paper summarizes our works from the perspective of both the frequency domain and the time domain, provides the tools for the analysis and control of power systems under a unified framework of stochastic processes, and discusses the underlying reasons that stochastic process-based approaches can perform better than conventional approaches on both computational efficiency and optimality. These work may shed a new light on the research of analysis, control and operation of renewable power ***, this paper outlooks the theoretic developments of stochastic processes in future’s renewable power systems.

关键词： renewable energy resources renewable power systems stochastic processes frequency domain time domain

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Darlington pair of quantum thermal transistors

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Physical Review B 2021年第4期104卷 045405-045405页

作者： Ravi T. Wijesekara Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory(AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

Recent progress in manipulating individual quantum systems has led to the development of quantum thermal transistors, which control the thermal conductivity between two of its terminals according to an input signal on its third terminal. With several models for individual thermal transistors already developed, the next natural step is to investigate how multiple thermal transistors can interconnect to build useful composite devices. In electronics literature, the Darlington pair is a two-transistor configuration commonly used to construct electronic amplifiers with higher gain than is possible with a single transistor. We create the electronic Darlington pair's thermal equivalent using two individual thermal transistors in a similar configuration. Unlike previously studied models, multitransistor configurations like this contain internal transistor interconnections whose temperatures cannot be biased externally but are determined by the individual transistors' internal dynamics. We refine previous models to incorporate these transistor-transistor interactions and introduce an intermediate thermal bath to facilitate the thermal energy exchange between the Darlington pair transistors. We investigate temperature-based and optical field-based control strategies of the Darlington pair in terms of both steady-state and transient thermal flow characteristics through numerical simulations. Under both control strategies, the thermal Darlington pair's steady-state performance exhibits superior thermal amplification, sensitivity, and thermodynamic efficiency than an equivalent single thermal transistor. Our results closely mirror those expected from the corresponding electronic Darlington pair. Hence, we envision that we may readily adapt the intermediate bath formalism we developed in this work to translate a wide variety of useful electronic multitransistor configurations to their thermal equivalents.

关键词： Thermal properties Nanostructures Transistors

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Emergence of Natural and Robust Bipedal Walking by Learning to Control Up to Ninety Muscles from Biologically Plausible Objectives

SSRN

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SSRN 2023年

作者： Schumacher, Pierre Geijtenbeek, Thomas Caggiano, Vittorio Kumar, Vikash Schmitt, Syn Martius, Georg Haeufle, Daniel F.B. Max-Planck Institute for Intelligent Systems Tübingen Germany Hertie Institute for Clinical Brain Research Center for Integrative Neuroscience Tübingen Germany Goatstream Netherlands Meta AI New York United States Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany Institute of Computer Engineering Heidelberg University Germany

Humans show unparalleled ability when maneuvering diverse terrains. While motor control researchers have studied these skills in simulation for a long time, most control approaches are only able to generate single motions. Recently, reinforcement learning~(RL) has shown great promise in the development of robust controllers capable of generating complex behaviors. The application to highly complex biomechanical models was, however, only possible under extensive use of motion capture data, limiting the abilities of the resulting agents. In this paper, we demonstrate that the combination of a recent RL algorithm with a biologically plausible reward function is capable of learning controllers for 4 different musculoskeletal human models and achieves bipedal motion with up to 90 muscles without demonstrations. Our controllers are robust enough to generalize to diverse and unseen terrains, while only a single objective function is needed for training. We validate our findings on 4 models in 2 different simulators and 2 different tasks, walking and running. We see this as a first step for the motor control, biomechanics, and rehabilitation communities to generate highly complex human movements with RL, without needing to collect large amounts of experimental data or limiting themselves to simple models that are not representative of the diverse skills that human beings exhibit every day. © 2023, The Authors. All rights reserved.

关键词： Muscle

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Quantum Computation of Dynamical Quantum Phase Transitions and Entanglement Tomography in a Lattice Gauge Theory

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PRX Quantum 2023年第3期4卷 030323-030323页

作者： Niklas Mueller Joseph A. Carolan Andrew Connelly Zohreh Davoudi Eugene F. Dumitrescu Kübra Yeter-Aydeniz Maryland Center for Fundamental Physics and Department of Physics University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Physics InQubator for Quantum Simulation (IQuS) University of Washington Seattle Washington 98195 USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Physics North Carolina State University Raleigh North Carolina 27695 USA Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA The MITRE Corporation Emerging Engineering and Physical Sciences Department 7515 Colshire Drive McLean Virginia 22102-7539 USA

Strongly coupled gauge theories far from equilibrium may exhibit unique features that could illuminate the physics of the early universe and of hadron and ion colliders. Studying real-time phenomena has proven challenging with classical-simulation methods, but is a natural application of quantum simulation. To demonstrate this prospect, we quantum compute nonequal-time correlation functions and perform entanglement tomography of nonequilibrium states of a simple lattice gauge theory, the Schwinger model, using a trapped-ion quantum computer by IonQ Inc. As an ideal target for near-term devices, a recently predicted [Zache et al., Phys. Rev. Lett. 122, 050403 (2019)] dynamical quantum phase transition in this model is studied by preparing, quenching, and tracking the subsequent nonequilibrium dynamics in three ways: (i) overlap echos signaling dynamical transitions, (ii) nonequal-time correlation functions with an underlying topological nature, and (iii) the entanglement structure of nonequilibrium states, including entanglement Hamiltonians. These results constitute the first observation of a dynamical quantum phase transition in a lattice gauge theory on a quantum computer, and are a first step toward investigating topological phenomena in nuclear and high-energy physics using quantum technologies.

关键词： Dynamical phase transitions Entanglement entropy Entanglement production Gauge theories Geometric & topological phases Quantum computation Trapped ions Lattice gauge theory

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