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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Vardoyan, Gayane Wehner, Stephanie Delft University of Technology QuTech Netherlands Quantum Computer Science Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Kavli Institute of Nanoscience Delft University of Technology Netherlands

ISBN: (纸本)9798350343236

Network Utility Maximization (NUM) is a mathe-matical framework that has endowed researchers with powerful methods for designing and analyzing classical communication protocols. NUM has also enabled the development of distributed algorithms for solving the resource allocation problem, while at the same time providing certain guarantees, e.g., that of fair treatment, to the users of a network. We extend here the notion of NUM to quantum networks, and propose three quantum utility functions - each incorporating a different entanglement measure. We aim both to gain an understanding of some of the ways in which quantum users may perceive utility, as well as to explore structured and theoretically-motivated methods of simultaneously servicing multiple users in distributed quantum systems. Using our quantum NUM constructions, we develop an optimization framework for networks that use the single-photon scheme for entanglement generation, which enables us to solve the resource allocation problem while exploring rate-fidelity tradeoffs within the network topologies that we consider. We learn that two of our utility functions, which are based on distillable entanglement and secret key fraction, are in close agreement with each other and produce similar solutions to the optimization problems we study. While these two utilities place a higher emphasis on end-to-end fidelity, our third utility- based on entanglement negativity - has more favorable mathematical properties, and tends to place a higher value on the rate at which users receive entangled resources. These contrasting behaviors thus provide ideas regarding the suitability of quantum network utility definitions to different quantum applications. © 2023 IEEE.

关键词： Resource allocation

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Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

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computer Modeling in engineering & Sciences 2024年第1期138卷 167-200页

作者： Yuexin Huang Suihuai Yu Jianjie Chu Zhaojing Su Yangfan Cong Hanyu Wang Hao Fan Key Laboratory of Industrial Design and Ergonomics Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi’an710072China School of Industrial Design Engineering Delft University of TechnologyDelft2628 CEThe Netherlands Department of Industrial Design College of ArtsShandong University of Science and TechnologyTsingtao266590China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product *** study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge ***,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product ***,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction *** comparison verified the effectiveness and accuracy of the proposed knowledge extraction *** case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

关键词： Conceptual product design design knowledge acquisition knowledge graph entity extraction relation extraction

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Minimal Input Structural Modifications for Strongly Structural Controllability 63

Minimal Input Structural Modifications for Strongly Structur...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Joseph, Geethu Moothedath, Shana Lin, Jiabin Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science CD Delft2628 Netherlands Iowa State University Department of Electrical and Computer Engineering IA50011 United States

ISBN: (纸本)9798350316339

This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured systems that rely on zero/nonzero/arbitrary structure, i.e., some entries of system matrices are zeros, some are nonzero, and the remaining entries can be zero or nonzero (arbitrary). We derive the feasibility conditions of the problem, and if it is feasible, we reformulate it into another equivalent problem. This new formulation leads to a greedy heuristic algorithm. However, we also show that the greedy algorithm can give arbitrarily poor solutions for some special systems. Our alternative approach is a randomized Markov chain Monte Carlo-based algorithm. Unlike the greedy algorithm, this algorithm is guaranteed to converge to an optimal solution with high probability. Finally, we numerically evaluate the algorithms on random graphs to show that the algorithms perform well. © 2024 IEEE.

关键词： Markov chains

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Prognostics for Lithium-ion batteries for electric Vertical Take-off andLanding aircraft using data-driven machine learning

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Energy and AI 2023年第2期12卷 145-162页

作者： Mihaela Mitici Birgitte Hennink Marilena Pavel Jianning Dong Faculty of Science Utrecht UniversityHeidelberglaan 83584 CSUtrechtThe Netherlands Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 1HS 2926DelftThe Netherlands Electrical Engineering Mathematics and Computer Science FacultyTU Delft2628 CDDelftThe Netherlands

The health management of batteries is a key enabler for the adoption of Electric Vertical Take-off and Landingvehicles (eVTOLs). Currently, few studies consider the health management of eVTOL batteries. One distinctcharacteristic of batteries for eVTOLs is that the discharge rates are significantly larger during take-off andlanding, compared with the battery discharge rates needed for automotives. Such discharge protocols areexpected to impact the long-run health of batteries. This paper proposes a data-driven machine learningframework to estimate the state-of-health and remaining-useful-lifetime of eVTOL batteries under varying flightconditions and taking into account the entire flight profile of the eVTOLs. Three main features are consideredfor the assessment of the health of the batteries: charge, discharge and temperature. The importance of thesefeatures is also quantified. Considering battery charging before flight, a selection of missions for state-ofhealth and remaining-useful-lifetime prediction is performed. The results show that indeed, discharge-relatedfeatures have the highest importance when predicting battery state-of-health and *** several machine learning algorithms, it is shown that the battery state-of-health and remaining-useful-lifeare well estimated using Random Forest regression and Extreme Gradient Boosting, respectively.

关键词： Electric Vertical Take-off and Landing vehicles Lithium-ion battery State-of-health Machine learning Remaining-useful-life

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Learn & drop: fast learning of cnns based on layer dropping

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Neural Computing and Applications 2024年第18期36卷 10839-10851页

作者： Cruciata, Giorgio Cruciata, Luca Lo Presti, Liliana van Gemert, Jan La Cascia, Marco Engineering Department University of Palermo V.le delle Scienze Ed. 6 Palermo90128 Italy Computer Vision Lab Delft University of Technology Van Mourik Broekmanweg 6 Delft2628 XE Netherlands

This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates scores to measure how much each layer’s parameters change and whether the layer will continue learning or not. Based on these scores, the network is scaled down such that the number of parameters to be learned is reduced, yielding a speed-up in training. Unlike state-of-the-art methods that try to compress the network to be used in the inference phase or to limit the number of operations performed in the back-propagation phase, the proposed method is novel in that it focuses on reducing the number of operations performed by the network in the forward propagation during training. The proposed training strategy has been validated on two widely used architecture families: VGG and ResNet. Experiments on MNIST, CIFAR-10 and Imagenette show that, with the proposed method, the training time of the models is more than halved without significantly impacting accuracy. The FLOPs reduction in the forward propagation during training ranges from 17.83% for VGG-11 to 83.74% for ResNet-152. As for the accuracy, the impact depends on the depth of the model and the decrease is between 0.26% and 2.38% for VGGs and between 0.4 and 3.2% for ResNets. These results demonstrate the effectiveness of the proposed technique in speeding up learning of CNNs. The technique will be especially useful in applications where fine-tuning or online training of convolutional models is required, for instance because data arrive sequentially. © The Author(s) 2024.

关键词： Backpropagation

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Harvesting Energy and Stability Insights in Internally Coupled Resonator Systems

Harvesting Energy and Stability Insights in Internally Coupl...

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18th IEEE International Conference on Advanced Motion Control, AMC 2024

作者： Alimohammadi, H. Vassiljeva, K. HosseinNia, S.H. Petlenkov, E. Tallinn University of Technology Department of Computer Systems Tallinn Estonia Delft University of Technology Department of Precision and Microsystems Engineering Delft Netherlands

ISBN: (纸本)9798350342819

This paper presents a comprehensive study on lumped parameter systems with internally coupled resonators, emphasizing stability analysis and energy harvesting potential. Both mechanical and electromechanical coupling mechanisms are explored, revealing the critical role of the resonator's spring constant and feedback shunt capacitance stiffness. Simulation analyses demonstrate the significant advantages of electrical internal coupling, such as enhanced tunability and superior energy harvesting capabilities. By harnessing negative stiffness through electromechanical coupling, the paper highlights possibilities for precise control over system dynamics and the creation of band gaps at lower frequencies. The findings underscore the potential of internally coupled resonators in metamaterial systems, particularly for vibration control and energy harvesting applications. © 2024 IEEE.

关键词： Energy harvesting

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Managing aging bridges under seismic hazards through deep reinforcement learning 12th

Managing aging bridges under seismic hazards through deep re...

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12th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2024

作者： Metwally, Z. Andriotis, C.P. Molaioni, F. Faculty of Architecture and the Build Environment Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

ISBN: (纸本)9781032770406

Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans. © 2024 The Author(s).

关键词： Life cycle

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Estimating Risk-aware Flexibility Areas for Electric Vehicle Charging Pools via AC Stochastic Optimal Power Flow

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Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1247-1256页

作者： Juan S.Giraldo Nataly Bañol Arias Pedro P.Vergara Maria Vlasiou Gerwin Hoogsteen Johann L.Hurink Energy Transition Studies Group Netherlands Organisation for Applied Scientific Research(TNO)Amsterdam1043 NTThe Netherlands Department of Electrical Engineering Mathematics and Computer ScienceUniversity of TwenteEnschede7522 NBThe Netherlands Intelligent Electrical Power Grids Group Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft2628 CDThe Netherlands

This paper introduces an AC stochastic optimal power flow(SOPF)for the flexibility management of electric vehicle(EV)charging pools in distribution networks under *** AC SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging *** application of the AC SOPF is described where a distribution system operator(DSO)requires flexibility to each charging pool in a day-ahead time frame,minimizing the cost for flexibility while guaranteeing technical *** areas are defined for each charging pool and calculated as a function of a risk parameter involving the uncertainty of the *** show that all players can benefit from this approach,i.e.,the DSO obtains a riskaware solution,while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

关键词： Electric vehicle flexibility management stochastic optimal power flow(SOPF) risk awareness compensation mechanism

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Adaptive Prescribed-Time Neural Control of Nonlinear Systems via Dynamic Surface Technique

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4948-4958页

作者： Wang, Ping Yu, Chengpu Lv, Maolong Zhao, Zilong Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710051 China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

The adaptive practical prescribed-time (PPT) neural control is studied for multiinput multioutput (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. First, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piecewise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator. © 2024 IEEE.

关键词： Flexible manipulators

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Near-Term Spin-Qubit Architecture Design via Multipartite Maximally Entangled States

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PRX Quantum 2025年第2期6卷 020307-020307页

作者： N. Paraskevopoulos M. Steinberg B. Undseth A. Sarkar L.M.K. Vandersypen X. Xue S. Feld QuTech Delft University of Technology Delft Netherlands Quantum and Computer Engineering Department Delft University of Technology Delft Netherlands Kavli Institute of Nanoscience Delft University of Technology Delft Netherlands

The design and benchmarking of quantum computer architectures traditionally rely on practical hardware restrictions, such as gate fidelities, control, and cooling. At the theoretical and software levels, numerous approaches have been proposed for benchmarking quantum devices, ranging from, inter alia, quantum volume to randomized benchmarking. In this work, we utilize the quantum information-theoretic properties of multipartite maximally entangled quantum states, in addition to their correspondence with quantum error-correction codes, permitting us to quantify the entanglement generated on near-term bilinear spin-qubit architectures. For this aim, we introduce four metrics that ascertain the quality of genuine multipartite quantum entanglement, along with circuit-level fidelity measures. As part of the task of executing a quantum circuit on a device, we devise simulations, which combine expected hardware characteristics of spin-qubit devices with appropriate compilation techniques; we then analyze three different architectural choices of varying lattice sizes for bilinear arrays, under three increasingly realistic noise models. We find that if the use of a compiler is assumed, sparsely connected spin-qubit lattices can approach comparable values of our metrics to those of the most highly connected device architecture. Even more surprisingly, by incorporating crosstalk into our last noise model, we find that, as error rates for crosstalk approach realistic values, the benefits of utilizing a bilinear array with advanced connectivity vanish. Our results highlight the limitations of adding local connectivity to near-term spin-qubit devices, and can be readily adapted to other qubit technologies. The framework developed here can be used for analyzing quantum entanglement on a device before fabrication, informing experimentalists on concomitant realistic expectations.

关键词： Quantum entanglement

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