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检索条件"任意字段=International Conference on Optical Network Design and Modeling"

共 29382 条记录，以下是131-140 订阅

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design of photovoltaic-energy storage-load forecasting and optimal control method for distributed distribution network based on reinforcement learning 9

Design of photovoltaic-energy storage-load forecasting and o...

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9th international conference on Energy Materials and Electrical Engineering, ICEMEE 2023

作者： Zhao, Chenxu Qin, Xiaodong Wang, An Song, Ruijun Zhou, Wenqi Sun, Zhebin Inner Mongolia Power Economic Technology Research Institute Inner Mongolia Hohhot010020 China Economic and Technical Research CO.LTD of Inner Mongolia Power Group Inner Mongolia Hohhot010020 China

ISBN: (纸本)9781510672765

The safe and stable operation of distribution network can improve the economic benefit and operational performance of power system. In order to improve the photovoltaic-energy storage-load effect of distribution network, a method of photovoltaic-energy storage-load prediction and optimal control of distributed distribution network based on reinforcement learning is proposed. Calculate the dynamic photovoltaic-energy storage-load and static photovoltaic-energy storage-load of distribution network, determine the change of the ratio of static and dynamic photovoltaic-energy storage-load of distribution network, and comprehensively predict the photovoltaic-energy storage-load of distribution network;Then, reinforcement learning is used to optimize the photovoltaic-energy storage-load control of distributed distribution network. The experimental results show that the proposed method shows better prediction accuracy and effect after optimization, and has higher control accuracy for photovoltaic output power, energy storage power and load power of distributed distribution network. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Photovoltaics Machine learning Solar energy Data modeling Data storage design and modelling Mathematical optimization Education and training Control systems Renewable energy

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Physics-aware Roughness Optimization for Diffractive optical Neural networks 60

Physics-aware Roughness Optimization for Diffractive Optical...

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60th ACM/IEEE design Automation conference (DAC)

作者： Zhou, Shanglin Li, Yingjie Lou, Minhan Gao, Weilu Shi, Zhijie Yu, Cunxi Ding, Caiwen Univ Connecticut Storrs CT 06269 USA Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9798350323481

As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption. However, there is a mismatch, i.e., significant prediction accuracy loss, between the DONN numerical modelling and physical optical device deployment, because of the interpixel interaction within the diffractive layers. In this work, we propose a physics-aware diffractive optical neural network training framework to reduce the performance difference between numerical modeling and practical deployment. Specifically, we propose the roughness modeling regularization in the training process and integrate the physics-aware sparsification method to introduce sparsity to the phase masks to reduce sharp phase changes between adjacent pixels in diffractive layers. We further develop 2p periodic optimization to reduce the roughness of the phase masks to preserve the performance of DONN. Experiment results demonstrate that, compared to state-of-the-arts, our physics-aware optimization can provide 35.7%, 34.2%, 28.1%, and 27.3% reduction in roughness with only accuracy loss on MNIST, FMNIST, KMNIST, and EMNIST, respectively.

关键词： Diffractive optical neural network Weight Sparsification Roughness modeling

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Improving Transfer Time Prediction of ML Models via Auto-correcting Dynamical Systems modeling 10

Improving Transfer Time Prediction of ML Models via Auto-cor...

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10th international conference on network Softwarization (NetSoft)

作者： Karanam, Venkat Sai Suman Lamba Ramamurthy, Byrav Univ Nebraska Lincoln Sch Comp Lincoln NE 68588 USA

ISBN: (纸本)9798350369588;9798350369595

Machine Learning (ML) is extensively used for predicting transfer times for general purpose Wide Area networks (WANs) or public Internet applications, but for Research and Education networks (RENs) two major gaps exist in literature. First, RENs i.e. networks carrying large data flows have received limited attention by the networking community. RENs behave differently compared to the general purpose Internet applications and other network types. Hence, ML models from other network types cannot be used interchangeably for large data transfers. Second, the ML models are used as blackboxes to train on measured network values and then used to predict transfer times or other runtime network parameters. In this paper, we present a dynamical systems model of the large data transfers typical of RENs in the form of a system of Ordinary Differential Equations (ODEs) inspired by the Lotka-Volterra competition model. We present a transfer time prediction component called Dynamic Transfer Time Predictor (DTTP) which solves the ODEs and predicts the future transfer times. Second we formulate a loss function based on Lyapunov function called Lyapunov Drift Correction (LDC) that self-corrects the transfer time prediction errors dynamically. To design and develop our model, we studied real-world datasets consisting of over 100 million transfer records collected from platforms such as Open Science Grid (OSG), Large Hadron Collider optical Private network (LHCOPN), Worldwide LHC Grid (WLCG), as well as the RENs of Internet2 and ESNet. We integrate our model into well-known neural network models and regressors and present evaluation results.

关键词： Lyapunov functions

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A CONTROL-ORIENTED HYBRID MODEL FOR NUCLEAR REACTORS BASED ON NEURAL network 31

A CONTROL-ORIENTED HYBRID MODEL FOR NUCLEAR REACTORS BASED O...

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31st international conference on Nuclear Engineering (ICONE) - 24/7 Clean Nuclear Energy - Solution for Global Warming

作者： Zhu, Ze Liang, Wenlong Ye, Baiqing Jiang, Qingfeng Wang, Pengfei Xi An Jiao Tong Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9780791888216

Accurate modeling is the basis for analyzing the dynamic response characteristics of the model. However, due to the complexity and time-varying nature of the internal mechanisms of the reactor, it is inevitable that inaccurate model parameters will be used in the modeling process. These will lead to discrepancies between the modeled mechanisms and the actual reactor. In this paper, the difference is evaluated and shortened by means of neural network hybrid modeling. Based on the MATLAB/Simulink simulation platform, this paper firstly obtains the parameters that have the greatest influence on the linear model through sensitivity analysis and takes them as the object of neural network correction, then obtains the data required for offline training of neural network according to the mechanism model, linear model and the deviation of the two under different working condition levels, retains the neural network weights and thresholds obtained from the offline training, and finally utilizes the gradient descent algorithm to update the neural network weights and thresholds in real time in order to achieve the online calibration of the linear model. The final results show that the hybrid model can effectively reduce the steady-state deviation between the two models, which indicates that the hybrid modeling can effectively improve the accuracy of the established model and provide a solid foundation for the subsequent design of the control system based on the linear model.

关键词： Hybrid modeling Mechanism model Linear model Neural network Gradient descent algorithm

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Blocking Probability Analysis modeling in Spectrally-Spatially Elastic optical networks: A Crosstalk-Avoided Perspective 24

Blocking Probability Analysis Modeling in Spectrally-Spatial...

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24th international conference on Transparent optical networks (ICTON)

作者： Ahmed, Imran Oki, Eiji Chatterjee, Bijoy Chand South Asian Univ New Delhi India Kyoto Univ Kyoto Japan

ISBN: (纸本)9798350377330;9798350377323

Crosstalk presents a well-defined challenging issue in spectrally-spatially elastic optical networks (SS-EONs), leading to increased blocking probability (BP). Analyzing BP analytically becomes complex due to additional constraints. Current assessments of BP in SS-EONs, particularly in multi-core multi-mode fiber (MCMMF)-based systems, heavily rely on simulation-based techniques. This paper analyzes an accurate analytical model based on a continuous-time Markov chain in SS-EONs, investigating various spectrum allocation policies with and without the spectrum contiguity constraint. To calculate BP, the analytical model produces all feasible states and their transitions while mitigating inter-core and inter-mode crosstalks. Numerical results show that analytical model and simulation studies have lower BP with CMS-FF and CMS-RF using without contiguity constraints compared to other schemes.

关键词： Markov chain elastic optical network space division multiplexing crosstalk blocking analysis

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Decision Transformer based Target-aware Feature Selection network for Behavior-oriented Recommendation 27

Decision Transformer based Target-aware Feature Selection Ne...

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27th international conference on Computer Supported Cooperative Work in design (CSCWD)

作者： Li, Tianyang Yan, Hongbin Xu, Hanwen Northeast Elect Power Univ Sch Comp Sci Jilin Jilin Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Learning dynamic user preferences has become an important component for E-commerce platforms to make sequential recommendations. Many works have been focused on modeling users' purchase behavior for sequential recommendations, while less work has considered users' dynamic behavioral intentions. With this concern, we introduce a new recommendation problem, Behavior-Oriented Recommendation (BOR), where recommenders must retrieve items based on users' diverse behavioral intentions. In this paper, We figure out that due to the discrepancy between BOR and traditional scenarios, existing recommendation models are struggling in discovering preference drift due to changes in behavioral intentions. To tackle this problem, we propose a Target-aware Feature Selection network (TFSN). More specifically, a sequence encoder based on low-rank self-attention is designed to enhance the sequence feature representation. Then, the Decision Transformer architecture is employed to model the complex dependencies between sequence features and target behaviors. Furthermore, a weighted contrastive learning method is proposed to distinguish the preference representations under various targets. Extensive experiments on three real-world E-commerce datasets demonstrate the superiority of TFSN over state-of-the-art methods.

关键词： Sequential Recommendation Multi-Behavior Recommendation Decision Transformer Contrastive Learning User modeling

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design and Development of a Collaborative Augmented Reality Environment for Systems Science 57

Design and Development of a Collaborative Augmented Reality ...

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57th Hawaii international conference on System Sciences (HICSS)

作者： Shrestha, Anish Demay, Loic Giabbanelli, Philippe J. Miami Univ Dept Comp Sci & Software Engn Oxford OH 45056 USA

ISBN: (纸本)9780998133171

Participatory modeling seeks to support a group in reaching a shared understanding of a complex system by identifying commonalities between individual views and navigating their differences. Traditional workshops where participants and facilitators are physically present face several barriers: complex systems span multiple domains hence they may involve experts living in various locations or rarely available together, while other stakeholder groups may struggle to access a workshop for logistical reasons such as time commitment or transportation costs. Switching to a fully remote environment through desktop applications alleviates some of these concerns but loses a sense of rapport, which can impair the collective learning experience normally fostered by systems thinking workshops. To address these limitations, we present the design and code implementation of a new augmented reality environment that aims to support remote participants in collectively arriving at a shared causal map. Our collaborative modeling application leverages graph-drawing algorithms, multiple synchronized views, and custom network protocols.

关键词： Augmented Reality Causal Maps design Extended Reality Multiple Views Participatory modeling Systems Thinking

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design and Development of a Waveguide Filter Using Artificial Neural network Based ReLU function 2

Design and Development of a Waveguide Filter Using Artificia...

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2nd international conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： El Houda Sara Senasli, Nour Damou, Mehdi Bouras, Bouhafs Bouasria, Fatima Chetioui, Mohammed Boudkhil, Abdelhakim Saida Univ Dr Tahar Moulay Technol Fac Telecommun Dept Lab Elect Adv Signal Proc & Microwaves Saida Algeria

ISBN: (纸本)9798350349740;9798350349757

Artificial Neural networks (ANNs) are highly used in microwave engineering for their ability of modeling accurate complex design structures. They are used to solve major modeling problems that cannot be surpassed previously. This research paper employs a specific ANN model to design an accurate microwave waveguide filter structure and extract highly optimized parameters. In this model, the network inputs are given by the filter's geometric variables and resonant frequency, while outputs are given by the filter S-parameters. The model uses a Rectified Linear Unit (ReLU) activation function, known by its deep fragment switching characteristic, to efficiently employ a large number of training parameters. Such a function allows the model to better understand the intricate relationship between the output and input variables in order to reducetthe number offtraining iterations and maintain a good level of accuracy for the network connections. Effectively, the proposed technique efficiency is demonstrated through the developed waveguide filter design electromagnetic (EM) response.

关键词： ANNs ReLU activation function Waveguide Filters Inputs-Outputs Deep Training design and Optimization

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Insight on the optimal design of non-Hermitian DFB lasers and experimental results 24

Insight on the optimal design of non-Hermitian DFB lasers an...

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24th international conference on Transparent optical networks (ICTON)

作者： Liang, Y. Gaimard, Q. Coudevylle, J. R. Garreau, A. Wilk, A. Delorme, O. Benisty, H. Ramdane, A. Lupu, A. Univ Paris Saclay Ctr Nanosci & Nanotechnol CNRS F-91120 Palaiseau France Univ Paris Saclay Inst Opt LCFIO Lab Charles Fabry F-91120 Palaiseau France III V Lab 1 Ave Augustin Fresnel F-91767 Palaiseau France

ISBN: (纸本)9798350377330;9798350377323

We address the problem of the optimal design of non-Hermitian Bragg gratings for DFB laser operation. A particular emphasizes is done on the impact of arbitrary facets termination on the performances of such DFB lasers in terms of threshold gain and robustness of single frequency operation. We also present experimental results on the electrically injected DFB lasers based on such design.

关键词： DFB lasers complex-coupled Bragg grating non-Hermitian photonics Parity-Time symmetry

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Proceedings of 2024 14th international Workshop on Resilient networks design and modeling, RNDM 2024

Proceedings of 2024 14th International Workshop on Resilient...

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14th international Workshop on Resilient networks design and modeling, RNDM 2024

ISBN: (纸本)9798331518189

The proceedings contain 14 papers. The topics discussed include: impact of channel provisioning strategies in the transient resiliency of SuperC+L-band networks;shared backup path protection with QoT guarantee in elastic optical networks;on the resilience of mutually dependent power and data networks;computing safest st-paths in backbone networks: efficiently solvable cases and fast heuristics;network sharing for fault resilience;fault-tolerant local recovery with preprocessing in multiple shared protection;resource efficiency and survivability of network slice isolation strategies after massive outages;and analyzing network routing resilience: a hybrid approach of face and tree routing.

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