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27th International Conference on Optical Fiber Sensors, OFS 2022

作者： Hu, Shicheng Liu, Haifeng Liu, Bo Lin, Wei Zhang, Hao Song, Binbin Wu, Jixuan Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Institute of Modern Optics Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Institute of Modern Optics Nankai University Tianjin300350 China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai519000 China Key Laboratory of Computer Vision and Systems School of Computer Science and Engineering Tianjin University of Technology Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems College of Electronic and Information Engineering Tianjin Polytechnic University Tianjin300387 China

We proposed a self-temperature-compensation approach for fiber specklegram sensor (FSS) based on polarization specklegram analysis, and designed a fiber specklegram magnetic field sensor with high stability and good r... 详细信息

ISBN: (纸本)9781957171142

关键词： Polarization

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Split Lohmann computer holography: fast generation of 3D hologram in single-step diffraction calculation

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Advanced Photonics Nexus 2024年第3期3卷 1-13页

作者： Chenliang Chang Xian Ding Di Wang Zhizhou Ren Bo Dai Qi Wang Songlin Zhuang Dawei Zhang University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringEngineering Research Center of Optical Instrument and SystemMinistry of Education and Shanghai Key Laboratory of Modern Optics SystemShanghaiChina Beihang University School of Instrumentation and Optoelectronic EngineeringBeijingChina

Holographic display stands as a prominent approach for achieving lifelike three-dimensional(3D)reproductions with continuous depth ***,the generation of a computer-generated hologram(CGH)always relies on the repetitive computation of diffraction propagation from point-cloud or multiple depthsliced planar images,which inevitably leads to an increase in computational complexity,making real-time CGH generation ***,we report a new CGH generation algorithm capable of rapidly synthesizing a 3D hologram in only one-step backward propagation calculation in a novel split Lohmann lens-based diffraction *** introducing an extra predesigned virtual digital phase modulation of multifocal split Lohmann lens in such a diffraction model,the generated CGH appears to reconstruct 3D scenes with accurate accommodation abilities across the display *** with the conventional layer-based method,the computation speed of the proposed method is independent of the quantized layer numbers,and therefore can achieve real-time computation speed with a very dense of depth *** simulation and experimental results validate the proposed method.

关键词： computer holography holographic display diffraction calculation.

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Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders

arXiv

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arXiv 2024年

作者： Abeer, A.N.M. Nafiz Jantre, Sanket Urban, Nathan M. Yoon, Byung-Jun Department of Electrical and Computer Engineering Texas A&M University College StationTX United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks aimed at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task is impractical. Furthermore, the black-box nature of typical downstream tasks–such as property prediction–makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose a novel approach for a model uncertainty-guided fine-tuning of a pre-trained variational autoencoder (VAE)-based GMD model through performance feedback in an active learning setting. The main idea is to quantify model uncertainty in the generative model, which is made efficient by working within a low-dimensional active subspace of the high-dimensional VAE parameters explaining most of the variability in the model’s output. The inclusion of model uncertainty expands the space of viable molecules through decoder diversity. We then explore the resulting model uncertainty class via black-box optimization made tractable by low-dimensionality of the active subspace. This enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties, using multiple VAE-based generative models, demonstrate that our uncertainty-guided fine-tuning approach consistently outperforms the original pre-trained models. © 2024, CC BY.

关键词： Molecules

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Energy Consumption Prediction of Electric Bus Based on CNN-XGBoost

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第2期10卷 1153-1167页

作者： Fang, Weidong Chi, Yufeng Pan, Jeng-Shyang School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Chaoyang University of Technology Taichung413310 Taiwan

The existing energy consumption prediction models for pure electric buses do not comprehensively consider various energy consumption influencing factors, and most models only use data from a single operating route. This paper proposes an electric bus trip energy consumption prediction model based on CNN-XGBoost to improve the prediction accuracy. Using the actual driving data of eight pure electric buses, firstly, we analyze the change rule of energy consumption under different ambient temperatures and propose the energy consumption influencing factors stratification to analyze the influence of driving conditions on energy consumption. Then, we innovatively input the speed-related three types of joint probability distribution matrix into the CNN model to predict the motor energy consumption and then input the predicted motor energy consumption and the influencing factors, such as ambient temperature and line information, into the XGBoost model to predict the energy consumption of bus trip. Finally, it is compared and analyzed with various prediction models such as SVR and LGBM, and the results show that the R2 of the CNN-XGBoost model is 0.923, the prediction performance is significantly higher than other models. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Normal distribution

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Thermal metasurface with tunable narrowband absorption from a hybrid graphene/silicon photonic crystal resonance

Thermal metasurface with tunable narrowband absorption from ...

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作者： Nagpal, Arun Zhou, Ming Ilic, Ognjen Yu, Zongfu Atwater, Harry A. Thomas J. Watson Laboratory of Applied Physics California Institute of Technology 1200 E. California Avenue PasadenaCA91125 United States Department of Electrical Engineering Stanford University 450 Serra Mall StanfordCA94305 United States Department of Mechanical Engineering University of Minnesota 111 Church Street SE MinneapolisMN55455 United States Department of Electrical and Computer Engineering University of Wisconsin 1415 Engineering Drive MadisonWI53706 United States

We report the design of a tunable, narrowband, thermal metasurface that employs a hybrid resonance generated by coupling a tunable permittivity graphene ribbon to a silicon photonic crystal. The gated graphene ribbon array, proximitized to a high quality factor Si photonic crystal supporting a guided mode resonance, exhibits tunable narrowband absorbance lineshapes (Q > 10,000). Actively tuned Fermi level modulation in graphene with applied gate voltage between high absorptivity and low absorptivity states gives rise to absorbance on/off ratios exceeding 60. We employ coupled-mode theory as a computationally efficient approach to elements of the metasurface design, demonstrating an orders of magnitude speedup over typical finite element computational methods. © 2023 Optica Publishing Group.

关键词： Absorbance

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Line-of-Sight Visual Target Tracking via Particle-Based Belief Propagation

Line-of-Sight Visual Target Tracking via Particle-Based Beli...

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American Control Conference (ACC)

作者： Tony X. Lin Manav Gagvani Sean Lindstrom Donald A. Sofge Fumin Zhang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Distributed Autonomous Systems Section U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper considers the problem of controlling a team of camera-equipped robots to ensure a mobile human target is observed by at least one robot at all times. We focus on determining team trajectories over a fixed time horizon such that the human target is in at least one robot's field of view even while the line of sight to the target can be obstructed by obstacles. Our approach to solving this problem lies in using a particle-based Belief Propagation method to estimate the most likely poses for each robot over time such that the visual tracking requirement is satisfied. Our approach is validated in simulation studies and an experimental trial where a human target must be tracked by a pair of miniature autonomous robot blimps.

关键词： Legged locomotion Visualization Target tracking Smoothing methods Soft sensors Robot vision systems Search problems

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Non-Linear Bias Mitigation in Multi-Sensor Multi-Track Fusion

Non-Linear Bias Mitigation in Multi-Sensor Multi-Track Fusio...

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International Conference on Information Fusion

作者： Shaun J. Hoyt W. Dale Blair Aaron D. Lanterman Applied Systems Laboratory Georgia Tech Research Institute Atlanta GA Sensors and Electromagnetic Applications Laboratory Georgia Tech Research Institute Atlanta GA Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

When performing track correlation and fusion in conjunction with bias estimation for sensor registration, the pattern match bias estimation is usually performed by modeling the biases as additive constants to the tracks in Cartesian space. Since sensor biases actually occur in sensor polar or spherical coordinates, the bias model of adding constants to the tracks can only be applied to a group of somewhat closely-spaced tracks before the linear assumption of the biases in Cartesian coordinates breaks down. A methodology to estimate sensor biases in the native coordinate frame in which they occur is presented, along with simulation results that illustrate its performance. Modeling the biases in sensor coordinates allow for tracks throughout the field of view to be used for sensor bias estimation, producing better sensor registration and track picture. In this research, sensor tracks are transmitted to a fusion center, where track correlation, bias estimation, and fusion are performed. Murty’s K-best hypotheses algorithm is utilized to generated the top K hypotheses for track-to-track correlation. Each hypothesis produces an estimate of the sensor biases. The correlation hypotheses are corrected for their sensor bias estimates and new correlation scores are computed, and the biascorrected correlation hypotheses are ranked to find the best. The best hypothesis is selected as the most recent system track picture. The system tracks produced by the best hypothesis are correlated against the previous system track picture to maintain system track number continuity. The performance of the bias estimation is assessed against the root mean squared error (RMSE) and normalized estimation error squared (NEES) errors of the estimated biases versus the true biases. A scenario with four tracks and two sensors is used to demonstrate the observability of these biases. The results show that the biases as applied to the remote sensor are observable and mitigated, allowing for a more acc

关键词： Correlation Prevention and mitigation Simulation Noise Estimation Noise measurement Observability Remote sensing Pattern matching Floors

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Hierarchical neural operator transformer with learnable frequency-aware loss prior for arbitrary-scale super-resolution 24

Hierarchical neural operator transformer with learnable freq...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xihaier Luo Xiaoning Qian Byung-Jun Yoon Computational Science Intiative Brookhaven National Laboratory Upton NY Computational Science Intiative Brookhaven National Laboratory Upton NY and Department of Electrical and Computer Engineering Texas A&M University College Station TX

In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the resolution of scientific data, which often involves complex challenges such as continuity, multi-scale physics, and the intricacies of high-frequency signals. Grounded in operator learning, the proposed method is resolution-invariant. The core of our model is a hierarchical neural operator that leverages a Galerkin-type self-attention mechanism, enabling efficient learning of mappings between function spaces. Sinc filters are used to facilitate the information transfer across different levels in the hierarchy, thereby ensuring representation equivalence in the proposed neural operator. Additionally, we introduce a learnable prior structure that is derived from the spectral resizing of the input data. This loss prior is model-agnostic and is designed to dynamically adjust the weighting of pixel contributions, thereby balancing gradients effectively across the model. We conduct extensive experiments on diverse datasets from different domains and demonstrate consistent improvements compared to strong baselines, which consist of various state-of-the-art SR methods.

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Sub-Nyquist Sampling with Optical Pulses for Photonic Blind Source Separation

Sub-Nyquist Sampling with Optical Pulses for Photonic Blind ...

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Frontiers in Optics + Laser Science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Shi, Taichu Qi, Yang Zhang, Weipeng Prucnal, Paul Wu, Ben Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Lightwave Communications Laboratory Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

We proposed and demonstrated an optical pulse sampling method for photonic blind source separation. It can separate large bandwidth of mixed signals by small sampling frequency, which can reduce the workload of digita... 详细信息

ISBN: (纸本)9781557528209

关键词： Blind source separation

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