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Asynchronous multi-rate systems of a transfer learning filtering algorithm using t-distribution

Kongzhi Lilun Yu Yingyong/Control Theory and Applications 2025年第5期42卷 947-954页

作者： Wang, Wei Zhao, Shun-Yi Zhang, Cheng-Xi Luan, Xiao-Li Liu, Fei Wu, Jin Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi214122 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology 999077 Hong Kong

This paper investigates a transfer learning filtering algorithm based on the t-distribution to address the problem of state estimation in asynchronous multi-rate sensor systems affected by outliers. By integrating full probability design and multi-scale system theory, we propose a novel transfer learning framework for asynchronous multi-rate systems. A multi-scale model is established to convert the asynchronous multi-rate system into a synchronous one. This design minimizes the Kullback-Leibler divergence between the predicted distribution in the source domain and the ideal distribution in the target domain, while allowing the sensor sampling rate ratio to be any positive integer. To account for outlier effects on state estimation, the source and target domains leverage the heavy-tailed properties of the t-distribution to model state and observation processes, with approximate estimation achieved through expectation-maximization and variational Bayesian methods. Simulation results on a planar position-velocity system demonstrate the superior performance of the proposed method. © 2025 South China University of Technology. All rights reserved.

关键词： Maximum likelihood estimation

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Red, green, and blue quantum rod based electroluminescent light-emitting diodes 57th

Red, green, and blue quantum rod based electroluminescent li...

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57th SID International Symposium, Seminar and Exhibition, Display Week, 2020

作者： Mallem, Kumar Prodanov, Maksym F. Gupta, Swadesh K. Tam, Bryan Siu Ting Vashchenko, Valerii V. Srivastava, Abhishek K. State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Quantum dot light emitting diodes (QLEDs) emerges as a potential display technology due to its ultra-saturated colors, small turn on voltage (Von) and high efficiency (n). However, the internal quantum efficiency (IQE) of QLED devices nearly reach its ideal level, and leave light outcoupling as the only possible way to improve the external quantum efficiency (EQE). This might need a different shape of the core shell, device structure and re-optimization of the device. In this paper, we synthesized CdSe with CdS/ZnS based red, blue and green quantum rods (QRs) and fabricated electroluminescent QRs light emitting diodes (QRLEDs), this is the first study to report for the blue QRLEDs. The solution processed zinc oxide (ZnO) nano-particles (NP) are used as an electron transport layer. The maximum luminance and EQE values of the devices are 1288, 420 and 13 cd/m2 and 5.7% and 0.18% and 0.093 %, for the red, green and blue LEDs, respectively. This kind of new QRLEDs shows a great potential for the use of next generation high color gamut display. © 2020 SID.

关键词： II-VI semiconductors

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Non-convex potential games for finding global solutions to sensor network localization

arXiv

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arXiv 2023年

作者： Xu, Gehui Chen, Guanpu Hong, Yiguang Fidan, Baris Parisini, Thomas Johansson, Karl H. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Sensor networks

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Collaborative Multi-BS Power Management for Dense Radio Access Network using Deep Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chang, Yuchao Chen, Wen Li, Jun Liu, Jianpo Wei, Haoran Wang, Zhendong Al-Dhahir, Naofal The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Electronic and Optical Engineering Nanjing University of Science Technology Nanjing210094 China Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai201800 China Department of Electrical and Computer Engineering The University of Texas at Dallas RichardsonTX75080 United States

Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively managing multi-BSs' radiated power levels with constraints on the users' traffic volume and achievable rate. Considering stochastic traffic arrivals at the users and time-varying network interference, we first formulate the problem as a Markov decision process (MDP) and then develop a novel deep reinforcement learning (DRL) framework based on the cloud-RAN operation scheme. To tackle the trade-off between complexity and performance, the overall optimization of multi-BSs' energy efficiency with the multiplicative complexity constraint is modeled to achieve nearoptimal performance by using a deep Q-network (DQN). In DQN, each BS first maximizes its individual energy efficiency, and then cooperates with other BSs to maximize the overall multi-BSs' energy efficiency. Simulation results demonstrate that the proposed algorithm can converge faster and enjoy a network energy efficiency improvement by 5% and 10% compared with the benchmarks of the Q-learning and sleep schemes, respectively. © 2023, CC0.

关键词： Reinforcement learning

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese Automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

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npj Computational Materials 2020年第1期6卷 964-970页

作者： Yabo Dan Yong Zhao Xiang Li Shaobo Li Ming Hu Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyang 550025China Department of Computer Science and Engineering University of South CarolinaColumbiaSC 29201USA Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang 550025China Department of Mechanical Engineering University of South CarolinaColumbiaSC 29201USA

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired *** effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials ***,we propose a generative machine learning model(MatGAN)based on a generative adversarial network(GAN)for efficient generation of new hypothetical inorganic *** with materials from the ICSD database,our GAN model can generate hypothetical materials not existing in the training dataset,reaching a novelty of 92.53% when generating 2 million *** percentage of chemically valid(charge-neutral and electronegativitybalanced)samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD,even though no such chemical rules are explicitly enforced in our GAN model,indicating its capability to learn implicit chemical composition rules to form *** algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

关键词： chemical inverse networks

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Output consensus control for linear port-Hamiltonian systems

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IFAC-PapersOnLine 2022年第30期55卷 230-235页

作者： Shuai Feng Yu Kawano Michele Cucuzzella Jacquelien M.A. Scherpen School of Automation Nanjing University of Science and Technology Nanjing 210094 China Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima 739-8527 Japan Department of Electrical Computer and Biomedical Engineering University of Pavia 27100 Pavia PV Italy Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen 9747 AG Groningen the Netherlands

In this paper, we study output consensus of coupled linear port-Hamiltonian systems on graphs in the presence of constant disturbances, where couplings are allowed to be both static and dynamic. Utilizing port-Hamiltonian structures, we present dynamic controllers achieving output consensus where the consensus values are determined by the disturbances. Finally, the utility of the proposed controller is illustrated by applying it to current sharing of DC microgrids.

关键词： port-Hamiltonian systems networked systems output consensus

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A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning

arXiv

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arXiv 2023年

作者： Abouheaf, Mohammed Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah College of Technology Architecture & Applied Engineering Bowling Green State University Bowling GreenOH43402 United States School of Electrical Engineering and Computer Science Department of Mechanical Engineering University of Ottawa OttawaONK1N 6N5 Canada Machine Intelligence Research Labs AuburnWA98071-2259 United States

Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive control strategies. The resulting solutions are often challenged by the complexity of the reference model and those of the derived control strategies. Additionally, the explicit dependence of the control strategies on the process dynamics and reference dynamical models may contribute in degrading their efficiency in the face of uncertain or unknown dynamics. A model-reference adaptive solution is developed here for autonomous systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based structure. The proposed approach describes the process with an integral temporal difference equation and solves it using an integral reinforcement learning mechanism. This is done in real-time without knowing or employing the dynamics of either the process or reference model in the control strategies. A class of aircraft is adopted to validate the proposed technique. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Time-Refraction Optics at Single Cycle Modulation

Time-Refraction Optics at Single Cycle Modulation

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Conference on Lasers and Electro-Optics (CLEO)

作者： Eran Lustig Ohad Segal Soham Saha Eliyahu Bordo Sarah N. Chowdhury Yonatan Sharabi Avner Fleischer Mustafa Ozlu Alexandra Boltasseva Oren Cohen Vladimir M. Shalaev Mordechai Segev Ginzton Laboratory and Department of Electrical Engineering Stanford University Stanford California USA Physics Department and Solid State Institute Technion-Israel Institute of Technology Haifa Israel Elmore Family School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA Birck Nanotechnology Center Purdue University West Lafayette IN USA School of Chemistry Tel Aviv University Israel

We present an experimental study of optical time-refraction in single-cycle time-interfaces. Specifically, we study the propagation of a probe pulse through a sample undergoing large refractive index changes induced b... 详细信息

ISBN: (纸本)9781957171258

关键词： Blue shift Hollow core fibers Modulation Modulators Refractive index Ultrashort pulses

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Tunable unidirectional surface plasmon-polaritons at the interface between gyrotropic and isotropic conductors 22

Tunable unidirectional surface plasmon-polaritons at the int...

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22nd International Conference on Electromagnetics in advanced Applications, ICEAA 2021

作者： Liang, Y. Pakniyat, S. Xiang, Y. Chen, J. Shi, F. Hanson, G.W. Cen, C. West Virginia University Department of Physics and Astronomy MorgantownWV26506 United States University of Wisconsin-Milwaukee Department of Electrical Engineering MilwaukeeWI53211 United States University of Pittsburgh Peterson Institute of Nano Science and Engineering Department of Electrical and Computer Engineering PittsburghPA15261 United States Guangxi Key Lab for Relativistic Astrophysics Center on Nanoenergy Research School of Physical Science and Technology Guangxi University Nanning Guangxi530004 China Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781665413862

The propagation of SPPs in solid state systems is usually reciprocal, which means that a forward-traveling SPP wave can be easily backscattered by disorders. Since such backscattering is detrimental for SPP-based information processing applications, the development of nonreciprocal plasmonic platforms that enforce unidirectional SPP propagations is of great importance. © 2021 IEEE.

关键词： Electromagnetic wave polarization

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