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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leandro José Evilásio Campos Marco Henrique Terra Ricardo Menotti Roberto Santos Inoue Graduate Program in Electrical Engineering Federal University of São Carlos São Carlos SP Brazil Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos SP Brazil Department of Computing Federal University of São Carlos

The efficient Kalman filter has been widely used in recent decades to obtain air navigation information in UAVs. However, for a good performance of the Kalman filter, the model that describes the system dynamics must not contain uncertainties. This paper presents the implementation of a robust Kalman filter to estimate the attitude, velocity, and position of UAVs. The robust filter considers uncertainties in the sensor models. A mathematical structure based on the solution of linear systems synthesizes the predictor-corrector robust estimation algorithm. The main contribution of this study is the proposed QR decomposition based on Givens rotation to solve the linear system. The simulated experiments used sensory data collected in Zürich-Switzerland and ground truth referencing attitude, velocity, and position. The offline simulation results express the effectiveness of the robust Kalman filter for this application, with a reduction of up to 18.9% in the estimation error, in relation to the standard Kalman filter. The proposal to use systolic arrays for numerical solutions has shown promise for implementation in parallel processing platforms, such as FPGAs.

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Minimizing File Transfer Time in Opportunistic Spectrum Access Model

arXiv

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arXiv 2022年

作者： Hu, Jie Doshi, Vishwaraj Eun, Do Young Department of Electrical and Computer Engineering United States Operations Research Graduate Program North Carolina State University RaleighNC United States

We study the file transfer problem in opportunistic spectrum access (OSA) model, which has been widely studied in throughput-oriented applications for max-throughput strategies and in delay-related works that commonly assume identical channel rates and fixed file sizes. Our work explicitly considers minimizing the file transfer time for a given file in a set of heterogeneous-rate Bernoulli channels, showing that max-throughput policy doesn’t minimize file transfer time in general. We formulate a mathematical framework for static extend to dynamic policies by mapping our file transfer problem to a stochastic shortest path problem. We analyze the performance of our proposed static and dynamic optimal policies over the max-throughput policy. We propose a mixed-integer programming formulation as an efficient alternative way to obtain the dynamic optimal policy and show a huge reduction in computation time. Then, we propose a heuristic policy that takes into account the performance-complexity tradeoff and consider the online implementation with unknown channel parameters. Furthermore, we present numerical simulations to support our analytical results and discuss the effect of switching delay on different policies. Finally, we extend the file transfer problem to Markovian channels and demonstrate the impact of the correlation of each channel. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Meta-optic accelerators for object classifier

Meta-optic accelerators for object classifier

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in a high-speed and robust classifier with 93.1% accurac...

ISBN: (纸本)9781957171258

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Investigating the effect of cerebral atrophy on brain deformation using subject-specific models

Investigating the effect of cerebral atrophy on brain deform...

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Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Hong, Allen Carass, Aaron Zuo, Lianrui Prince, Jerry L. Alshareef, Ahmed Department of Biomedical Engineering The College of New Jersey EwingNJ08618 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD20892 United States Biomedical Engineering Program University of South Carolina ColumbiaSC29208 United States

ISBN: (纸本)9781510660410

Traumatic brain injuries (TBIs) are a major health risk that increases with age. Natural brain aging results in cerebral atrophy and the enlargement of the ventricular regions. The objective of this study is to investigate the effect of cerebral atrophy on brain biomechanics with subject-specific models to determine the risk of traumatic brain injury (TBI). Utilizing subjects from a longitudinal study of aging in healthy volunteers, we created subject-specific brain models of a small cohort with progressive age-related cerebral atrophy. We then simulate concussive loading conditions to study changes in brain deformation, a correlate to risk of TBI. The results display differing trends with increasing ventricle volume, with some subjects exhibiting increases and others showing decreasing strain. Additional subject simulations are needed to clarify these the causes of these trends. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Health risks

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An ANN-Based Electronic Nose for Monitoring Pollutant Gases in Landfills

An ANN-Based Electronic Nose for Monitoring Pollutant Gases ...

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IEEE Instrumentation and Measurement Technology Conference

作者： Juliene dos Santos Davi Muniz Francisco Auriberto Ferreira Marques Junior Cleonilson Protasio De Souza William De Paiva Post-Graduate Program of Electrical Engineering Federal University of Paraíba João Pessoa PB Brazil Post-Graduate Program of Environmental Science and Technology State University of Paraíba Campina Grande PB Brazil Department of Electrical Engineering Federal University of Paraíba João Pessoa PB Brazil Department of Sanitary and Environmental Engineering State University of Paraíba João Pessoa PB Brazil

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Biogas generated from urban solid waste degradation in landfills is composed of various types of gases, some of them are very toxic and harmful to the health of both humans and animals, and contribute directly to global warming as well. In this way, the monitoring of biogas concentration in landfills and their surroundings is very important because this can assess the air contamination around landfills, as well as to propose means to minimize it. In this work, an electronic nose for gas identification and measurement of gases present in landfill biogas using an Artificial Neural Network (ANN) is introduced. The ANN training data have been collected by the developed electronic nose, which consisted on a set of low-cost gas sensors and on a signal acquisition system controlled by the LabViewsoftware. After the ANN training, a neural architecture has been obtained and implemented in a microcontrolled digital system to measure different gas concentrations, mainly, methane. The main results of the proposed ANN-based Electronic Nose show that it was possible to measure the gases concentrations, with RMSE less than 1%, for the most of target gases, confirming the accuracy of the developed system.

关键词： Gases Semiconductor device measurement Accuracy Landfills Artificial neural networks Electronic noses Biogas

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Hidayaturrahman Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Computer Science Bina Nusatara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies.

关键词： computer science Adaptation models Neural networks Feature extraction Data models Data mining Information technology

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

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International Conference on Information Technology, Information Systems and electrical engineering (ICITISEE)

作者： Tri Agus Setiawan Yaya Heryadi Lukas Lili Ayu Wulandari Ilvico Sonata Computer Science Department BINUS Graduate Program -Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia - Atma Jaya Jakarta Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets.

关键词： Training Accuracy Machine learning Generative adversarial networks Reliability engineering Skin Data models Medical diagnostic imaging Diseases Synthetic data

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Output Feedback Control of TS Fuzzy Systems with Unmeasured Nonlinearities via a Local Separation Principle

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IFAC-PapersOnLine 2023年第2期56卷 2164-2169页

作者： Anh-Tu Nguyen Pedro Coutinho Márcia Peixoto Thierry-Marie Guerra Reinaldo Palhares LAMIH UMR CNRS 8201 Université Polytechnique Hauts-de-France France Federal University of Minas Gerais Graduate Program in Electrical Engineering Brazil Federal University of Minas Gerais Department of Electronics Engineering Brazil

This paper presents a fuzzy observer-based control framework for TS fuzzy models with nonlinear consequents (N-TS). The N-TS fuzzy model is constructed such that only measured nonlinearities are considered to obtain the fuzzy membership functions, and all unmeasured nonlinearities are modeled as local consequent parts. Based on the differential mean value theorem, the closed-loop system is rewritten in an adequate format, which allows proving the separation principle for asymptotic TS fuzzy observer-based control design. Sufficient design conditions, expressed in terms of linear matrix inequalities, are derived via delayed nonquadratic Lyapunov functions for the synthesis of both stabilizing fuzzy controller and Luenberger-like fuzzy observer. Using Lyapunov-based arguments of a local separation principle, a constructive procedure is provided to maximize the closed-loop domain of attraction (DoA) estimation, which is guaranteed to be included inside the predefined fuzzy modeling region. Numerical examples are given to illustrate the effectiveness of the proposed approach.

关键词： Takagi-Sugeno fuzzy systems observer-based fuzzy control separation principle unmeasured premise variables linear matrix inequality

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Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

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2023 Flat Optics: Components to Systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

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OPTIMIZING TRADING STRATEGIES IN QUANTITATIVE MARKETS USING MULTI-AGENT REINFORCEMENT LEARNING

arXiv

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arXiv 2023年

作者： Zhang, Hengxi Shi, Zhendong Hu, Yuanquan Ding, Wenbo Kuruoglu, Ercan E. Zhang, Xiao-Ping Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electrical Computer and Biomedical Engineering Ryerson University Toronto Canada

Quantitative markets are characterized by swift dynamics and abundant uncertainties, making the pursuit of profit-driven stock trading actions inherently challenging. Within this context, Reinforcement Learning (RL) - which operates on a reward-centric mechanism for optimal control - has surfaced as a potentially effective solution to the intricate financial decision-making conundrums presented. This paper delves into the fusion of two established financial trading strategies, namely the constant proportion portfolio insurance (CPPI) and the time-invariant portfolio protection (TIPP), with the multi-agent deep deterministic policy gradient (MADDPG) framework. As a result, we introduce two novel multi-agent RL (MARL) methods: CPPI-MADDPG and TIPP-MADDPG, tailored for probing strategic trading within quantitative markets. To validate these innovations, we implemented them on a diverse selection of 100 real-market shares. Our empirical findings reveal that the CPPI-MADDPG and TIPP-MADDPG strategies consistently outpace their traditional counterparts, affirming their efficacy in the realm of quantitative trading. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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