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Machine learning structure preserving brackets for forecasting irreversible processes

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arXiv 2021年

作者： Lee, Kookjin Trask, Nathaniel A. Stinis, Panos School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM87123 United States Pacific Northwest National Laboratory RichlandWA99354 United States

Forecasting of time-series data requires imposition of inductive biases to obtain predictive extrapolation, and recent works have imposed Hamiltonian/Lagrangian form to preserve structure for systems with reversible dynamics. In this work we present a novel parameterization of dissipative brackets from metriplectic dynamical systems appropriate for learning irreversible dynamics with unknown a priori model form. The process learns generalized Casimirs for energy and entropy guaranteed to be conserved and nondecreasing, respectively. Furthermore, for the case of added thermal noise, we guarantee exact preservation of a fluctuation-dissipation theorem, ensuring thermodynamic consistency. We provide benchmarks for dissipative systems demonstrating learned dynamics are more robust and generalize better than either "black-box" or penalty-based approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Dynamics

UAV-miniUGV Hybrid System for Hidden Area Exploration and Manipulation

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arXiv 2022年

作者： Pushp, Durgakant Kalhapure, Swapnil Das, Kaushik Liu, Lantao School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Automatic Control and Systems Engineering Department University of Sheffield Mappin St SheffieldS1 3JD United Kingdom TCS Research India

— We propose a novel hybrid system (both hardware and software) of an Unmanned Aerial Vehicle (UAV) carrying a miniature Unmanned Ground Vehicle (miniUGV) to perform a complex search and manipulation task. This system leverages heterogeneous robots to accomplish a task that cannot be done using a single robot system. It enables the UAV to explore a hidden space with a narrow opening through which the miniUGV can easily enter and escape. The hidden space is assumed to be navigable for the miniUGV. The miniUGV uses Infrared (IR) sensors and a monocular camera search for an object in the hidden space. The proposed system takes advantage of a wider field of view (fov) of the camera as well as the stochastic nature of the object detection algorithms to guide the miniUGV in the hidden space to find the object. Upon finding the object the miniUGV grabs it using visual servoing and then returns back to its start point from where the UAV retracts it back and transports the object to a safe place. In case there is no object found in the hidden space, UAV continues the aerial search. The tethered miniUGV gives the UAV an ability to act beyond its reach and perform a search and manipulation task which was not possible before for any of the robots individually. The system has a wide range of applications and we have demonstrated its feasibility through repetitive experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach

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Computers and Education: Artificial Intelligence 2022年 3卷

作者： Akour, Iman A. Al-Maroof, Rana Saeed Alfaisal, Raghad Salloum, Said A. Information Systems Department College of Computing & Informatics University of Sharjah United Arab Emirates English Language & Linguistics Department Al Buraimi University College Al Buraimi Oman Faculty of Art Computing and Creative Industries Universiti Pendidikan Sultan Idris Malaysia School of Science Engineering and Environment University of Salford United Kingdom

The metaverse is a kind of imagined world with immersive digital spaces that increase, allowing a more interactive environment in educational settings. The metaverse is an expansion of the synchronous communication that embraces an effective number of users to share different experiences. The study aims to investigate the students' perceptions towards metaverse system for educational purposes in the Gulf area. The conceptual model comprises the adoption properties, namely trialability, observability, compatibility, and complexity, users' satisfaction, personal innovativeness, and Technology Acceptance Model (TAM) constructs. The novelty of the paper lies in its conceptual model that correlates both personal-based characteristics and technology-based features. In addition, the novel approach of hybrid analysis will be used in the current study to perform deep-learning-based analysis of structural equation modelling (SEM) and artificial neural network (ANN). Moreover, the importance-performance map analysis (IPMA) is used in the current study to evaluate the involved factors for their importance and performance. The study identified Perceived Usefulness (PU) to be an essential predictor of the factor of Users’ Intention to Use the Metaverse System (MS). The fact was discovered during ANN and IPMA analysis. Furthermore, this study is practically significant, as it helped the concerned authorities in educational sector in understanding the significance of each factor and allowed them to make efforts and plans according to the order of significance of factors. Another important implication of the study is methodological in nature. It validates that deep ANN architecture can offer deep insight into non-linear relationships shared by various factors of a theoretical model. © 2022 The Authors

关键词： Neural networks

Convolutional neural network regression for low-cost microalgal density estimation

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 9卷

作者： Nguyen, Linh Nguyen, Dung K. Nguyen, Thang Nghiem, Truong X. Institute of Innovation Science and Sustainability Federation University Australia Churchill 3842 VIC Australia College of Agriculture and Science Life Chonnam National University Buk-gu Gwangju 61186 South Korea School of Engineering & Computing Sciences Texas A&M University-Corpus Christi Corpus Christi 78412 TX United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff 86011 AZ United States

Density of microalgae is critical information for production of algae in a closed cultivation system since it can be used to optimally control their growth rate, biomass concentration and quality of the products. Given advancement in image processing techniques and thanks to low-cost camera sensors, image based methods are increasingly widely utilized to indirectly estimate the density. Advantages of the image based techniques include being less invasive and more nondestructive and biosecured. However, most of the existing techniques rely on averaging all pixels of a microalgae image, which may eliminate crucial information of their spatial correlation. Therefore, in this work we propose to exploit a convolutional neural network (CNN) to efficiently extract information from the microalgae images, which are then employed to regress the density. Interestingly, the proposed deep CNN regression model accepts the whole color image as its input while the density is calculated in the output. The proposed CNN regression architecture was validated in real-world experiments where the microalgal strain Chlorella vulgaris was cultured and their images were captured by our low-cost camera sensor system. The obtained results demonstrate that the averaged estimation accuracy of the proposed model is 99.45% (± 0.68%) while the R² value between the density predictions and the ground truths is 0.9997, which is highly accurate and practical. © 2024 The Author(s)

关键词： Convolutional neural network Deep learning Image processing Microalgae Microalgal density Precision agriculture Regression

Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking

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Predictive Modeling of Periodic Behavior for Human-Robot Sym...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Geoffrey Clark Joseph Campbell Seyed Mostafa Rezayat Sorkhabadi Wenlong Zhang Heni Ben Amor School of Computing Informatics and Decision Systems Engineering Arizona State University The Polytechnic School Arizona State University Mesa AZ USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21° in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.

关键词： Legged locomotion Robot sensing systems Biomechanics Predictive models Prosthetics Probabilistic logic

Non-linear Analysis for Operational Safety Verification of Cyber Physical systems

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Non-linear Analysis for Operational Safety Verification of C...

Industrial Cyber-Physical systems (ICPS)

作者： Ayan Banerjee Imane Lamrani Sandeep K.S. Gupta School of Computer Informatics and Decision Systems Engineering Arizona State University

ISBN: (数字)9781728163895

ISBN: (纸本)9781728163901

Reach set approximation is an important tool for operational safety analysis of Cyber -Physical systems (CPS). However, industrial CPS typically experience non-linear dynamics. Piece-wise linearization is popularly employed to simplify the continuous dynamics with certain approximation errors, which typically increase non-linearly with respect to size of each piece (simplex). In this paper, we utilize ExReach that employs a novel reach set estimation algorithm for non-linear hybrid systems and show the use on practical medical cyber-physical system examples such as artificial pancreas and ventilators. In addition, we compare the performance of ExReach with existing tools such as Flow* on six non-linear systems with dimensions varying from 2 to 28. ExReach outputs a tighter over-approximation of the reach set of the CPS than Flow * or C2E2.

关键词： Safety Trajectory Blood Splines (mathematics) Mathematical model Reachability analysis Pancreas

Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics

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arXiv 2022年

作者： Guo, Xiaoyuan Duan, Jiali Kuo, C.-C. Jay Gichoya, Judy Wawira Banerjee, Imon Department of Computer Science Emory University GA United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California CA United States Department of Radiology and Imaging Sciences Emory University GA United States School of Computing Informatics Decision Systems Engineering Arizona State University AZ United States Department of Radiology Mayo Clinic AZ United States

Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning. In contrast, visual modality is inherently continuous and high-dimensional, which potentially prohibits the alignment as well as fusion between vision and language modalities. We therefore propose to "discretize" the visual representation by joint learning a codebook that imbues each visual token a semantic. We then utilize these discretized visual semantics as self-supervised ground-truths for building our Masked Image Modeling objective, a counterpart of Masked Language Modeling which proves successful for language models. To optimize the codebook, we extend the formulation of VQ-VAE which gives a theoretic guarantee. Experiments validate the effectiveness of our approach across common vision-language benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

BRDS: An FPGA-based LSTM accelerator with row-balanced dual-ratio sparsification

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arXiv 2021年

作者： Ghasemzadeh, Seyed Abolfazl Tavakoli, Erfan Bank Kamal, Mehdi Afzali-Kusha, Ali Pedram, Massoud School of Electrical and Computer Engineering University of Tehran Tehran14399-57131 Iran School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

In this paper, first, a hardware-friendly pruning algorithm for reducing energy consumption and improving the speed of Long Short-Term Memory (LSTM) neural network accelerators is presented. Next, an FPGA-based platform for efficient execution of the pruned networks based on the proposed algorithm is introduced. By considering the sensitivity of two weight matrices of the LSTM models in pruning, different sparsity ratios (i.e., dual-ratio sparsity) are applied to these weight matrices. To reduce memory accesses, a row-wise sparsity pattern is adopted. The proposed hardware architecture makes use of computation overlapping and pipelining to achieve low-power and high-speed. The effectiveness of the proposed pruning algorithm and accelerator is assessed under some benchmarks for natural language processing, binary sentiment classification, and speech recognition. Results show that, e.g., compared to a recently published work in this field, the proposed accelerator could provide up to 272% higher effective GOPS/W and the perplexity error is reduced by up to 1.4% for the PTB dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

Few-shot learning based histopathological image classification of colorectal cancer

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Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories