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Koopman-Based data-Driven Techniques for Adaptive Cruise Control System Identification 27

Koopman-Based Data-Driven Techniques for Adaptive Cruise Con...

引用

27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Meng, Yiming Li, Hangyu Ornik, Melkior Li, Xiaopeng University of Wisconsin-Madison Department of Civil and Environmental Engineering MadisonWI53706 United States University of Illinois Urbana-Champaign Coordinated Science Laboratory UrbanaIL61801 United States University of Illinois Urbana-Champaign Department of Aerospace Engineering UrbanaIL61801 United States

ISBN: (纸本)9798331505929

Accurately identifying the intrinsic model of Adaptive Cruise Control has the potential to enhance the prediction of automated car-following behavior, helping vehicles' decision-making and contributing to safer and more efficient traffic flows. Moreover, white box models offer an analytical base for evaluating the impact of automated driving functions on macroscopic traffic dynamics, consequently aiding the management of the whole intelligent transportation system. Many existing system identification techniques have been applied to automated vehicles. However, most of these studies focus on identifying parameters for models of a fixed prototype. Their reliance on accurate estimation of state time derivatives prevents their real applications, challenged by low sampling rates, noisy measurements, and limited observation periods. In contrast, the Koopman operator learning framework presents a promising improvement that can identify the nonlinear evolutionary properties of continuous-time systems. In this study, we apply Koopman-based methods to data driven Adaptive Cruise Control model identification. Additionally, as the challenge remains in establishing a practical relationship between identification accuracy and sampling rate, we numerically compared the performance of three Koopman-based learning frameworks, finite-difference, Koopman-logarithm, and a newly devised resolvent-type method, with that of a commonly used offline simulation-based batch optimization approach. We introduce a novel modification to the resolvent-type method, and the experimental results demonstrate its state of the art performance, particularly in identifying the potential existence of parametric noise at lower sampling rates. © 2024 IEEE.

关键词： Continuous time systems

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Energy Efficient ECG Classifier Using Smart Lead Switching and Apache TVM 22

Energy Efficient ECG Classifier Using Smart Lead Switching a...

引用

22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Ayad, Ahmad Barhoush, Mahdi Volker, Benedikt Leonhardt, Steffen Schmeink, Anke Information Theory and Data Analytics Chair or Medical Information Technology Helmholtz-Institute for Biomedical Engineering Rwth Aachen University AachenD-52074 Germany

ISBN: (纸本)9798350345346

The classification of Electrocardiograms (ECG) sig-nals is vital for diagnosing and monitoring cardiovascular diseases. Recently, there has been increasing interest in leveraging efficient machine learning models for ECG classification. Op-timizing computational resources in this domain is crucial for real-time analysis and reducing power consumption. This paper explores two approaches to enhance the efficiency of ECG classification on edge devices. Firstly, a smart lead-switching mechanism intelligently switches between a high-power model and a low- power model. Secondly, Apache Tensor Virtual Machine (TVM) is employed to optimize the implementation of the developed models on the Jetson Nano single-board computer. Experimental results demonstrate noticeable achievements, including up to 96% reduction in FLOPS and 95% reduction in inference time. Our results hold significant promise for improving ECG classification on edge devices, enabling early detection of abnormalities, and enhancing patient care. © 2023 IEEE.

关键词： Deep learning Electrocardiogram Energy-efficient Temporal Convolutional Network Tensor Virtual Machine

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Symbolic-Based Recurrent Neural Networks for Metamodeling of Nonlinear Structural Models 22

Symbolic-Based Recurrent Neural Networks for Metamodeling of...

引用

22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Jia, Yiming Sasani, Mehrdad Northeastern University Department of Civil and Environmental Engineering Boston United States

ISBN: (纸本)9798350345346

Nonlinear time history analyses of structural models, widely used in civil engineering, can be time-consuming. For an urban scale, modeling a large number of structures and carrying out nonlinear time history analyses under different levels of hazard intensity is not practical. This can be resolved by the use of metamodeling, which can effectively reduce the computational cost while maintaining necessary engineering accuracy. Neural networks have been proven to be a powerful tool for metamodel development. However, training a reliable deep learning model requires a large training dataset that contains representative input-output relationships, which typically cannot be satisfied in practical applications. Practically, a trained deep learning model is a 'black box', which has limited (or no) generalizability. In this paper, a symbolic-based neural network that does not require an immense amount of data is proposed to capture nonlinear structural behavior. In symbolic-based neural networks, symbolic activation functions are capable of finding mathematical expressions to describe the mechanism of input-output relationship, and the hidden state can store the sequence information of nonlinearity. Case studies are carried out for reinforced concrete structures subjected to a series of selected pulse-type ground motions. The results show that the symbolic-based neural network is a promising approach for estimating the nonlinear building responses without a large training dataset. © 2023 IEEE.

关键词： metamodeling neural network nonlinear structural response numerical integration ordinary differential equation

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Meta-learning Based Feature Selection for Clustering 22nd

Meta-Learning Based Feature Selection for Clustering

引用

22nd international conference on intelligent data engineering and automated learning

作者： Taratukhin, Oleg Muravyove, Sergey ITMO Univ St Petersburg Russia

ISBN: (纸本)9783030916077;9783030916084

Clustering is a highly demanded task nowadays. However, it requires the attention of human experts and it might certainly benefit from automation. This paper presents a method to perform simultaneous automatic algorithm selection and hyperparameter tuning with feature selection for clustering. The algorithm also features a meta-model to predict promising algorithm configurations based on dataset properties. Experimental results are provided for a set of benchmark datasets, it is shown that the proposed method outperforms known alternatives.

关键词： Machine learning Clustering Feature selection Meta-learning

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Safety or Not? A Comparative Study for Deep learning Apps on Smartphones 22

Safety or Not? A Comparative Study for Deep Learning Apps on...

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22nd IEEE international conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023

作者： Au-Yeung, Jin Wang, Shanshan Liu, Yuchen Chen, Zhenxiang University of Jinan Information Science and Engineering Jinan China

ISBN: (纸本)9798350381993

Recent years have witnessed an astonishing explosion in the evolution of mobile applications powered by deep learning (DL) technologies. Considering that inference of DL models in the cloud requires transferring user data to server, which is prone to the risk of user privacy leakage, many developers choose to deploy the models on local devices for executing the inference process. However, this also raises a number of other security issues, such as adversarial attacks, model stealing attacks, etc. To explore the security issues that exist in deep learning applications (DL apps), we conducted the first comprehensive comparative study of the top 200 apps in each category on Google Play. We built DLApplnspector, a vulnerability detection tool that combines dynamic and static analysis methods for dissecting apps, which helped us automate our empirical study on real-world mobile DL apps. First, we identify DL apps and extract their models by using DL Checker. Subsequently, Static Scoper is provided to detect encryption and reusability of DL models. Finally, within Dynamic Scoper, we use reverse engineering techniques on the network traffic to parse out the packets and collect side-channel information during application runtime. Our research shows that the majority of developers prefer to use open-source models, with almost 92% of models successfully parsed. This suggests that most models are unprotected. DL apps are more likely to upload user behaviour and collect private data than Non-DL apps. We provide security recommendations for developers and users to address the issues discovered. © 2023 IEEE.

关键词： Mobile security

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AssistTaxi: A Comprehensive dataset for Taxiway Analysis and Autonomous Operations 22

AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and...

引用

22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Ganeriwala, Parth Bhattacharyya, Siddhartha Gunther, Sean Kish, Brian Khan, Mohammed Abdul Hafeez Dhadoti, Ankur Neogi, Natasha Florida Institute of Technology Dept. of Computer Science MelbourneFL United States Florida Institute of Technology Dept. of Aerospace Engineering MelbourneFL United States Univeristy of Michigan Dept. of Mechanical Engineering Ann ArborMI United States Bits Pilani Dept. of Computer Science Dubai Campus Dubai United Arab Emirates Langley Research Center Nasa HamptonVA United States

ISBN: (纸本)9798350345346

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Addition-ally, the dataset serves as a valuable resource for validating and enhancing existing algorithms, facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique. © 2023 IEEE.

关键词： aircraft perception autonomous driving computer vision dataset labeling line identification taxiway data

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PointSplit: Towards On-device 3D Object Detection with Heterogeneous Low-power Accelerators 22

PointSplit: Towards On-device 3D Object Detection with Heter...

引用

22nd ACM/IEEE international conference on Information Processing in Sensor Networks (IPSN)

作者： Park, Keondo Choi, You Rim Lee, Inhoe Kim, Hyung-Sin Seoul Natl Univ Grad Sch Data Sci Seoul South Korea

ISBN: (纸本)9798400701184

Running deep learning models on resource-constrained edge devices has drawn significant attention due to its fast response, privacy preservation, and robust operation regardless of Internet connectivity. While these devices already cope with various intelligent tasks, the latest edge devices that are equipped with multiple types of low-power accelerators (i.e., both mobile GPU and NPU) can bring another opportunity;a task that used to be too heavy for an edge device in the single-accelerator world might become viable in the upcoming heterogeneous-accelerator world. To realize the potential in the context of 3D object detection, we identify several technical challenges and propose PointSplit, a novel 3D object detection framework for multi-accelerator edge devices that addresses the problems. Specifically, our PointSplit design includes (1) 2D semantics-aware biased point sampling, (2) parallelized 3D feature extraction, and (3) role-based group-wise quantization. We implement PointSplit on TensorFlow Lite and evaluate it on a customized hardware platform comprising both mobile GPU and EdgeTPU. Experimental results on representative RGB-D datasets, SUN RGB-D and Scannet V2, demonstrate that PointSplit on a multi-accelerator device is 24.7x faster with similar accuracy compared to the full-precision, 2D-3D fusion-based 3D detector on a GPU-only device.

关键词： 3D object detection On-device machine learning Edge computing Quantization

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Survey on Emotion Recognition databases 22

Survey on Emotion Recognition Databases

引用

22nd international conference on Control, Automation and Systems (ICCAS)

作者： Hong, Juyoung Hwang, Yujin Lee, Gwangjin Choi, Yukyung Sejong Univ Dept Intelligent Mechatron Engn Seoul 05006 South Korea

ISBN: (纸本)9788993215243

The social robots market has grown in recent years owing to advancements in robotics. Accordingly, there is a growing interest in emotion recognition technology, one of the fundamental technologies of social robots. Early research in emotion recognition predicted emotions into a Facial Action Coding System (FACS) that recognizes facial expressions by analyzing action units (AU), the fundamental action components of face muscles. However, with the advent of deep learning, models can now recognize facial emotions straight from images. For industry application of this deep learning based emotion recognition approach, supervised learning are usually applied, and this model training approach is highly dependent on training datasets. This survey gives an overview of datasets that can be used when the emotion recognition model is implemented. This paper focused on identifying the availability of datasets relevant to the elderly and children, the primary goals of social robots, as well as reviewing the data collection and annotation processes. This survey sheds insight on the current status of emotion datasets and presents suggestions for future development.

关键词： Emotion Recognition Infant Emotional Expression Child Emotional Expression Facial Expression dataset Recognition dataset

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data-Driven intelligent Feeding System for Pet Care 22

Data-Driven Intelligent Feeding System for Pet Care

引用

22nd international conference on Control, Automation and Systems, ICCAS 2022

作者： Ravi, Ghimire Choi, Jae Weon Pusan National University Department of Mechanical Engineering Busan46241 Korea Republic of

ISBN: (纸本)9788993215243

The rapid development of artificial intelligence, the internet of things, and digital information processing technology has a huge impact on our daily lives with smart devices and wearables. The well-being of companion animals such as dogs and cats has become a large challenge. An increasing number of pet owners, their emotional attachment with their pets, and the 21st-century's lifestyle importantly need the safety and welfare of pets by harnessing a smart technological approach. This paper analyzes and compares different machine learning algorithms for data-driven intelligent feeding system for pet care application. Different parameters such as body weight growth, body temperature, heart rate, eating habits, activity, sleep, and urine pH have been considered with other correlated sub-variables in creating virtual datasets. The supervised machine learning models: linear regression, gaussian process regression, narrow neural network, linear support vector machine, and fine tree are evaluated and discussed for estimating feed quantity. The machine learning model was verified by training, validation, and testing datasets. The developed model will be an innovative breakthrough for pet care applications. Feed estimation can be automated using the pet's health parameters, this will help the pet to prevent obesity and related disorders. © 2022 ICROS.

关键词： Feeding

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Improving Adversarial Robustness of Few-Shot learning with Contrastive learning and Hypersphere Embedding 22

Improving Adversarial Robustness of Few-Shot Learning with C...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Buwaneswaran, Madhushan Fonseka, Tehara Narayan, Apurva Grolinger, Katarina Western University Department of Electrical and Computer Engineering LondonON Canada Western University Department of Computer Science LondonON Canada

ISBN: (纸本)9798350345346

Few-shot image classification (FSIC) is a computer vision task from the few-shot learning (FSL) category in which the model learns to classify images using only a few training samples. It has been demonstrated that even neural networks trained on large scale datasets are vulnerable to adversarial samples. This vulnerability is magnified in FSIC due to the low volume of training data. This paper proposes the use of hypersphere embedding and supervised contrastive learning to improve the adversarial robustness of representation learning-based FSIC. Contrastive learning contributes through its ability to bring together similar samples while pushing away dissimilar ones. On the other hand, hypersphere embedding has been successful in the representation learning tasks by restricting the embeddings to a hypersphere manifold. The proposed approach was evaluated on both 5-shot and 1-shot learning using two standard FSL networks and the standard Mini-ImageNet benchmark dataset. The evaluation shows that supervised contrastive training provides inherent adversarial robustness to the FSIC model while hypersphere embedding with cosine distance metrics improves the accuracy of the FSIC model and, when used in conjunction with an adversarial defense mechanism, boosts the adversarial performance. © 2023 IEEE.

关键词： Adversarial defense Contrastive learning Few-shot learning Hypersphere embedding

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