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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Shilpi Shah Brendan Franz Travis Forgach Milan Jostes Joshua Siegel Chan-Jin Chung Dept. of Computer Science Rutgers University New Brunswick NJ USA Dept. of Computer Science Harvard University Cambridge MA USA Dept. of Computer Science and Engineering University of Michigan Ann Arbor MI USA Dept. of Math and Computer Science Lawrence Technological University Southfield MI USA Dept. of Computer Science and Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

This research develops, compares, and analyzes both a traditional algorithm using computer vision and a deep learning model to deal with dynamic road conditions. In the final testing, the deep learning model completed the target of five laps for both the inner and outer lane, whereas the computer vision algorithm only completed almost three laps for the inner lane and slightly over four laps for the outer. After conducting statistical analysis on the results of our deep learning model by finding the p-value between the absolute error and squared error of the self-driving algorithm in the outer lane and inner lane, we find that our results are statistically significant based on a two-tailed T test with unequal variances where the p-value for absolute error is 0.009, and 0.001 for squared error. Self-driving vehicles are not only complex, but they are growing in necessity—therefore, finding an optimal solution for lane detection in dynamic conditions is crucial to continue innovation.

关键词： Deep learning computer vision Lane detection Heuristic algorithms Computational modeling Lighting Data models

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A Structure-Behavior Coalescence Process Algebra Approach to Unifying Multiple Views of a System 5

A Structure-Behavior Coalescence Process Algebra Approach to...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Wang, Hsien-Tzu Ma, Wei-Ming Chao, William S. National Kaohsiung University of Science and Technology College of Management Kaohsiung Taiwan R.O.C. Air Force Academy Math. and Phy. Group General Education Center Kaohsiung Taiwan National Sun Yat-sen University Dept. of Information Management Kaohsiung Taiwan

ISBN: (纸本)9798350314694

We proposed a system metamodel called channel-based multi-queue structure-behavior coalescence process algebra (CM-SBC-PA). An example of a system metamodel using CM-SBC-PA and diagramming techniques was shown. CM-SBC-PA can be used as the basis for unifying multiple views of the system. Possible ways for CM-SBC-PA to project their views on the system are presented. © 2023 IEEE.

关键词： system metamodel unifying multiple views

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Local recognition of the point graphs of some Lie incidence geometries

arXiv

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arXiv 2023年

作者： Ihringer, Ferdinand Jansen, Paulien Lambrecht, Linde Neyt, Yannick Rijpert, Daan Van Maldeghem, Hendrik Victoor, Magali Dept. of Mathematics: Analysis Logic and Discrete Math. Ghent University Belgium Dept. of Mathematics Southern University of Science and Technology Shenzhen China Dept. of Mathematics: Algebra and Geometry Ghent University Belgium

Given a finite Lie incidence geometry which is either a polar space of rank at least 3 or a strong parapolar space of symplectic rank at least 4 and diameter at most 4, or the parapolar space arising from the line Grassmannian of a projective space of dimension at least 4, we show that its point graph is determined by its local structure. This follows from a more general result which classifies graphs whose local structure can vary over all local structures of the point graphs of the aforementioned geometries. In particular, this characterises the strongly regular graphs arising from the line Grassmannian of a finite projective space, from the half spin geometry related to the quadric Q+(10, q) and from the exceptional group of type E6(q) by their local structure. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Numerical Simulation of the Time-domain Electromagnetic Field for Thaw Zone Detection

Numerical Simulation of the Time-domain Electromagnetic Fiel...

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2022 IEEE International Multi-Conference on Engineering, computer and Information sciences, SIBIRCON 2022

作者： Shtabel, Nadezhda V. Dobrolyubova, Darya V. Lab. of Math. Modeling of Multiphase Processes in Native and Artif. Multiscale Heterogeneous Media Dept. of Geophysical Systems Trofimuk Institute of Petroleum Geology and Geophysics SBRAS Novosibirsk State Technical University Novosibirsk Russia Lab. of Math. Modeling of Multiphase Processes in Native and Artif. Multiscale Heterogeneous Media Dept. of Computer Technologies Novosibirsk State Technical University Trofimuk Institute of Petroleum Geology and Geophysics SBRAS Novosibirsk Russia

ISBN: (纸本)9781665464802

Permafrost thaw in the Arctic and Northern regions has a great impact on both northern ecosystems and human activities in the region. Therefore, efficient and noninvasive methods of thaw monitoring are of utmost importance. In this paper, we explore the applicability of the near-field electromagnetic pulsed sounding method to the problem of thaw zone detection. We consider several variants of the medium composition: a layered model with a thaw top layer;a permafrost half-space with a vertical well with and without the thaw zone along the wellbore, and examine whether the electromotive force or apparent resistivity curves yielded by the numerical simulation of the pulsed electromagnetic field indicate the effect of the thaw zones. The numerical simulations show that the pulsed electromagnetic sounding setup considered in the paper successfully identifies even relatively thin thaw upper layer, whereas a small thaw zone around the wellbore, or an open wellbore itself, are non-detectable by the surface measurements. © 2022 IEEE.

关键词： Permafrost

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A stochastic modeling approach for traffic analysis of a tramway system with virtual tags and local positioning

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Journal of the Brazilian computer Society 2021年第1期27卷 1-38页

作者： da Silva, Leandro Dias Lollini, Paolo Mongelli, Diamantea Bondavalli, Andrea Mandò, Gianluca Computing Institute Federal University of Alagoas Maceió Brazil Resiltech S.R.L. Pontedera Italy Dept. of Math. and Computer Science University of Florence Firenze Italy University of Florence Firenze Italy Innovation & Technology Thales Italia SpA Pontedera Italy

Traditional solutions for tramway interlocking systems are based on physical sensors (balizes) distributed along the infrastructure which detect passing of the trams and trigger different actions, like the communications with the ground infrastructure and the interlocking system. This approach is not easily scalable and maintainable, and it is costly. The SISTER project designed new architectural solutions for addressing the previous problems based on the virtualization of the sensors and on the local positioning of each tram. The key idea is to trigger actions when the computed local position corresponds to a virtual tag. However, the computed position can be affected by errors, compared to the real one. Therefore, it is important to understand the impact of these new solutions on the traffic that can be supported by the tramway network. This paper presents a stochastic modeling approach for analysing the performability of a tramway system based on the SISTER architectural solutions, aiming to identify the parts of the tramway network that are more critical and sensible to the variation of the traffic conditions and to the setting of the key architectural parameters. We build a model using Stochastic Activity Networks and run sensitivity analyses on (i) the accuracy of the positioning, (ii) the different SISTER parameters, and (iii) considering possible outages temporarily blocking the journey of a tram. This analysis allows to properly set and fine-tune the key architectural parameters, to understand the impact of the accuracy on the positioning, to understand the impact of the outages. © 2021, The Author(s).

关键词： Stochastic systems

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Deep Learning Patch-Based Approach for Hyperspectral Image Classification

Deep Learning Patch-Based Approach for Hyperspectral Image C...

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IEEE International Conference on Electro-Information Technology

作者： Papia F. Rozario Eleana Ruehmann Tyler Pham Tianqi Sun Jacob Jensen Hengrui Jia Zhongyue Yu Rahul Gomes Dept. of Geography and Anthropology University of Wisconsin-Eau Claire Eau Claire WI USA Dept. of Computer Science University of Minnesota-Twin Cities Minneapolis MN USA Dept. of Computer Science University of Wisconsin-Eau Claire Eau Claire WI USA Dept. of Math and Computer Science Minot State University Minot ND USA Dept. of Math Statistics and Computer Science Macalester College Saint Paul MN USA

Classification of hyperspectral images is an important step of image interpretation from high spatial resolution imagery. Different studies demonstrate that spatial features can provide complementary information for increasing the accuracy of hyperspectral image classification. In this study, we evaluate different methods of spectral-spatial classification of hyperspectral images that are based on denoising methods using convolutional autoencoders. The resulting high-dimensional vectors of spectral features are classified by supervised algorithms such as support vector machine (SVM), maximum likelihood (ML), and random forest (RF). The experiments are performed on several widely known hyperspectral images that reveal a patch-based 3D convolutional autoencoder is more effective in reducing noise in the dataset and retaining spectral-spatial information. Random Forest classifier provides the highest classification accuracy across all the models.

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Norm Decay Rates of the Fourier Oscillatory Integral Operators for a Class of Homogeneous-Type Polynomial Hybrid Phases

SSRN

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SSRN 2024年

作者： Nguyen, Minh Tuan Anh, Pham Tuan Huy, Vu Nhat Dept. of Math. University of Transport and Communications 3 Cau Giay str. Dong Da dist. Hanoi Viet Nam Dept. of Math. VNU University of Science Viet Nam National University 334 Nguyen Trai str. Thanh Xuan dist. Hanoi Viet Nam Dept. of Math. VNU University of Education Viet Nam National University G7 Build. 144 Xuan Thuy rd. Cau Giay dist. Hanoi Viet Nam

This paper presents a new approach to the L2(R) norm decay rates of the Fourier oscillatory integral operators for some classes of degenerate phases. In particular, the sharp norm decay rates of the Fourier oscillatory integral operators for homogeneous-type polynomial phases, and those for a class of nonsmooth polynomial hybrid phase functions are obtained. © 2024, The Authors. All rights reserved.

关键词： Choquet integral

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Convergence of high dimensional Toeplitz and related matrices with correlated inputs

arXiv

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arXiv 2023年

作者： Adhikari, Kartick Bose, Arup Maurya, Shambhu Nath Dept. of Math. Indian Institute of Science Education and Research Bhopal India Statistics and Mathematics Unit Indian Statistical Institute Kolkata India

We investigate the joint convergence of independent random Toeplitz matrices with complex input entries that have a pair-correlation structure, along with deterministic Toeplitz matrices and the backward identity permutation matrix. Further, we study the joint convergence of independent generalized Toeplitz matrices along with other related matrices. The limits depend only on the correlation structure but are universal otherwise, in that they do not depend on the underlying distributions of the entries. In particular, these results provide the joint convergence of asymmetric Hankel matrices. Earlier results in the literature on the joint convergence of random symmetric Toeplitz and symmetric Hankel matrices with real entries follow as special *** Codes Primary 15B05, Secondary 15B52, 60B20 Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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An AR-Based Tool for Acquisition and Automatic Labeling of Human-Object Interactions From First Person Vision

An AR-Based Tool for Acquisition and Automatic Labeling of H...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Luigi Seminara Francesco Ragusa Rosario Leonardi Giovanni Maria Farinella Antonino Furnari Dept. Math and Computer Science (DMI) University of Catania (UNICT) Catania Italy Dept. Math and Computer Science (DMI) University of Catania (UNICT) Next Vision S.r.l. - Spinoff of the University of Catania

We consider the problem of detecting Human-Object Interactions (HOIs) from images acquired through the use of wearable devices. Understanding human-object interactions allows to support users in different scenarios, ranging from everyday activities to industrial contexts. In particular, by detecting interactions, it is possible to provide assistance in using specific objects or enhance worker safety by alerting them when they are interacting with dangerous tools. The current approaches for detecting egocentric human-object interactions (EHOIs) require the collection and labeling of domain-specific data in order to fine-tune the models to work in a specific target environment. To reduce the labeling costs generally associated with this process, we developed a new tool that uses spatial mapping, hand poses estimation, and camera tracking capabilities available in the Augmented Reality stack of Microsoft HoloLens2 to collect and automatically label images of human-object interactions performed in the real-world. To assess the effectiveness of the proposed tool, we collected and automatically labeled a dataset of human-object interactions performed on an industrial panel. Experiments with two EHOI recognition models suggest that using the data collected by the proposed tool can improve model performance in the considered target domain.

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AI-augmented Automation for Real Driving Prediction: an Industrial Use Case

arXiv

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arXiv 2024年

作者： Eramo, Romina Salman, Hamzeh Eyal Spezialetti, Matteo Stern, Darko Quinton, Pierre Cicchetti, Antonio Dept. of Communication Science University of Teramo Italy Dept. of Software Engineering IT Faculty Mutah University Jordan Dept. of Inf. Eng. Comp. Science and Math. University of L'Aquila Italy Dept. of Research Program Management AVL Austria Dept. of Methodology and R&D AVL Austria Dept. of IDT Mälardalen University Sweden

The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to the need to perform the tests in real driving conditions. As a consequence, agile/iterative processes like DevOps are largely hindered by the necessity of triggering frequent tests. This paper reports on a practical experience of developing an AI-augmented solution - based on Machine Learning and Model-based Engineering - to support continuous vehicle development and testing. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator and hence enable performance tests in virtual environments. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior. © 2024, CC BY.

关键词： Machine learning

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