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Health Monitoring of Structural and Biological Systems XVI 2022

作者： Prasad, Sneha Kumar, David Kalra, Sumit Chiang, Chih-Hung Khandelwal, Arpit Interdisciplinary Research Platform Internet of Things and Applications Indian Institute of Technology Jodhpur Jodhpur342030 India Department of Aeronautical Engineering Center for NDT Chaoyang University of Technology Taichung413310 Taiwan Department of Computer Science and Engineering Indian Institute of Technology Jodhpur Jodhpur342030 India Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur342030 India

ISBN: (数字)9781510649729

ISBN: (纸本)9781510649712

An automatic lightweight feature detection algorithm is developed to perform real-time structural health monitoring (SHM) of large structures. The algorithm works on the specified region of interest (ROI) and applies canny edge detection with k-means clustering for identifying the displaced pixel location in an image sequence. The location of detected edges (white pixels) in the selected ROI is first validated and then given as input to the k-means clustering algorithm for centroid calculation. The pixel movement tracing method is validated by image simulation, indoor digital micrometer experiment and then an outdoor field experiment on wind turbine. The image simulation experiment was performed to generate sample data and ground truth values. In this experiment, the algorithm was able to detect the defined pixel translations. With this validation, other two experiments were conducted. The indoor experiment was implemented for experimental verification where it successfully identifies the moving bar's 20mm displacement. Likewise, it also accurately measures the natural frequency of the tower of a utility-scale wind turbine. Hence, the algorithm was built on parallel processing with multi-ROI selection to optimize the space and time complexity for real-time vibration analysis. The present study proclaims that the developed algorithm can be used to perform real-time SHM of large-scale structures. © 2022 SPIE

关键词： Pixels

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Long Live O-PCMs: Understanding Reliability Challenges of Optical Phase Change Materials

Long Live O-PCMs: Understanding Reliability Challenges of Op...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Popescu, Cosmin-Constantin Mills, Brian Monier, Louis Martin Ranno, Luigi Zhang, Yifei Du, Qingyang Ríos, Carlos Vitale, Steven Roberts, Christopher Miller, Paul Liberman, Vladimir Kim, Hyun Jung Aryana, Kiumars Kang, Myungkoo Richardson, Kathleen Callahan, Dennis Geiger, Sarah Gu, Tian Hu, Juejun Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Zhejiang Hangzhou311121 China Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States NASA Langley Research Center HamptonVA23681 United States CREOL The College of Optics and Photonics University of Central Florida OrlandoFL32816 United States Charles Stark Draper Laboratory CambridgeMA02139 United States

The endurance of optical phase change material-based devices for integrated and free-space applications is explored and improvements are proposed based on limiting PCM dewetting, protective layer delamination, and ele... 详细信息

ISBN: (纸本)9781957171258

关键词： Phase change materials

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Prediction of water quality parameters by IoT and machine learning

Prediction of water quality parameters by IoT and machine le...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Srinivas Kolli M. Ranjani P. Kavitha D. Arul Pon Daniel Awadhesh Chandramauli Department Of Information Technology VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India Computer Applications Jawaharlal Nehru college for women Ulundurpet India Muthayammal center for Advanced Research Muthayammal College of Arts & Science Department of Computer Science and Applications Loyola College of Arts & Science Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehrradun India

Increases in domestical applications and other applications drive the need for better clean Water Management in India. What makes up the “chemical, physical, and biological” quality of freshwater There are three stages in the conventional method of monitoring water quality: sample, analysis, and inquiry. The scientists do these by hand. This method needs to be foolproof and predict a water supply’s quality. They’re Using the development of wireless sensor technologies, has also been some investigation into the possibility of monitoring water quality with wireless sensors put in water and communicating with farmers through brief messages regarding water. There has also been a study into the possibility of employing machine learning algorithms to analyze water quality. In light of recent advances in Machine Communication, which allow for devices to communicate with one another and smart ly analyze the data they collect. We have developed an “smart water quality monitoring system by using wireless sensonrs” for residential areas that rely on water storage tanks. A pH sensor and a total dissolved solids meter are used in this system to provide accurate readings of the water’s pH and dissolved solids levels. K-Means clustering, a machine learning technique, has been used to forecast water quality using a training data set consisting of various water samples. This system is now in its prototype stage, used with low-cost embedded devices like the Arduino Uno and Raspberry Pi3.

关键词： Wireless communication Meters Wireless sensor networks Water storage Training data Prototypes Water quality

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AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations

arXiv

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arXiv 2024年

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

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. Additionally, 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. © 2024, CC BY-NC-SA.

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AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations

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

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International Conference on Machine Learning and applications (ICMLA)

作者： Parth Ganeriwala Siddhartha Bhattacharyya Sean Gunther Brian Kish Mohammed Abdul Hafeez Khan Ankur Dhadoti Natasha Neogi Dept. of Computer Science Florida Institute of Technology Melbourne Florida Dept. of Aerospace Engineering Florida Institute of Technology Melbourne Florida Dept. of Mechanical Engineering Univeristy of Michigan Ann Arbor Michigan Dept. of Computer Science BITS Pilani Dubai Campus Dubai United Arab Emirates Langley Research Center NASA Hampton Virginia

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.

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Industrial Digitalization Solutions for Precision Forestry Towards Forestry 4.0 1

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7th International Conference on Smart City applications, SCA 2022

作者： Torres, Pedro M. B. Spencer, Geoffrey Neto, Luís Gonçalves, Gil Dionísio, Rogério Instituto Politécnico de Castelo Branco Castelo Branco6000-084 Portugal SYSTEC - Research Center for Systems and Technologies Porto4200-465 Portugal DiSAC - R &D Unit for Digital Services Applications and Content Castelo Branco6000-767 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal

ISBN: (数字)9783031268526

ISBN: (纸本)9783031268519

This paper presents machine digitalization solutions with particular application in forest machines, such as harvesters and wood processing machines. In line with all the requirements of Industry 4.0, this type of machines also needs digitization to align with the concept defined as Forestry 4.0, where we think of a smarter forest in which all stakeholders, humans, forest producers, machines and factories communicate. For machine manufacturers is a step that must be taken to modernize machines, enabling remote access services for maintenance, productivity monitoring, and management of forest operations. It consists of developing cyber-physical systems around the machines with digital twins that allow the simulation and identification of faults that may occur. A solution is presented to enable CAN Bus communication between the controller, operator joysticks, and sensors/actuators, as well as a Digital Twin solution to emulate machine operations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Industry 4.0

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Dynamic Regret with Unknown Delays

SSRN

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SSRN 2023年

作者： Wu, Ping Liu, Zhengyang Lu, Haolin Huang, Heyan School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

We study the problem of online convex optimization in dynamic environments with unknown and arbitrary delays. Without delays, existing work has achieved the optimal $\Theta(\sqrt{(1+\Delta_T)T})$ regret bound for any given sequence of comparators, where $T$ is the time horizon and $\Delta_T$ is the path-length of the comparator sequence. However, their algorithm assumes that the loss function can be revealed immediately to the agent after making the decision. In this paper, we propose a novel algorithm to tackle the delayed setting. We show that it enjoys $\mathcal{O}(\max\{\sqrt{(1+\Delta_T)D_T},(\hat{d}-1)\Delta_T\})$ regret bound, where $\hat{d}$ is the maximum delay and $D_T$ is the cumulative delay over the whole horizon. To complement our algorithm, we also establish an $\Omega(\sqrt{(1+\Delta_T)D_T})$ lower bound. The basic idea is to employ a delayed meta algorithm to track parallel delayed expert algorithms with varying step sizes. We further extend our algorithm to the case that we have no idea about the time horizon and cumulative delay using a novel doubling trick. Finally, numerical simulations are provided to illustrate the validity of our theoretical results. © 2023, The Authors. All rights reserved.

关键词： Comparators (optical)

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Dynamic Regret with Unknown Delays

SSRN

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SSRN 2023年

In this paper, we delve into the realm of delayed online convex optimization, addressing scenarios where unknown delays exist between the querying and revealing of feedback. Obtaining dynamic regret in this case poses challenges arising from the uncertainties associated with both comparator sequence and delay sequence. First, to handle the uncertainty of comparator sequence, we introduce an adaptive delayed algorithm, yielding an $\mathcal{O}(\max{\sqrt{(1+\Delta_T)D_T},(\hat{d}-1)\Delta_T})$ dynamic regret bound for arbitrary comparator sequences. Here, $\Delta_T$ represents the path-length of the comparator sequence, $\hat{d}$ denotes the maximum delay, and $D_T$ signifies the cumulative delay over the entire horizon. Our approach centers around employing a delayed meta algorithm to track parallel delayed expert algorithms with varying step sizes. To complement our theoretical analysis, we establish an $\Omega(\sqrt{(1+\Delta_T)D_T})$ lower bound. Furthermore, to address the uncertainty of delay sequence, we extend our adaptive algorithm to scenarios where the time horizon and cumulative delay are unknown, incorporating a delayed doubling trick tailored to the number of missing feedback instances, thereby achieving a comparable dynamic regret. Finally, numerical simulations are presented to validate the effectiveness of our proposed approach. © 2023, The Authors. All rights reserved.

关键词： Comparators (optical)

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Distributed Dual Averaging for Composite Optimization

SSRN

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SSRN 2023年

作者： Wu, Ping Lu, Haolin Liu, Zhengyang Heyan, Huang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

In the context of a multi-agent network, we investigate the distributed online composite optimization problem where the optimization objective of each local agent is a combination of a convex cost function and a convex regularized function. To handle this problem, we introduce two distributed algorithms based on approximate dual averaging. The first online distributed composite dual averaging algorithm, under the gradient feedback, achieves a composite regret bound of $\mathcal{O}(\sqrt{T})$ and $\mathcal{O}(\ln T)$ for general convex and strongly convex regularized functions, respectively, across a total time horizon of $T$. Furthermore, the second one, relying exclusively on the values of cost function rather than the full information of gradient, attains a comparable composite regret bound against the first algorithm. To illustrate the effectiveness of our proposed algorithms, we present a plethora of numerical simulations for both the distributed regularized regression task and the distributed regularized classification task. © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Neural P3M: a long-range interaction modeling enhancer for geometric GNNs 24

Neural P3M: a long-range interaction modeling enhancer for g...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yusong Wang Chaoran Cheng Shaoning Li Yuxuan Ren Bin Shao Ge Liu Pheng-Ann Heng Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University University of Illinois Urbana-Champaign Department of Computer Science and Engineering The Chinese University of Hong Kong University of Science and Technology of China Microsoft Research AI4Science

ISBN: (纸本)9798331314385

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M.

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