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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Sun, Yiwei Wei, Yangjie Chen, Sike Zhao, Ji College of Computer Science and Engineering Northeastern University LN Shenyang China College of Mechanical Engineering and Automation Northeastern University LN Shenyang China

ISBN: (纸本)9798350315196

Existing design methods for off-axis reflective optical systems have certain limitations, such as complicated design processes, lengthy calculations and experience dependence. This study establishes an integrated generation method for the initial system of off-axis reflective optical systems and proposes it as a solution to the aforementioned limitations and for improvement of the design quality and efficiency of off-axis reflective systems. First, the tilt matrix and displacement matrix are introduced to consider changes in the shapes and space poses of the mirrors, and a simplified mathematical modeling method for arbitrary pose mirrors in an off-axis reflective system is proposed. Then, a mirror mathematical model of an arbitrary mirror relative to an arbitrary reference mirror is established by introducing the relative pose relationship between the two mirrors. Then, a model for the mirror-to-mirror and mirror-to-image transfer optical path in the off-axis mirror system is constructed, and the initial system for an off-axis reflective optical system can be automatically and quantitatively calculated. Experimental results show that the method proposed in this paper exhibits the advantages of high practicability, low requirements for designers' experience, easy to obtain design and manufacturing automation and intellectualization. © 2023 IEEE.

关键词： Mirrors

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VISION-iT:A Framework for Digitizing Bubbles and Droplets

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Energy and AI 2024年第1期15卷 26-35页

作者： Youngjoon Suh Sanghyeon Chang Peter Simadiris Tiffany B.Inouye Muhammad Jahidul Hoque Siavash Khodakarami Chirag Kharangate Nenad Miljkovic Yoonjin Won Department of Mechanical and Aerospace Engineering University of CaliforniaIrvine4200 Engineering GatewayCA 92617-2700USA Department of Electrical Engineering and Computer Science University of CaliforniaIrvine5200 Engineering HallCA 92617-2700USA Department of Mechanical Science and Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Mechanical and Aerospace Engineering Department Case Western Reserve UniversityClevelandOH44106USA Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA Materials Research Laboratory University of Illinois at Urbana-ChampaignUrbanaIL61801USA International Institute for Carbon Neutral Energy Research(WPI-12CNER) Kyushu University744 Moto-okaNishi-kuFukuoka 819-0395Japan

Quantifying the nucleation processes involved in liquid-vapor phase-change phenomena,while dauntingly challenging,is central in designing energy conversion and thermal management *** technological advances in the deep learning and computer vision field offer the potential for quantifying such complex two-phase nucleation processes at unprecedented *** leveraging these new technologies,a multiple object tracking framework called“vision inspired online nuclei tracker(VISION-iT)”has been proposed to extract large-scale,physical features residing within boiling and condensation ***,extracting high-quality features that can be integrated with domain knowledge requires detailed discussions that may be field-or case-specific *** this regard,we present a demonstration and discussion of the detailed construction,algorithms,and optimization of individual modules to enable adaptation of the framework to custom *** concepts and procedures outlined in this study are transferable and can benefit broader audiences dealing with similar problems.

关键词： Deep learning computer vision Nucleation Heat transfer Phase-change phenomena

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Bi-Criteria Diverse Plan Selection via Beam Search Approximation 17th

Bi-Criteria Diverse Plan Selection via Beam Search Approxima...

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17th International Symposium on Combinatorial Search, SoCS 2024

作者： Zhong, Shanhe Shati, Pouya Cohen, Eldan Department of Mechanical and Industrial Engineering University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Vector Institute Toronto Canada

ISBN: (纸本)9781577358916

Recent work on diverse planning has focused on a two-step setting where the first step consists of generating a large number of plans, and the second step consists of selecting a subset of plans that maximizes diversity. For the second step, previous work has focused on solving a combinatorial optimization problem for diverse subset selection that can be approximated using greedy search. In this work, we propose a flexible, bi-criteria framework for diverse plan selection. Our framework consists of optimizing both quality and diversity, generalizing previous work and providing flexibility to prioritize one objective over the other. We consider two quality and two diversity measures and show that greedy search guarantees an approximation with a constant ratio for certain configurations based on established results in the literature. To allow users to trade off additional computation for better solutions, we introduce a beam search approximation that generalizes the greedy search, and we provide approximation guarantees on the obtained solutions. Finally, we conduct extensive experiments that show that: (1) our flexible bi-criteria framework allows us to obtain solutions of better quality while still maintaining a high degree of diversity;(2) our beam search approximation obtains significant improvement in performance over greedy search and, for a large number of instances, is able to generate solutions that are equal to or better than those obtained by an exact MIP solver with a significantly higher runtime limit. © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Economic and social effects

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Advanced Tracking Algorithm for Multiple Dynamic Vehicles using Attention Mechanisms and Kinematic Models 3

Advanced Tracking Algorithm for Multiple Dynamic Vehicles us...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ravikumar, Kishore Sutaria, Kamal Pathak, Ganesh Pandit Rastogi, Shailesh Chennai Institute of Technology Department of Mechanical Engineering Kundrathur India Parul University Faculty of Engineering and Technology Deparment of Computer Science and Engineering Vadodara India Balaji Institute of Modern Management Pune India Pune India

ISBN: (纸本)9798331508456

Efficient vehicle tracking in urban environments is crucial for intelligent transportation systems but faces challenges such as nonlinear motion, dynamic backgrounds, and frequent occlusions. To address these complexities, we propose the AM-Vehicle-Track system, designed under the tracking-by-detection paradigm. This system integrates the Lightweight Convolutional Block Attention Module (LCBAM), which enhances detection accuracy by refining feature maps through channel-wise and spatial attention, and the Noise-Adaptive Extended Kalman Filter (NSA-EKF), which dynamically adjusts observation noise based on detection confidence to handle nonlinear vehicle motion. Additionally, the Byte data association technique is employed to mitigate issues with low-confidence detections and reduce identity switches. Tested on the challenging VisDrone-MOT dataset, AM-Vehicle-Track achieves a mean object tracking accuracy (MOTA) of 42.2%, an identity F1 score (IDF1) of 51.2%, and processes at 72 frames per second (FPS), outperforming existing state-of-the-art systems. These results highlight its robustness and efficiency in real-time scenarios, making it suitable for intelligent transportation applications. By significantly improving detection accuracy, tracking precision, and scalability, AM-Vehicle-Track establishes a new benchmark in multi-object tracking technologies and demonstrates its potential to enhance the reliability and efficiency of vehicle tracking in complex urban environments. © 2025 IEEE.

关键词： Urban transportation

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Soft Humanoid Hand with C-Shaped joint and Granular-Jamming Palm 16th

Soft Humanoid Hand with C-Shaped joint and Granular-Jamming ...

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Zheng, Haoxian Fang, Bin Yan, Junxia Liu, Huaping Sun, Fuchun School of Mechanical Engineering Jiangnan University Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819964857

In this paper, we propose a soft humanoid hand with a C-shaped joint and granular-jamming palm. The C-shaped joint with spring steel strip has low forward stiffness and can be easily bent, while its inherent elasticity enables it to return to its original position. The reverse stiffness of the joint is higher, providing strong support. The granular-jamming palm endows the soft humanoid hand with superhuman capabilities. Then the required bending moment for joint bending was derived through theoretical analysis of the C-shaped joint. The experimental platform was built, and the grasping, adhesion, and weight-bearing experiments were carried out. The results demonstrate the superior performance of the soft humanoid hand with a C-shaped joint and granular-jamming palm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Stiffness

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Clarity Method of Low-illumination and Dusty Coal Mine Images Based on Improved AMEF

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第7期47卷 51-62页

作者： Su, Chang Li, Ziqiang Wei, Zhongliang Xu, Naizhong Yuan, Quan State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine Anhui University of Science and Technology Huainan China School of Mechanical Engineering Anhui University of Science and Technology Huainan China School of Computer Science and Engineering Anhui University of Science and Technology Huainan China School of Mining Anhui University of Science and Technology Huainan China

The existing image processing methods based on physical models can have a significant impact on defogging performance due to inaccurate estimation of the depth of field information. These methods often encounter problems such as low brightness, invisible color distortion, and loss of detail when processing images with poor lighting conditions, such as those taken in coal mines. To address these issues, this paper proposes a new algorithm based on artificial multi-exposure image fusion. The proposed method performs global exposure on images with uneven illumination by combining S-type functions and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm in the Hue-Saturation-Value (HSV) color space. This reduces the spatial dependence of brightness during processing and avoids color distortion problems that may arise in the Red-Green-Blue (RGB) color space. To mitigate the issue of detail loss, a gradient-domain guided filter is used to preserve fine structures in images, while an improved homomorphic filtering algorithm is introduced during the Laplacian pyramid decomposition process to reduce image content loss arising from large dark areas. This paper also conducted subjective, objective, and computational time comparisons to evaluate performance, providing reliable results regarding speed, quality, and reliability in processing hazy images. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Luminance

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Cognitive Digital Twin Systems for Predictive Security in AI-Enhanced IoT Environments 1

Cognitive Digital Twin Systems for Predictive Security in AI...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Ravi Kumar, M. Jain, Upma Francis, Flory Senthil Kumar, S. Adhvaryu, Rachit Natrayan, L. Cambridge Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Ramaiah Institute of Technology Department of Electronics and Communication Engineering Bengaluru India R.M.K College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This thesis represents progress in that direction. This thesis covers research that aims to use AI to tackle significant online security challenges. Security in Internet of Things (IoT) settings powered by artificial intelligence may be improved with the help of cognitive digital twin systems (CDTS). In order to provide proactive security measures, real-time monitoring, and predictive analysis, these systems use cutting-edge AI methods to digitally represent physical IoT devices. In order to anticipate and lessen the impact of security risks, this research introduces a new CDTS architecture that combines cognitive learning skills, anomaly detection, and machine learning models. The suggested approach reduced reaction time to security events by 87.5% and identified zero-day threats with a detect

关键词： Risk management

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TEACHING A DEVELOPED FIRST-YEAR FLIPPED CLASSROOM

TEACHING A DEVELOPED FIRST-YEAR FLIPPED CLASSROOM

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ASME 2023 International mechanical engineering Congress and Exposition, IMECE 2023

作者： Stephan Thamban, P.L. Fadda, Dani Rios, Oziel Mechanical Engineering Department The Erik Jonsson School of Engineering and Computer Science The Jonsson School The University of Texas at Dallas DallasTX United States

ISBN: (纸本)9780791887653

Teaching a mechanical engineering class for the first time requires significant preparation and content development. While a flipped classroom canbe effective for student learning, course development can bequitedifficult for the faculty. However, when a course is developed in a modular flipped classroom model, faculty, teaching the course for the first time, can benefit from the developed content. They can use and make improvementsto modules based on their areas of expertise without affecting the entire course. The paper provides the perspective of a faculty member teaching a developed first year introduction to mechanical engineering course with the modular flipped classroom model. © 2023 by ASME.

关键词： Teaching

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A Machine Learning Approach for Revenue Management in Cloud Manufacturing 16

A Machine Learning Approach for Revenue Management in Cloud ...

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16th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2022

作者： Adomat, Vincent Ehrhardt, Jonas Kober, Christian Ahanpanjeh, Maryam Wulfsberg, Jens P. Institute of Production Engineering Helmut Schmidt University Hamburg Germany Institute of Computer Science in Mechanical Engineering Helmut Schmidt University Hamburg Germany

Cloud Manufacturing matches supplier capacities and customer demands via an algorithmic agent. The agent aims to maximize its own and the platform participant's revenue, hence the platform's gross revenue. Volatilities in supply and demand, as well as individual pricing models and willingness to pay, exacerbate the problem for deterministic solvers. In Revenue Management, comparable problems are approached with linear programming and regression models. Within this paper, we introduce a new Machine Learning (ML) based approach: Machine Learning for Cloud Manufacturing Cost Prediction (ML-ConCord). ML-ConCord aims to improve prediction quality for Revenue Management in Cloud Manufacturing by combining a recurrent neural network (RNN) with a linear programming pricing algorithm. We evaluate our approach on a self-generated dataset and compare its performance with standard regression-based approaches. Models and datasets are published on GitHub. © 2023 Elsevier B.V.. All rights reserved.

关键词： Economics

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A New Federated Learning Framework Against Gradient Inversion Attacks 39

A New Federated Learning Framework Against Gradient Inversio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Pengxin Zeng, Shuang Chen, Wenhao Zhang, Xiaodan Ren, Weihong Zhou, Yuyin Qu, Liangqiong School of Computing and Data Science The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Beijing University of Technology China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering UC Santa Cruz United States

ISBN: (纸本)157735897X

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively "breaks the direct connection" between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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