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2020 International Virtual Conference on Sustainable Materials, IVCSM 2k20

作者： Renugadevi, N. Saravanan, S. Naga Sudha, C.M. Department of Computer Science and Engineering Indian Institute of Information Technology Tiruchirappalli India Department of Mechanical Engineering K. Ramakrishnan College of Technology Tiruchirappalli India Department of Computer Technology Anna University MIT Campus Chennai India

In present scenario, each and every activity in basic lifestyle of people is becoming smarter with the day-to-day flourishing development of technology such as Internet of Things, Artificial Intelligence, Machine Learning and Blockchain. Among the smart improvements, smart city holds the highest rank due to the initiatives provided by the administration. Smart city mission has influenced the citizen's life with respect to the automation on fundamental needs. Energy resources are considered as one of the important requirements for the survival of people. With the advent of Internet of Things, smart grid systems are designed in order to meet the sustainable demands. It also provides information about the energy resources consumed and alert the consumers about the demand prevails on the resources. Technological improvement is more essential for a country, only if it can be considered and adaptable for sustainable development. Therefore these papers outline the Internet of Things based smart grid which helps in the energy conservation process to meet the future demands. Energy management processes has to be well-versed among the citizens as it helps in understanding the possible ways on storage of energy resources. However, when energy storage is concerned, security issues are the counterpart which has to be focused at higher level. Therefore, blockchain techniques applied for energy storage systems are also outlined along with the applications and future directions of smart grid. © 2021 Elsevier Ltd. All rights reserved.

关键词： Energy resources

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mechanically Reconfigurable Terahertz Quasi-Optical Components for Spectral Filtering and Polarization Control

Mechanically Reconfigurable Terahertz Quasi-Optical Componen...

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Microwaves Antennas and Propagation Conference (MAPCON)

作者： Sakib Quader Xiaojing Lv Xiaolong You Rajour T. Ako Madhu Bhaskaran Sharath Sriram Christophe Fumeaux Withawat Withayachumnankul Terahertz Engineering Laboratory The University of Adelaide Adelaide Australia School of Mechanical and Mechatronic Engineering University of Technology Sydney Sydney Australia Functional Materials and Microsystems Research Group RMIT University Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798350379693

ISBN: (纸本)9798350379709

Terahertz technologies present unique opportunities for high throughput communications, short-distance high-resolution radar, and spectral sensing/imaging. This spectral range presents some interesting challenges in terms of tunability of transmissive quasi-optical components for spectral filtering or polarization control. The concept of multi-layer periodic structures with sub-wavelength elements, such as frequency-selective surfaces or metasurfaces, allows to create terahertz optical components with unique properties. In this context, this paper reviews three distinctive realizations of such multi-layer transmissive devices with reconfigurable properties. The controlled dynamic adaptation of transmission properties is achieved through mechanical reconfiguration based on relative translation or rotation of layers. These presented devices are illustrative of three efficient implementations based on different physical mechanisms, with application for tunable spectral filtering and dynamic polarization control.

关键词： Translation Filtering Surface waves Bandwidth Terahertz materials Metasurfaces Throughput Sensors Tuning Optical surface waves

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Advanced Multimodal Prediction of Components of Livestock Feed Materials Using Knowledge Distillation

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 272-285页

作者： Oluwabukunmi, Owoeye Babatunde Joseph, Akomolafe Ayobami Udurume, Miraculous Njoku, Judith Nkechinyere Nwakanma, Cosmas Ifeanyi Asem-Hiablie, Senorpe Mallipeddi, Rammohan Park, Tusan Uyeh, Daniel Dooyum Michigan State University Department of Biosystems and Agricultural Engineering East LansingMI48824 United States West Virginia University Department of Computer Science and Electrical Engineering MorgantownWV26505 United States Shell International Exploration and Production Inc. Shell Technology Center Biotechnology Department HoustonTX77082 United States Kyungpook National University Department of Artificial Intelligence Daegu41566 Korea Republic of Kyungpook National University Smart Agricultural Innovation Center Daegu41566 Korea Republic of Kyungpook National University Department of Applied Biosciences Daegu41566 Korea Republic of Kyungpook National University Department of Smart Bio-Industrial Mechanical Engineering Daegu41566 Korea Republic of

Accurate analysis of livestock feed quality is critical for enhancing productivity and supporting sustainable farming practices. This study explores the integration of red, green, and blue (RGB) and near-infrared (NIR) imaging modalities, leveraging their complementary strengths, RGB for physical properties and NIR for chemical compositions, to predict key nutritional metrics. A novel knowledge distillation model was developed to transfer insights from a complex teacher model to a simpler student model. The approach involved training three types of models: single-channel (RGB or NIR), double-channel (RGB and NIR), and knowledge distillation models. Key evaluation metrics, including mean squared error (MSE), mean absolute error, and root MSE, validated the model's predictive accuracy. Experimental results demonstrated that the knowledge distillation model significantly outperformed both single- and double-channel models, achieving a 91.86% reduction in the MSE compared to RGB single-channel models, an 89.68% reduction compared to NIR single-channel models, and a 63.43% improvement over double-channel models. This study provides a robust, efficient, and cost-effective solution for feed quality assessment, highlighting the transformative potential of multimodal imaging and machine learning in precision agriculture. © 2023 IEEE.

关键词： Agricultural robots

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A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted Machine Learning Model

A Robust Development of Calorie Prediction Methodology based...

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Intelligent Technologies (CONIT), International Conference on

作者： G Naga Venkata RamaKrishna S Sreelakshmi Meenakshi Diwakar S. Ramkumar T Vinithra Banu T. Thilagam Faculty of Engineering University of Technology and Applied Sciences Al Musannah Sultanate of Oman Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of CSE (AI and ML) S.A.Engineering College Chennai Tamil Nadu India Department of Electronics and communication engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering S.A Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Modern people’s hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it’s not hard to keep track of one’s calorie intake. There aren’t many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. This paper presents an innovative ML model for accurate calorie prediction; it’s called the Improved Learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we’ll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object’s known volume to determine the food item’s volume. Once the volume has been determined, the food item’s mass may be determined using formulae. Then, the connection between weight and calories can be used to compute the food item’s calories.

关键词： Obesity Schedules Accuracy Target recognition Computational modeling Predictive models Feature extraction

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MK-RRT: Multi-Robot Kinodynamic RRT Trajectory Planning

MK-RRT: Multi-Robot Kinodynamic RRT Trajectory Planning

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： Cain, Brennan Kalaitzakis, Michail Vitzilaios, Nikolaos University of South Carolina Department of Computer Science and Engineering ColumbiaSC29208 United States University of South Carolina Department of Mechanical Engineering ColumbiaSC29208 United States

ISBN: (纸本)9780738131153

This paper introduces MK-RRT: a Multi-robot Kinodynamic RRT-based framework for trajectory planning of multiple dynamically-modeled robots. The framework includes both tightly-coupled and loosely-coupled methods for planning. The simultaneous, tightly-coupled, method provides an asymptotically optimal solution to the multi-robot trajectory planning problem. A sequential, loosely-coupled, method is also developed to compare costs of the generated trajectories and computational complexity of both methods. An application of this framework to multi-UAV trajectory planning is presented and experimentally evaluated using a team of Ryze Tello EDU UAVs. © 2021 IEEE.

关键词： Trajectories

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CFD-DRIVEN TOPOLOGY OPTIMIZATION FOR PERSONALIZED INTRACRANIAL ANEURYSM IMPLANT DESIGN

CFD-DRIVEN TOPOLOGY OPTIMIZATION FOR PERSONALIZED INTRACRANI...

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ASME 2024 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2024

作者： Lei, Weijun Sadasivan, Chander Gu, Xianfeng David Chen, Shikui Department of Mechanical Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Department of Neurological Surgery Stony Brook University Medical Center Stony BrookNY11794 United States Department of Computer Science Department of Applied Mathematics and Statistics State University of New York at Stony Brook Stony BrookNY11794 United States

ISBN: (纸本)9780791888377

Intracranial aneurysm rupture causes life-threatening subarachnoid hemorrhage. Current endovascular devices like coils, flow diverters, and intravascular implants aim to thrombose the aneurysm but have limitations and varying success rates depending on aneurysm characteristics. We propose a new computational framework integrating CFD and topology optimization to design personalized aneurysm implants. The optimization problem aims to reduce blood flow velocity within the aneurysm while ensuring adequate structural integrity of the implant. The fluid dynamics are governed by the Navier-Stokes equations, while the solid mechanics are described by the linear elasticity equations. A Darcy-Brinkman model is employed to simulate flow through the porous implant in the fluid domain, while the Solid Isotropic Material with Penalization (SIMP) method is used to interpolate between solid and void regions in the structural domain during topology optimization. The objective combines fluid energy dissipation ratio and solid strain energy with spatially varying weights. Global and local volume constraints generate personalized implants with porosity and flow-diverting architectures. The approach is demonstrated on patient-specific aneurysm geometries from rotational angiography. This CFD-driven topology optimization method enables personalized aneurysm implant design to potentially improve occlusion rates and reduce complications compared to current devices. Further studies will validate the optimized designs and investigate their efficacy in vitro and in vivo. Copyright © 2024 by ASME.

关键词： Navier Stokes equations

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Contour-guided Image Completion with Perceptual Grouping 32

Contour-guided Image Completion with Perceptual Grouping

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32nd British Machine Vision Conference, BMVC 2021

作者： Rezanejad, Morteza Gupta, Sidharth Gummaluru, Chandra Marten, Ryan Wilder, John Gruninger, Michael Walther, Dirk B. Department of Psychology University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Mechanical & Industrial Engineering University of Toronto Toronto Canada Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Humans are excellent at perceiving illusory outlines. We are readily able to complete contours, shapes, scenes, and even unseen objects when provided with images that contain broken fragments of a connected appearance. In vision science, this ability is largely explained by perceptual grouping: a foundational set of processes in human vision that describes how separated elements can be grouped. In this paper, we revisit an algorithm called Stochastic Completion Fields (SCFs) that mechanizes a set of such processes - good continuity, closure, and proximity - through contour completion. This paper implements a modernized model of the SCF algorithm, and uses it in an image editing framework where we propose novel methods to complete fragmented contours. We show how the SCF algorithm plausibly mimics results in human perception. We use the SCF completed contours as guides for inpainting, and show that our guides improve the performance of state-of-the-art models. Additionally, we show that the SCF aids in finding edges in high-noise environments. Overall, our described algorithms resemble an important mechanism in the human visual system, and offer a novel framework that modern computer vision models can benefit from. © 2021. The copyright of this document resides with its authors.

关键词： computer vision

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Superior Polymeric Gas Separation Membrane Designed by Explainable Graph Machine Learning

arXiv

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arXiv 2024年

作者： Xu, Jiaxin Suleiman, Agboola Liu, Gang Perez, Michael Zhang, Renzheng Jiang, Meng Guo, Ruilan Luo, Tengfei Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States Department of Chemical and Biomolecular Engineering University of Notre Dame Notre DameIN46556 United States Department of Computer Science and Engineering University of Notre Dame IN46556 United States

Gas separation using polymer membranes promises to dramatically drive down the energy, carbon, and water intensity of traditional thermally driven separation, but developing the membrane materials is challenging. Here, we demonstrate a novel graph machine learning (ML) strategy to guide the experimental discovery of synthesizable polymer membranes with performances simultaneously exceeding the empirical upper bounds in multiple industrially important gas separation tasks. Two predicted candidates are synthesized and experimentally validated to perform beyond the upper bounds for multiple gas pairs (O2/N2, H2/CH4, and H2/N2). Notably, the O2/N2 separation selectivity is 1.6-6.7× higher than existing polymer membranes. The molecular origin of the high performance is revealed by combining the inherent interpretability of our ML model, experimental characterization, and molecule-level simulation. Our study presents a unique explainable ML-experiment combination to tackle challenging energy material design problems in general, and the discovered polymers are beneficial for industrial gas separation. © 2024, CC BY.

关键词： Machine learning

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A Context-free Grammar based Association Rule Mining Technique for Network Dataset

A Context-free Grammar based Association Rule Mining Techniq...

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2020 International E- Conference on Data Analytics, Intelligent Systems and Information Security, ICDIIS 2020

作者： Saranyadevi, S. Murugeswari, R. Bathrinath, S. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu626126 India Department of Mechanical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu626126 India

Among various data mining concepts like prediction, clustering, classification, association and outlier discovery, association is a useful technique to extract the interesting relations among data items effectively. Association technique is applied in a number of applications like marketing, education, chemical, bioinformatics, computational linguistics and etc. The important purpose of association is to provide useful information of buying preferences of customers in supermarket in order to increase the sales opportunity, which is called as market- basket analysis. Till now there are many algorithms were developed, but the usage of formal grammars in association rule mining (ARM) is a latest technique to mine required data by means of grammars. In this paper ARM is performed using Context -free Grammar (CFG) - (ARM - Grammar) and the experiments are conducted on MATLAB 2017 software using network dataset, KDDCUP'99. Experimental outcomes prove that the proposed ARM - Grammar is effective than the traditional ARM approach. © Published under licence by IOP Publishing Ltd.

关键词： Association rules

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A Digital Twin Enabled Internet of Living Things (IoLT) Framework for Soil Carbon Management

A Digital Twin Enabled Internet of Living Things (IoLT) Fram...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Di An YangQuan Chen Electrical Engineering & Computer Science University of California Merced CA USA Department of Mechanical Engineering University of California Merced CA USA

ISBN: (纸本)9781665455718

Due to the lack of cost-effective methods for soil carbon content accounting, soil carbon emissions cannot be managed properly for a long time. The traditional methods are labor intensive which usually need tedious preprocessing and expensive analyzers to quantify the total soil carbon content. In this paper, we propose that a Digital Twin with AIoLT framework could perfectly solve the challenge mentioned above. By using an AIoLT-enabled proximity radar sensor to measure soil carbon content in real-time, our proposed soil carbon management digital twin (SCMDT) has the ability to deal with these soil carbon data to be “smart” big data, which are from missionaware sampling strategies. We also provide an evaluation metric for our proposed SCMDT in order to quantify its performance.

关键词： Mechatronics Machine learning algorithms Soil measurements Radar measurements Carbon dioxide Soil Real-time systems

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