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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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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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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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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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Rule-based High-level Hardware-RTL Synthesis of Algorithms, Virtualizing Machines, and Communication Protocols with FPGAs based on Concurrent Communicating Sequential Processes and the ConPro Synthesis Framework

arXiv

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arXiv 2023年

作者： Bosse, Stefan University of Bremen Dept. of Mathematics and Computer Science Bremen Germany University of Siegen Dept. of Mechanical Engineering Siegen Germany Institute for Digitization Bremen Germany

Virtualization is the abstraction of details. Algorithms and programming languages provide abstraction, too. Virtualization of hardware and embedded systems is becoming more and more important in heterogeneous environments and networks, e.g., distributed and material-integrated sensor networks. Communication and data processing with a broad range of hardware and low-level protocols can be unified and accessed uniquely by introducing virtualization layers implemented directly in hardware on chip. Hardware design is today still component-driven (like a circuit board), rather than transforming algorithms as an abstraction layer directly into hardware designs. Programs and protocols are algorithms, so do not handle them as devices like in traditional high-level synthesis design flows! Complex reactive systems with dominant and complex control paths play an increasing role in SoC-design. The major contribution to concurrency appears at the control path level. This article gives an in-depth introduction to SoC-design methodology using the Highest-Level Synthesis ConPro compiler framework and a process-oriented programming language that provides a programming model based on concurrently executing and communicating sequential processes (CCSP) with an extensive set of interprocess-communication primitives. Circuits are modelled and programmed on an algorithmic level, more convenient and natural than component-driven designs. Extended case studies of a smart communication protocol router and an advanced stack-based processor providing a programmatical virtualization layer are shown and evaluated. Both are used together as a smart node architecture deployed in high density sensor-actuator-networks, e.g., for material-integrated intelligent systems. © 2023, CC BY.

关键词： Real time systems

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A Development of Cloud Based Robotics Design Networks for Industry Applications

A Development of Cloud Based Robotics Design Networks for In...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Sathi G Neeraj Varshney Praveen Sharma B.Jasmine Punitha R. Sundar S P V SubbaRao Dept of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Engineering and Applications (CEA) GLA University Mathura Department of Computer Science & Engineering (CS & E) IES Institute of Technology and Management IES University Bhopal M.P India Department of Information Technology Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore TN Department of Electronics and Communication Engineering (ECE) Sreenidhi Institute of Science and Technology Hyderabad

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In modern robots, the usage of computationally expensive models involving deep neural networks, also referred to as DNNs, for tasks such as the localization of operations awareness, planning, and object recognition is becoming prominent. Nevertheless, resource-constrained machinery, such as low-power aerial vehicles, often lack the requisite internal computing resources to easily run cutting-edge simulations of neural networks. Cloud robotics appears as an answer, allowing robots to offload processing to centralized computers for greater precision models. Nonetheless, the ignored downside of cloud robots lies in the possible delay and data loss experienced during contact over crowded wireless networks. This study discusses the Robot Transferring responsibility Problem, exploring when and where robots should offload sense tasks that improve accuracy while reducing the costs involved with cloud communication. The method involves framing shifting as a sequence decision-making issue concerning robots and suggesting a remedy using sophisticated reinforcement learning. Through models and hardware tests employing advanced thinking DNNs, what was suggested sharing strategy improves vision task efficacy by 1.3 2.6 times as a result of standard strategies, allowing robots to increase their sense accuracy while incurring minimal communication via cloud costs.

关键词： computers Costs Accuracy Service robots Computational modeling Reinforcement learning Robot sensing systems

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Enhancing Non-Destructive Examining of Composite Materials Using Lightweight Structures And Machine Learning

Enhancing Non-Destructive Examining of Composite Materials U...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Prashant Kumar Gangwar Balachandra Pattanaik Madiajagan M Mohammed Ali Sohail M. Dinesh Babu B. Sarala Department of Construction Technology & Management Woldia University Amhara Region Ethiopia Department of Electrical and Computer Engineering College of Engineering and Technology Wollega University Nekemte Ethiopia Africa School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer & Network Engineering College of Computer Science & Information Technology Jazan University Jazan K.S.A Department of Mechanical Engineering Rajalakshmi Institute of Technology Chembarabakkam Chennai Department of Electronics and Communication Engineering R.M.K Engineering College Chennai

Composite materials and structures are becoming more efficient and cost-effective, and new special features are being developed to improve their specific properties. Their use in a variety of applications such as wind turbines, aeronautical, medical equipment, transportation, and other load-bearing structures is becoming increasingly important. It is necessary for non-destructive composite examining to be robust and trustworthy in order to limit the risk of damage as well as the cost of maintenance. In order to ensure the product quality throughout its life cycle, many non-destructive examining procedures based on a wide range of concepts have been created. In this paper, nondestructive examining was used to obtain vibration measurements of composites in high-frequency regimes, which were then analysed. Both end-to-end classifications of the data and classifications of data acquired by feature engineering with modal analysis employ Support Vector Machine (SVM) to carry out damage localization in their respective applications. Finally, the findings display a large number of plates made of various materials and with defects of varying magnitudes.

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Research on Network Performance and Availability Modeling Based on Data Link

Research on Network Performance and Availability Modeling Ba...

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International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS)

作者： Yanfang Fu Dengdeng Guo Tao Zhao Shaochun Qu Yinfan Chen School of Computer Science and Engineering Xi'an Technological University Xi'an China Northwest Institute of Mechanical & Electrical Engineering Xian Yang China

As the basis of information war, military network plays a vital role in the development of war. Network availability is one of the most important performance of military network construction and operation. It is also an important index to measure the quality of system structure design and system performance stability. Therefore, based on the analysis of the factors affecting the availability of military network, starting from the basic concept of network availability and using Markov random process theory, this paper puts forward the Markov model of multi state repairable system of communication network, and analyzes and calculates the delay and throughput of military network system in Link16 data link. It provides some references for the development of military network in the future.

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