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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Jeffrey Jeeva Dhas Adhip Koshy Nishit Dua D. Ajitha Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

A popular biometric identification method, renowned for its dependability and individuality in personal identification, is fingerprint recognition. This article presents an efficient fingerprint identification system that significantly reduces processing times for 100,000 fingerprints from 8 minutes to less than 5 seconds by utilizing multithreading, ANSI 378 templates, and Source AFIS. The system overcomes the time-consuming image-to-template conversion procedure and increases overall storage efficiency by utilizing parallel processing and preformatted templates (ANSI/ISO). The fingerprint matching system incorporates Directional Field, CNN models for identifying fingerprint types, and Finger Code methods to classify fingerprints, improving precision and efficiency.

关键词： Technological innovation Multithreading Law enforcement System performance Organizations Machine learning Fingerprint recognition Biometric identification Real-time systems Informatics

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Study of monolithically integrated 940 nm AlGaAs distributed Bragg reflectors on graded GaAsP/bulk Si substrates

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Optical Materials Express 2023年第4期13卷 1077-1091页

作者： Guo, Jia Zhao, Yunlong Feifel, Markus Cheng, Hao-Tien Yang, Yun-Cheng Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui Department of Materials Engineering The University of British Columbia VancouverBCV6 T 1Z4 Canada Fraunhofer Institute for Solar Energy Systems ISE Heidenhofstraße 2 Freiburg79110 Germany Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Photonics and Optoelectronics National Taiwan University Taiwan Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6 T 1Z4 Canada

We report the fabrication of monolithically integrated 940 nm AlGaAs distributed Bragg reflectors (DBRs) on graded GaAsP/Si substrates. Low-density surface bumps and cross-hatch patterns were observed on the DBR surfaces. Cross-sectional DBR layers are smooth and flat. The reflectance spectra of the GaAsP/Si DBRs have lower intensities than those of the GaAs DBRs and have double peaks. Transfer matrix method calculations, surface scratch and polishing tests were conducted, which suggest that the surface cross-hatch was the cause of the inferior DBR reflectance spectra. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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Predicting the Performance of ATL Model Transformations 23

Predicting the Performance of ATL Model Transformations

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14th Annual ACM/SPEC International Conference on Performance engineering, ICPE 2023

作者： Groner, Raffaela Bellmann, Peter Höppner, Stefan Thiam, Patrick Schwenker, Friedhelm Tichy, Matthias Institute of Software Engineering and Programming Languages Ulm University Ulm Germany Institute of Neural Information Processing Ulm University Ulm Germany Institute of Medical Systems Biology Ulm University Ulm Germany

ISBN: (纸本)9798400700682

Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. Typically, developers create their transformations based on small input models which systematically cover the language of the input models. This makes it difficult for the developers to estimate how the transformations would perform for a large and diverse set of input models. Hence, developers would benefit from an approach for predicting the performance of model transformations based on just abstract characteristics of input models. Regression approaches based on machine learning lend themselves well to such predictions. However, it is currently unknown, whether and which regression approach is suitable in this context as well as how a model should be abstractly characterized for this purpose. We conducted several experiments to analyze how well different machine learning methods predict the execution time of model transformations defined in the Atlas Transformation Language (ATL) transformations for distinct sets of model characteristics. As possible methods, we have investigated linear regression, random forests and support vector regression using a radial basis function kernel. The results of our experiments show that support vector regression is the best choice in terms of usability and prediction accuracy for the model transformation modules covered in our experiments and is thus suited for a prediction approach. In addition, simple model characterizations based only on the number of model elements, the number of references, and the number of attributes are a suitable way to easily describe a model and to achieve decent prediction accuracy. © 2023 Owner/Author.

关键词： Forecasting

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Combining Simulation and Recurrent Neural Networks for Model-Based Condition Monitoring of Machines 24

Combining Simulation and Recurrent Neural Networks for Model...

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Proceedings of the Winter Simulation Conference

作者： Alexander Wuttke Markus Rabe Joachim Hunker Jan-Philipp Diepenbrock Dept. IT in Production and Logistics TU Dortmund University Dortmund GERMANY Fraunhofer Institute for Software and Systems Engineering Dortmund GERMANY IVA Schmetz GmbH Menden GERMANY

ISBN: (纸本)9798331534202

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation's a-priori knowledge and machine learning's data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces.

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Developing Rib Bone Surrogates for High Dynamic Impact Assessment with Additive Manufacturing and Post-mortem Human Subjects (PMHS)-Based Evaluation

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Human Factors and Mechanical engineering for Defense and Safety 2024年第1期8卷 1-13页

作者： Jenerowicz, Marcin Haase, Thomas Linnenberg, Markus Musienko, Eugen Hoschke, Klaus Boljen, Matthias Hiermaier, Stefan Departments of Material Dynamics and Impact Physics Fraunhofer-Institute for High-Speed-Dynamics Ernst-Mach-Institute EMI Ernst-Zermelo-Straße 4 Freiburg 79104 Germany Department of Mechanical Engineering Manufacturing Engineering Laboratory Helmut-Schmidt-University Holstenhofweg 85 Hamburg 22043 Germany Department of Sustainable Systems Engineering - INATECH Albert-Ludwigs-University Emmy-Noether-Straße 2 Freiburg 79110 Germany

The conception of ballistic personal protective equipment requires a comprehensive understanding of the human body’s response to dynamic loads. The objective of this study is to develop rib bone surrogates enhancing new anthropomorphic test devices for personal protective equipment evaluation at high dynamic impacts. These are fabricated with additive manufacturing and compared to post-mortem human subjects (PMHS) data from literature. The 5th rib of the finite element Global Human Body Model Consortium (GHBMC) male 50th percentile (M50) model was extracted and transferred to a CAD model. This CAD model was divided into 30 sections with specific cortical bone thicknesses in all directions (caudal, cranial, cutaneous and pleural) from an equivalent rib of an M50 PMHS. Three different additive manufacturing technologies (direct metal laser melting, fused filament fabrication and multi jet modeling) were used to reproduce the M50 PMHS 5th rib surrogate. A total of 57 specimens were dynamically (500 mm/s) loaded to failure in a bending scenario imitating a frontal thoracic impact. Force, displacement, stiffness, and energy at failure were determined. Also, the strain distribution using 3D digital image correlation was recorded and compared to PMHS data from literature. The rib surrogates show deviations from the PMHS characteristic values. Nevertheless, there are also common characteristics in key variables to certain age groups of the PMHS data, which will facilitate the further development and improvement of adequate surrogates for a more realistic representation of the human body’s response to high dynamic loads. © The Author(s) 2024.

关键词： Additive manufacturing Anthropomorphic test devices High dynamic impacts Rib surrogates

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IoT Based Drip Infusion Monitoring System

IoT Based Drip Infusion Monitoring System

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Maniktalia, Riddhi Tanwar, Satwik Billa, Ritvik Deepa, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9781665458832

During recent years, we have seen many technological advancements which help to take better care of patient's health and assure them fast and safe recovery. The most basic item necessary is competent patient care in hospitals, as well as proper management of fluid and electrolytes. In hospital almost all patients, mainly ICU patients, have to regulate the volume of fluids and electrolyte into the bloodstream 247 which is done using drip. These drips need regular monitoring or changing to maintain constant flow of fluids or to prevent any infection to patients. Though this system is simple, overcrowded hospitals and the risk of deficiency of nurses can put patients at health risk which can deteriorate patients' health more aggressively or in worse case it can lead to death. Almost in all hospitals, nurses or the hospital staff are responsible for monitoring the drip level. But unfortunately, because of their hectic schedule, the observer may forget to change the bottle at the appropriate time. During the pandemic, hospitals were overrun with patients, and nurses were unable to do manual regular checks on the drip conditions and drip level of every patient, even after working extra shifts. Many patients even died due to not being able to get proper care from nurses. The next step in providing more effective and easy healthcare is to automate such vital procedures. To overcome this critical situation, we are proposing an IoT (Internet of Things) based Drip Monitor System using Arduino UNO which eases the process of measuring and solves the issues of bubble formation in drips. © 2022 IEEE.

关键词： Health risks

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection 40

RobFL: Robust Federated Learning via Feature Center Separati...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhou, Ting Liu, Ning Song, Bo Lv, Hongtao Guo, Deke Liu, Lei National University of Defense Technology Science and Technology Laboratory on Information Systems Engineering Changsha China School of Software Shandong University Jinan China Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798350317152

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module -feature center separation learning -that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks. © 2024 IEEE.

关键词： Anomaly detection

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Mechanism of superlubricity of a DLC/Si_(3)N_(4) contact in the presence of castor oil and other green lubricants

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Friction 2022年第10期10卷 1693-1706页

作者： Yun LONG Yang WANG Volker WEIHNACHT Stefan MAKOWSKI Momoji KUBO Jean Michel MARTIN Maria-Isabel DE BARROS BOUCHET Ecole Centrale de Lyon Laboratory of Tribology and System DynamicsUniversity of LyonEcully 69134France Institute for Materials Research Tohoku UniversitySendai 980-8577Japan Department of Mechanical Systems Engineering Graduate School of EngineeringTohoku UniversitySendai 980-8579Japan Fraunhofer Institute for Material and Beam Technology(IWS) Dresden 01277Germany

To meet the surging needs in energy efficiency and eco-friendly lubricants,a novel superlubricious technology using a vegetable oil and ceramic materials is *** coupling different hydrogen-free amorphous carbon coatings with varying fraction of sp^(2) and sp^(3) hybridized carbon in presence of a commercially available silicon nitride bulk ceramic,castor oil provides superlubricity although the liquid vegetable oil film in the contact is only a few nanometres thick at *** a partial liquid film possibly separating surfaces in contact,local tribochemical reactions between asperities are essential to maintain superlubricity at low *** local pressure activates chemical degradation of castor oil generating graphitic/graphenic-like species on top of asperities,thus helping both the chemical polishing of surface and its chemical passivation by H and OH ***,the formation of the formation of–(CH_(2)–CH_(2))n–noligomers have been evidenced to have a major role in the friction *** simulation unveils that formation of chemical degradation products of castor oil on friction surfaces are favoured by the quantity of sp^(2)-hybridized carbon atoms in the amorphous carbon ***,tuning sp^(2)-carbon content in hydrogen-free amorphous carbon,in particular,on the top layers of the coating,provides an alternative way to control superlubricity achieved with castor oil and other selected green lubricants.

关键词： superlubricity diamond-like carbon(DLC) sp^(2)-hybridized carbon hydrogen passivation castor oil

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IPL Data Analysis by Web Scraping and Using PowerBI

IPL Data Analysis by Web Scraping and Using PowerBI

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Mogith P Mothiswar T B G Mohammad Abubak J. Jagannathan N. Balaganesh Software Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Cricket fans throughout the world avidly follow the Indian Premier League (IPL), a competition of great fame. The IPL data is examined in this using Exploratory Data Analysis (EDA) approaches to find hidden trends, patterns, and insights. Key findings are summarized in EDA, a critical stage of data processing that is frequently complemented with visual aids. Through EDA, the goal is to investigate several factors, including team performance, player data, match results, and venue effects. Data transformation, cleaning, and visualization will all be part of producing insightful findings. Win percentages, player averages, and run distributions are examples of key performance indicators (KPIs) that will be looked at. The analysis's findings will give teams, athletes, and supporters important new information.

关键词： Visualization Technological innovation Fans Data analysis Key performance indicator Data visualization Market research Knowledge discovery Cleaning

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