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arXiv 2023年

作者： Singh, Ritwiz Kashyap, Keshav Mukherjee, Rajesh Bera, Asish Chakraborty, Mamata Dalui Department of Computer Science and Engineering Haldia Institute of Technology WB India Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333 031 India Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances, and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset. © 2023, CC BY.

关键词： Biometrics

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Shaping the Sub-Reflector of a Ring Focus Antenna for Tailored Beam Width Applications

Shaping the Sub-Reflector of a Ring Focus Antenna for Tailor...

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European Conference on Antennas and Propagation, EuCAP

作者： Giuseppe Dell'Aere Christophe Mellé Pascal Cousin Nelson J.G. Fonseca Ronan Sauleau David González-Ovejero Mauro Ettorre CNRS Univ Rennes IETR Rennes France Safran Data Systems La Teste de Buch France Safran Data Systems Colombelles France European Space Agency Noordwijk The Netherlands Dept. of Electrical and Computer Engineering Michigan State University East Lansing USA

This paper introduces a comprehensive investigation on the possibility to tailor the ring focus antenna's beamwidth by manipulating the shape of the sub-reflector, and more specifically its eccentricity, determining the location of the focal points of the dual reflector system. Eccentricity variation affects the sub-reflector's reflected field and thus the main reflector's illumination. The examined antenna configuration includes a main reflector with a diameter of 13.5 m, a sub-reflector with a diameter of 1.35 m, and employs a linearly polarized Gaussian beam as feed. Changing the sub-reflector's eccentricity from the original value of 0.6 to 0.66 reduces the directivity from 70 dB to 55 dB, widening the beamwidth from 0.05° to 0.45° at 26 GHz. This feature is of great interest for applications needing adjustable beamwidth, such as satellite location acquisition. The beamwidth switching may be implemented using a reconfigurable transmitarray providing two states equivalent to the two sub-reflector shapes.

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning

Machine Learning for Climate Change Impact Assessment and Ad...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： T. Suresh Balakrishnan Prabhakar Krishnan U. Samson Ebenezar M. Mohammed Nizarudeen N. Kamal Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-Campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level.

关键词： Climate change Machine learning Planning Data analysis Quality assessment Meteorology Support vector machines Convolutional neural networks Statistical analysis Random forests Long short term memory Feedback loop Evidence theory

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Teaching DevOps Security Education with Hands-on Labware: Automated Detection of Security Weakness in Python

arXiv

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arXiv 2023年

作者： Akter, Mst Shapna Rodriguez-Cardenas, Juanjose Rahman, Md Mostafizur Shahriar, Hossain Rahman, Akond Wu, Fan Dept. of Intelligence and Robotics Systems University of West Florida United States Dept. of Information Technology Kennesaw State University United States Dept. of Information Technology University of West Florida United States Center of Cyber Security University of West Florida United States Dept. of Computer Science and Software Engineering Auburn University Auburn United States Dept of Computer Science Tuskegee University Tuskegee United States

The field of DevOps security education necessitates innovative approaches to effectively address the ever-evolving challenges of cybersecurity. In adopting a student-centered approach, there is the need for the design and development of a comprehensive set of hands-on learning modules. In this paper, we introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses, based on taint tracking to accurately pinpoint vulnerable code. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post lab sections. They all provide introduction to specific DevOps topics and software security problems at hand, followed by practicing with real world code examples having security issues to detect them using tools. The initial evaluation results from a number of courses across multiple schools show that the hands-on modules are enhancing the interests among students on software security and cybersecurity, while preparing them to address DevOps security vulnerabilities. © 2023, CC BY.

关键词： computer aided instruction

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Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks

Improving the Efficiency of a Multistage Evaporator Station ...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko det. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine

ISBN: (纸本)9798350346848

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department.

关键词： Productivity Soft sensors Predictive models Solids Sugar industry Data models Real-time systems

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On the Use of MPC Techniques to Decide Intervention Policies against COVID-19

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IFAC-PapersOnLine 2021年第14期54卷 476-481页

作者： Zonglin Liu Olaf Stursberg Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Germany.

This paper aims at demonstrating how and that model predictive control (MPC) strategies can be used to determine optimal intervention policies against the COVID-19 pandemic. Especially for the time after a first wave of infection and before a vaccine can be safely distributed to a sufficient extent, the intervention experience from the first outbreak can be utilized to guide the policy decision in this period. The MPC problem in this paper takes the pandemic in different regions of a country and its neighboring countries into account, while policies such as wearing masks or social distancing are selected as inputs to be optimized. This optimized policy balances the risk of a second outbreak and socio-economic costs, while considering that the measure should not be too severe to be rejected by the population. Effectiveness of this policy compared to standard intervention policies is compared through numerical simulations.

关键词： Biomedical systems Networked systems Nonlinear Model Predictive control

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Evaluation of the Energy Budget of a Solar Powered Autonomous Surface Vehicle

Evaluation of the Energy Budget of a Solar Powered Autonomou...

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OCEANS

作者： Jessica Simmerman Wolfe Gary D. Chesser Gray Turnage J. Wes Lowe Jane Moorhead Robert Moorhead James Herman Geosystems Research Institute Mississippi State University Mississippi State Mississippi Dept. of Agricultural and Biological Engineering Mississippi State University Mississippi State Mississippi Dept. of Electrical and Computer Engineering Mississippi State University Mississippi State Mississippi SeaTrac Systems Inc. Marblehead Massachusetts

With the increasing need for real-time monitoring and management of aquatic ecosystems, autonomous surface vehicles (ASV) are a vital tool for in situ water quality sampling. Compared to conventional monitoring methods, solar powered ASVs (SP-ASV) serve as mobile monitoring stations equipped with various sensor platforms to enhance spatio-temporal water quality data collection by providing extended-duration, continuous monitoring of diverse ecosystem. The SeaTrac SP-48 is ASV designed to surface navigate varying aquatic ecosystems. The SP-48’s solar power system, navigational dashboard, payload capacity, and sensor agnostic design provide capabilities to host and power a variety of sensors for multi-task over-the-horizon operations to persistently and autonomously monitor aquatic ecosystems for extended durations of *** objective of this research was to evaluate the energy budget (energy production/consumption) of a commercially available multi-purpose SP-ASV (SeaTrac SP-48) equipped with an integrated payload package of environmental monitoring sensors during an extended-duration mission. For this study, operation of the integrated sensors package and SP-48 navigation system resulted in a mean power load of 0.14 kW. Due to decreasing solar energy potential over the 29-day period (photoperiod (PD), sun angle, and weather conditions), energy consumption was slightly higher than energy *** results of this work will benefit ASV operators when planning missions in order to manage an energy budget. The results will also help researchers and ASV operators optimize energy consumption. Future work should assess ASV energy budget in summer months as photoperiod and solar irradiance are at peak levels to establish peak performance baselines

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New control Method for Multi-phase Buck DC-DC Converters Used with Photovoltaic Panels

New Control Method for Multi-phase Buck DC-DC Converters Use...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Dobroslav Kovac Viacheslav Melnykov Dept. of Theoretical and Industrial Electrical Engineering Faculty of Electrical Engineering and Informatics Technical University of Kosice Kosice Slovak Republic Institute of Electromechanics Energy Saving and Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

The paper deals with a new algorithm for controlling a multi-phase buck DC-DC converter. The principle of its control is explained in detail using a flowchart. It is based on the continuous change of the converter topology using the number of its currently controlled phases depending on the size of the input power. In connection with this, its microprocessor control is also mentioned. The whole issue is explained on the design, simulation and measurement of a laboratory sample of a four-phase buck converter.

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Foreword to the 6th International Workshop on engineering and Cybersecurity of Critical systems (EnCyCriS)

Foreword to the 6th International Workshop on Engineering an...

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engineering and Cybersecurity of Critical systems (EnCyCriS), IEEE/ACM International Workshop on

作者： Coralie Esnoul Eunkyoung Jee John Eidar Simensen Ita Ryan Mary Sánchez-Gordón Nathan Lau Risk and Safety Department Institute for Energy Technology Halden Norway School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea University College Cork Cork Ireland Dept. of Computer Science and Communication Østfold University College Halden Norway Dept. of Industrial and Systems Engineering Virginia Tech Virginia USA

ISBN: (数字)9798331538101

ISBN: (纸本)9798331538118

This marks the 6th edition of the International Workshop on engineering and Cybersecurity of Critical systems (EnCyCriS) at the ICSE conference. For industrial critical systems, although the previous premises of increasing system interconnectivity, decentralization, and introduction of new, more intelligent technologies still hold true, there is an increased societal awareness with regards to cybersecurity. This has led to clearer regulation, sharper requirements, and higher expectations for industry. At the same time, the availability of readily deployable competence, methods, tools, and solutions is lacking, which should be considered a critical societal risk. In the current international political climate, cyber security, and safety of critical infrastructures across industry, are more important than ever before. The EnCyCriS workshop facilitates discourse and discussion amongst researchers, practitioners, and students who are working on challenges and solutions related to industrial critical infrastructure. It has a particular focus on sharing industry experience and project results pertaining to cyber threats on critical systems, secure systems engineering, and attack detection and response mechanisms.

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