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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 24-31页

作者： Zivelonghi, Alessandro Giuseppi, Alessandro Dep. of Applied Mathematical Science IIS "Calabrese-Levi" VR San Pietro in Cariano Italy PowerConsulting Studio San Pietro in Cariano Verona Italy Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Via Ariosto 25 Rome00185 Italy

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes. © 2023 The Authors

关键词： Internet of things

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System for Talent Acquisition: Integrating AI, Automation, and Data Analysis in HR 6

System for Talent Acquisition: Integrating AI, Automation, a...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Abitova, Gulnara Serikov, Ayanbek Nikulin, Vladimir Rakhimzhanova, Mira Shuteyeva, Gulnur Kulniyazova, Korlan Astana IT University Dep. Computer Engineeering Astana Kazakhstan Binghamton University Dept. Electical Engineering and Computer Science Binghamton United States Eurasian National University Dept. System Anslysis and Managemnt Astana Kazakhstan

ISBN: (纸本)9798350344349

This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as *** for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies. © 2024 IEEE.

关键词： Machine learning

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The Influence of Applying Digital Toolkits to Assist 3D Software Development on Individual Work Performance and Perceived Stress 10

The Influence of Applying Digital Toolkits to Assist 3D Soft...

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10th International Conference on Applied System Innovation, ICASI 2024

作者： Wu, Wen-Chuan Lee, Ming-Han National Ilan University Dep. of Computer Science and Information Engineering Yilan City Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan City Taiwan

ISBN: (纸本)9798350394924

Digital content industry, encompassing areas such as digital games, computer animation, digital audio-visual applications, and digital art, plays a pivotal role in today's and future developmental trends. Maya 3D software stands out as a crucial development tool in these domains. However, the multitude of built-in tools in Maya 3D can lead to overly complex interfaces, impacting development efficiency for software developers. To address this issue, this paper designs a digital toolkit for Maya 3D software, presented in a Menu Bar format. This toolkit offers features like naming, sorting, categorization, and automatic saving to JSON files for future use. Through a crossover experimental design, this paper investigates the impact of this proposed toolkit on 3D software developers' individual work performance, work engagement, and stress perception. Results shows a significant positive influence of that toolkit on software developers, particularly in enhancing work performance. It means that the proposed digital toolkit effectively improves the performance of software developers' work and reduces their stress during the software development process. © 2024 IEEE.

关键词： Digital devices

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Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

arXiv

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arXiv 2024年

作者： Varotto, Matteo Heinrichs, Florian Schürg, Timo Tomasin, Stefano Valentin, Stefan Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy Dep. of Medical Engineering and Technomathematics FH Aachen University of Applied Sciences Germany

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine-learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time. © 2024, CC BY-NC-ND.

关键词： 5G mobile communication systems

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Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

Two-Rated-Power, Dual-Winding, Three-Phase Induction Motor

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International Conference on Electrical Machines, ICEM

作者： Fernando J. T. E. Ferreira Dep. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9798350370607

ISBN: (纸本)9798350370614

This paper proposes using star-delta and star-series connection modes in three-phase dual-winding squirrelcage induction motors to have available 100% and 55% of the original rated power for a given supply line-to-line voltage. The connection mode can be changed on a nine-terminal block. The experimental results obtained with a commercial 4-kW, 400-V, 50-Hz, 4-pole induction motor whose stator windings were redesigned to provide 4-kW and 2.2-kW output powers are presented and validate the proposed solution. Two-rated-power motors with the proposed output power ratio could be used as spare motors to replace faulty/damaged motors with the same frame size. The proposed solution allows fine-tuning the motor power to the actual maximum power required by the driven application/load and can be considered cost-effective as it may lead to significant gains in the motor efficiency and power factor when the low-power mode is adopted. In many cases, it may avoid the underloaded operation of induction motors, while keeping their original rated power available.

关键词： Reactive power Stator cores Costs Induction motors Torque Windings Stator windings Voltage Transformers Power generation

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Machine-Learning-Assisted Optimization of Type-II Superlattices for Enhanced Vertical Hole Mobility 23

Machine-Learning-Assisted Optimization of Type-II Superlatti...

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23rd International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2023

作者： Glennon, John Bellotti, Enrico Boston University Div. of Material Science and Engineering BostonMA02215 United States Boston University Div. of Material Science and Engineering Dep. of Electrical and Computer Engineering BostonMA02215 United States

ISBN: (纸本)9798350314298

Gaussian process regression is used to develop a model for predicting the superlattice (SL) structures grown on a 6.3 Å lattice. This model is used to search for optimized hole transport properties. Nonequilibrium Green's functions calculations are used to determine the vertical hole mobility of the structure which is compared with both the calculated and experimental results for an experimental SL grown on a GaSb substrate. Ultimately, a simple analytical model is used to approximate and compare the quantum efficiency of a photodiode containing each structure demonstrating an improvement in device performance using the optimized SL structure. © 2023 IEEE.

关键词： Gallium compounds

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A Latent Causal Inference Framework for Ordinal Variables

arXiv

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arXiv 2025年

作者： Scauda, Martina Kuipers, Jack Moffa, Giusi Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland

Ordinal variables, such as on the Likert scale, are common in applied research. Yet, existing methods for causal inference tend to target nominal or continuous data. When applied to ordinal data, this fails to account for the inherent ordering or imposes well-defined relative magnitudes. Hence, there is a need for specialised methods to compute interventional effects between ordinal variables while accounting for their ordinality. One potential framework is to presume a latent Gaussian Directed Acyclic Graph (DAG) model: that the ordinal variables originate from marginally discretizing a set of Gaussian variables whose latent covariance matrix is constrained to satisfy the conditional indep.ndencies inherent in a DAG. Conditioned on a given latent covariance matrix and discretisation thresholds, we derive a closed-form function for ordinal causal effects in terms of interventional distributions in the latent space. Our causal estimation combines naturally with algorithms to learn the latent DAG and its parameters, like the Ordinal Structural EM algorithm. Simulations demonstrate the applicability of the proposed approach in estimating ordinal causal effects both for known and unknown structures of the latent graph. As an illustration of a real-world use case, the method is applied to survey data of 408 patients from a study on the functional relationships between symptoms of obsessive-compulsive disorder and dep.ession. © 2025, CC BY-NC-SA.

关键词： Directed graphs

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Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbor...

引用

IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Matteo Varotto Florian Heinrichs Timo Schürg Stefano Tomasin Stefan Valentin Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Medical Engineering and Technomathematics FH Aachen University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy Sciences Germany

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning (ML). We propose to detect jamming at the physical layer with an ML model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.

关键词： Support vector machines Accuracy 5G mobile communication Computational modeling Wireless networks Nearest neighbor methods Convolutional neural networks Jamming Principal component analysis Narrowband

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On the classification of weather based on the production of photovoltaic installations

On the classification of weather based on the production of ...

引用

IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Paweł Parczyk Tobiasz Puślecki Robert Burduk Dep. of Systems and Computer Networks Wrocław University of Science and Technology Wrocław Poland

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

In recent years, there has been an energy transition in which fossil fuel-fired power plants are being replaced by renewable energy sources such as photovoltaics. While the influence of factors such as location and method of installation or the sun’s position above the horizon is deterministic, the weather factor is rather random. Weather data can be easily obtained from nearby weather stations, but some weather phenomena are so dynamic that data obtained in this way can become useless. When considering the problem of classifying weather conditions based on production data of photovoltaic installations, we propose two new features extracted from the production data that describe the overall level of sunshine and the variability of sunlight. Three supervised learning methods were used to classify the dataset: CNN, Random Forest classifier and Decision Tree Classifier. Reference values were obtained by generating classification results from the raw output and compared with those generated from the two new features. The experiments showed a statistically significant advantage for the models fitted to the data extended with the new features.

关键词： Photovoltaic systems Renewable energy sources Clouds Supervised learning Production Feature extraction Data models Data mining Random forests Meteorology

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