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SSRN 2024年

作者： Miranda, Israel Riverón Gomez-Gonzalez, Jose Francisco Mendez-Perez, Juan Albino Dep. Computer Science and Systems Engineering University of La Laguna Spain Dep. Industrial Engineering University of La Laguna Spain

This paper introduces an innovative Energy Management System (EMS) designed to reduce the cost of energy flows in a Hybrid Renewable Energy System (HRES) connected to a grid with an Energy Storage System (ESS) based on lithium-ion electric batteries. This study considers power purchase and sale tariffs, as well as costs associated with the battery wear contingent upon its State of Charge (SoC). The proposed EMS combines three Fuzzy Logic Controllers (FLCs) arranged in a cascade to regulate battery intensity with a Hysteresis-Based Calculation algorithm (HBC) in a supervisory control hierarchy to establish charge and discharge setpoints. Its goal is to minimise the energy flow costs at the current instant while maintaining the power supply to the loads. Two generation and consumption scenarios were simulated, one of which was a test case for examining all possible power-balanced situations. The results were compared with the optimal costs obtained by the Bellman-Ford algorithm (BFA). This research concludes that the intelligent EMS proposed is reliable in reducing the operating costs of the HRES, and it posits itself as a more reliable and computationally cost-effective alternative to the optimisation techniques. © 2024, The Authors. All rights reserved.

关键词： Energy management systems

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One-Class Classification as GLRT for Jamming Detection in Private 5G Networks

arXiv

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arXiv 2024年

作者： Varotto, Matteo Valentin, Stefan Ardizzon, Francesco Marzotto, Samuele Tomasin, Stefano Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering University of Padova Italy

Fifth-generation (5G) mobile networks are vulnerable to jamming attacks that may jeopardize valuable applications such as industry automation. In this paper, we propose to analyze radio signals with a dedicated device to detect jamming attacks. We pursue a learning approach, with the detector being a convolutional neural network (CNN) implementing a generalized likelihood ratio test (GLRT). To this end, the CNN is trained as a two-class classifier using two datasets: one of real legitimate signals and another generated artificially so that the resulting classifier implements the GLRT. The artificial dataset is generated mimicking different types of jamming signals. We evaluate the performance of this detector using experimental data obtained from a private 5G network and several jamming signals, showing the technique's effectiveness in detecting the attacks. © 2024, CC BY-NC-ND.

关键词： 5G mobile communication systems

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One-Class Classification as GLRT for Jamming Detection in Private 5G Networks

One-Class Classification as GLRT for Jamming Detection in Pr...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Matteo Varotto Stefan Valentin Francesco Ardizzon Samuele Marzotto Stefano Tomasin Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Information Engineering University of Padova Italy

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

关键词： Wireless communication Accuracy 5G mobile communication Supervised learning Detectors Signal processing Threat assessment Convolutional neural networks Jamming Unsupervised learning

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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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Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

Planing It by Ear: Convolutional Neural Networks for Acousti...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Deschênes, Anthony Georges, Rémi Subakan, Cem Ugulino, Bruna Henry, Antoine Morin, Michael Dep. of Wood and Forest Sciences Université Laval QC Canada Dep. of Computer Science and Software Engineering Université Laval QC Canada Dep. of Operations and Decision Systems Université Laval QC Canada FORAC Research Consortium Université Laval QC Canada Mila-Quebec AI Institute QC Canada FPInnovations QC Canada Concordia University QC Canada

ISBN: (纸本)9798350368741

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities. © 2025 IEEE.

关键词： Acoustic Anomaly detection Convolutional neural networks Transformer Wood planer

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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...

引用

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

引用

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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