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2023 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2023

作者： Bassolillo, Salvatore Rosario Notaro, Immacolata D'Amato, Egidio Mattei, Massimiliano University of Campania Luigi Vanvitelli Department of Engineering CE Aversa81031 Italy University of Naples Parthenope Dep. of Science and Technology Naples80143 Italy University of Naples Federico Ii Dep. of Electrical Engineering and Information Technologies Naples80125 Italy

ISBN: (纸本)9798350319392

In the last years, Unmanned Aerial Vehicles (UAVs) have been widely used for several types of missions, including aerial reconnaissance, search and rescue, and military operations. In first rescue missions, the ability to detect missing people after avalanche events in a short time is fundamental to increase the probability of saving them. The use of unmanned aerial vehicles in such scenarios can improve results with respect to multiple points of view: on the one hand, it can decrease the danger for rescuers, while on the other hand, it can speed up the search process. An effective solution can be the involvement of a formation of multiple drones to cover a greater research space and accelerate the process. However, an important challenge in dep.oying a formation of robots in emergency scenarios is the autonomy in terms of system scalability, since drones are usually teleoperated in a one-to-one ratio with operators, requiring a large crew of rescuers. In order to improve the situational awareness and distribute the communication burden, this paper deals with a decentralized Kalman filtering algorithm using sensor data from multiple drones to estimate a target position, besides UAVs state to support guidance and control algorithms. Such decentralized Kalman filtering algorithm combines the characteristics of Consensus on information and Consensus on Measurement techniques. The proposed technique is preliminarily validated by means of numerical simulations on an example scenario. © 2023 IEEE.

关键词： Drones

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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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A Method to Design SOA-Based Software Architecture and Goal Models from BPMN Models

A Method to Design SOA-Based Software Architecture and Goal ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Blal Redouane Abderrahmane Leshob Mili Hafedh Dep. Analytics Operations and IT University of Quebec at Montreal Montreal Canada Dep. Computer Science University of Quebec at Montreal Montreal Canada

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

In today's digital economy, organisations build soft-ware systems to support their Collaborative Business Processes (CBPs). Building such systems is a diligent and time-consuming task that requires both business and technical skills. Service-Oriented Architecture (SOA) offers design principals based on the service concept to help align CBPs with their supporting systems while helping organisations achieve their business goals. This paper aims to design a model-driven method that bridges the gap between CBPs and the systems that support them. Its objective is two-fold: i) design SOA-based software solutions that support CBPs expressed in BPMN and ii) build a set of goal models that link each service to the collaborative activity it automates and the business goals it satisfies.

关键词： Software architecture Biological system modeling Computational modeling Design methodology Buildings Collaboration Software

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An Intelligent Energy Management for an On-Grid Hybrid Energy System Cost Reduction

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

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

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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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Comparison of Milk Quality Grades Using the Logistic Model Tree Algorithm

Comparison of Milk Quality Grades Using the Logistic Model T...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Phanintorn Suaprae Thanakrit Janchidfah Brijesh Bhatt Dep. Of Business Information Technology Management Phibulsongkram Rajabhat University Phitsanulok Thailand Dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Dep. Of Computer Engineering Faculty of Technology Dharmsinh Desai University India

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This study aims to track and categorize milk quality grades using the Logistic Model Tree (LMT) algorithm, based on the analysis of 1,059 milk samples. The focus of the research is to evaluate key factors such as pH value, taste, smell, fat content, concentration, and color, which play a crucial role in determining the overall quality of milk. The goal is to create an efficient and standardized process for sorting cow's milk, which can save significant time and resources in the dairy industry. To identify the most suitable algorithm for this task, the research team initially tested 12 different algorithms, assessing their performance in predicting milk quality. Among these, the Logistic Model Tree (LMT) emerged as the most effective model, achieving an impressive accuracy rate of 99.76%. The LMT algorithm, known for combining the advantages of decision trees and logistic regression, demonstrated its ability to classify milk quality grades with high precision. The results of this study indicate that the LMT algorithm can be a valuable tool for automating the milk quality grading process. By leveraging key attributes such as pH, fat content, and color, it is possible to efficiently sort and grade milk, reducing the need for manual inspection and improving overall productivity in the dairy industry. This method offers a significant improvement over traditional quality assessment techniques and has the potential to be widely adopted for milk sorting and quality control applications.

关键词： Analytical models Dairy products Accuracy Standardization Prediction algorithms Data models Classification algorithms Quality assessment Logistics Sorting

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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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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Automated Validation of Spatial Data

Automated Validation of Spatial Data

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Joao Victor T. Salgado Demostenes Z. Rodriguez Vinicius Vitor Dos S. Dias Renata L. Rosa Dep. of Computer Science Univ. of Lavras Federal Minas Gerais Brazil

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Spatial databases, are often based on relational database management systems (RDBMS), to incorporate geographical data essential for data planning and optimization. However, maintaining the integrity of spatial data poses challenges, particularly in enforcing topological rules and preventing performance degradation during query execution. In this context, we propose an automated geometry validation application tailored for database environments, complemented by a user-friendly web-based interface. Our solution addresses the issue of invalid geometries often encountered in datasets, enhancing the reliability and efficiency of spatial database operations. Through performance and accuracy evaluations, we demonstrate the effectiveness of our approach in improving the overall user experience and trust in data planning processes with minimal implementation efforts.

关键词： Geometry Urban planning Transportation Spatial databases User experience Planning Telecommunications Reliability Optimization Geographic information systems

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Blockchain-based Device Identity Management with Consensus Authentication for IoT Devices 19

Blockchain-based Device Identity Management with Consensus A...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Mukhandi, Munkenyi Damiao, Francisco Granjal, Jorge Vilela, Joao P. University of Coimbra Cisuc Dep. of Informatics Engineering Coimbra Portugal Pdmfc Lisbon Portugal University of Porto Faculty of Sciences Cracs Inesctec Cisuc Dep. of Computer Science Porto Portugal

ISBN: (纸本)9781665431620

To decrease the IoT attack surface and provide protection against security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity and authentication management. This work proposes a blockchain-based identity management approach with consensus authentication as a scalable solution for IoT device authentication management. The proposed approach relies on having a blockchain secure tamper proof ledger and a novel lightweight consensus-based identity authentication. The results show that the proposed decentralised authentication system is scalable as we increase number of nodes. © 2022 IEEE.

关键词： Authentication

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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