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SPLANDID — Optimal Sizing, PLacement, And management of centralized aNd DIstributed shareD battery energy storage systems in residential communities: Application to smart grids
No full textInternational audienceThis paper introduces SPLANDID, a novel techno-economic methodology for the optimal sizing, placement, and management of shared Battery Energy Storage Systems (BESSs) in residential communities that minimizes both capital and operational costs, along with energy losses within the community. To address the installation of two types of shared BESSs (i.e., single centralized BESS, multiple distributed BESSs), our methodology offers two distinct approaches: one optimizes the centralized BESS, while the other focuses on optimizing distributed BESSs. We formulate each approach as a constrained optimization problem and solve it using the particle swarm optimization (PSO) algorithm. To validate our methodology, we use real consumption and production patterns collected from households in a residential community in the United Kingdom (UK). We propose and compare three scenarios (i.e., no BESS installation, single BESS installation, and multiple BESSs installation) using various numerical metrics, such as total energy losses and total costs. Simulation results underscore the effectiveness of BESS installation, demonstrating an impressive 82.24% reduction in total costs compared to the benchmark scenario without BESS. Moreover, the installation of multiple distributed BESSs outperforms a single centralized BESS, reducing total costs and energy losses by 17.4% and 49.4%, respectively. Furthermore, the distributed installation proves efficient by decreasing the required storage capacity by 11.82% in contrast to the centralized approach. Compared to the large existing body of literature, this study contributes on several fronts. First, it explores the feasibility of installing multiple distributed shared BESSs in residential communities, departing from the dominant single centralized shared BESS installation in existing studies. The results obtained with this alternative installation strategy are highly promising from several perspectives. Secondly, our methodology is uniquely designed to base planning on actual batteries available on the market, enhancing its practical applicability in real-life scenarios. This approach contrasts with studies that often propose optimal sizes without considering the constraints imposed by the capabilities of existing batteries
Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS)
No full textInternational audienceDistributed intrusion detection systems (DIDS) are a specialized subset of conventional IDSs designed for implementation in distributed environments. Each IDS is integrated into distinct entities within a monitored network, potentially distributed across various locations. These participating IDSs can be configured to detect either a particular or multiple attack types. Although DIDS has found extensive application in diverse IoT systems, its utilization in unmanned aerial vehicles (UAVs) still needs to be explored. Consequently, it is imperative to devise a comprehensive framework tailored explicitly for UAVs. It combines multiple detection units to enhance security. Based on the insights gained from previous studies, we propose an exhaustive DIDS for UAVs security enforcement in this paper. Our proposed solution offers a robust and scalable security approach. Through distributing the workload across interconnected IDSs deployed on the UAV, our solution was optimized for UAVs attacks detection to achieve high detection performance while reducing the complexity. To the best of our insight, there is no recorded DIDS for UAVs security, and attack detection has been proposed and evaluated. Furthermore, our paper provides a detailed analysis, outlining the development basis and the achieved results. We performed multiple experiments over different cases using different datasets. The achieved experimental results demonstrate that the proposed IDS has significantly high accuracy detection and low loss rates. Our proposed E-DIDS efficiently detects multiple attacks on different UAVs subsets with good global accuracy that reached 98.6% and low resource consumption
Indirect Flow-Shop Coding Using Rank: Application to Indirect QAOA
No full textInternational audienceAbstract Metaheuristic optimization algorithms are powerful for solving complex optimization problems, almost when we lack information about the nature of their objective function. Meanwhile, the Finite Element Method (FEM) is a very powerful method for predicting and simulating the behavior of systems subjected to various stresses, whether electrical, thermal, mechanical, vibratory or magnetic. In the current study, we have proposed an optimization methodology that couples a metaheuristic algorithm - the Search and Rescue Algorithm (SAR) - with the finite element method. We then investigated the proposed methodology for optimizing the reliability of solder balls with respect to their geometry in a Ball Grid Array (BGA) assembly subjected to thermal cycling load. First, we performed a sensitivity analysis to determine the parameters influencing solder ball reliability, then we optimized the plastic deformation of solder balls, hence increasing their reliability as a function of their geometry through the proposed methodology, which results in improving the reliability of the entire electronic component
Enhanced Cough Analysis Using 1-Dimensional CNN Features for Respiratory Health Diagnosis
No full textInternational audienceCough, a prevalent respiratory symptom, plays a crucial role in disease diagnosis. The accurate differentiation between wet and dry coughs is essential for effective respiratory health assessment. This work presents a robust method for classifying cough audios into wet and dry utilizing one dimensional Convolutional Neural Network (1D-CNN) model as a feature extractor. A hyperparameters tuning approach is introduced which in turn optimizes the CNN model and tunes the size of the pre-last dense layer. Features are extracted from undersampled and oversampled versions of the dataset and are introduced to Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) classifiers for performance comparison. The research demonstrates that the hyper-tuned feature extractor combined with the RF classification model using the augmented version of the dataset surpasses the other trials by achieving a validation accuracy of 99.29% and ROC AUC of 100%. A remarkable increase of 4 to 16% in the accuracy and of 14% in the ROC AUC is obtained when comparing with the literature. The results indicate the effectiveness of the proposed approach in enhancing the automatic detection of cough types in the healthcare sector. Overall, this work holds great promise for improving healthcare diagnostics and enabling remote disease detection and monitoring
Rail Infrastructure Management Optimization Through Digital Twins: Focus on Data Reliability and Quality
No full textInternational audienceThe digital twin of the rail system plays a pivotal role in advancing infrastructure management, ushering in greater punctuality and safety for trains. By harnessing data from diverse sources, it facilitates real-time updates throughout the lifecycle of railway assets. This comprehensive digital twin encompasses every aspect of infrastructure management, offering multifaceted functionalities beyond mere asset oversight. These include providing insights into the current state of the rail system and furnishing simulation tools to bolster decision-making processes across all stakeholders.Given the fundamental reliance on digital data within this framework, our primary focus is on ensuring its usability and quality. Accurate analysis of asset conditions hinges upon the trustworthiness of the data, a requirement shared by maintainers and operators alike. Consequently, we’ve diligently crafted a set of digital data confidence indicators aimed at instilling trust in the information at hand.In tandem with the endeavors to develop a digital twin for the rail system, there arises a pressing need to establish digital solutions that capitalize on data pertinent to business objectives. This necessity is underscored by the efforts of SNCF’s network description department in modeling both processes and data. However, the efficacy of these digital solutions is contingent upon the relevance, efficiency, and high quality of the data they rely on. To this end, we’ve embedded the notion of trust within the digital data used to delineate the condition of railway assets.While these indicators amalgamate various factors impacting user confidence in digital data, further expansion is warranted to ensure the widespread effectiveness and adoption of digital tools by end-users
Perception de l’utilisation de réponses produites par une IA dans la communication interpersonnelle
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La fabrication des émojis : un langage universel, entre technique et politique
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El Risitas : itinéraire d’un mème, en ligne et hors ligne
No full textInternational audienceCet article porte sur la genèse et l’utilisation du mème El Risitas dans le forum en ligne Blabla 18-25 ans et aborde quatre aspects : l’utilisation du mème par les utilisateurs et utilisatrices du forum, sa genèse, les facteurs qui ont favorisé le processus de mémification et sa vie hors ligne. À travers cette étude de cas, cet article propose d’analyser l’itinéraire d’un mème, en ligne et hors ligne
