La surveillance est la fonction d'observer toutes activités humaine ou environnementales dans le but de superviser, contrôler ou même réagir sur un cas particulier; ce qu’on appelle la supervision ou le monitoring. La technologie de la radio-identification, connue sous l’abréviation RFID (de l’anglais Radio Frequency IDentification), est l’une des technologies utilisées pour récupérer des données à distance de les mémoriser et même de les traiter. C’est une technologie d’actualité et l’une des technologies de l’industrie 4.0 qui s'intègre dans de nombreux domaines de la vie quotidienne notamment la surveillance et le contrôle d’accès. L’objectif de cet article est de montrer comment protéger et surveiller en temps réel des zones industrielles critiques et de tous types d'accès non autorisés de toute personne (employés, visiteurs…) en utilisant la technologie RFID et cela à travers des exemples de simulation à l'aide d’un simulateur dédié aux réseaux de capteurs.

As commonly known that living beings cannot survive without natural sources available on earth, technology is no exception; it cannot develop without the inspiring help given by the same nature.

The field of biology has extensively participated in the computing field through the "code of life" DNA (Deoxyribo Nucleic Acid) since it was discovered by Adelman in the past century. This combination gave birth to DNA Computing, which is a very interesting new aspect of biochemistry. It works massively parallel with high energy efficiency, and requiring almost no space.

The field of molecular computing is still new and as the field progresses from concepts to engineering, researchers will address these important issues.

By the use of encoding data into DNA strands, many NP-complete problems have been solved and many new efficient techniques have been proposed in cryptography field.

The aim of this paper is to give an overview of bio-inspired system and to summarize the great role of DNA molecule in servicing of the technology field.

Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective

Benbouzid, Mohamed, and Tarek Berghout. 2023. “Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective”. Electronics 12 (3) : 527. Publisher's Version Abstract

Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have been pushed back to include more decentralized learning challenges. In this context, this perspective paper describes these challenges and provides future directions based on a relevant state-of-the-art review.

Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach

Berghout, Tarek, et al. 2023. “Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach”. IEEE Transactions on Transportation Electrification. Publisher's Version Abstract

Lithium-ion (Li-ion) batteries play an important role in providing necessary energy when acting as a main or backup source of electricity. Indeed, the unavailability of battery aging discharge data in most real-world applications makes the State of Health (SoH) assessment very challenging. Alternatively, accelerated aging is therefore adopted to emulate the degradation process and to achieve an SoH estimate. However, accelerated aging generates limited deterioration patterns suffering from a higher level of complexity due to the non-linearity and non-stationarity imposed by harsh conditions. In this context, this paper aims to provide a predictive model capable of solving incomplete data problems by providing two main solutions for each of the problems of complexity and missing patterns, respectively. First, to overcome the problem of lack of patterns, a robust collaborative feature extractor (RCFE) is designed by collaborating between a set of improved restricted Boltzmann machines (I-RBMs) to be able to share learning knowledge among different locally trained I-RBMs to create a more generalized global extraction model. Second, a set of RCFEs is then evolved through a neural network with an augmented hidden layer (NAHL) to enhance the predictive ability by further exploring representation learning to overcome pattern complexity issues. The designed RCFE-NAHL is trained to predict SoH using constant current (CC) discharge characteristics by implying multiple characteristics recorded through the constant voltage (CV) charging process as indicators of health. The proposed SoH prediction approach performances are evaluated on a set of battery life cycles from the well-known NASA database. In this context, the achieved results clearly highlight the higher accuracy and robustness of the proposed learning model.

2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Othmane, Friha, et al. 2023. “2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT”. Computers & Security 127. Publisher's Version Abstract

Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP) Federated Learning (FL)-based IDS (2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three building blocks, namely: a key exchange protocol (for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of cyber attacks in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based IDS solutions.

Faulty Detection System Based on SPC and Machine Learning Techniques

Benrabah, Mohamed-Elamine, Ouahab Kadri, and Nadia-Kenza Mouss. 2023. “Faulty Detection System Based on SPC and Machine Learning Techniques”. Revue de l’Intelligence Artificielle : 969-977. Publisher's Version Abstract

Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.

IoT-based food traceability system: Architecture, technologies, applications, and future trends

Mehannaoui, Raouf, Kinza-Nadia Mouss, and Karima Aksa. 2023. “IoT-based food traceability system: Architecture, technologies, applications, and future trends”. Food Control 145. Publisher's Version Abstract

An effective Food Traceability System (FTS) in a Food Supply Chain (FSC) should adequately provide all necessary information to the consumer(s), meet the requirements of the relevant agencies, and improve food safety as well as consumer confidence. New information and communication technologies are rapidly advancing, especially after the emergence of the Internet of Things (IoT). Consequently, new food traceability systems have become mainly based on IoT. Many studies have been conducted on food traceability. They mainly focused on the practical implementation and theoretical concepts. Accordingly, various definitions, technologies, and principles have been proposed. The “traceability” concept has been defined in several ways and each new definition has tried to generalize its previous ones. Nevertheless, no standard definition has been reached. Furthermore, the architecture of IoT-based food traceability systems has not yet been standardized. Similarly, used technologies in this field have not been yet well classified. This article presents an analysis of the existing definitions of food traceability, and thus proposes a new one that aims to be simpler, general, and encompassing than the previous ones. We also propose, through this article, a new architecture for IoT-based food traceability systems as well as a new classification of technologies used in this context. We do not miss discussing the applications of different technologies and future trends in the field of IoT-based food traceability systems. Mainly, an FTS can make use of three types of technologies: Identification and Monitoring Technologies (IMT), Communication Technologies (CT), and Data Management Technologies (DMT). Improving a food traceability system requires the use of the best new technologies. There is a variety of promising technologies today to enhance FTS, such as fifth-generation (5G) mobile communication systems and distributed ledger technology (DLT).

Laboratoire D'automatique et Productique

Publications Nationales

Recent Publications

Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective

Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach

2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Faulty Detection System Based on SPC and Machine Learning Techniques

IoT-based food traceability system: Architecture, technologies, applications, and future trends