Equipe 1 com

2015

Djamel, Bellala. 2015. “Optimal Planning of Nurses’ Redeployment at Patient Overflow, ISSN / ISBN 978-94-62520-74-5 / 2352-538X”. Proceedings of the 2015 AASRI International Conference on Circuits and Systems. Publisher's Version Abstract

In order to provide to his patients a quality medical service at lower cost, the General Administration Department of a University Hospital wants to maintain the number of the allocated nurses as low as possible while guaranteeing a satisfying level of health care. The nurses’ redeployment is an optimization problem that falls under the category of integer linear programming problems whose graphical model is a digraph. The mathematical model is composed of an objective function of several interdependent variables to be obtained and some equality and side constraints that the General Administration Department should not violate in order to achieve satisfaction. The solution of this kind of problems rests on the use of an iterative method known as the simplex algorithm.

Rezgui, Wail. 2015. “Smart diagnosis algorithm of the open-circuit fault in a photovoltaic generator, ISBN: 978-1-4799-8212-7”. CEIT, 2015 3rd International Conference on Control, Engineering & Information Technology. Publisher's Version Abstract

This article proposed a new smart diagnosis algorithm of the open-circuit fault in a PV generator. For the faults conventional diagnosis, it used the analysis of the actual operation parameters of the PV generator. For the faults smart diagnosis, it based on the optimization of SVM technique by the neural network for the classification of observations located on its margin. The resulting algorithm can ensure a better monitoring function of the open-circuit fault within the PV generator, with a high classification rate and a low error rate.

Hayet, Mouss Leila. 2015. “New Algorithm for the IV Characteristic Modeling of the Photovoltaic Generator Malfunction within Impedance and Reversed Polarity Faults”. IEEE EFEA Third International Symposium on Environment Friendly Energies and Applications : pp. 506-512. Publisher's Version Abstract

In this paper, we proposed a new methodology that can improved and developed the faults detection and diagnosis methods of the photovoltaic generator, especially when it subjected to the impedance and reversed polarity defects. This proposed algorithm is based on the mathematical modeling of the IV characteristic, of the faulty photovoltaic generator hierarchies as: cell, cells group, module, string and the entire generator, when they submitted to one or more of: cells, bypass and blocking diodes in impedance and reversed polarity faults. This new methodology can facilitated the study of the faulty generator characteristics, and obtained a database for the learning phase and the classification of the new observations collected on the system during its operation. NOMENCLATURE I_phi = Photo-Current. N_Cells = Cells Number in Each Group. N_Groups = Groups Number in Each Module. N_Modules = Modules Number in Each String. N_Strings = Strings Number in the Generator. V_Cell_Imp = Cell Voltage Imposed. I_Cells = Cell Current. V_Cells = Cell Voltage. I_PV = Generator Current. V_PV = Generator Voltage. R_S = Cell Series Resistance. R_SH = Cell Shunt Resistance. I_S1 = Reverse Saturation Current of 1 st Diode. I_S2 = Reverse Saturation Current of 2 nd Diode. m1 = Ideality Factors of 1 st Diode. m2 = Ideality Factors of 2 nd Diode.

Wail, Rezgui. 2015. “Smart diagnosis algorithm of the open-circuit fault in a photovoltaic generator, ISBN: 978-1-4799-8212-7 ”. (CEIT), 2015 3rd International Conference onControl, Engineering & Information Technology. Publisher's Version

Leila-Hayet, Mouss. 2015. “Photovoltaic module simultaneous open-and short-circuit faults modeling and detection using the I–V characteristic”. (ISIE 2015 IEEE) The 24th International Symposium on Industrial Electronics. Publisher's Version

2014

Hanane, Zermane. 2014. “Remote control and supervision of a manufacturing system Application in a cement factory”. ICEE 2014 The Second International Conference on Electro – Energy .

Ouhab, Kadri. 2014. “L’application des algorithmes de colonies de fourmis pour le diagnostic des systèmesdynamiques et complexes”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing. Publisher's Version

Samia, Brahmi. 2014. “La logique floue pour l’anesthésie inhalatoire”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing. Publisher's Version

Kherredine, Bouhafna. 2014. “MaSE, une méthode innovante pour la modélisation d’un système de production réel”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing. Publisher's Version

Khadidja, Abid. 2014. “Designing a mobile agent based M-maintenance: Formal modelling and simulation”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing. Publisher's Version

Adel, Abdelhadi. 2014. “Une nouvelle méthode basée sur le système multi-agents et le système immunitaire artificielpour la maintenance systématique”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing. Publisher's Version

Wail, Rezgui. 2014. “Development of a smart algorithm for the prognosis and the diagnosis of the short circuit and the reversed polarity faults in a photovoltaic generator, May, 11-13, Batna, Algeria ”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 245-251. Publisher's Version

Djamil, Rezki. 2014. “An automatic control of cement production quality May, 11-13, Batna, Algeria,”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 28-33. Publisher's Version

Nafissa, Zeghichi. 2014. “Implantation d'un Algorithme Pour l'Allocation des Ressources dans un Contexte de Maintenance Distribuée pour une Entreprise Etendue (Approche SMA).May, 11-13, Batna, Algeria,”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 367-373. Publisher's Version

Khadidja, Abid. 2014. “An e-medicine approach based on Multi Agent system. . May, 11-13, Batna, Algeria ”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 640-645. Publisher's Version

Nafissa, Rezki. 2014. “An agent based approach for quality control and maintenance in production line: Modelling with AUMLMay, 11-13, Batna, Algeria,”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 634-639. Publisher's Version

Khadidja, Abid. 2014. “Formal Specification of a Mobile Agent Based Maintenance for Manufacturing System”. ISIA International Symposium on Informatics and its Applications.

Khadidja, Abid. 2014. “Designing a mobile agent based M-maintenance: Formal modelling and simulation. . May, 11-13, Batna, Algeria”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 353-359. Publisher's Version

Nafissa, Rezki. 2014. “Joint quality and Maintenance in Manufacturing System. May, 11-13, Batna, Algeria”. ICIEM’14, International Conference on Industrial Engineering and Manufacturing : pp. 46-49. Publisher's Version

Rabie, Hamidene. 2014. “ Remote control and supervision of a manufacturing system (Application in a cement factory), SSN : 2335-1012”. (ICDSD 2014) The second International Conference on Distributed System and Decision.

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