2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017
To ensure complex systems reliability and to extent their life cycle, it is crucial to properly a... more To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely prognose faults. In this context, this paper describes a new intelligent approach to estimate the remaining useful life in complex systems. This approach is based on the combination of several intelligent techniques. This approach is based on Echo State Network (ESN) and Particle Swarm Optimization (PSO) technique to set the ESN with optimal parameters. The input of this model are the measurements of signals correlated to the component degradation state, whereas the model output is the component RUL. To validate the feasibility of the proposed approach, real life fault historical data from turbofan engines system were analyzed and used to obtain the optimal prediction of RUL.
Nous exposons dans ce memoire differentes formes d'utilisation de la modelisation bond graph ... more Nous exposons dans ce memoire differentes formes d'utilisation de la modelisation bond graph pour la detection et la localisation des pannes dans les systemes complexes. Dans le premier chapitre, nous presentons les objectifs de la surveillance en mettant l'accent sur son role, le principe de la redondance, la specification de la surveillance et le cahier des charges. Ensuite, nous exposons la modelisation bond graph sous ses deux formes graphique et matricielle. Dans le deuxieme chapitre, nous nous interessons de pres a l'approche par projection orthogonale et nous determinons l'expression analytique de la matrice de projection permettant de generer les relations de redondance. Nous comparons, par la suite, nos resultats a ceux des methodes existantes. Le troisieme chapitre se consacre a l'approche structurelle. Il explicite des methodes de generation de relations de redondance par l'etude d'un couplage dans la structure du systeme et la construction d&#...
2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 2017
Reinforcement learning comprises an attractive solution to the multi-agent cooperation problem, d... more Reinforcement learning comprises an attractive solution to the multi-agent cooperation problem, due to its robustness for learning in unknown and uncertain environments. The objective of this paper is to provide learning capabilities to a group of autonomous agents in order to efficiently perform a cooperative foraging task in a distributed manner. Firstly, the D-DCM-Multi-Q learning method, presented in [1], is evaluated. To overcome the shortcomings of this method, new cooperative action selection strategies are developed. A new exploration alternative, favoring least recently visited states, is also proposed. The conducted simulation tests indicate the efficiency of suggested improvements in the case of large, unknown and stationary environments.
This paper presents two analytical redundancy relation generation methods used for fault detectio... more This paper presents two analytical redundancy relation generation methods used for fault detection. These methods are then applied to bond graph models. The first method rests on the structural and the behavioural properties of the system. The basic idea is to collect all information the bond graph is able to give in order to construct a matrix which might be considered as a bipartite graph constituted of a set of relations between all the variables of the system, The analytical redundancy relations may be generated by dividing the system of relations into over, just and under-determined subsystems; a special handling of the over-determined subsystem leads to the result. The second method, which can be used to reach the same results but is limited to linear model, rests on the projection of all the relations on a vector space orthogonal to that generated by all the unknown variables.
Inference systems are intelligent software performed generally to help people take appropriate de... more Inference systems are intelligent software performed generally to help people take appropriate decisions and solve problems in specific domains. Fuzzy inference systems are a kind of these systems that are based on fuzzy knowledge. To handle the fuzziness in the inference, the compositional rule of inference is used, which has two parameters: a t-norm and an implication operator. However, most of the combinations of t-norm/implication do not give an adequate inference result that coincides with human intuitions. This was the motivation for several works to study these combinations and to identify those that are compatible, in order to guarantee a performance close to that of humans. We are interested in this paper to a more general form of rules, which is complex rules, whose premise is a conjunction of propositions. To obtain the consequence in a fuzzy inference system using the compositional rule of inference with a complex rule, we study, in this work, Lukasiewicz t-norm which wa...
To ensure complex systems reliability and to extent their life cycle, it is crucial to properly a... more To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely correct eventual faults. In this context, this paper propose an intelligent approach to detect single and multiple faults in complex systems based on soft computing techniques. This approach is based on the combination of fuzzy logic reasoning and Artificial Fish Swarm optimization. The experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.
This paper focuses on distributed reinforcement learning in cooperative multi-agent systems, wher... more This paper focuses on distributed reinforcement learning in cooperative multi-agent systems, where several simultaneously and independently acting agents have to perform a common foraging task. To do that, a novel cooperative action selection strategy and a new kind of agents, called ”relay agent”, are proposed. The conducted simulation tests indicate that our proposals improve coordination between learners and are extremely efficient in terms of cooperation in large, unknown and stationary environments.
Abstract The healthcare supply chain begins with the production of medical products. Different st... more Abstract The healthcare supply chain begins with the production of medical products. Different stages of supply chain flow may have their own objectives. The healthcare supply chain management process can be inefficient and fragmented because supply chain goals are not always matched within the medical requirements. To choose a certain product, healthcare organizations must consider a variety of demands and perspectives. The MRI team contributes a wealth of knowledge to the essential task of improving our clients’ supply chain and logistics performance. MRI is considered the most efficient medical device for the acquisition and treatment of medical imaging. Medical devices and manufacturing are classified as crucial factors for the supply chain in radiology. The objective of this paper is to present an approach that consists of modeling in 3D a segment of a stenosing aorta with a parallel treatment in order to determine the cost and the time of treatment for the reporting, which can be considered a promoter element to optimize the course of supply chain from manufacture to medical industry. Acceleration has sought to reduce the imaging speed in parallel architecture convergence for cardiac MRI. The image computation time is comparatively long owing to the iterative reconstruction process of 3D models. The aim of this paper is to suggest a CPU-GPU parallel architecture based on multicore to increase the speed of mesh generation in a 3D model of a stenosis aorta. A retrospective cardiac MRI scan with 74 series and 3057 images for a 10-year-old patient with congenital valve and valvular aortic stenosis on close MRI and coarctation (operated and dilated) in the sense of shone syndrome. The 3D mesh model was generated in Standard Tessellation Language (STL), as well as the libraries used to operate with Pymesh and Panda, and the time spent in tracing, decomposing, and finalizing the mesh crucially depends on the number of nuclei used in the parallel processing and the mesh quality chosen. A parallel processing based on four processors are required for the 3D shape refinement.To improve the efficiency of image processing algorithms and medical applications acquired in real-time analysis and control, a hybrid architecture (GPU/GPU) was proposed. The response time of parallel processing based on the CPU-GPU architecture used at the mesh level to achieve a 3D model is critically dependent on the number of kernels required.
2014 14th International Conference on Hybrid Intelligent Systems, 2014
The presence of faults in complex systems can cause serious damage and even generate fatal situat... more The presence of faults in complex systems can cause serious damage and even generate fatal situations. Proper and timely diagnosis of behavior of such systems is then crucial to detect the presence of eventual faults and isolate their causes. In this context, this paper describes a new intelligent approach to diagnose multiple faults in complex systems. This approach is based on the combination of a fuzzy system optimized by cultural algorithm and causal reasoning. The ongoing experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.
Artificial Intelligence Techniques for a Scalable Energy Transition, 2020
Residential energy smart management (RESM) has received considerable momentum in the recent decad... more Residential energy smart management (RESM) has received considerable momentum in the recent decade considering its strong impact on the total energy consumption and the elaboration of the smart grid. Non-Intrusive Load Monitoring (NILM) is the first brick of the smart grid. In this paper, the importance of NILM in the smart grid is highlighted and its impact on different smart grid issues is discussed. Challenges facing NILM are also explained and existing solutions are reviewed. Mainly, an overview of different machine learning approaches is presented and these methods’ limits are discussed giving rise to open problems in the state of the art.
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), 2017
To ensure complex systems reliability and to extent their life cycle, it is crucial to properly a... more To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely prognose faults. In this context, this paper describes a new intelligent approach to estimate the remaining useful life in complex systems. This approach is based on the combination of several intelligent techniques. This approach is based on Echo State Network (ESN) and Particle Swarm Optimization (PSO) technique to set the ESN with optimal parameters. The input of this model are the measurements of signals correlated to the component degradation state, whereas the model output is the component RUL. To validate the feasibility of the proposed approach, real life fault historical data from turbofan engines system were analyzed and used to obtain the optimal prediction of RUL.
Nous exposons dans ce memoire differentes formes d'utilisation de la modelisation bond graph ... more Nous exposons dans ce memoire differentes formes d'utilisation de la modelisation bond graph pour la detection et la localisation des pannes dans les systemes complexes. Dans le premier chapitre, nous presentons les objectifs de la surveillance en mettant l'accent sur son role, le principe de la redondance, la specification de la surveillance et le cahier des charges. Ensuite, nous exposons la modelisation bond graph sous ses deux formes graphique et matricielle. Dans le deuxieme chapitre, nous nous interessons de pres a l'approche par projection orthogonale et nous determinons l'expression analytique de la matrice de projection permettant de generer les relations de redondance. Nous comparons, par la suite, nos resultats a ceux des methodes existantes. Le troisieme chapitre se consacre a l'approche structurelle. Il explicite des methodes de generation de relations de redondance par l'etude d'un couplage dans la structure du systeme et la construction d&#...
2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 2017
Reinforcement learning comprises an attractive solution to the multi-agent cooperation problem, d... more Reinforcement learning comprises an attractive solution to the multi-agent cooperation problem, due to its robustness for learning in unknown and uncertain environments. The objective of this paper is to provide learning capabilities to a group of autonomous agents in order to efficiently perform a cooperative foraging task in a distributed manner. Firstly, the D-DCM-Multi-Q learning method, presented in [1], is evaluated. To overcome the shortcomings of this method, new cooperative action selection strategies are developed. A new exploration alternative, favoring least recently visited states, is also proposed. The conducted simulation tests indicate the efficiency of suggested improvements in the case of large, unknown and stationary environments.
This paper presents two analytical redundancy relation generation methods used for fault detectio... more This paper presents two analytical redundancy relation generation methods used for fault detection. These methods are then applied to bond graph models. The first method rests on the structural and the behavioural properties of the system. The basic idea is to collect all information the bond graph is able to give in order to construct a matrix which might be considered as a bipartite graph constituted of a set of relations between all the variables of the system, The analytical redundancy relations may be generated by dividing the system of relations into over, just and under-determined subsystems; a special handling of the over-determined subsystem leads to the result. The second method, which can be used to reach the same results but is limited to linear model, rests on the projection of all the relations on a vector space orthogonal to that generated by all the unknown variables.
Inference systems are intelligent software performed generally to help people take appropriate de... more Inference systems are intelligent software performed generally to help people take appropriate decisions and solve problems in specific domains. Fuzzy inference systems are a kind of these systems that are based on fuzzy knowledge. To handle the fuzziness in the inference, the compositional rule of inference is used, which has two parameters: a t-norm and an implication operator. However, most of the combinations of t-norm/implication do not give an adequate inference result that coincides with human intuitions. This was the motivation for several works to study these combinations and to identify those that are compatible, in order to guarantee a performance close to that of humans. We are interested in this paper to a more general form of rules, which is complex rules, whose premise is a conjunction of propositions. To obtain the consequence in a fuzzy inference system using the compositional rule of inference with a complex rule, we study, in this work, Lukasiewicz t-norm which wa...
To ensure complex systems reliability and to extent their life cycle, it is crucial to properly a... more To ensure complex systems reliability and to extent their life cycle, it is crucial to properly and timely correct eventual faults. In this context, this paper propose an intelligent approach to detect single and multiple faults in complex systems based on soft computing techniques. This approach is based on the combination of fuzzy logic reasoning and Artificial Fish Swarm optimization. The experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.
This paper focuses on distributed reinforcement learning in cooperative multi-agent systems, wher... more This paper focuses on distributed reinforcement learning in cooperative multi-agent systems, where several simultaneously and independently acting agents have to perform a common foraging task. To do that, a novel cooperative action selection strategy and a new kind of agents, called ”relay agent”, are proposed. The conducted simulation tests indicate that our proposals improve coordination between learners and are extremely efficient in terms of cooperation in large, unknown and stationary environments.
Abstract The healthcare supply chain begins with the production of medical products. Different st... more Abstract The healthcare supply chain begins with the production of medical products. Different stages of supply chain flow may have their own objectives. The healthcare supply chain management process can be inefficient and fragmented because supply chain goals are not always matched within the medical requirements. To choose a certain product, healthcare organizations must consider a variety of demands and perspectives. The MRI team contributes a wealth of knowledge to the essential task of improving our clients’ supply chain and logistics performance. MRI is considered the most efficient medical device for the acquisition and treatment of medical imaging. Medical devices and manufacturing are classified as crucial factors for the supply chain in radiology. The objective of this paper is to present an approach that consists of modeling in 3D a segment of a stenosing aorta with a parallel treatment in order to determine the cost and the time of treatment for the reporting, which can be considered a promoter element to optimize the course of supply chain from manufacture to medical industry. Acceleration has sought to reduce the imaging speed in parallel architecture convergence for cardiac MRI. The image computation time is comparatively long owing to the iterative reconstruction process of 3D models. The aim of this paper is to suggest a CPU-GPU parallel architecture based on multicore to increase the speed of mesh generation in a 3D model of a stenosis aorta. A retrospective cardiac MRI scan with 74 series and 3057 images for a 10-year-old patient with congenital valve and valvular aortic stenosis on close MRI and coarctation (operated and dilated) in the sense of shone syndrome. The 3D mesh model was generated in Standard Tessellation Language (STL), as well as the libraries used to operate with Pymesh and Panda, and the time spent in tracing, decomposing, and finalizing the mesh crucially depends on the number of nuclei used in the parallel processing and the mesh quality chosen. A parallel processing based on four processors are required for the 3D shape refinement.To improve the efficiency of image processing algorithms and medical applications acquired in real-time analysis and control, a hybrid architecture (GPU/GPU) was proposed. The response time of parallel processing based on the CPU-GPU architecture used at the mesh level to achieve a 3D model is critically dependent on the number of kernels required.
2014 14th International Conference on Hybrid Intelligent Systems, 2014
The presence of faults in complex systems can cause serious damage and even generate fatal situat... more The presence of faults in complex systems can cause serious damage and even generate fatal situations. Proper and timely diagnosis of behavior of such systems is then crucial to detect the presence of eventual faults and isolate their causes. In this context, this paper describes a new intelligent approach to diagnose multiple faults in complex systems. This approach is based on the combination of a fuzzy system optimized by cultural algorithm and causal reasoning. The ongoing experiments focus on a simulation of the three-tank hydraulic system, a benchmark in the diagnosis domain.
Artificial Intelligence Techniques for a Scalable Energy Transition, 2020
Residential energy smart management (RESM) has received considerable momentum in the recent decad... more Residential energy smart management (RESM) has received considerable momentum in the recent decade considering its strong impact on the total energy consumption and the elaboration of the smart grid. Non-Intrusive Load Monitoring (NILM) is the first brick of the smart grid. In this paper, the importance of NILM in the smart grid is highlighted and its impact on different smart grid issues is discussed. Challenges facing NILM are also explained and existing solutions are reviewed. Mainly, an overview of different machine learning approaches is presented and these methods’ limits are discussed giving rise to open problems in the state of the art.
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