Hubert Kadima

SysML and safety analysis for mechatronic systems

Model-based system engineering is an efficient approach to specifying, designing, simulating and validating mechatronic systems. This approach allows errors to be detected as soon as possible in the design process, and thus reduces the overall cost of the product. Uniformity in a complex mechatronic project, which is by definition multidisciplinary, is achieved by expressing the models in a common modeling language such as SysML. This paper presents the state of the art of integrating risk and reliability studies with SysML in the design process of safety-critical systems. An Electro Mechanical Actuator system for light aircraft is used to illustrate the integration process, showing how a failure modes and effects analysis is automatically carried out from SysML structural and behavioral diagrams. Through our industry-relevant case study, the advantages and drawbacks of the employed integration methodology are analyzed.

Toward ontology-based personalization of a recommender system in social network

Personalized search, navigation and content delivery techniques have attracted interest in the recommender systems as a means to decrease search ambiguity and return results most relevant to a particular user preferences. In this paper, we study the effect of incorporating user semantic profile derived from past users behavior and preferences on the accuracy of a recommender system. We present a preliminary work which aims at tackling the most technical issues due to the integration of an ontology-based semantic user profile within a hybrid recommender system based on our early released guided recommender algorithm. A semantic user profile context is represented as an instance of a reference domain ontology in which concepts are annotated by interest scores.

Searching Frequent Itemsets by Clustering Data: Towards a Parallel Approach Using Mapreduce

We propose a new algorithm for searching frequent itemsets in large data bases. The idea is to start searching from a set of representative examples instead of testing the 1-itemset,the k-itemset and so on. A clustering algorithm is firstly applied in order to cluster the transactions into k clusters. The set of the k representative examples will be used as the starting point for searching frequent itemsets. Each cluster is represented by the most representative example. We show some preliminary results and we then propose a parallel version of this algorithm based on the MapReduce Framework.

Safety analysis integration in a SysML-based complex system design process

Model-based system engineering is an efficient approach to specifying, designing, simulating and validating complex systems. This approach allows errors to be detected as soon as possible in the design process, and thus reduces the overall cost of the product. Uniformity in a system engineering project, which is by definition multidisciplinary, is achieved by expressing the models in a common modeling language such as SysML. This paper presents an approach to integrate safety analysis in SysML at early stages in the design process of safety-critical systems. Qualitative analysis is performed through functional as well as behavioral safety analysis and strengthened by formal verification method. This approach is applied to a real-life avionic system and contributes to the integration of formal models in the overall safety and systems engineering design process of complex systems.

A Consistent Preliminary Design Process for Mechatronic Systems

In this paper, a consistent and collaborative preliminary design process for mechatronic systems is described. First, a functional analysis is carried out from user requirements with SysML. This allows one to define suitable architectures and associated test cases. Each of them has to be analysed and optimized separately in order to select the best architecture and the best set of key parameters. The next step of the preliminary design is a modelling of its architecture and its behaviour. In order to merge multi-physical and geometrical parameters, our generic method relies on a topological analysis of the system and generates a set of equations with physical and topological constraints previously defined. Finally, an interval analysis is implemented, allowing one to explore exhaustively the search space resulting from a declarative statement of constraints, in order to optimize the parameters under the constraint of the relevant test cases. An automotive power lift gate scenario has been chosen to test this design process.

Graph Searching Algorithms for Semantic-Social Recommendation

In this paper we present two recommendation algorithms, called Node-Edge-Based and Node-Based recommendation algorithms. These algorithms are designed to recommend items to users connected via social network. Our algorithms are based on three main features: a social network analysis measure (degree centrality), the graph searching algorithm (Depth First Search algorithm), and the semantic similarity measure (which measures the closeness between the input item and users). We apply these algorithms to a real dataset (Amazon dataset) and we compare them with item-based collaborative filtering and hybrid recommendation algorithms. Our results show good precision as well as in a good performance in terms of runtime. Moreover, Node-Edge-Based and Node-Based algorithms search a small part of the dataset, compared to item-based and hybrid recommendation algorithms.

Exploiting Semantic and Social Information in Recommendation Algorithms

In this paper we present algorithms for recommender systems. Our algorithms rely on a semantic relevance measure and a social network analysis measure to partially explore the network using depth-first search and breath-first search strategies. We apply these algorithms to a real data set and we compare them with item-based collaborative filtering and hybrid recommendation algorithms. Our experiments show that our algorithms outperform existing recommendation algorithms, while providing good precision and F-measure results.

CBR-Mining Approach to Improve Learning System Engineering in a Collaborative E-Learning Platform.

System engineering (SE) is an approach that involves customers and users in the development process and more particularly during the definition of requirements and system functionalities. In order to meet the challenges and increasing complexity of system engineering, the training of engineering students in this field is necessary. It enables learners to acquire sound theoretical and practical knowledge, and to adapt to the majority of profiles of the position related to system engineering field proposed by industrial companies. In this paper, we present a continuity of our research work (Berriche et al., 2015), we study the feasibility of the CBR-mining (case based reasoning and process mining) approach in the context of our platform dedicated to the learning of system engineering. First, we apply the CBR-mining approach to monitor student interactions from log files. Secondly, we propose clusters that bring together all the educational processes most performed by students. We have experimented this approach using the ProM Framework.

Closed-Loop Product Lifecycle Management Based on a Multi-agent System for Decision Making in Collaborative Design

In the collaborative design environment, there is an increasing demand for information exchange and sharing to reduce lead time and to improve product quality and value. Software and communication technologies can be a relevant approach in this context, using for instance PLM (Product Lifecycle Management) systems. Each product lifecycle development phase generates knowledge, and managing this knowledge can be placed in a closed-loop. In this paper, we present a research in progress that exposes a collaborative architecture based on a multi-agent system which aims to support the knowledge management process in the closed-loop. This is a new strategic approach to manage the product lifecycle information efficiently in a distributed environment. The purpose of this paper is to illustrate the use of DOCK (Design based on Organization, Competence and Knowledge) methodology for the design of our multi-agent system and to demonstrate how to handle intelligent knowledge via a use case study.

Toward Social-Semantic Recommender Systems

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.