This course is intended for computing sophomores and aims at presenting basic principles of relational DBMS and the practice of these fundamentals. The course content is mainly the following: Chapter 1: Introduction to databases Chapter 2: Relational Model Chapter 3: Relational Algebra Chapter 4: Standardization Chapter 5: SQL Language Chapter 6: Practical work A set of exercises are included at the end of the document. We added a tutorial section and directed to allow students to apply the concepts learned in the five chapters.
In this book, we propose several modules of diagnosis for complex and dynamic systems. These modules are based on the three algorithms colony of ants, which are AntTreeStoch, Lumer & Faieta and Binary ant colony. These algorithms have been chosen for their simplicity and their vast field of application. However, these algorithms cannot be used under their basal form for the development of diagnostic modules since they have several limitations. We have also proposed several adaptations in order that these algorithms can be used in diagnostic modules. We have proposed a parallel version of the algorithm AntTreeStoch based on a reactive multi-agents system. This version allows minimizing the influence of initial sort on the outcome of classification. We have also introduced a new parameter called Sid, which allows several ants to connect to the same position, and we have modified the movements of ants by promoting the path of the ant the most similar. For the algorithm Lumer & Faieta, we have accelerated the speed of construction of classes by adding a speed setting different for each Ant. To reduce the number of movements, we have proposed a new variable that allows saving the identifiers of objects displaced by the same Ant. To improve the quality of classification, we have also added to the algorithm of the indices to report the classes trunks constructed. For the algorithm Binary ant colony, we have proposed a variant called "Hybrid wrapper/filter-based ACO-SVM". This algorithm allows the selection of parameters. It combines the techniques of filters and enveloping methods in taking advantage of the rapidity of the Fisher report and the adaptation of selected settings to the classifier SVM. It improves the quality of classification according to the data nature in the database for learning and the type of the kernel function used. It also allows adjusting the hyperparameters of the kernel function. We tested these algorithms based on data from two industrial systems, which are the sintering system and the pasteurization system, as well on a few databases of UCI (University of California, Irvine).
This paper proposes a novel hybrid algorithm for the integration of systematic preventive maintenance policies in hybrid flow shop scheduling to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling
This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.
This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.
This paper proposes an improved approach based on MAS Architecture and Heuristic Algorithm for systematic maintenance to minimize makespan. We have implemented a problem-solving approach for optimizing the processing time, methods based on metaheuristics. The proposed approach is inspired by the behavior of the human body. This hybridization is between a multi-agent system and inspirations of the human body, especially genetics. The effectiveness of our approach has been demonstrated repeatedly in this paper. To solve such a complex problem, we proposed an approach which we have used advanced operators such as uniform crossover set and single point mutation. The proposed approach is applied to three preventive maintenance policies. These policies are intended to maximize the availability or to maintain a minimum level of reliability during the production chain. The results show that our algorithm outperforms existing algorithms. We assumed that the machines might be unavailable periodically during the production scheduling.
This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in unsupervised process monitoring.
