This work appears in pattern recognition in the agronomic domain, especially for the identification of the leaves of plants, while using the adaptive technique of neuronal networks. In this article, we will expose our tool; which is intended for two categories of specialists, the first consisting of researchers in the field of botany, as the second, so all scientists, who may use this work in their own applications. We will expose also, the capacities of generalization of the neuronal networks and their implementation to our problem.

This paper created a new research space in the faults modeling and detection area of the industrial systems, especially photovoltaic generators. It reserved for modeling and detection the hybrid defects, like the presence of cells open- and short-circuit within the same photovoltaic cells group. For a small investment, the new algorithm created a new platform. It exposed a display screen of the database, which presented the power of the PV module production in each period. The display screen allows real-time monitoring of the PV module production throughout the year, and detecting its anomalies.

The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give ASKANDIA, by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26% and could be generalised to other manufacturing or service systems.

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors.

In recent years, companies have emerged in an advanced competitive environment. To meet the requirements of cost reduction, customer demand, minimising delays, quality and variety improvement, companies must improve their performance to remain competitive, survive and expand. To achieve this goal, several models are used such as total quality management, Kaizen, just in time, enterprise resource planning, business process reengineering and Six Sigma, etc. In this work, we look for an effective model (drawn from Six Sigma approach) used mainly to warrant the competitiveness of a company denoted as the weighted defects per million opportunities model. The aim of this paper is to apply this model to measure process levels (weights) and assess the company competitiveness. The results of this model are applied in a real manufacturing system which produces gas bottles.

This study aims are data acquisition, control and online modeling of an oil collection pipeline network using a SCADA «Supervisory Control and Data Acquisition» system, allowing the optimization of this network in real time by creating more exact models of onsite facilities. Indeed, fast development of computing systems makes obsolete usage of old systems for which maintenance became more and more expensive and their performances don't comply any more with modern company operations. SCADA system is a telemetry and control system adapted for particular requirements of an oilfield management. Thanks to its different functions, we take advantage of this system to solve production problems especially those related to oil collecting pipeline network. In fact this network is confronted to some problems, in particular pressure losses which has significant effect on the production. This problem can be taken under control by the awareness of pipeline network operation and all its process data (especially junctions) in real time. This will allow online creation of representative and accurate computerized models for the oil collecting pipeline network including producing wells, collecting pipelines, manifolds and others facilities.

The purpose of this paper is to compare between three methods of intellectual capital (IC) measurement; intellectual capital dynamic valuation (IC-dVal), value added intellectual coefficient (VAIC), and national intellectual capital index (NICI). The three methods are the most used in practice; we used 24 criteria covering important aspects of IC to do general comparison. According to ten criteria, we compared and prioritised them using analytic hierarchy process (AHP). The results of this comparison show that the methods are close for some criteria and distant for other criteria. The prioritisation with AHP found that NICI method is the most method responding to the criteria, namely: macro measure, guidelines of the method, dynamic valuation, involved levels of business, usability by stakeholders, covered aspects of IC, quantifiability, frequency of use and applicability. IC-dVal is the second one and VAIC is the third method responding to the criteria. The analysis could give more significant results using larger set of criteria. This is the first research prioritising methods of measurement of IC using AHP analysis.

This paper deals with a new smart algorithm allowing open-circuit and reversed polarity faults prognosis in photovoltaic generators. Its contribution lies on the optimization of support vector regression (SVR) technique by a k-NN regression tool (k-NNR) for undetermined outputs. To testing the performance of the proposed algorithm, we used a significant data base containing the generator functioning history, and as indicators we selected variance, standard deviation, Confidence interval, absolute and relative errors.

The purpose of this paper is to propose a hybrid method SKACICM of development of knowledge management systems. Based on weaknesses of the method of performance dashboards SKANDIA, we proposed a pragmatisation and adaptation of Skandia to give ASKANDIA, by enrichment of its performance book. We ameliorated CICM model against the requirements of GERAM to give ACICM model by mappings between their proposed metamodels. We tried to hybridise ACICM, ASKANDIA and business intelligence to propose a new method SKACICM of development of knowledge management systems. We applied SKACICM on a cement company to develop software containing three main modules, module knowledge management, module business intelligence and performance dashboard system. The developed system ameliorated the performance of the enterprise by 26% and could be generalised to other manufacturing or service systems.

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.

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.

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.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.