Predicting Faultiness of Program Modules Using Mamdani Model by Fuzzy Profile Development of Software Metrics

This research seminar proposed and implemented a new approach toward reliability and quality measurement of software systems by building a fault prediction model and faultiness degree estimation before starting the testing phase. The main goals of this model were to support decision making with regard to testing phase which leads to reduce the testing efforts, and to optimally assign the needed resources for testing activities. This research used KC2 dataset originated from National Aeronautics and Space Administration (NASA) project to evaluate the predictive accuracy of the proposed model. Software metrics in this dataset are of fuzzy nature, consequently, this work used MATLAB system to build a Mamdani fuzzy inference model. Then, this research applied and validated a published methodology for fuzzy profile development from data as an important requirement to build the model. Moreover, the proposed model utilized the capabilities of k-mean clustering algorithm as a machine learning technique to extract the fuzzy inference rules that were also required to build the model. Finally, this paper used suitable approaches to validate and evaluate the model. Accordingly, the results show that the proposed model provides significant capabilities in fault prediction and estimation

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Software quality and reliability prediction using Dempster -Shafer theory

As software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Most accurate prediction models require extensive testing effort, implying increased cost and slowing down the development life cycle. We developed two novel statistical models based on Dempster-Shafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. The models are flexible enough to incorporate information generated throughout the development life-cycle to improve the prediction accuracy.;Our first contribution is an original algorithm for building Dempster-Shafer Belief Networks using prediction logic. This model has been applied to software quality prediction. We demonstrated that the prediction accuracy of Dempster-Shafer Belief Networks is higher than that achieved by logistic regression, discriminant analysis, random forests, as well as the algorithms in two machine learning software packages, See5 and WEKA. The difference in the performance of the Dempster-Shafer Belief Networks over the other methods is statistically significant.;Our second contribution is also based on a practical extension of Dempster-Shafer theory. The major limitation of the Dempsters rule and other known rules of evidence combination is the inability to handle information coming from correlated sources. Motivated by inherently high correlations between early life-cycle predictors of software reliability, we extended Murphy\u27s rule of combination to account for these correlations. When used as a part of the methodology that fuses various software reliability prediction systems, this rule provided more accurate predictions than previously reported methods. In addition, we proposed an algorithm, which defines the upper and lower bounds of the belief function of the combination results. To demonstrate its generality, we successfully applied it in the design of the Online Safety Monitor, which fuses multiple correlated time varying estimations of convergence of neural network learning in an intelligent flight control system

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Fault Diagnosis of HVDC Systems Using Machine Learning Based Methods

With the development of high-power electronic technology, HVDC system is applied in the power system because of advantages in large-capacity and long-distance transmission, stability, and flexibility. Therefore, as the guarantee of reliable operating of HVDC system, fault diagnosis of the HVDC system is of great significance. In the current variety methods used in fault diagnosis, Machine Learning based methods have become a hotspot. To this end, the performance of several commonly used machine learning classifiers is compared in HVDC system. First of all, nine faults both in AC systems and DC systems of the HVDC system are set in the HVDC model in Simulink. Therefore, 10 operating states corresponding to the faults and normal operating are considered as the output classes of classifier. Seven parameters, such as DC voltage and DC current, are selected as fault feature parameters of each sample. By simulating the HVDC system in 10 operating states (including normal operating state) correspondingly, 20000 samples, each containing seven parameters, be obtained during the fault period. Then, the training sample set and the test sample set are established by 80% and 20% of the whole sample set. Subsequently, Decision Trees, the Support Vector Machine (SVM), K-Nearest Neighborhood Classifier (KNN), Ensemble classifiers, Discriminant Analysis, Backward Propagation Neural Network (BP-NN), long Short-Term Memory Neural Network (LSTM-NN), Extreme Learning Machine (ELM) was trained and tested. The accuracy of testing is used as the performance index of the model. In particular, for BP-NN, the impact of different transfer functions and learning rules combinations on the accuracy of the model was tested. For ELM, the impact of different activation functions on accuracy is tested. The results have shown that ELM and Bagged Trees have the best performance in HVDC fault diagnosis. The accuracy of these two methods are 92.23% and 96.5% respectively. However, in order to achieve better accuracy in ELM model, a large number of hidden layer nodes are set so that training time increases sharply