Fuzzy Logic Based Software Product Quality Model for Execution Tracing

This report presents the research carried out in the area of software product quality modelling. Its main endeavour is to consider software product quality with regard to maintainability. Supporting this aim, execution tracing quality, which is a neglected property of the software product quality at present in the quality frameworks under investigation, needs to be described by a model that offers possibilities to link to the overall software product quality frameworks. The report includes concise description of the research objectives: (1) the thorough investigation of software product quality frameworks from the point of view of the quality property analysability with regard to execution tracing; (2) moreover, extension possibilities of software product quality frameworks, and (3) a pilot quality model developed for execution tracing quality, which is capable to capture subjective uncertainty associated with the software quality measurement. The report closes with concluding remarks: (1) the present software quality frameworks do not exhibit any property to describe execution tracing quality, (2) execution tracing has a significant impact on the analysability of software systems that increases with the complexity, and (3) the uncertainty associated with execution tracing quality can adequately be expressed by type-1 fuzzy logic. The section potential future work outlines directions into which the research could be continued. Findings of the research were summarized in two research reports, which were also incorporated in the thesis, and submitted for publication: 1. Tamas Galli, Francisco Chiclana, Jenny Carter, Helge Janicke, “Towards Introducing Execution Tracing to Software Product Quality Frameworks,” Acta Polytechnica Hungarica, vol. 11, no. 3, pp. 5-24, 2014. doi: 10.12700/APH.11.03.2014.03.1 2. Tamas Galli, Francisco Chiclana, Jenny Carter, Helge Janicke “Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic,” Acta Polytechnica Hungarica, vol. 10, no. 8, pp. 49-67, 2013. doi: 10.12700/APH.10.08.2013.8.

Heuristic design of fuzzy inference systems: a review of three decades of research

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper offers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems

Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

In this research, the improved mass spring model is presented to simulate the human liver deformation. The underlying MSM is redesigned where fuzzy knowledge-based approaches are implemented to determine the stiffness values. Results show that fuzzy approaches are in very good agreement to the benchmark model. The novelty of this research is that for liver deformation in particular, no specific contributions in the literature exist reporting on real-time knowledge-based fuzzy MSM for liver deformation

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

Using fuzzy inference system to predict Pb (II) removal from aqueous solutions by magnetic Fe3O4/H2SO4-activated Myrtus Communis leaves carbon nanocomposite

In this research study, a magnetic nanocomposite consisting of the Fe3O4 nanoparticles immobilized on Myrtus Communis-derived activated carbon (MM-AC) was synthesized and then, characterized by FE-SEM and FT-IR analytical methods. The results showed that the sizes of the Fe3O4 nanoparticles were about 54¿nm, and the changes in the intensities of the major peaks were associated with the binding process. The adsorption efficiency of the MM-AC was evaluated for Pb (II) removal from aqueous solutions. The effective parameters such as pH, adsorbent dosage, contact time and initial metal ion concentration were optimized to reach maximum Pb (II) removal efficiency (%). The equilibrium amount of Pb (II) adsorbed onto the MM-AC suggested that the removal of Pb (II) followed Langmuir model. The kinetic studies on the removal of Pb (II) revealed that the adsorption process obeyed pseudo-second-order kinetic model. The maximum Pb (II) removal efficiency by the MM-AC was obtained at pH¿=¿5. The adsorption capacity of Pb (II) onto the MM-AC changed from 88.65 to 480.90¿mg/g by increasing the initial concentration of Pb (II) in the range of 100–400¿mg/L. The comparison of maximum monolayer adsorption capacity of the MM-AC with other adsorbents reported in the literatures for removal of Pb (II) indicated that the MM-AC had better removal efficiency. In order to predict Pb (II) removal efficiency, a methodology based on fuzzy inference system (FIS) including multiple inputs and one output was developed. Four input variables namely pH, contact time (min), adsorbent dosage (g), and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. A Mamdani-type of fuzzy inference system was applied to implement a total of 18 rules in IF-THEN format along with a fuzzy subset consisting of a combination of Triangular and Trapezoidal membership functions in eight levels. The max-min method was employed as fuzzy inference operator, while defuzzification process was conducted using the center of gravity (COG, centroid) method. The achieved coefficient of determination value (R2>¿0.99) confirmed the excellent accuracy of fuzzy logic model as a trustworthy prediction tool for Pb (II) removal efficiency. The overall results suggested that the developed material can be employed as an efficient adsorbent for Pb (II) removal from polluted aqueous solutions on a full-scale operation.Peer ReviewedPostprint (author's final draft

Dynamic Analysis of Cracked Rotor in Viscous Medium and its Crack Diagnosis Using Intelligent Techniques

Fatigue cracks have high potential to cause catastrophic failures in the rotor which can lead to catastrophic failure if undetected properly and in time. This fault may interrupt smooth, effective and efficient operation and performance of the machines. Thereby the importance of identification of crack in the rotor is not only for leading safe operation but also to prevent the loss of economy and lives.The condition monitoring of the engineering systems is attracted by the researchers and scientists very much to invent the automated fault diagnosis mechanism using the change in dynamic response before and after damage. When the rotor with transverse crack immersed in the viscous fluid, analysis of cracked rotor is difficult and complex. The analysis of cracked rotor partially submerged in the viscous fluid is widely used in various engineering systems such as long spinning shaft used drilling the seabed for the extracting the oil, high-speed turbine rotors, and analysis of centrifuges in a fluid medium. Therefore, dynamic analysis of cracked rotor partially submerged in the viscous medium have been presented in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Amplitude) of multiple cracked mild steel rotor partially submerged in the viscous medium. The presence of the crack in rotor generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic characteristics of cracked rotor in a viscous fluid. The local stiffness matrix has been calculated by the inverse of local dimensionless compliance matrix. The finite element analysis has been carried out to measure the vibration characteristics of cracked rotor partially submerged in the viscous medium using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the viscosity of fluid, depth and position of the cracks affect the performance of the rotor and effectiveness of crack detection techniques. Various Artificial Intelligent (AI) techniques such as fuzzy logic, hybrid BPNN-RBFNN neural network, MANFIS and hybrid fuzzy-rule base controller based multiple faults diagnosis systems are developed using the dynamic response of rotating cracked rotor in a viscous medium to monitor the presence of crack. Experiments have been conducted to authenticate the performance and accuracy of proposed methods. Good agreement is observed between the results

Fuzzy model predictive control. Complexity reduction by functional principal component analysis

En el Control Predictivo basado en Modelo, el controlador ejecuta una optimización en tiempo real para obtener la mejor solución para la acción de control. Un problema de optimización se resuelve para identificar la mejor acción de control que minimiza una función de coste relacionada con las predicciones de proceso. Debido a la carga computacional de los algoritmos, el control predictivo sujeto a restricciones, no es adecuado para funcionar en cualquier plataforma de hardware. Las técnicas de control predictivo son bien conocidos en la industria de proceso durante décadas. Es cada vez más atractiva la aplicación de técnicas de control avanzadas basadas en modelos a otros muchos campos tales como la automatización de edificios, los teléfonos inteligentes, redes de sensores inalámbricos, etc., donde las plataformas de hardware nunca se han conocido por tener una elevada potencia de cálculo. El objetivo principal de esta tesis es establecer una metodología para reducir la complejidad de cálculo al aplicar control predictivo basado en modelos no lineales sujetos a restricciones, utilizando como plataforma, sistemas de hardware de baja potencia de cálculo, permitiendo una implementación basado en estándares de la industria. La metodología se basa en la aplicación del análisis de componentes principales funcionales, proporcionando un enfoque matemáticamente elegante para reducir la complejidad de los sistemas basados en reglas, como los sistemas borrosos y los sistemas lineales a trozos. Lo que permite reducir la carga computacional en el control predictivo basado en modelos, sujetos o no a restricciones. La idea de utilizar sistemas de inferencia borrosos, además de permitir el modelado de sistemas no lineales o complejos, dota de una estructura formal que permite la implementación de la técnica de reducción de la complejidad mencionada anteriormente. En esta tesis, además de las contribuciones teóricas, se describe el trabajo realizado con plantas reales en los que se han llevado a cabo tareas de modelado y control borroso. Uno de los objetivos a cubrir en el período de la investigación y el desarrollo de la tesis ha sido la experimentación con sistemas borrosos, su simplificación y aplicación a sistemas industriales. La tesis proporciona un marco de conocimiento práctico, basado en la experiencia.In Model-based Predictive Control, the controller runs a real-time optimisation to obtain the best solution for the control action. An optimisation problem is solved to identify the best control action that minimises a cost function related to the process predictions. Due to the computational load of the algorithms, predictive control subject to restric- tions is not suitable to run on any hardware platform. Predictive control techniques have been well known in the process industry for decades. The application of advanced control techniques based on models is becoming increasingly attractive in other fields such as building automation, smart phones, wireless sensor networks, etc., as the hardware platforms have never been known to have high computing power. The main purpose of this thesis is to establish a methodology to reduce the computational complexity of applying nonlinear model based predictive control systems subject to constraints, using as a platform hardware systems with low computational power, allowing a realistic implementation based on industry standards. The methodology is based on applying the functional principal component analysis, providing a mathematically elegant approach to reduce the complexity of rule-based systems, like fuzzy and piece wise affine systems, allowing the reduction of the computational load on modelbased predictive control systems, subject or not subject to constraints. The idea of using fuzzy inference systems, in addition to allowing nonlinear or complex systems modelling, endows a formal structure which enables implementation of the aforementioned complexity reduction technique. This thesis, in addition to theoretical contributions, describes the work done with real plants on which tasks of modeling and fuzzy control have been carried out. One of the objectives to be covered for the period of research and development of the thesis has been training with fuzzy systems and their simplification and application to industrial systems. The thesis provides a practical knowledge framework, based on experience

An Explainable Artificial Intelligence Approach Based on Deep Type-2 Fuzzy Logic System

Artificial intelligence (AI) systems have benefitted from the easy availability of computing power and the rapid increase in the quantity and quality of data which has led to the widespread adoption of AI techniques across a wide variety of fields. However, the use of complex (or Black box) AI systems such as Deep Neural Networks, support vector machines, etc., could lead to a lack of transparency. This lack of transparency is not specific to deep learning or complex AI algorithms; other interpretable AI algorithms such as kernel machines, logistic regressions, decision trees, or rules-based algorithms can also become difficult to interpret for high dimensional inputs. The lack of transparency or explainability reduces the effectiveness of AI models in regulated applications (such as medical, financial, etc.), where it is essential to explain the model operation and how it arrived at a given prediction. The need for explainability in AI has led to a new line of research that focuses on developing Explainable AI techniques. There are three main avenues of research that are being explored to achieve explainability; first, Deep Explanations, which involves the modification of existing Deep learning models to add explainability. The methods proposed to do Deep explanations generally provide details about all the input features that affect the output, generally in a visual format as there might be a large number of features. This type of explanation is useful for tasks such as image recognition, but in other tasks, it might be hard to distinguish the most important features. Second, Model induction, which involves methods that are model agnostic, but these methods might not be suitable for use in regulated applications. The third method is to use existing interpretable models such as decision trees, fuzzy logic, etc., but the problem with them is that they can also become opaque for high dimensional data. Hence, this thesis presents a novel AI system by combining the predictive power of Deep Learning with the interpretability of Interval Type-2 Fuzzy Logic Systems. The advantages of such a system are, first, the ability to be trained via labelled and unlabelled data (i.e., mixing supervised and unsupervised learning). Second, having embedded feature selection abilities (i.e., can be trained by hundreds and thousands of inputs with no need for feature selection) while delivering explainable models with small rules bases composed of short rules to maximize the model’s interpretability. The proposed model was developed with data from British Telecom (BT). It achieved comparable performance to the deep models such as Stacked Autoencoder (SAE) and Convolution Neural Networks (CNN). In categorical datasets, the model outperformed the SAE by 2%, performed within 2-3% of the CNN and outperformed Multi-Layer Perceptron (MLP) and IT2FLS by 4%. In the regression datasets, the model performed slightly worse than the SAE, MLP and CNN models, but it outperformed the IT2FLS with a 15% lower error. The proposed model achieved excellent interpretability in a survey where it was rated within 2% of the highly interpretable IT2FLS. It was also rated 20% and 17% better than Deep learning XAI tools LIME and SHAP, respectively. The proposed model shows a small loss in performance for significantly higher interpretability, making it a suitable replacement for the other AI models in applications with many features where interpretability is paramount

Mission Aware Energy Saving Strategies For Army Ground Vehicles

Fuel energy is a basic necessity for this planet and the modern technology to perform many activities on earth. On the other hand, quadrupled automotive vehicle usage by the commercial industry and military has increased fuel consumption. Military readiness of Army ground vehicles is very important for a country to protect its people and resources. Fuel energy is a major requirement for Army ground vehicles. According to a report, a department of defense has spent nearly $13.6 billion on fuel and electricity to conduct ground missions. On the contrary, energy availability on this plant is slowly decreasing. Therefore, saving energy in Army ground vehicles is very important. Army ground vehicles are embedded with numerous electronic systems to conduct missions such as silent and normal stationary surveillance missions. Increasing electrical energy consumption of these systems is influencing higher fuel consumption of the vehicle. To save energy, the vehicles can use any of the existing techniques, but they require complex, expensive, and time consuming implementations. Therefore, cheaper and simpler approaches are required. In addition, the solutions have to save energy according to mission needs and also overcome size and weight constraints of the vehicle. Existing research in the current literature do not have any mission aware approaches to save energy. This dissertation research proposes mission aware online energy saving strategies for stationary Army ground vehicles to save energy as well as to meet the electrical needs of the vehicle during surveillance missions. The research also proposes theoretical models of surveillance missions, fuzzy logic models of engine and alternator efficiency data, and fuzzy logic algorithms. Based on these models, two energy saving strategies are proposed for silent and normal surveillance type of missions. During silent mission, the engine is on and batteries power the systems. During normal surveillance mission, the engine is on, gear is on neutral position, the vehicle is stationary, and the alternator powers the systems. The proposed energy saving strategy for silent surveillance mission minimizes unnecessary battery discharges by controlling the power states of systems according to the mission needs and available battery capacity. Initial experiments show that the proposed approach saves 3% energy when compared with the baseline strategy for one scenario and 1.8% for the second scenario. The proposed energy saving strategy for normal surveillance mission operates the engine at fuel-efficient speeds to meet vehicle demand and to save fuel. The experiment and simulation uses a computerized vehicle model and a test bench to validate the approach. In comparison to vehicles with fixed high-idle engine speed increments, experiments show that the proposed strategy saves fuel energy in the range of 0-4.9% for the tested power demand range of 44-69 kW. It is hoped to implement the proposed strategies on a real Army ground vehicle to start realizing the energy savings