This diploma thesis deals with the design of fuzzy models to support decision making for selecting the most suitable suppliers for PSL, a.s. Describes methods and procedures for modeling in MS Excel and MATLAB. The goal is to create a decision-making system that will evaluate suppliers to optimize the choice of the most suitable supplier based on the requirements of the selected company.

Master's thesis deals with the evaluation of suppliers of selected company using fuzzy logic. Designed fuzzy system allows firm to evaluate individual offers and serves as a support for decision-making.

I denna rapport utreds olika metoder för att kunna reglera vattennivån i vattenkraftverket Avesta Lillfors i Dalarna. Två kraftverk ligger endast 900 m uppströms och detta gör att svarstiderna blir korta och regleringen blir lätt nervös. Att använda sig av vattennivåreglering i ett kraftverk för-enklar dess styrning då anpassning till inflödet sker automatiskt. En flödestabell har tagits fram genom mätningar i turbinen, med hjälp av Winter-Kennedy-metoden. Denna tabell används för att kunna fram-koppla regulatorn och därmed dämpa stora variationer i inflödet. Dessu-tom har en modell av älven skapats och testats med en återkopplad PID-regulator. Utefter dessa tester har lämpliga parametrar tagits fram, som ger önskad stabilitet, noggrannhet och snabbhet. Simuleringar har även gjorts med reglermetoden Fuzzy logic. / This report evaluates different methods to create a stable regulation of the water level in the hydro power plant Avesta Lillfors, in county Dalar-na. Another pair of plants are located just 900 m up the stream, which is why the regulation has to act fast. If the water level can be regulated and automatically adjust to the incoming flow, it facilitates the control of the plant. A flow chart is created from measurements in the turbine, using the Win-ter-Kennedy method. The results are used for feedforward control. A PID-regulator with feedback is also simulated in a model of the river. This helps finding the parameters that provide a stable, accurate and fast regu-lation. Fuzzy logic control has also been simulated.

Indiana University-Purdue University Indianapolis (IUPUI) / Electronic Fuel Control (EFC) valve regulates fuel flow to the injector fuel supply line in the Cummins Pressure Time (PT) fuel system. The EFC system controls the fuel flow by means of a variable orifice that is electrically actuated. The supplier of the EFC valves inspects all parts before they are sent out. Their inspection test results provide a characteristic curve which shows the relationship between pressure and current provided to the EFC valve. This curve documents the steady state characteristics of the valve but does not adequately capture its dynamic response. A dynamic test procedure is developed in order to evaluate the performance of the EFC valves. The test itself helps to understand the effects that proposed design changes will have on the stability of the overall engine system. A by product of this test is the ability to evaluate returned EFC valves that have experienced stability issues. The test determines whether an EFC valve is faulted or not before it goes out to prime time use. The characteristics of a good valve and bad valve can be observed after the dynamic test. In this thesis, a mathematical model has been combined with experimental research to investigate and understand the behavior of the characteristics of different types of EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. System Identification has been addressed to determine the transfer functions of the different types of EFC valves that were experimented. Methods have been used both in frequency domain as well as time domain. Also, based on the characteristic patterns exhibited by the EFC valves, fuzzy logic has been implemented for the use of pattern classification.

Master of Science / Department of Computing and Information Sciences / David A. Gustafson / IEDs are a prevailing threat to today’s armed forces and civilians. With some IEDs being well concealed and planted sometimes days or weeks prior to detonation, it is extremely difficult to detect their presence. Remotely triggered IEDs do offer an indirect method of detection as an insurgent must monitor the IED’s kill zone and detonate the device once the intended target is in range. Within the safe confines of a video game we can model the behavior of an insurgent using remotely triggered IEDs. Specifically, we can build a model of the sequence of actions an insurgent goes through immediately prior to detonating an IED. Using this insurgent model, we can recognize the behavior an insurgent would exhibit before detonating an IED. Once the danger level reaches a certain threshold, we can then react by changing our original course to a new one that does not cross the area we believe an IED to be in. We can show proof of concept of this by having human players take on the role of an insurgent in an online video game in which they try to destroy an autonomous agent. Successful tactics used by the autonomous agent should then be good tactics in the real world as well.

The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Malgorzata Rys / Catheter associated urinary tract infections (CAUTIs) are a source of concern in the healthcare industry because they occur more frequently than other healthcare associated infections and the rates of CAUTI have not improved in recent years. The use of urinary catheters is common among patients; between 15 and 25 percent of all hospital patients will use a urinary catheter at some point during their hospitalization (CDC, 2016). The prevalence of urinary catheters in hospitalized patients and high CAUTI occurrence rates led to the application of human factors engineering to develop a tool to help hospitals reduce CAUTI rates. Human reliability analysis techniques are methods used by human factors engineers to quantify the probability of human error in a system. A human error during a catheter insertion has the opportunity to introduce bacteria into the patient’s system and cause a CAUTI; therefore, human reliability analysis techniques can be applied to catheter insertions to determine the likelihood of a human error. A comparison of three human reliability analysis techniques led to the selection of the Cognitive Reliability and Error Analysis Method (CREAM). To predict a patient’s probability of developing a CAUTI, the human error probability found from CREAM is incorporated with several health factors that affect the patient’s risk of developing CAUTI. These health factors include gender, duration, diabetes, and a patient’s use of antibiotics, and were incorporated with the probability of human error using fuzzy logic. Membership functions were developed for each of the health factors and the probability of human error, and the centroid defuzzification method is used to find a crisp value for the probability of a patient developing CAUTI. Hospitals that implement this tool can choose risk levels for CAUTI that places the patient into one of three zones: green, yellow, or red. The placement into the zones depends on the probability of developing a CAUTI. The tool also provides specific best practice interventions for each of the zones.

In an era where use and dependence of information systems is significantly high, the threat of incidents related to information security that could jeopardize the information held by organizations is more and more serious. Alarming facts within the literature point to inadequacies in information security practices, particularly the evaluation of information security controls in organizations. Research efforts have resulted in various methodologies developed to deal with the information security controls assessment problem. A closer look at these traditional methodologies highlights various weaknesses that can prevent an effective information security controls assessment in organizations. This dissertation develops a methodology that addresses such weaknesses when evaluating information security controls in organizations. The methodology, created using the Fuzzy Logic Toolbox of MATLAB based on fuzzy theory and fuzzy logic, uses fuzzy set theory which allows for a more accurate assessment of imprecise criteria than traditional methodologies. It is argued and evidenced that evaluating information security controls using fuzzy set theory addresses existing weaknesses found in the literature for traditional evaluation methodologies and, thus, leads to a more thorough and precise assessment. This, in turn, results in a more effective selection of information security controls and enhanced information security in organizations. The main contribution of this research to the information security literature is the development of a fuzzy set theory-based assessment methodology that provides for a thorough evaluation of ISC in organizations. The methodology just created addresses the weaknesses or limitations identified in existing information security control assessment methodologies, resulting in an enhanced information security in organizations. The methodology can also be implemented in a spreadsheet or software tool, and promote usage in practical scenarios where highly complex methodologies for ISC selection are impractical. Moreover, the methodology fuses multiple evaluation criteria to provide a holistic view of the overall quality of information security controls, and it is easily extended to include additional evaluation criteria factor not considered within this dissertation. This is one of the most meaningful contributions from this dissertation. Finally, the methodology provides a mechanism to evaluate the quality of information security controls in various domains. Overall, the methodology presented in this dissertation proved to be a feasible technique for evaluating information security controls in organizations.

Inspired by the idea of vision zero, there is a lot of work that needs to be done in the field of advance driver assistance systems to develop more safer systems. Driver intention detection with a prediction of upcoming behavior of the driver is one possible solution to reduce the fatalities in road traffic. Driver intention detection provides an early warning of the driver's behavior to an Advanced Driver Assistance Systems (ADAS) and at the same time reduces the risk of non-essential warnings. This will significantly reduce the problem of warning dilemma and the system will become more safer. A driving maneuver prediction can be regarded as an implementation of driver's behavior. So the aim of this thesis is to determine the driver's intention by early prediction of a driving maneuver using Controller Area Network (CAN) bus data. The focus of this thesis is to optimize and further develop an algorithm for driver intention detection with fuzzy logic and edit distance method. At first the basics concerning driver's intention detection are described as there exists different ways to determine it. This work basically uses CAN bus data to determine a driver's intention. The algorithm overview with the design parameters are described next to have an idea about the functioning of the algorithm. Then different implementation tasks are explained for optimization and further development of the algorithm. The main aim to execute these implementation tasks is to improve the overall performance of the algorithm concerning True Positive Rate (TPR), False Positive Rate (FPR) and earliness values. At the end, the results are validated to check the algorithm performance with different possibilities and a test drive is performed to evaluate the real time capability of the algorithm. Lastly the use of driver intention detection algorithm for an ADAS to make it more safer is described in details. The early warning information can be feed to an ADAS, for example, an automatic collision avoidance or a lane change assistance ADAS to further improve safety for these systems.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Map matching method plays an important role in vehicle location and navigation systems. It employs the information in a digital map to compensate the positioning error. This paper presents a fuzzy-logic-based probabilistic map-matching algorithm used in GPS/INS integrated navigation systems, in which the reliability degree of map matching resolution is given explicitly as the decision basis in selecting matching road segment by utilizing the fuzzy comprehensive judgement. The results of experimental simulations have shown that the system performance gained significant enhancement by introducing this algorithm.