The availability of water can be considered as one of the key ingredients to the human life, yet this resource remains scarcely available to those living in the rural parts of Africa. When water does present itself, it is often impure and requires extensive treatment. Water treatment systems, particularly those capable of treating water in rural areas, are currently areas of research and entrepreneurship focus, making a number of potential solutions available, and other still coming in. Unfortunately, these systems are not always capable of performing in particular socio-cultural and economic contexts, or are often deployed in the wrong rural areas. Therefore these systems do not perform at their optimal level of design. Rural areas in Africa have different socio-cultural and economic context from each other, and this needs to be taken into account if one is going to select the right water treatment system for a particular area. Using industrial engineering tools, two water treatment system selection models; an Additive Analytic Hierarchy Process model and a Fuzzy Logic based model, are presented and then integrated. These models take into account the context of selected rural area by pitting available water purification systems against selected criteria to determine if it is the right fit for the rural area considered. Both models are then pitted against each other to determine which is more adept at selecting the appropriate water purification system. Three water treatment alternatives were considered after an analysis was conducted on the available solutions on the market. The water treatment systems under consideration were the Biosand Filter with Zeolites (BSFZ), the Silver Impregnated Porous Pot, and A Borehole system. Makwane, a rural village in Limpopo, South Africa was used as a case study to demonstrate the application of the selection models. The BSFZ was selected as the ideal water treatment system to be implemented in Makwane / Dissertation (MEng)--University of Pretoria, 2016. / Industrial and Systems Engineering / MEng / Unrestricted

This thesis investigates the application of Knowledge Based systems and Fuzzy Logic to automatic control. The knowledge used by a human operator is put in a computer usable form and applied to a control problem. The idea is not to attempt to enhance the stability or response of the system but given a basically stable and controllable system we apply human type control methods via a computer controller. A system can never be modelled exactly and therefore a controller design must allow for the uncertainty in the model. With fuzzy logic, the system inputs, outputs, parameters, reactions and cross coupling are represented in fuzzy or inexact variables, knowledge and reasoning. An exact (or nearly exact) model of the system is not necessary. A simple aircraft is the process to which this control method is applied. Knowledge, reasoning and feedback similar to what a human pilot utilizes are applied in the control of the process. / Thesis / Master of Engineering (ME)

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

Yes / The next generation in mobility management will enable different mobile networks to interoperate with each other to ensure terminal and personal mobility and global portability of network services. However, in order to ensure global mobility, the deployment and integration of both satellite and terrestrial components are necessary. This article is focused on issues reltaed to mobility management in a future mobile communications system, in a scenario where a multisegment access network is integrated into an IP core network by exploiting the principles of Mobile IP. In particular, attention is given to the requirements for location, address, and handover management. In a heterogeneous environment, the need to perform handover between access networks imposes particular constraints on the type of information available to the terminal and network. In this case, consideration will need to be given to parameters other than radio characteristics, such as achievable quality of service and user preference. this article proposes a new approach to handover management by applying the fuzzy logic concept to a heterogeneous environment. The article concludes with the presentation of mobility management signaling protocols.

No / This paper presents an investigation of the design optimization in microstrip lines to reduce the crosstalk level using Fuzzy Logic. In microstrip lines length and spacing, termination conditions of interconnection and output impedance of gates are the major components that cause crosstalk. In order to design high speed printed circuit board (PCB) with optimum interconnection configuration, it is essential to reduce the crosstalk to its minimum tolerance level. A design methodology is proposed to correlate electrical parameters and physical configuration of lines to the crosstalk phenomena. This design is subsequently optimized using Fuzzy Logic to reduce the level of crosstalk. A set of experiments is carried out to demonstrate the capabilities of the design and optimization methods. The effect of the geometrical configuration of the lines on crosstalk, particularly the spacing, is highlighted.

The testing of any physiological diagnostic system in-vivo depends critically on the stability of the anesthetized animal used. That is, if the systemic physiological parameters are not tightly controlled, it is exceedingly difficult to assess the precision and accuracy of the system or interpret the consequence of disease. In order to ensure that all measurements taken using the experimental system are not affected by fluctuations in physiological state, the animal must be maintained in a tightly controlled physiologic range. The main goal of this project was to develop a robust monitoring and control system capable of maintaining the physiological parameters of the anesthetized animal in a predetermined range, using the instrumentation already present in the laboratory, and based on the LabVIEWR software interface. A single user interface was developed that allowed for monitoring and control of key physiological parameters including body temperature (BT), mean arterial blood pressure (MAP) and end tidal CO2 (ETCO2). Embedded within this interface was a fuzzy logic based control system designed to mimic the decision making of an anesthetist. The system was tested by manipulating the blood pressure of a group of anesthetized animal subjects using bolus injections of epinephrine and continuous infusions of phenylephrine (a vasoconstrictor) and sodium nitroprusside (a vasodilator). This testing showed that the system was able to significantly reduce the deviation from the set pressure (as measured by the root mean square value) while under control in the hypotension condition (p < 0.10). Though both the short-term and hypertension testing showed no significant improvement, the control system did successfully manipulate the anesthetic percentage in response to changes in MAP. Though currently limited by the control variables being used, this system is an important first step towards a fully automated monitoring and control system and can be used as the basis for further research.

Finding a product with high quality and reasonable price online is a difficult task due to the fuzzy nature of data and queries. In order to handle the fuzzy problem, a new type-2 fuzzy reasoning based decision support system, the Web Shopping Expert for online users is proposed. In the Web Shopping Expert, an interval type-2 fuzzy logic system is used and a fuzzy output can be obtained using the up-low limit technique, which offers an opportunity to directly employ all the rules and methods of the type-1 fuzzy sets onto the type-2 fuzzy sets. To achieve the best performance the fuzzy inference system is optimized by the least square and numerical method. The key advantages of the least square method are the efficient use of samples and the simplicity of the implementation. The Web Shopping Expert based on the interval type-2 fuzzy inference system provides more reasonable conclusions for online users.

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Master’s thesis deals with designing decision making system based on fuzzy logic principles which evaluates suppliers offers. In this Master’s thesis there were created two decision making systems based on the demands of the company. First decision making system was designed within the environment of MS Excel, the second one was designed in MATLAB software. In addition of creating the decision making system the thesis puts emphasis on the analysis of the current decision making model in the company and this section will serve as a springboard to designing a decision making system based on fuzzy logic. At the beginning of the work there are included all the necessary theoretical framework on which I based the writing of this thesis.

Today’s society is facing a large increase of automation and smart devices. Everything from coffee machines to fridges include some kind of electronics and embedded systems. The focus of this Bachelor’s thesis was to dive deeper into how these automated devices can be controlled and more specifically a robot arm. The main purpose revolved around constructing a robotic arm that could be controlled through three different methods using MATLAB. These three were manual control, numerical analysis control and with a neural network based control. The prototype was created by assembling six servo motors onto 3D-printed parts. The arm consisted of three main parts which were a base, an arm and a gripper. The system was controlled by an Arduino micro-controller connected to a computer. The results show that the manual control method was easy to implement, fast and reliable. It allows control of all the angels for each servo motor, which also means controlling each individual degree of freedom. The numerical way, using Newton-Raphson’s method, broadened the abilities to control the arm but was slower. The third and final solution was to use fuzzy-logic. This ended up being a powerful method allowing for great control with low latency. While unreliable, the method showed great potential and with refinement could surpass the others. The conclusion was that the neural network method was the overall best method for controlling and manoeuvring the robot arm using MATLAB. / Dagens samhälle står inför en stor ökning av automatisering och smarta produkter. Allt från kaffemaskiner till kyl och frys innehåller någon form av elektronik och inbäddade system. Det huvudsakliga syftet med detta kanditatexamensarbete var att gräva djupare i hur dessa automatiserade produkter kan kontrolleras och mer specifikt i detta fall, en robotarm. Projektet handlade om att konstruera en robotarm som kunde styras och kontrolleras genom tre olika metoder i programmet MATLAB. Dessa tre har vi valt att kalla manuell kontroll, numerisk kontroll och neuralt nätverksbaserad kontroll. Prototypen tillverkades genom att montera sexservomotorer på 3D-utskrivna delar. Armen bestod av tre huvuddelar, en bas, en arm och en gripklo. Systemet styrdes av en Arduino mikrokontroll ansluten till en dator. Resultaten visar att den manuella kontrollmetoden var enkel att implementera, snabb samt var tillförlitlig. Den gav precis styrning av alla vinklar för varje servomotor, vilket också innebar att den gav god styrning av varje frihetsgrad. Den numeriska metoden, mer bestämt Newton Raphson’s metod, vidgade möjligheterna att kontrollera armen men var långsammare. Den tredje och sista lösningen var att använda ett neuralt nätverk, fuzzy logic. Detta visade sig vara ett kraftfullt sätt att styra roboten med låg latens. Det neurala nätverket visade sig dock vara opålitligt, men metoden visade stor potential för vidare utveckling och kan då prestera mycket bättre än de andra två metoderna. Slutsatsen var att det neurala nätverket var den generellt bästa metoden för att kontrollera och manövrera robotarmen via programmeringsprogrammet MATLAB.