The power grid has evolved over the course of many decades with the usage of cyber systems and communications such as Supervisory Control And Data Acquisition (SCADA); however, due to their connectivity to the internet, the cyber-power system can be infiltrated by malicious attackers. Encryption is not a singular solution. Currently, there are several cyber security measures in development, including those based on artificial intelligence. However, there is a need for a varying but consistent attack algorithm to serve as a testbed for these AI or other practices to be trained and tested. This is important because in the event of a real attacker, it is not possible to know exactly where they will attack and in what order. Therefore, the proposed method in this thesis is to use criminology concepts and fuzzy logic inference to create this algorithm and determine its effectiveness in making decisions on a cyber-physical system model. The method takes various characteristics of the attacker as an input, builds their ideal target node, and then compares the nodes to the high-impact target and chooses one as the goal. Based on that target and their knowledge, the attackers will attack nodes if they have resources. The results show that the proposed method can be used to create a variety of attacks with varying damaging effects, and one other set of tests shows the possibility for multiple attacks, such as denial of service and false data injection. The proposed method has been validated using an extended cyber-physical IEEE 13-node distribution system and sensitivity tests to ensure that the ruleset created would take each of the inputs well. / Master of Science / For the last decades, information and communications technology has become more commonplace for electric power and energy systems around the world. As a result, it has attracted hackers to take advantage of the cyber vulnerabilities to attack critical systems and cause damage, e.g., the critical infrastructure for electric energy. The power grid is a wide-area, distributed infrastructure with numerous power plants, substations, transmission and distribution lines as well as customer facilities. For operation and control, the power grid needs to acquire measurements from substations and send control commands from the control center to substations. The cyber-physical system has its vulnerabilities that can be deployed by hackers to launch falsified measurements or commands. Much research is concerned with how to detect and mitigate cyber threats. These methods are used to determine if an attack is occurring, and, if so, what to do about it. However, for these techniques to work properly, there must be a way to test how the defense will understand the purpose and target of an actual attack, which is where the proposed modeling and simulation method for an attacker comes in. Using a set of values for their resources, motivation and other characteristics, the defense algorithm determines what the attacker's best target would be, and then finds the closest point on the power grid that they can attack. While there are still resources remaining based on the initial value, the attacker will keep choosing places and then execute the attack. From the results, these input characteristic values for the attacker can affect the decisions the attacker makes, and the damage to the system is reflected by the values too. This is tested by looking at the results for the high-impact nodes for each input value, and seeing what came out of it. This shows that it is possible to model an attacker for testing purposes on a simulation.

Baigiamajame magistro darbe nagrinėjamos neraiškiųjų verslo taisyklių naudojimo informacinėse sistemose problemos, „minkštųjų skaičiavimų“ intelektinėse informacinėse sistemose problematika, neuroninių-neraiškiųjų sistemų principai. Išnagrinėti pagrindiniai neraiškiosios logikos dėsniai, kuriais remiantis naudojamos neraiskiosios verslo taisyklės intelektinėse informacinėse sistemose. Pateiktas būdas, kaip neuroninės-neraiškiosios sistemos gali būti naudojamos verslo taisyklių sistemose naudojant RuleML, taisyklių žymėjimo kalbos, standartą. Baigiamajame darbe aprašomas eksperimentas, atliktas naudojant Matlab aplinką, XMLBeans taikomąją programą ir autoriaus sukurta neraiškaus išvedimo sistemos perkelimo į RuleML formatą taikomąją programą. Išnagrinėjus teorinius ir praktinius neuroninių-neraiškiųjų sistemų naudojimo aspektus, pateikiamos baigiamojo darbo išvados ir siūlymai. Darbą sudaro 5 dalys: įvadas, analitinė-metodinė dalis, eksperimentinė-tiriamoji dalis, išvados ir siūlymai, literatūros sąrašas. Darbo apimtis – 58 p. teksto be priedų, 30 iliustr., 30 bibliografiniai šaltiniai. Atskirai pridedami darbo priedai. / This work investigates the problems of use of fuzzy business rules in information systems, „soft computing“ in intelligent information systems issues, neuro-fuzzy systems principles. Main fuzzy logic laws are considered, which are used as the basis of fuzzy business rules in intelligent information systems. Suggested an approach, based on RuleML standard, how neuro-fuzzy systems could be used together with business rules engines. This paper describes the experiment carried out using the Matlab environment, XMLBeans application and the author created application for fuzzy inference system migration to RuleML standard format. Structure: introduction, analysis , project, conclusions and suggestions, references. Thesis consist of: 58 p. text without appendixes, 30 pictures, 30 bibliographical entries. Appendixes included.

The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan iv and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within ±1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance. According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects’ data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within ±1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.

Orientador: Sandra Regina Monteiro Masalskiene Roveda / Resumo: A nova perspectiva rumo à conservação do meio ambiente como fato categórico de subsistência planetária tem colocado a sustentabilidade em primeiro plano como o grande desafio da universidade, responsável e equipada para a formação daqueles que terão o poder decisório sobre as questões relacionadas a um futuro viável. Este estudo se refere à sustentabilidade na universidade por meio do que é percebido pelos diversos atores que nela transitam. Teve como objetivo desvendar, em algumas instituições do setor público e do setor privado, quais as impressões que professores/coordenadores, alunos e funcionários possuem sobre as atuações da instituição em seu campus, os projetos e pesquisas voltados à sustentabilidade elaborados pela equipe docente e os aprendizados efetivos na formação dos novos profissionais, que deverão atuar nas diversas áreas de atividades em nossa sociedade. Para poder medir essas impressões, foram aplicados questionários e desenvolvido um modelo fuzzy com um índice associado, que apresenta o nível de sustentabilidade de uma Instituição de Ensino Superior – IES. Isso nos leva a concluir que os sistemas de inferência fuzzy são capazes de fazer uma avaliação do que pode ser percebido pela comunidade universitária sobre a sustentabilidade de sua instituição. / Doutor

With the development of three-dimensional (3D) technologies, the demand for high-quality 3D content, 3D visualization, and flexible and natural interactions are increasing. As a result, semi-transparent Augmented-Reality (AR) systems are emerging and evolving rapidly. Since there are currently no well-recognized models to evaluate the performance of these systems, we proposed a Quality-of-Experience (QoE) taxonomy for semi-transparent AR systems containing three levels of influential QoE parameters, through analyzing existing QoE models in other related areas and integrating the feedbacks received from our user study. We designed a user study to collect training and testing data for our QoE model, and built a Fuzzy-Inference-System (FIS) model to estimate the QoE evaluation and validate the proposed taxonomy. A case study was also conducted to further explore the relationships between QoE parameters and technical QoS parameters with functional components of Microsoft HoloLens AR system. In this work, we illustrate the experiments in detail and thoroughly explain the results obtained. We also present the conclusion and future work.

In this paper I describe a motion planning technique for intelligent ground vehicles. The technique is an implementation of a path selection algorithm based on fuzzy inference. The approach extends on the motion planning algorithm known as driving with tentacles. The selection of the tentacle (a drivable path) to follow relies on the calculation of a weighted cost function for each tentacle in the current speed set, and depends on variables such as the distance to the desired position, speed, and the closeness of a tentacle to any obstacles. A Matlab simulation and the practical implementation of the fuzzy inference rule on a Clearpath Husky robot within the Robot Operating System (ROS) framework are provided.

The stability of the Internet is at risk if the amount of voice and video traffic continues to increase at the current pace. While current transport layer protocols do work well for most applications, they still present some problems. TCP is reliable, tracks the state of some network conditions and reacts drastically to an indication of congestion. TCP serves data-oriented applications very well but it can lead to unacceptably low quality for streaming applications by multiplicatively reducing the congestion window upon a sign of congestion. The other main transport layer protocol, UDP, provides good service for streaming applications but is not friendly to TCP and can cause the well-known existing congestion collapse problem in the Internet. This thesis proposes a new protocol to provide a good service for voice and video applications while being friendly to TCP and solving the congestion collapse problem. The protocol utilizes a fuzzy logic controller that considers network related information to govern the applications sending rate while satisfying the users needs. Using network information such as the available bandwidth, Packet Loss Rates (PLR), and Round Trip Times (RTT) a fuzzy inference system optimizes the applications send rate to meet the requested rate in a smooth manner without wasting network resources unnecessarily. The fuzzy logic controller is designed and its performance evaluated using MATLAB model simulations. The results indicate that the fuzzy controller solves the congestion collapse problem by reducing the number of undelivered packets into the network by nearly 100%. It provides smooth transition changes as demonstrated by the controlled UDP flow utilizing an estimated 44% more of the available bandwidth to smooth the send rate than the TCP flow in a highly varying bandwidth environment. The controller also remains friendly with TCP which was demonstrated to share the bandwidth at nearly 50% with one other competing controlled UDP flow.

Fine-scale resolution digital elevation models (DEMs) created from data collected using high precision instruments have become ubiquitous in fluvial geomorphology. They permit a diverse range of spatially explicit analyses including hydraulic modeling, habitat modeling and geomorphic change detection. Yet, the intercomparison of survey technologies across a diverse range of wadeable stream habitats has not yet been examined. Additionally, we lack an understanding regarding the precision of DEMs derived from ground-based surveys conducted by different, and inherently subjective, observers. This thesis addresses current knowledge gaps with the objectives i) to intercompare survey techniques for characterizing instream topography, and ii) to characterize observer variability in instream topographic surveys. To address objective i, we used total station (TS), real-time kinematic (rtk) GPS, terrestrial laser scanner (TLS), and infrared airborne laser scanning (ALS) topographic data from six sites of varying complexity in the Lemhi River Basin, Idaho. The accuracy of derived bare earth DEMs was evaluated relative to higher precision TS point data. Significant DEM discrepancies between pairwise techniques were calculated using propagated DEM errors thresholded at a 95% confidence interval. Mean discrepancies between TS and rtkGPS DEMs were relatively low (≤ 0.05 m), yet TS data collection time was up to 2.4 times longer than rtkGPS. ALS DEMs had lower accuracy than TS or rtkGPS DEMs, but ALS aerial coverage and floodplain topographic representation was superior to all other techniques. The TLS bare earth DEM accuracy and precision were lower than other techniques as a result of vegetation returns misinterpreted as ground returns. To address objective ii, we used a case study where seven field crews surveyed the same six sites to quantify the magnitude and effect of observer variability on DEMs interpolated from the survey data. We modeled two geomorphic change scenarios and calculated net erosion and deposition volumes at a 95% confidence interval. We observed several large magnitude elevation discrepancies across crews, however many of these i) tended to be highly localized, ii) were due to systematic errors, iii) did not significantly affect DEM-derived metric precision, and iv) can be corrected post-hoc.

The purpose of this work is to develop a web based decision support system, based onfuzzy logic, to assess the motor state of Parkinson patients on their performance in onscreenmotor tests in a test battery on a hand computer. A set of well defined rules, basedon an expert’s knowledge, were made to diagnose the current state of the patient. At theend of a period, an overall score is calculated which represents the overall state of thepatient during the period. Acceptability of the rules is based on the absolute differencebetween patient’s own assessment of his condition and the diagnosed state. Anyinconsistency can be tracked by highlighted as an alert in the system. Graphicalpresentation of data aims at enhanced analysis of patient’s state and performancemonitoring by the clinic staff. In general, the system is beneficial for the clinic staff,patients, project managers and researchers.

Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Due to the free nature of Wikipedia and allowing open access to everyone to edit articles the quality of articles may be affected. As all people don’t have equal level of knowledge and also different people have different opinions about a topic so there may be difference between the contributions made by different authors. To overcome this situation it is very important to classify the articles so that the articles of good quality can be separated from the poor quality articles and should be removed from the database. The aim of this study is to classify the articles of Wikipedia into two classes class 0 (poor quality) and class 1(good quality) using the Adaptive Neuro Fuzzy Inference System (ANFIS) and data mining techniques. Two ANFIS are built using the Fuzzy Logic Toolbox [1] available in Matlab. The first ANFIS is based on the rules obtained from J48 classifier in WEKA while the other one was built by using the expert’s knowledge. The data used for this research work contains 226 article’s records taken from the German version of Wikipedia. The dataset consists of 19 inputs and one output. The data was preprocessed to remove any similar attributes. The input variables are related to the editors, contributors, length of articles and the lifecycle of articles. In the end analysis of different methods implemented in this research is made to analyze the performance of each classification method used.