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A Statistical Approach to Bridge the Gap Between Fault and No-FaultEndre, Hjalmar January 2022 (has links)
No description available.
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Development of an Automated Method for Identification of Wet and Dry Channel Segments Using LiDAR Data and Fuzzy Logic Cluster AnalysisRowney, Chris 01 January 2015 (has links)
Research into the use of LiDAR data for purposes other than simple topographic elevation determination, such as urban land cover classification and the identification of forest biomass, has become prominent in recent years. In many cases, alternative analysis methodologies conducted using airborne LiDAR data are possible because the raw data collected during a survey can include information other than the classically used elevation and coordinate points, the X, Y, and Z of the plane. In particular, intensity return values for each point in a LiDAR grid have been found to provide a useful data set for wet and dry channel classification. LiDAR intensity return data are, in essence, a numeric representation of the characteristic light reflectivity of the object being scanned; the more reflective the object is, the higher the intensity return will be. Intensity data points are collected along the course of the channel network and within the perceived banks of the channel. Intensity data do not crisply reflect a perfectly wet or dry condition, but instead vary over a range such that each location can be viewed as partially wet and partially dry. It is advantageous to assess problems of this type using the methods of fuzzy logic. Specifically, the variance in LiDAR intensity return data is such that the use of fuzzy logic to identify intensity cluster centers, and thereby assign wet and dry condition identifiers based on fuzzy memberships, is a possibility. Membership within a fuzzy data set is characterized by a value representing the degree of membership. Typically, membership values range from 0 (representing non-membership) through 1 (representing full membership), with many observations found to be not at either extreme but instead at some intermediate value representing partial membership. The ultimate goal of this research was to design and develop an automated algorithm to identify wet and dry channel sections, given a previously identified channel network based on topographic elevation, using a combination of intensity return values from LiDAR data and fuzzy logic clustering methods, and to implement that algorithm in such a way as to produce reliable multi-class channel segments in ArcGIS. To enable control of calculations, limiting parameters were defined, specifically including the maximum allowable bank slope, and a filtering percentage to more accurately accommodate the study area. Alteration of the maximum allowable bank slope has been shown to affect the comparative quantity of high and low intensity centroids, but only in extreme bank slope conditions are the centroids changed enough to hamper results. However, interference from thick vegetation has been shown to lower intensity values in dry channel sections into the range of a wet channel. The addition of a filtering algorithm alleviates some of the interference, but not all. Overall results of the tool show an effective methodology where basic channel conditions are identified, but refinement of the tool could produce more accurate results.
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Intelligent phishing website detection system using fuzzy techniquesAburrous, Maher R., Hossain, M. Alamgir, Thabatah, F., Dahal, Keshav P. January 2008 (has links)
Phishing websites are forged web pages that are created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information.
Detecting and identifying Phishing websites is really a complex and dynamic problem involving many factors and criteria, and
because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Logic model can be an effective
tool in assessing and identifying phishing websites than any other
traditional tool since it offers a more natural way of dealing with
quality factors rather than exact values. In this paper, we present
novel approach to overcome the `fuzziness¿ in traditional website phishing risk assessment and propose an intelligent resilient and effective model for detecting phishing websites. The proposed
model is based on FL operators which is used to characterize the
website phishing factors and indicators as fuzzy variables and
produces six measures and criteria¿s of website phishing attack
dimensions with a layer structure. Our experimental results
showed the significance and importance of the phishing website
criteria (URL & Domain Identity) represented by layer one, and
the variety influence of the phishing characteristic layers on the
final phishing website rate.
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Call admission control using cell breathing concept for wideband CDMAMishra, Jyoti L., Dahal, Keshav P., Hossain, M. Alamgir January 2006 (has links)
This paper presents a Call Admission Control
(CAC) algorithm based fuzzy logic to maintain the quality of
service using cell breathing concept. When a new call is accepted
by a cell, its current user is generally affected due to cell
breathing. The proposed CAC algorithm accepts a new call only
if the current users in the cell are not jeopardized. Performance
evaluation is done for single-cell and multicell scenarios. In
multicell scenario dynamic assignment of users to the
neighboring cell, so called handoff, has been considered to
achieve a lower blocking probability. Handoff and new call
requests are assumed with handoff being given preference using
a reserved channel scheme. CAC for different types of services
are shown which depend upon the bandwidth requirement for
voice, data and video. Distance, arrival rate, bandwidth and nonorthogonality
factor of the signal are considered for making the
call acceptance decision. The paper demonstrates that fuzzy logic
with the cell breathing concept can be used to develop a CAC
algorithm to achieve a better performance evaluation.
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Phishing website detection using intelligent data mining techniques. Design and development of an intelligent association classification mining fuzzy based scheme for phishing website detection with an emphasis on E-banking.Abur-rous, Maher Ragheb Mohammed January 2010 (has links)
Phishing techniques have not only grown in number, but also in sophistication. Phishers might
have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of
the phishing attacks, which are mainly on-line banking consumers and payment service
providers, are facing substantial financial loss and lack of trust in Internet-based services. In
order to overcome these, there is an urgent need to find solutions to combat phishing attacks.
Detecting phishing website is a complex task which requires significant expert knowledge and
experience. So far, various solutions have been proposed and developed to address these
problems. Most of these approaches are not able to make a decision dynamically on whether the
site is in fact phished, giving rise to a large number of false positives. This is mainly due to
limitation of the previously proposed approaches, for example depending only on fixed black
and white listing database, missing of human intelligence and experts, poor scalability and their
timeliness.
In this research we investigated and developed the application of an intelligent fuzzy-based
classification system for e-banking phishing website detection. The main aim of the proposed
system is to provide protection to users from phishers deception tricks, giving them the ability
to detect the legitimacy of the websites. The proposed intelligent phishing detection system
employed Fuzzy Logic (FL) model with association classification mining algorithms. The
approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic
phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception
behaviour techniques have been conducted to cover all phishing concerns. A layered fuzzy
structure has been constructed for all gathered and extracted phishing website features and
patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack
type. To reduce human knowledge intervention, Different classification and association
algorithms have been implemented to generate fuzzy phishing rules automatically, to be
integrated inside the fuzzy inference engine for the final phishing detection.
Experimental results demonstrated that the ability of the learning approach to identify all
relevant fuzzy rules from the training data set. A comparative study and analysis showed that
the proposed learning approach has a higher degree of predictive and detective capability than
existing models. Experiments also showed significance of some important phishing criteria like
URL & Domain Identity, Security & Encryption to the final phishing detection rate.
Finally, our proposed intelligent phishing website detection system was developed, tested and
validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results
obtained are promising and showed that our intelligent fuzzy based classification detection
system can provide an effective help for real-time phishing website detection. The toolbar
successfully recognized and detected approximately 92% of the phishing websites selected from
our test data set, avoiding many miss-classified websites and false phishing alarms.
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Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only.Bradley, William J. January 2013 (has links)
Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads.
This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications. / The full-text was made available at the end of the embargo period on 29th Sept 2017.
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An Integrated Intelligent Approach to Enhance the Security Control of IT Systems. A Proactive Approach to Security Control Using Artificial Fuzzy Logic to Strengthen the Authentication Process and Reduce the Risk of PhishingSalem, Omran S.A. January 2012 (has links)
Hacking information systems is continuously on the increase. Social engineering
attacks is performed by manipulating the weakest link in the security chain; people.
Consequently, this type of attack has gained a higher rate of success than a technical
attack.
Based in Expert Systems, this study proposes a proactive and integrated
Intelligent Social Engineering Security Model to mitigate the human risk and reduce the
impact of social engineering attacks.
Many computer users do not have enough security knowledge to be able to
select a strong password for their authentication. The author has attempted to implement
a novel quantitative approach to achieve strong passwords. A new fuzzy logic tool is
being developed to evaluate password strength and measures the password strength
based on dictionary attack, time crack and shoulder surfing attack (social engineering).
A comparative study of existing tools used by major companies such as Microsoft,
Google, CertainKey, Yahoo and Facebook are used to validate the proposed model and
tool.
A comprehensive literature survey and analytical study performed on phishing
emails representing social engineering attacks that are directly related to financial fraud
are presented and compared with other security threats. This research proposes a novel
approach that successfully addresses social engineering attacks. Another intelligent tool
is developed to discover phishing messages and provide educational feedback to the user focusing on the visible part of the incoming emails, considering the email’s source
code and providing an in-line awareness security feedback.
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[en] KNOWLEDGE BASED FOR HYDROELECTRIC MACHINES DIAGNOSIS / [pt] SISTEMA DE CONHECIMENTO PARA DIAGNÓSTICO DE MÁQUINAS HIDROGERADORASLUCIANO R CHAGAS COSTA JUNIOR 18 September 2006 (has links)
[pt] O Sistema elétrico brasileiro é baseado quase que
integralmente em energia produzida por Usinas
Hidroelétricas. Estas Máquinas Hidroelétricas possuem um
comportamento diferenciado das máquinas turbo geradoras,
cujo comportamento já foi identificado e classificado em
pesquisas anteriores. Este trabalho investiga o uso de um
Sistema baseado em Conhecimento para o diagnóstico precoce
de falhas em Máquinas Hidrogeradoras, visando redução de
custos advindos principalmente de paradas operacionais não
necessárias na máquina para manutenção. O sistema foi
criado com informação obtida a partir dos seguintes meios:
sistemática de manutenção executada nas Usinas
Hidroelétricas, através de entrevistas à equipe
responsável pela manutenção da usina de Furnas (MG); da
identificação do comportamento eletromecânico da máquina;
e do estudo de casos. O Sistema é capaz de identificar, a
partir dos sensores localizados nas máquinas, eventuais
falhas, permitindo executar paradas programadas de maneira
otimizada. Foi criado um protótipo de um sistema
computacional baseado em Conhecimento implementando tal
modelo de forma bem flexível. A modelagem criada, a
implementação do protótipo computacional e,
principalmente, a explicação do raciocínio empregado,
agregado com a possibilidade da modificação do
conhecimento através da aquisição automática, são
contribuições inovadoras deste trabalho. É descrito o
Domínio do Problema de diagnosticar falhas em Máquinas
Hidrogeradoras, identificado durante análise das
informações coletadas da equipe de manutenção na usina de
Furnas e de especialistas no comportamento eletromecânico
das máquinas.
É descrito também o modelo simbólico criado,
representativo do domínio, utilizando interface projetada,
visando a implementação prática nas usinas. É apresentado
uma solução de desacoplamento das informações advindas dos
sensores eletromecânicos da máquina e o sistema, através
de módulo baseado em Lógica Nebulosa (Fuzzy Logic) que
converte as informações numéricas em informações
simbólicas compreendidas pelo sistema de diagnóstico,
permitindo o uso do sistema, sem alteração em máquinas que
possuam características diversas. Finalmente, é
apresentada a metodologia de testes adotada para validação
do modelo implementado através da simulação de dados de
vibração e oscilação, cujo relacionamento com eventuais
falhas é parcialmente conhecido, assim como uma conclusão
sobre a viabilidade e praticidade de um modelo simbólico
na solução do diagnóstico das máquinas hidrogeradoras.
Durante o desenvolvimento da tese verificou-se que o
conhecimento sobre falhas em Máquinas Hidrogeradoras ainda
não está consolidado e que então, um Sistema baseado em
Conhecimento com aquisição de conhecimento automático
mostra-se uma excelente ferramenta de modelagem para os
especialistas. / [en] The Brazilian Electrical Energy supply is almost entirely
based on the energy produced by the Hydroeletric Power
Station Machines. These Hydroeletric Machines own
particular behavior in comparison to the turbogenerator
behavior. This work investigates the use of Knowledge
based system Hydroeletric Machines fault diagnosis. The
system was modeled using information obtained by: the
maintenance s systematic executed Hydroeletric Power
Stations, though Furnas (Minas Gerais) maintenance team
interviews; the Machine electromechanical behavior; and a
Case Based study. The system is able to identify, from
machine located sensors data analysis, eventual faults,
allowing the execution of programmed operational
interrupts in the machine in a optimized manner. A
computational prototype and, mainly, the interface explain
engine in addition to the knowledge modification through
acquisition, are the innovative contributions of this work.
The machine fault diagnosis problem domain is described,
identified in the information, collected from the
maintenance team and the electromechanical behavior
experts, analysis. It is also described the projected
symbolic model, the domain representation, using graphical
and friendly interface, aiming its practical
implementation in real Power Stations. It is shown a
sensor information detach solution, through a Fuzzy Logic
based module which converts the numerical data in a
symbolic one, known by the diagnosis system, allowing its
use, without any modification, in a sort of different
machines. Finally, it is shown the test methodology
adopted for the prototype validation through oscillation
data simulation, which relationship with machine faults is
partially known, and the symbolic model praticality and
feasibility in the Hidrogenerator Diagnosis solution.
Through the thesis development, it was verified that the
Hydrogenerator fault knowledge wasn t still consolidated.
So, the Knowledge Based system with knowledge acquisition
became an excelent modeling tool for the domain experts.
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Fuzzy Integral-based Rule Aggregation in Fuzzy LogicTomlin, Leary, Jr 07 May 2016 (has links)
The fuzzy inference system has been tuned and revamped many times over and applied to numerous domains. New and improved techniques have been presented for fuzzification, implication, rule composition and defuzzification, leaving rule aggregation relatively underrepresented. Current FIS aggregation operators are relatively simple and have remained more-or-less unchanged over the years. For many problems, these simple aggregation operators produce intuitive, useful and meaningful results. However, there exists a wide class of problems for which quality aggregation requires nonditivity and exploitation of interactions between rules. Herein, the fuzzy integral, a parametric non-linear aggregation operator, is used to fill this gap. Specifically, recent advancements in extensions of the fuzzy integral to “unrestricted” fuzzy sets, i.e., subnormal and non-convex, makes this now possible. The roles of two extensions, gFI and the NDFI, are explored and demonstrate when and where to apply these aggregations, and present efficient algorithms to approximate their solutions.
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Group based fault-tolerant physical intrusion detection system using fuzzy based distributed RSSI processingRaju, Madhanmohan January 2013 (has links)
No description available.
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