Global ETD Search

11	Data Mining Methods For Clustering Power Quality Data Collected Via Monitoring Systems Installed On The Electricity Network Guder, Mennan 01 September 2009 (has links) (PDF) Increasing power demand and wide use of high technology power electronic devices result in need for power quality monitoring. The quality of electric power in both transmission and distribution systems should be analyzed in order to sustain power system reliability and continuity. This analysis is possible by examination of data collected by power quality monitoring systems. In order to define the characteristics of the power system and reveal the relations between the power quality events, huge amount of data should be processed. In this thesis, clustering methods for power quality events are developed using exclusive and overlapping clustering models. The methods are designed to cluster huge amount of power quality data which is obtained from the online monitoring of the Turkish Electricity Transmission System. The main issues considered in the design of the clustering methods are the amount of the data, efficiency of the designed algorithm and queries that should be supplied to the domain experts. This research work is fully supported by the Public Research grant Committee (KAMAG) of TUBITAK within the scope of National Power quality Project (105G129).
12	Brain State Classification in Epilepsy and Anaesthesia Lee, Angela 07 January 2011 (has links) Transitions between normal and pathological brain states are manifested differently in the electroencephalogram (EEG). Traditional discrimination of these states is often subject to bias and strict definitions. A fuzzy logic-based analysis can permit the classification and tracking of brain states in a non-subjective and unsupervised manner. In this thesis, the combination of fuzzy c-means (FCM) clustering, wavelet, and information theory has revealed notable frequency features in epilepsy and anaesthetic-induced unconsciousness. It was shown that entropy changes in membership functions correlate to specific epileptiform activity and changes in anaesthetic dosages. Seizure episodes appeared in the 31-39 Hz band, suggesting changes in cortical functional organization. The induction of anaesthetics appeared in the 64-72 Hz band, while the return to consciousness appeared in the 32-40 Hz band. Changes in FCM activity were associated with the concentration of anaesthetics. These results can help with the treatment of epilepsy and the safe administration of anaesthesia. EEG signal processing epilepsy anaesthesia anesthesia neuroscience fuzzy logic entropy fuzzy c-means clustering wavelet classification brain state seizure membership function 0544 0541 0317
13	Brain State Classification in Epilepsy and Anaesthesia Lee, Angela 07 January 2011 (has links) Transitions between normal and pathological brain states are manifested differently in the electroencephalogram (EEG). Traditional discrimination of these states is often subject to bias and strict definitions. A fuzzy logic-based analysis can permit the classification and tracking of brain states in a non-subjective and unsupervised manner. In this thesis, the combination of fuzzy c-means (FCM) clustering, wavelet, and information theory has revealed notable frequency features in epilepsy and anaesthetic-induced unconsciousness. It was shown that entropy changes in membership functions correlate to specific epileptiform activity and changes in anaesthetic dosages. Seizure episodes appeared in the 31-39 Hz band, suggesting changes in cortical functional organization. The induction of anaesthetics appeared in the 64-72 Hz band, while the return to consciousness appeared in the 32-40 Hz band. Changes in FCM activity were associated with the concentration of anaesthetics. These results can help with the treatment of epilepsy and the safe administration of anaesthesia. EEG signal processing epilepsy anaesthesia anesthesia neuroscience fuzzy logic entropy fuzzy c-means clustering wavelet classification brain state seizure membership function 0544 0541 0317
14	Machinery fault diagnostics based on fuzzy measure and fuzzy integral data fusion techniques Liu, Xiaofeng January 2007 (has links) With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults. condition monitoring fault diagnosis fuzzy measures fuzzy integrals fuzzy c-means clustering membership degree feature selection feature level data fusion decision level data fusion
15	Analysis of Quality of Experience by applying Fuzzy logic : A study on response time Ataeian, Seyed Mohsen, Darbandi, Mehrnaz Jaberi January 2011 (has links) To be successful in today's competitive market, service providers should look at user's satisfaction as a critical key. In order to gain a better understanding of customers' expectations, a proper evaluations which considers intrinsic characteristics of perceived quality of service is needed. Due to the subjective nature of quality, the vagueness of human judgment and the uncertainty about the degree of users' linguistic satisfaction, fuzziness is associated with quality of experience. Considering the capability of Fuzzy logic in dealing with imprecision and qualitative knowledge, it would be wise to apply it as a powerful mathematical tool for analyzing the quality of experience (QoE). This thesis proposes a fuzzy procedure to evaluate the quality of experience. In our proposed methodology, we provide a fuzzy relationship between QoE and Quality of Service (QoS) parameters. To identify this fuzzy relationship a new term called Fuzzi ed Opinion Score (FOS) representing a fuzzy quality scale is introduced. A fuzzy data mining method is applied to construct the required number of fuzzy sets. Then, the appropriate membership functions describing fuzzy sets are modeled and compared with each other. The proposed methodology will assist service providers for better decision-making and resource management. Quality of Experience (QoE) Fuzzy set theory Fuzzy C-Means Clustering (FCM) Fuzzified Opinion Score (FOS) Mathematical Analysis Matematisk analys Computer Sciences Datavetenskap (datalogi) Telecommunications Telekommunikation
16	Využití fuzzy množin ve shlukové analýze se zaměřením na metodu Fuzzy C-means Clustering / Fuzzy Sets Use in Cluster Analysis with a Special Attention to a Fuzzy C-means Clustering Method Camara, Assa January 2020 (has links) This master thesis deals with cluster analysis, more specifically with clustering methods that use fuzzy sets. Basic clustering algorithms and necessary multivariate transformations are described in the first chapter. In the practical part, which is in the third chapter we apply fuzzy c-means clustering and k-means clustering on real data. Data used for clustering are the inputs of chemical transport model CMAQ. Model CMAQ is used to approximate concentration of air pollutants in the atmosphere. To the data we will apply two different clustering methods. We have used two different methods to select optimal weighting exponent to find data structure in our data. We have compared all 3 created data structures. The structures resembled each other but with fuzzy c-means clustering, one of the clusters did not resemble any of the clustering inputs. The end of the third chapter is dedicated to an attempt to find a regression model that finds the relationship between inputs and outputs of model CMAQ.
17	Data Mining the Effects of Storage Conditions, Testing Conditions, and Specimen Properties on Brain Biomechanics Crawford, Folly Martha Dzan 10 August 2018 (has links) Traumatic brain injury is highly prevalent in the United States yet there is little understanding of how the brain responds during injurious loading. A confounding problem is that because testing conditions vary between assessment methods, brain biomechanics cannot be fully understood. Data mining techniques were applied to discover how changes in testing conditions affect the mechanical response of the brain. Data were gathered from literature sources and self-organizing maps were used to conduct a sensitivity analysis to rank considered parameters by importance. Fuzzy C-means clustering was applied to find any data patterns. The rankings and clustering for each data set varied, indicating that the strain rate and type of deformation influence the role of these parameters. Multivariate linear regression was applied to develop a model which can predict the mechanical response from different experimental conditions. Prediction of response depended primarily on strain rate, frequency, brain matter composition, and anatomical region. multivariate linear regression neighborhood component analysis principal component analysis fuzzy c-means clustering self-organizing maps traumatic brain injury data mining
18	Driving data pattern recognition for intelligent energy management of plug-in hybrid electric vehicles Munthikodu, Sreejith 19 August 2019 (has links) This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada. / Graduate Driving Data Pattern Recognition Clean Transportation
19	Decision Making System Algorithm On Menopause Data Set Bacak, Hikmet Ozge 01 September 2007 (has links) (PDF) Multiple-centered clustering method and decision making system algorithm on menopause data set depending on multiple-centered clustering are described in this study. This method consists of two stages. At the first stage, fuzzy C-means (FCM) clustering algorithm is applied on the data set under consideration with a high number of cluster centers. As the output of FCM, cluster centers and membership function values for each data member is calculated. At the second stage, original cluster centers obtained in the first stage are merged till the new numbers of clusters are reached. Merging process relies upon a &ldquo / similarity measure&rdquo / between clusters defined in the thesis. During the merging process, the cluster center coordinates do not change but the data members in these clusters are merged in a new cluster. As the output of this method, therefore, one obtains clusters which include many cluster centers. In the final part of this study, an application of the clustering algorithms &ndash / including the multiple centered clustering method &ndash / a decision making system is constructed using a special data on menopause treatment. The decisions are based on the clusterings created by the algorithms already discussed in the previous chapters of the thesis. A verification of the decision making system / v decision aid system is done by a team of experts from the Department of Department of Obstetrics and Gynecology of Hacettepe University under the guidance of Prof. Sinan Beksa&ccedil / .
20	Channel Probing for an Indoor Wireless Communications Channel Hunter, Brandon 13 March 2003 (has links) (PDF) The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated. multipath multipath propagation propagation environment indoor propagation indoor multipath indoor multipath propagation channel probing channel probing indoor wireless communications channel indoor wireless communications indoor wireless communications wireless communications wireless communications channel MIMO SISO multiple input multiple output single input single output antenna diversity antenna diversity antenna array channel modeling modeling model wireless propagation clustering methods clustering algorithms clustering parameter estimation parameter channel matrix h matrix ray tracing point spread function psf deconvolution clean algorithm clean algorithm antenna pattern network analyzer temporal pattern 2.4 ghz system 6 ghz system channel capacity capacity estimate channel capacity estimate propagation environment scattering transfer matrix matrix water filling solution water filling method water filling Saleh Valenzuela Saleh and Valenzuela Model arrival time of arrival angle of arrival direction of arrival arrival amplitude arrivals antenna positioner data simulations measurements angle of departure direction of departure correlation convolution errors error CFA CFAD constant false alarm constant false alarm detection signal signal dependent clutter clutter returns transmitter transmit receive receiver transmit antenna receive antenna spatial correlation system PSF system point spread function mapping fuzzy c-means clustering fuzzy clustering c-means clustering c-means fuzzy k-means euclidean distance time amplitude delay performance prediction performance prediction errors fuzzy weighted distance method rectangular method weighted rectangular method statistics statistical statistical model Electrical and Computer Engineering

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