5 research outputs found
Development of Deep Learning Hybrid Models for Hydrological Predictions
The Abstract is currently unavailable, due to the thesis being under Embargo
Recommended from our members
Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods
This thesis is concerned with the development and study of a pattern recognition system for siphon and sewer condition/defect analysis based on acoustic characteristics. Pattern recognition has been studied and used widely in many fields including: identification and authentication; medical diagnosis and musical modelling. Audio based classification and research has been mainly focusing on speech recognition and music retrieval, but few applications have attempted to use acoustic characteristics for underground pipe condition classification. Traditional CCTV inspection methods are relatively expensive and subjective so remote techniques have been developed to overcome this concern and increase the inspection efficiency. The acoustic environment provides a rich source of information about the
internal conditions of a pipe. This thesis reports on a classification system based on measuring the direct and reflected acoustic signals and describing the energy spectrum for each condition/pipe defect. A K-nearest neighbour classifier (KNN) and Support vector machines (SVMs) classifier have been adopted to train the classification system to identify sediment and pipe surface defects by comparing the measured acoustic signals with a database containing a range of typical conditions. Laboratory generated data and field collected data were used to train the proposed system and evaluate its ability. The overall accuracy of the system recognizing blockage and structural aspects in each of the series of experiments varies between 70% and 95%
Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering
Over the last decade kernel based learning algorithms known as Support Vector Machines (SVMs) have become an attractive tool to solve pattern recognition problems. Choosing an appropriate kernel still is a trial and error approach for SVM however. This research provides some insights into the data characteristics that suit particular kernels. Our approach consists of four main stages. First, the performance of six kernels is examined across a collection of 33 classification problems from the machine learning literature. Secondly, a collection of statistics that describe each of the 33 problems in terms of data complexity is collected. After that, fuzzy C-means (FCM) is used to cluster, and construct a decision tree is used to generate the rules of the 33 problems based on these measurea of complexity. Each cluster represents a group of classification problems with similar data characteristics. The performance of each kernel within each cluster and the rules among the tree is then examined in the final stage to provide both quantitative and qualitative insights into which kernels perform best on certain problem types
Matching SVM kernel's suitability to data characteristics using tree by fuzzy C-means clustering
Over the last decade kernel based learning algorithms known as Support Vector Machines (SVMs) have become an attractive tool to solve pattern recognition problems. Choosing an appropriate kernel still is a trial and error approach for SVM however. This research provides some insights into the data characteristics that suit particular kernels. Our approach consists of four main stages. First, the performance of six kernels is examined across a collection of 33 classification problems from the machine learning literature. Secondly, a collection of statistics that describe each of the 33 problems in terms of data complexity is collected. After that, fuzzy C-means (FCM) is used to cluster, and construct a decision tree is used to generate the rules of the 33 problems based on these measurea of complexity. Each cluster represents a group of classification problems with similar data characteristics. The performance of each kernel within each cluster and the rules among the tree is then examined in the final stage to provide both quantitative and qualitative insights into which kernels perform best on certain problem types