The sintering temperature effect on the shrinkage behavior of cobalt chromium alloy

Problem Statement: Co-Cr based alloys which is well known for its high Young’s modulus, fatigue strength, wear resistance and corrosion resistance is an important metallic bio�material. However, till date there are only two type of Co-Cr alloy which are the castable and wrought cobalt alloy. Powder Metallurgy route for cobalt is expected to give better result of Co-Cr alloy. The purpose of this research was mainly to study the sintering temperature effect to the shrinkage behavior of Cobalt Chromium (Co-Cr) alloy of the powder metallurgy route. Approach: Co-Cr was produced following P/M route under sintering temperature of 1000, 1100, 1200, 1300 and 1400oC. The sintering time was fixed at 60 min. Several tests has been conducted to determine this effect such as the rate of shrinkage measurement, the bulk density and porosity percentage measurement, compression and hardness tests and micro structural study. Result: From the study, it was found that the sintering temperature has caused the shrinkage of Co-Cr. The increasing of the sintering temperature has caused to the increasing of shrinkage of Co-Cr. This has resulted to the reduction of the pore volume and hence increased it density. In conjunction to that, the strength and the hardness of Co-Cr was increased. Conclusion: Therefore, it is hope that it will bring new view of powder metallurgy Co-Cr alloy as bio-material

The sintering temperature effect on the shrinkage behavior of cobalt chromium alloy

Problem Statement: Co-Cr based alloys which is well known for its high Young’s modulus, fatigue strength, wear resistance and corrosion resistance is an important metallic bio�material. However, till date there are only two type of Co-Cr alloy which are the castable and wrought cobalt alloy. Powder Metallurgy route for cobalt is expected to give better result of Co-Cr alloy. The purpose of this research was mainly to study the sintering temperature effect to the shrinkage behavior of Cobalt Chromium (Co-Cr) alloy of the powder metallurgy route. Approach: Co-Cr was produced following P/M route under sintering temperature of 1000, 1100, 1200, 1300 and 1400oC. The sintering time was fixed at 60 min. Several tests has been conducted to determine this effect such as the rate of shrinkage measurement, the bulk density and porosity percentage measurement, compression and hardness tests and micro structural study. Result: From the study, it was found that the sintering temperature has caused the shrinkage of Co-Cr. The increasing of the sintering temperature has caused to the increasing of shrinkage of Co-Cr. This has resulted to the reduction of the pore volume and hence increased it density. In conjunction to that, the strength and the hardness of Co-Cr was increased. Conclusion: Therefore, it is hope that it will bring new view of powder metallurgy Co-Cr alloy as bio-material

Automated myocardial infarction diagnosis from ECG

In the present dissertation, an automated neural network-based ECG diagnosing system was designed to detect the presence of myocardial infarction based on the hypothesis that an artificial neural network-based ECG interpretation system may improve the clinical myocardial infarction. 137 patients were included. Among them 122 had myocardial infarction, but the remaining 15 were normal. The sensitivity and the specificity of present system were 92.2% and 50.7% respectively. The sensitivity was consistent with relevant research. The relatively low specificity results from the rippling of the low pass filtering. We can conclude that neural network-based system is a promising aid for the myocardial infarction diagnosis

Sistemas de deep-learning no apoio ao rastreio do bloqueio do ramo direito através de sinais ECG

Com o aumento exponencial dos casos de doenças cardiovasculares, a idealização de um algoritmo que possibilita a distinção de doenças é um grande aliado no diagnóstico. O bloqueio do ramo direito, apesar de ser uma doença que pode nunca vir a apresentar sintomas, é um excelente indicador de possíveis doenças cardiovasculares futuras. De forma a detetar o aparecimento do BBB nas suas fases iniciais, neste trabalho aplicou-se aos sinais ECG a Transformada Wavelet Discreta, o que permitiu extrair características na forma de energia, entropia e coerência de três níveis diferentes da decomposição do sinal. A discriminação dos sinais foi realizada através da CNN no processo de validação cruzada 30-fold. A precisão na comparação entre o BBB e as outras doenças, presentes na mesma base de dados, cifrou-se entre 98,90% e 100% utilizando pequenas porções de sinal com pares de entrada/saída para as CNNs. No caso da medida energética as CNNs conseguiram precisões entre 91,14% e 66,51%, para a entropia, 91,68% e 64,31% e utilizando a coerência obteve-se uma precisão máxima de 90,83%.With the exponential growing up in the number of cases for cardiovascular diseases, the idealization of an algorithm that can distinguish pathologies is a great ally in diagnosis. The Right Bundle Branch Block, even though is a disease that can never present symptoms, it is an excellent indicator for future cardiovascular diseases. In order to detect the apppearence of the BBB in the early stages, in this work the Discrete Wavelet Transform was applied to the ECG signals, which allowed to extract characteristics such as energy, entropy and coherence from three different levels of signal decomposition. The signal discrimination was performed through CNN in the 30-fold cross-validation process. The comparison accuracy between BBB and other diseases, present in the database, ranged from 98,90% and 100% using small portions of signal as input/output pairs fotr the CNN. In the case of the energy measurement, CNN provided an accuracy between 91,14% and 66,51%, for the entropy, 91,68% and 64,31% and using coherences, a maximum accuracy of 90,83%

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

Bottom-up design of artificial neural network for single-lead electrocardiogram beat and rhythm classification

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined in terms of perfecting individual sub-systems to improve the over all system performance is used. Sub-systems include pre-processing, QRS detection and fiducial point estimations, feature calculations, and pattern classification. Inaccuracies in time-domain fiducial point estimations are overcome with the derivation of features in the frequency domain. Feature extraction in frequency domain is based on a spectral estimation technique (combination of simulation and subtraction of a normal beat). Auto-regressive spectral estimation methods yield a highly sensitive spectrum, providing several local features with information on beat classes like flutter, fibrillation, and noise. A total of 27 features, including 16 in time domain and 11 in frequency domain are calculated. The entire data and problem are divided into four major groups, each group with inter-related beat classes. Classification of each group into related sub-classes is performed using smaller feed-forward neural networks. Input feature sub-set and the structure of each network are optimized using an iterative process. Optimal implementations of feed-forward neural networks provide high accuracy in beat classification. Associated neural networks are used for the more deterministic rhythm-classification task. An accuracy of more than 85% is achieved for all 13 classes included in this study. The system shows a graceful degradation in performance with increasing noise, as a result of the noise consideration in the design of every sub-system. Results indicate a neural network-based bottom-up design of single-lead ECG classification is able to provide very high accuracy, even in the presence of noise, flutter, and fibrillation

Efficient piecewise linear classifiers and applications

Supervised learning has become an essential part of data mining for industry, military, science and academia. Classification, a type of supervised learning allows a machine to learn from data to then predict certain behaviours, variables or outcomes. Classification can be used to solve many problems including the detection of malignant cancers, potentially bad creditors and even enabling autonomy in robots. The ability to collect and store large amounts of data has increased significantly over the past few decades. However, the ability of classification techniques to deal with large scale data has not been matched. Many data transformation and reduction schemes have been tried with mixed success. This problem is further exacerbated when dealing with real time classification in embedded systems. The real time classifier must classify using only limited processing, memory and power resources. Piecewise linear boundaries are known to provide efficient real time classifiers. They have low memory requirements, require little processing effort, are parameterless and classify in real time. Piecewise linear functions are used to approximate non-linear decision boundaries between pattern classes. Finding these piecewise linear boundaries is a difficult optimization problem that can require a long training time. Multiple optimization approaches have been used for real time classification, but can lead to suboptimal piecewise linear boundaries. This thesis develops three real time piecewise linear classifiers that deal with large scale data. Each classifier uses a single optimization algorithm in conjunction with an incremental approach that reduces the number of points as the decision boundaries are built. Two of the classifiers further reduce complexity by augmenting the incremental approach with additional schemes. One scheme uses hyperboxes to identify points inside the so-called “indeterminate” regions. The other uses a polyhedral conic set to identify data points lying on or close to the boundary. All other points are excluded from the process of building the decision boundaries. The three classifiers are applied to real time data classification problems and the results of numerical experiments on real world data sets are reported. These results demonstrate that the new classifiers require a reasonable training time and their test set accuracy is consistently good on most data sets compared with current state of the art classifiers.Doctor of Philosoph

Novel Algorithms for Modern Power Systems

The restructuring of the electric power industry has brought about many interesting and new problems to be solved by researchers. Some of the problems have been considered and suitable intelligent techniques that have been developed are described in this thesis. A new technique using wavelet transform and neural networks for fault location and protection of a practical tee-circuit has been developed. Fault simulation is carried out using EMTP software. The waveforms obtained from the simulation are then used in wavelet analysis to generate patterns for training and validation, which is carried out using Radial Basis Function network. Dynamic Protection Modelling (DPM) software developed by the University of Strathclyde, Centre for Electrical Power Engineering is used for the evaluation of relay settings. A new approach is proposed to use object oriented techniques and improved genetic algorithms in developing software to estimate generator excitation control system parameters. Simulation studies are carried using data representing a generator, with its transformer connected to an infinite busbar. Application of evolutionary programming to Optimal Power Flow is another technique that is proposed in this work. The objective is to minimize fuel cost keeping the system secure under both normal and contingent states. Studies are carried out using the IEEE - 30 bus test system. A short-term load-forecasting model using Artificial Neural Networks and Genetic Algorithm has been developed and tested on data obtained from a power company. It is found that the time taken to obtain a satisfactory solution is long, as the problem is very complex. This points in the direction of evolutionary computing being integrated with parallel processing techniques to solve such practical problems. A novel approach to the formulation and evaluation of transmission loss and line flow through a set of new loss coefficients and distribution factors respectively which are efficient, exact and robust and suitable for real time application is proposed. These loss coefficients and distribution factors are generated from the available load flow solution with trivial computational burden. Results on IEEE Test systems show that the coefficients need not be re-evaluated for wide changes in system operating condition