Creating Video Processing Suite Using Java Media Framework

This project is to develop a video processing suite using Java Media Framework. Previously an advanced video processing suite has been successfully been developed in MATLAB. However, due to the expensive licensing fees of MATLAB, not all computers are able to be equipped with it and this has limited a wider usage of the suite

Co-clustering algorithm for the identification of cancer subtypes from gene expression data

Cancer has been classified as a heterogeneous genetic disease comprising various different subtypes based on gene expression data. Early stages of diagnosis and prognosis for cancer type have become an essential requirement in cancer informatics research because it is helpful for the clinical treatment of patients. Besides this, gene network interaction which is the significant in order to understand the cellular and progressive mechanisms of cancer has been barely considered in current research. Hence, applications of machine learning methods become an important area for researchers to explore in order to categorize cancer genes into high and low risk groups or subtypes. Presently co-clustering is an extensively used data mining technique for analyzing gene expression data. This paper presents an improved network assisted co-clustering for the identification of cancer subtypes (iNCIS) where it combines gene network information with gene expression data to obtain co-clusters. The effectiveness of iNCIS was evaluated on large-scale Breast Cancer (BRCA) and Glioblastoma Multiforme (GBM). This weighted co-clustering approach in iNCIS delivers a distinctive result to integrate gene network into the clustering procedure

IoT Load Classification and Anomaly Warning in ELV DC Pico-grids using Hierarchical Extended k-Nearest Neighbors

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc pico-grids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended k-nearest neighbors (HE-kNN) technique that exploits the use of distance in kNN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This paper presents the successful implementation of HE-kNN technique in three different circuits: lightings, air-conditioning and multiple load dc pico-grids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data

Load Disaggregation Using One-Directional Convolutional Stacked Long Short-Term Memory Recurrent Neural Network

Reliable information about the active loads in the energy system allows for effective and optimized energy management. An important aspect of intelligent energy monitoring system is load disaggregation. The proliferation of direct current (dc) loads has spurred the increasing research interest in extra low voltage (ELV) dc grids. Artificial intelligence, such as deep learning algorithms of stacked recurrent neural network (RNN), improved results on a variety of regression and classification tasks. This paper proposes a 1-D convolutional stacked long short-term memory RNN technique for the bottom-up approach in load disaggregation using single sensor multiple loads ELV dc picogrids. This eliminates the requirement for communication and intelligence on every load in the grid. The proposed technique was applied on two different dc picogrids to test the algorithm's robustness. The proposed technique produced excellent result of over 98% accuracy for smart loads and over 99% accuracy for dumb loads in ELV dc picogrid

Forecasting of wind energy generation using Self-Organizing Maps and Extreme Learning Machines

This paper aims to forecast wind energy generation. With accurate forecasting of energy generation, it will aid the energy sector in managing of stability and grid planning for supplied energy. The main focus of this project is Artificial Neural Network (ANN) while the training algorithms used in this project is a combination of Self-Organizing Maps (SOM) and Extreme Learning Machines (ELM). Furthermore, the training algorithm is applied into MATLAB and simulated several times in order to obtain the optimal parameters setting so as to accurately forecast wind energy generation

Multi-Agent System for Control and Management of Distributed Power Systems

Ph.DDOCTOR OF PHILOSOPH

The importance of data classification using machine learning methods in microarray data

The detection of genetic mutations has attracted global attention. several methods have proposed to detect diseases such as cancers and tumours. One of them is microarrays, which is a type of representation for gene expression that is helpful in diagnosis. To unleash the full potential of microarrays, machine-learning algorithms and gene selection methods can be implemented to facilitate processing on microarrays and to overcome other potential challenges. One of these challenges involves high dimensional data that are redundant, irrelevant, and noisy. To alleviate this problem, this representation should be simplified. For example, the feature selection process can be implemented by reducing the number of features adopted in clustering and classification. A subset of genes can be selected from a pool of gene expression data recorded on DNA micro-arrays. This paper reviews existing classification techniques and gene selection methods. The effectiveness of emerging techniques, such as the swarm intelligence technique in feature selection and classification in microarrays, are reported as well. These emerging techniques can be used in detecting cancer. The swarm intelligence technique can be combined with other statistical methods for attaining better results

Survival analysis for the identified cancer gene subtype from the co-clustering algorithm

Cancer gene subtype information is significant for understanding tumour heterogeneity. The early detection of cancer and subsequent treatment can be lifesaving. However, it is hard clinically and computationally to detect cancer and its subtypes in their early stages. Therefore, we extend the analysis and results from Machap et al. (2019), to include the KaplanMeier survival analysis with the integration of gene expression and clinical features data. There are two cancer datasets used for the analysis : breast cancer and glioblastoma multiforme. The luminal type was the common subtype of breast cancer, showing a higher survival rate. Whereas the Proneural subtype in glioblastoma multiforme has a little longer survival rate than the other three subtypes. These molecular differences between subtypes have been shown to correlate very well with clinical features and survival parameters to help understand the disease and develop better therapeutic targets

Preeclampsia and syncytiotrophoblast membrane extracellular vesicles (STB-EVs)

Preeclampsia (PE) is a hypertensive complication of pregnancy that affects 2–8% of women worldwide and is one of the leading causes of maternal deaths and premature birth. PE can occur early in pregnancy (34 weeks gestation). Whilst the placenta is clearly implicated in early onset PE (EOPE), late onset PE (LOPE) is less clear with some believing the disease is entirely maternal whilst others believe that there is an interplay between maternal systems and the placenta. In both types of PE, the syncytiotrophoblast (STB), the layer of the placenta in direct contact with maternal blood, is stressed. In EOPE, the STB is oxidatively stressed in early pregnancy (leading to PE later in gestation- the two-stage model) whilst in LOPE the STB is stressed because of villous overcrowding and senescence later in pregnancy. It is this stress that perturbs maternal systems leading to the clinical manifestations of PE. Whilst some of the molecular species driving this stress have been identified, none completely explain the multisystem nature of PE. Syncytiotrophoblast membrane vesicles (STB-EVs) are a potential contributor to this multisystem disorder. STB-EVs are released into the maternal circulation in increasing amounts with advancing gestational age, and this release is further exacerbated with stress. There are good in vitro evidence that STB-EVs are taken up by macrophages and liver cells with additional evidence supporting endothelial cell uptake. STB-EV targeting remains in the early stages of discovery. In this review, we highlight the role of STB-EVs in PE. In relation to current research, we discuss different protocols for ex vivo isolation of STB-EVs, as well as specific issues involving tissue preparation, isolation (some of which may be unique to STB-EVs), and methods for their analysis. We suggest potential solutions for these challenges

Housing Development Building Management System (HDBMS) For Optimized Electricity Bills

Smart Buildings is a modern building that allows residents to have sustainable comfort with high efficiency of electricity usage. These objectives could be achieved by applying appropriate, capable optimization algorithms and techniques. This paper presents a Housing Development Building Management System (HDBMS) strategy inspired by Building Energy Management System (BEMS) concept that will integrate with smart buildings using Supply Side Management (SSM) and Demand Side Management (DSM) System. HDBMS is a Multi-Agent System (MAS) based decentralized decision making system proposed by various authors. MAS based HDBMS was established on an IEEE FIPA compliant multi-agent platform named JADE which is also a JAVA extension software. This allows agents to communicate, interact and negotiate with energy supply and demand of the smart buildings to provide the optimal energy usage and minimal electricity costs. This results in reducing the load of the power distribution system in smart buildings. This simulation studies show the potential of proposed HDBMS strategy to provide the optimal solution for Smart Building energy management