Robust standard gradient descent algorithm for ARX models using Aitken acceleration technique

A robust standard gradient descent (SGD) algorithm for ARX models using the Aitken acceleration method is developed. Considering that the SGD algorithm has slow convergence rates and is sensitive to the step size, a robust and accelerative SGD (RA-SGD) algorithm is derived. This algorithm is based on the Aitken acceleration method, and its convergence rate is improved from linear convergence to at least quadratic convergence in general. Furthermore, the RA-SGD algorithm is always convergent with no limitation of the step size. Both the convergence analysis and the simulation examples demonstrate that the presented algorithm is effective

Machine Learning: A Review

Due to the complexity of data, interpretation of pattern or extraction of information becomes difficult; therefore application of machine learning is used to teach machines how to handle data more efficiently. With the increase of datasets, various organizations now apply machine learning applications and algorithms. Many industries apply machine learning to extract relevant information for analysis purposes. Many scholars, mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems. In this paper, we focus on general review of machine learning including various machine learning techniques. These techniques can be applied to different fields like image processing, data mining, predictive analysis and so on.The paper aims at reviewing machine learning techniques and algorithms.The research methodology is based on qualitative analysis where various literatures is being reviewed based on machine learning.

Genetic Resources and Adaptive Management of Conifers in a Changing World

Climatic change causes a mismatch between tree populations on sites they currently occupy and the climate to which they have adapted in the past. The maintenance of productivity and of ecological and societal services requires resilient populations and ecosystems, particularly close to the vulnerable trailing (xeric) range limits. The studies confirm the selective effect of diverse habitat/climate conditions across the species ranges. Soil conditions may mask climate effects and should be considered separately. The unique potential of provenance tests is illustrated by growth response projections that may be less dramatic than provided by usual inventory data analyses. Assisted migration appears to be a feasible management action to compensate for climatic warming. However, the choice of populations needs special care under extreme conditions and outside the limits of current natural distribution ranges. The proper differentiation of measures according to the present and future adaptive challenges require the continuation of long-term analyses and the establishment of better focused field trials in disparate climates that contain populations from a representative range of habitats. The studies present results obtained from diverse regions of the temperate forest zone, from Central and Northwestern Europe, the Mediterranean, Russia, China, North and Central America

Optimization of System Identification for Multi-Rail DC-DC Power Converters

Ph. D. Thesis.There have been many recursive algorithms investigated and introduced in real time parameter estimation of Switch Mode Power Converters (SMPCs) to improve estimation performance in terms of faster convergence speed, lower computational cost and higher estimation accuracy. These algorithms, including Dichotomous Coordinate Descent (DCD) - Recursive Least Square (RLS), Kalman Filter (KF) and Fast Affine Projection (FAP), etc., are commonly applied for performance comparison of system identification of single-rail power converters. When they need to be used in multi-rail architectures with a single centralized controller, the computational burden on the processor becomes significant. Typically, the computational effort is directly proportional to the number of converters/rails. This thesis presents an iterative decimation approach to significantly alleviate the computational burden of centralized controllers applying real-time recursive system identification algorithms in multirail power converters. The proposed approach uses a flexible and adjustable update rate rather than a fixed rate, as opposed to conventional adaptive filters. In addition, the step size/forgetting factors are varied, as well, corresponding to different iteration stages. As a result, reduced computational burden and faster model update can be achieved. Recursive algorithms, such as Recursive Least Square (RLS), Affine Projection (AP) and Kalman Filter (KF), contain two important updates per iteration cycle. Covariance Matrix Approximation (CMA) update and the Gradient Vector (GV) update. Usually, the computational effort of updating Covariance Matrix Approximation (CMA) requires greater computational effort than that of updating Gradient Vector (GV). Therefore, in circumstances where the sampled data in the regressor does not experience significant fluctuations, re-using the Covariance Matrix Approximation (CMA), calculated from the last iteration cycle for the current update can result in computational cost savings for real- time system identification. In this thesis, both iteration rate adjustment and Covariance Matrix Approximation (CMA) re-cycling are combined and applied to simultaneously identify the power converter model in a three-rail power conversion architecture. Besides, in multi-rail architectures, due to the high likelihood of the at-the-same-time need for real time system identification of more than one rail, it is necessary to prioritize each rail to guarantee rails with higher priority being identified first and avoid jam. In the thesis, a workflow, which comprises sequencing rails and allocating system identification task into selected rails, was proposed. The multi-respect workflow, featured of being dynamic, selectively pre-emptive, cost saving, is able to flexibly change ranks of each rail based on the application importance of rails and the severity of abrupt changes that rails are suffering to optimize waiting time and make-span of rails with higher priorities

Hydraulic traits and drought mortality risk of tree species

Increased drought frequency and severity associated with global climate changehas contributed to large scale forest dieback on all vegetated continents. Forest dieback may alter community composition, leading to cascading negative impacts on ecosystem function and service, and creating a positive feedback loop between biosphere and atmosphere. Traits-based approaches have emerged as a promising way to accurately predict the impacts of climate change on vegetation dynamics. Yet predicting the forest mortality pattern resulting from drought stress remains challenging, largely because of a lack of knowledge of the plant traits determining the risk and modulating the process of drought-induced mortality, and how these traits vary across and within species. Hydraulic traits define species distributions along local or regional gradients of water availability, and recent advances in modelling forest dynamics highlight the critical role of hydraulic traits in improving model predictive power with respect to mortality events. Using various ecologically and economically important tree species from New South Wales, Australia, my PhD thesis was designed to examine inter-specific variation of various hydraulic traits across a wide range of species native to five different vegetation types: Rainforest (Acmena smithii), Wet sclerophyll forest (Eucalyptus grandis, E. viminalis), Dry sclerophyll forest (Angophora costata, Corymbia gummifera, E. sideroxylon), Grassy woodland (E. blakelyi, E. macrorhyncha, E. melliodora) and Semi-arid woodland (Acacia aneura, E. largiflorens, E. populnea). In addition, intra-specific variation of key hydraulic traits was examined for Banksia serrata. The primary objective of my work was to provide trait values that will help to predict the dynamics of tree species upon climate change with vegetation models. Furthermore, the correlative relationships among hydraulic traits and between traits and climate presented in this study broaden our understanding of plant hydraulic strategies and plant adaptation to low-rainfall environments

Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis

The development of highly accurate, quantitative automatic medical image segmentation techniques, in comparison to manual techniques, remains a constant challenge for medical image analysis. In particular, segmenting the pancreas from an abdominal scan presents additional difficulties: this particular organ has very high anatomical variability, and a full inspection is problematic due to the location of the pancreas behind the stomach. Therefore, accurate, automatic pancreas segmentation can consequently yield quantitative morphological measures such as volume and curvature, supporting biomedical research to establish the severity and progression of a condition, such as type 2 diabetes mellitus. Furthermore, it can also guide subject stratification after diagnosis or before clinical trials, and help shed additional light on detecting early signs of pancreatic cancer. This PhD thesis delivers a novel approach for automatic, accurate quantitative pancreas segmentation in mostly but not exclusively Magnetic Resonance Imaging (MRI), by harnessing the advantages of machine learning and classical image processing in computer vision. The proposed approach is evaluated on two MRI datasets containing 216 and 132 image volumes, achieving a mean Dice similarity coefficient (DSC) of 84:1 4:6% and 85:7 2:3% respectively. In order to demonstrate the universality of the approach, a dataset containing 82 Computer Tomography (CT) image volumes is also evaluated and achieves mean DSC of 83:1 5:3%. The proposed approach delivers a contribution to computer science (computer vision) in medical image analysis, reporting better quantitative pancreas segmentation results in comparison to other state-of-the-art techniques, and also captures detailed pancreas boundaries as verified by two independent experts in radiology and radiography. The contributions’ impact can support the usage of computational methods in biomedical research with a clinical translation; for example, the pancreas volume provides a prognostic biomarker about the severity of type 2 diabetes mellitus. Furthermore, a generalisation of the proposed segmentation approach successfully extends to other anatomical structures, including the kidneys, liver and iliopsoas muscles using different MRI sequences. Thus, the proposed approach can incorporate into the development of a computational tool to support radiological interpretations of MRI scans obtained using different sequences by providing a “second opinion”, help reduce possible misdiagnosis, and consequently, provide enhanced guidance towards targeted treatment planning

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table