3,075 research outputs found

    Patterns of coal sedimentation in the Ipswich Basin Southeast Queensland

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    The intermontane Ipswich Basin, which is situated 30km south-west of Brisbane, contains coal measures formed in the Late Triassic Epoch following a barren non-depositional period. Coal, tuff, and basalt were deposited along with fluvial dominated sediments. The Ipswich Coal Measures mark the resumption of deposition in eastern Australia after the coal hiatus associated with a series of intense tectonic activity in Gondwanaland during the Permo-Triassic interval. A transtensional tectonic movement at the end of the Middle Triassic deformed the Toogalawah Group before extension led to the formation of the Carnian Ipswich Coal Measures in the east. The Ipswich Coal Measures comprise the Brassall and Kholo Subgroups. The Blackstone Formation, which forms the upper unit of the Brassall Subgroup, contains seven major coal seams. The lower unit of the Brassall Subgroup, the Tivoli Formation, consists of sixteen stratigraphically significant coal seams. The typical thickness of the Blackstone Formation is 240m and the Tivoli Formation is about 500m. The coal seams of the Ipswich Basin differ considerably from those of other continental Triassic basins. However, the coal geology has previously attracted little academic attention and the remaining exposures of the Ipswich coalfield are rapidly disappearing now that mining has ceased. The primary aim of this project was to study the patterns of coal sedimentation and the response of coal seam characteristics to changing depositional environments. The coal accumulated as a peat-mire in an alluvial plain with meandering channel systems. Two types of peat-mire expansion occurred in the basin. Peat-mire aggradation, which is a replacement of water body by the peatmire, was initiated by tectonic subsidence. This type of peat-mire expansion is known as terrestrialisation. It formed thick but laterally limited coal seams in the basin. Whereas, peat-mire progradation was related to paludification and produced widespread coal accumulation in the basin. The coal seams were separated into three main groups based on the mean seam thickness and aerial distribution of one-meter and four-meter thickness contour intervals. Group 1 seams within the one-meter thickness interval are up to 15,000m2 in area, and seams within the four-meter interval have an aerial extent of up to 10,000m2. Group 1A contains the oldest seam with numerous intraseam clastic bands and shows a very high thickness to area ratio, which indicates high subsidence rates. Group 1B seams have moderately high thickness to area ratios. The lower clastic influx and slower subsidence rates favoured peat-mire aggradation. The Group 1A seam is relatively more widespread in aerial extent than seams from Group 1B. Group 1C seams have low mean thicknesses and small areas, suggesting short-lived peat-mires as a result of high clastic influx. Group 2 seams arebetween 15,000 and 35,000m2 in area within the one-meter interval, and between 5,000 and 10,000m2 within the four-meter interval. They have moderately high area to thickness ratios, indicating that peat-mire expansion occurred due to progressively shallower accommodation and a rising groundwater table. Group 3 seams, which have aerial extents from 35,000 to 45,000m2 within the one-meter thickness contour interval and from 10,000 to 25,000m2 within the four-meter interval, show high aerial extent to thickness ratios. They were deposited in quiet depositional environments that favoured prolonged existence of peat-mires. Group 3 seams are all relatively young whereas most Group 1 seams are relatively old seams. All the major fault systems, F1, F2 and F3, trend northwest-southeast. Apart from the West Ipswich Fault (F3), the F1 and F2 systems are broad Palaeozoic basement structures and thus they may not have had a direct influence on the formation of the much younger coal measures. However, the sedimentation patterns appear to relate to these major fault systems. Depocentres of earlier seams in the Tivoli Formation were restricted to the northern part of the basin, marked by the F1 system. A major depocentre shift occurred before the end of the deposition of the Tivoli Formation as a result of subsidence in the south that conformed to the F2 system configuration. The Blackstone Formation depocentres shifted to the east (Depocentre 1) and west (Depocentre 2) simultaneously. This depocentre shift was associated with the flexural subsidence produced by the rejuvenation of the West Ipswich Fault. Coal accumulation mainly occurred in Depocentre 1. Two types of seam splitting occurred in the Ipswich Basin. Sedimentary splitting or autosedimentation was produced by frequent influx of clastic sediments. The fluvial dominant depositional environments created the random distribution of small seam splits. However, the coincidence of seam splits and depocentres found in some of the seams suggests tectonic splitting. Furthermore, the progressive splitting pattern, which displays seam splits overlapping, was associated with continued basin subsidence. The tectonic splitting pattern is more dominant in the Ipswich Basin. Alternating bright bands shown in the brightness profiles are a result of oscillating water cover in the peat-mire. Moderate groundwater level, which was maintained during the development of the peat, reduced the possibility of salinisation and drowning of the peat swamp. On the other hand, a slow continuous rise of the groundwater table, that kept pace with the vertical growth of peat, prevented excessive oxidation of peat. Ipswich coal is bright due to its high vitrinite content. The cutinite content is also high because the dominant flora was pteridosperms of Dicroidium assemblage containing waxy and thick cuticles. Petrographic study revealed that the depositional environment was telmatic with bog forest formed under ombrotrophic to mesotrophic hydrological conditions. The high preservation of woody or structured macerals such as telovitrinite and semifusinite indicates that coal is autochthonous. The high mineral matter content in coal is possibly due to the frequent influx of clastic and volcanic sediments. The Ipswich Basin is part of a much larger Triassic basin extending to Nymboida in New South Wales. Little is known of the coal as it lacks exposures. It is apparently thin to absent except in places like Ipswich and Nymboida. This study suggests that the dominant control on depocentres of thick coal at Ipswich has been the tectonism. Fluvial incursions and volcanism were superimposed on this

    Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition

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    Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform accurate classification. This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time. Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones. Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time. It gives accuracy of 97.62 % and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU)

    Recognition of Promoters in DNA Sequences Using Weightily Averaged One-dependence Estimators

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    AbstractThe completion of the human genome project in the last decade has generated a strong demand in computational analysis techniques in order to fully exploit the acquired human genome database. The human genome project generated a perplexing mass of genetic data which necessitates automatic genome annotation. There is a growing interest in the process of gene finding and gene recognition from DNA sequences. In genetics, a promoter is a segment of a DNA that marks the starting point of transcription of a particular gene. Therefore, recognizing promoters is a one step towards gene finding in DNA sequences. Promoters also play a fundamental role in many other vital cellular processes. Aberrant promoters can cause a wide range of diseases including cancers. This paper describes a state-of-the-art machine learning based approach called weightily averaged one-dependence estimators to tackle the problem of recognizing promoters in genetic sequences. To lower the computational complexity and to increase the generalization capability of the system, we employ an entropy-based feature extraction approach to select relevant nucleotides that are directly responsible for promoter recognition. We carried out experiments on a dataset extracted from the biological literature for a proof-of-concept. The proposed system has achieved an accuracy of 97.17% in classifying promoters. The experimental results demonstrate the efficacy of our framework and encourage us to extend the framework to recognize promoter sequences in various species of higher eukaryotes

    Glass break detection system using deep auto encoders with fuzzy rules induction algorithm

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    Main uses of glass windows in commercial and residential buildings are prevalent. While a glass-based material has its advantages, it also poses security risks. Therefore, glass break detectors play an important role in security protection for offices and residential buildings. Conventional vibration-based and acoustic-based glass break detectors are designed to detect predetermined temporal and frequency feature thresholds of glass breakage sound signals. This leads to the inability to differentiate glass break from environmental sounds (such as the sound of striking objects, heavy sounds and shouted sounds) that are similar in their amplitude threshold and frequency pattern. Machine learning based acoustic audio classification has been popular in security surveillance applications. Researchers are interested in this research area, and different approaches have been proposed for anomaly event detection (such as gunshots, glass breakage sounds, etc.). This paper proposes a new design of a glass break detection algorithm based on Fuzzy Deep Auto-encoder Neural Network. The algorithm reduces false alarms and improves detection accuracy. Experimental results indicate that proposed fuzzy deep auto-encoder network system attained 95.5% correct detection for the proposed audio dataset

    Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting

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    Automatic and intelligent object sorting is an important task that can sort different objects without human intervention, using the robot arm to carry each object from one location to another. These objects vary in colours, shapes, sizes and orientations. Many applications, such as fruit and vegetable grading, flower grading, and biopsy image grading depend on sorting for a structural arrangement. Traditional machine learning methods, with extracting handcrafted features, are used for this task. Sometimes, these features are not discriminative because of the environmental factors, such as light change. In this study, Hierarchical Extreme Learning Machine (HELM) is utilized as an unsupervised feature learning to learn the object observation directly, and HELM was found to be robust against external change. Reinforcement learning (RL) is used to find the optimal sorting policy that maps each object image to the object’s location. The reason for utilizing RL is lack of output labels in this automatic task. The learning is done sequentially in many episodes. At each episode, the accuracy of sorting is increased to reach the maximum level at the end of learning. The experimental results demonstrated that the proposed HELM-RL sorting can provide the same accuracy as the labelled supervised HELM method after many episodes

    Construction of Diaphragm Wall Support Underground Car Park in Historical Area of Bangkok

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    Geotechnical aspects in construction of diaphragm-wall-support 2 level underground car park building, located in the historically and culturally significant area of Bangkok is presented in this paper. Results of the preliminary analyses showed that the deflection of the thin diaphragm wall of 0.60 m width would be large if it was to be fully cantilevered to fulfill the architectural and utility aspects of the car park structure. It was therefore decided to use buttress to minimize the diaphragm wall deflection. Performance of buttressed-support diaphragm wall is demonstrated based on the inclinometer monitoring results. Intensive modification of construction sequence in actual work execution with “value engineering options” different from tender stage design is demonstrated along with application of observational method

    Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis

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    Machine learning has opened up the opportunity for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension.We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) with different number of components of fMRI data. In addition to that, six different types of machine learning algorithm have been used. In particular, the Haxby dataset is chosen for our experiment. The dataset comprises 9 classes for object recognition. 10-fold cross validation step has been employed. We have discovered that RP outperforms PCA when the former is paired with logistic regression, Gaussian Naive Bayes and linear support vector machine. The best pair for this study was found to be PCA and k-nearest neighbors. Nevertheless, each algorithm was found to have its own strengths for fMRI classification approach
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