2,319 research outputs found

    Trees under attack: a Ray-Knight representation of Feller's branching diffusion with logistic growth

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    We obtain a representation of Feller's branching diffusion with logistic growth in terms of the local times of a reflected Brownian motion HH with a drift that is affine linear in the local time accumulated by HH at its current level. As in the classical Ray-Knight representation, the excursions of HH are the exploration paths of the trees of descendants of the ancestors at time t=0t=0, and the local time of HH at height tt measures the population size at time tt (see e.g. \cite{LG4}). We cope with the dependence in the reproduction by introducing a pecking order of individuals: an individual explored at time ss and living at time t=Hst=H_s is prone to be killed by any of its contemporaneans that have been explored so far. The proof of our main result relies on approximating HH with a sequence of Harris paths HNH^N which figure in a Ray-Knight representation of the total mass of a branching particle system. We obtain a suitable joint convergence of HNH^N together with its local times {\em and} with the Girsanov densities that introduce the dependence in the reproduction

    The effect of trust on knowledge sharing between project teams

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    Although there has been many studies that were conducted over the years about trust and its influence on knowledge sharing, the phenomenon in the context of project teams still remains ambiguous in theory and in practice. Moreover, managers often have difficulty in creating a workspace that team members are able to generate trust in order to encourage knowledge sharing. For this reason, this current research aims to examine three forms of trust namely: affect-based trust, cognition-based trust, and information-based trust, and their influences on the sharing of knowledge of project teams. In order to successfully conduct the research, the researcher shall provide an empirical investigation to explain the proposed relationships between the three forms of trust and knowledge sharing regarding project team members in various organizations by implementing a quantitative methodology research. A survey data of 256 responses was used to conduct confirmatory factor analysis and hierarchical multiple regression, and the hypotheses were tested. The results indicate that all the three proposed relationships between trust and knowledge sharing are positively and significantly supported, with affect-based trust being the most significant influence on knowledge sharing. Consequently, this research provides insights for managers about the value of project team members’ trust on their sharing of knowledge and proposes future directions for improvement

    Accurate modelling and positioning of a magnetically-controlled catheter tip

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    This thesis represents the initial phase of a proposed operator and patient friendly method designed to semi-automate the positioning and directing of an intravascular catheter in the human heart using a variable electromagnetically induced field to control a catheter tip equipped with three tiny fixed magnets oriented in XYZ planes. Here we demonstrate a comprehensive mathematical model which accurately calculates the magnetic field generated by the electromagnet system, and the magnetic torques and forces exerted on a three-magnet tip catheter. From this we have developed an iterative predictive computer algorithm to show the displacement and deflection of the catheter tip. Using an eight variable power electromagnet system around a 250mm sphere of air we have proven the ability of this to accurately move the catheter tip from an initial position to a designated position within the field

    Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

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    In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

    Height and the total mass of the forest of genealogical trees of a large population with general competition

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    We consider branching processes with interaction in continuous time, both with values in the integers and in the reals (in the second case we restrict ourselves to continuous processes), which model the evolution of the size of a population. We assume that for large population size the interaction is of the type of a competition, which limits the size of the population. We discuss in which cases the interaction is strong enough so that the extinction time (or equivalently the height of the forest of genealogical trees) remains finite, as the number of ancestors tends to infinity, or even such that the length of the forest of genealogical trees (which in the case of continuous state is rather called its total mass) remains finite, as the ancestral population size tends to infinity.Comment: ESAIM: Probability and Statistics, 201

    Precision of Disability Estimates for Southeast Asians in the American Community Survey 2008-2010 Microdata

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    Detailed social data about the United States (US) population was collected as part of the US decennial Census up until 2000. Since then, the American Community Survey (ACS) has replaced the long form previously administered in decennial years. The ACS uses a sample rather than the entire US population and therefore, only estimates can be created from the data. This investigation computes disability estimates, standard error, margin of error, and a more comprehensive “range of uncertainty” measure for non-Latino-whites (NLW) and four Southeast Asian groups. Findings reveal that disability estimates for Southeast Asians have a much higher degree of imprecision than for NLW. Within Southeast Asian groups, Vietnamese have the highest level of certainty, followed by the Hmong. Cambodians and Laotians disability estimates contain high levels of uncertainty. Difficulties with self-care and vision contain the highest level of uncertainty relative to ambulatory, cognitive, independent living, and hearing difficulties

    Investigating the influence of design parameters on the indoor environmental quality and thermal comfort in primary schools in Ho Chi Minh City, Vietnam

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    Indoor environmental quality significantly impacts on comfort levels which affect students’ performance and productivity. Currently in Vietnam, very few studies have dealt with the issue, and the current trend is to install energy-intensive air-conditioning in primary schools as this is perceived as more comfortable. In this study, indoor comfort and users’ perceptions were investigated in three primary schools in Ho Chi Minh City during the mid-season (September 2015), the hottest season (April 2016) and the coldest season (December 2016 – January 2017) to provide a good overview. In-situ spot and long-term measurements were recorded. Questionnaires were completed by 4411 children (age range from 8 to 11 years) and 116 teachers to inform the study about their experiences and the extent of their interaction with the buildings in 124 classrooms. The results were analysed by correlating the conditions measured and the comfort votes on a seven-point scale. In free-running schools, more than 90% of children were satisfied with the overall indoor conditions, although the classrooms were found to be out of thermal comfort for more than 20% of the school time. Furthermore, the classrooms were usually in noisy and dim conditions. The conflicting between the quantitative and qualitative results shows that the current standards are not reflecting the current expectation in the free running classrooms. In the air- conditioned classrooms, the CO2 concentration levels were over 2000ppm and affected children’s alertness. The calculated neutral temperature in the free running classrooms was 31.3oC with the relative humidity of 60% to 70% and the average air velocity of 0.56m/s; and the benchmark for overheating calculations was suggested at 33oC. The adjusted neutral temperature with a normal airspeed was 29.4oC. In this study, the adaptive thermal comfort model for Vietnamese children in primary schools was proposed. The thermal comfort criteria of design parameters for renovation projects and new-built buildings were recommended through parametric and optimisation studies. The findings suggested that air conditioning all year round may be unnecessary from a comfort perspective. These findings could help and encourage architects and engineers to design and deliver schools that provide thermal comfort and minimise the use of air conditioning systems. The results of this work could inform design standards to deliver high quality, low-energy indoor environmental classrooms in primary schools in Ho Chi Minh City, Vietnam

    Performances of the LBP based algorithm over CNN models for detecting crops and weeds with similar morphologies

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    Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, ResNet-50 and InceptionV3. The experimental results on the “bccr-segset” dataset in the laboratory testbed setting show that the accuracy of CNN models with fine-tuned hyper-parameters is slightly higher than the k-FLBPCM method, while the accuracy of the k-FLBPCM algorithm is higher than the CNN models (except for VGG-16) for the more realistic “fieldtrip_can_weeds” dataset collected from real-world agricultural fields. However, the CNN models require a large amount of labelled samples for the training process. We conducted another experiment based on training with crop images at mature stages and testing at early stages. The k-FLBPCM method outperformed the state-of-the-art CNN models in recognizing small leaf shapes at early growth stages, with error rates an order of magnitude lower than CNN models for canola–radish (crop–weed) discrimination using a subset extracted from the “bccr-segset” dataset, and for the “mixed-plants” dataset. Moreover, the real-time weed–plant discrimination time attained with the k-FLBPCM algorithm is approximately 0.223 ms per image for the laboratory dataset and 0.346 ms per image for the field dataset, and this is an order of magnitude faster than that of CNN models

    Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

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    Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. © 2018 China Agricultural Universit
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