Determining stream networks automatically from digital elevation models is an issue that is actively being studied. The quality of elevation models has increased over time, but many hydrologically critical features, s...
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Determining stream networks automatically from digital elevation models is an issue that is actively being studied. The quality of elevation models has increased over time, but many hydrologically critical features, such as culverts, are often missing from the elevation data. To analyze the surficial water flow, one must either prepare a special elevation model or post-process an already-existing model. This study builds on the traditional, well-established method of determining the stream network from digital elevation models. We have extended the traditional method by locating culverts automatically, using road network data as an input. We show, by comparison to the reference data, that the culverts being most relevant for the stream network can be found with good accuracy. We demonstrate that by including the automatically located culverts in the automatic stream network determination, the quality of the generated network can be noticeably improved.
Both equivalent radar reflectivity factor (Z_e) and specific attenuation (k) in several snow events are measured using a dual Ka-band radar system. Different k-Z_e relations are obtained depending on surface air tempe...
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ISBN:
(纸本)9781479911127
Both equivalent radar reflectivity factor (Z_e) and specific attenuation (k) in several snow events are measured using a dual Ka-band radar system. Different k-Z_e relations are obtained depending on surface air temperature. When surface air temperature is just above 0 ℃, k-Z_e relations scatter and larger k values than those of rain appear. On the other hand, when surface air temperature is below 0 ℃, both k and Z_e are small and a slight positive trend appears in the k-Z_e relations. The difference of k-Z_e relations can be attributed to the difference of the backscattering and attenuation characteristics between wet and dry snow. To confirm wet/dry snow existences, 2D-video-disdrometer data are analyzed. Velocity-size distributions of wet snow events are different from those of dry snow events. Graupels also show different velocity-size distributions from wet and dry snow particles with different k-Z_e relations.
The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discri...
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This paper looks at the possibility of creating an algorithm that will combine liveness and coercion modalities, along with organisational factors such as workforce composition. The hypothesis is that the algorithm ca...
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This paper looks at the possibility of creating an algorithm that will combine liveness and coercion modalities, along with organisational factors such as workforce composition. The hypothesis is that the algorithm can produce a value that can further be used to compare different setups of biometric security for self-optimisation by taking in the context of technique compatibility and user requirements. To this end, the algorithm focuses on four main aspects: time, participants, anomalous user-bases, and device redundancy inside a typical organisation. An experimental methodology has been used, focusing on the development of the algorithm, its associated effects, and how different parameters can be reliably estimated. After testing, the algorithm is proved to work as it creates an appropriate value, called the security value, which can be used to discover the best combinations of modalities for fusion development or practical installation for a given situation. There are some issues with this primarily due to data provision, the requirements for more data to parse through the algorithm, and finally, the need for a suitable interface, otherwise it may be too complex for efficient usage in a traditional security environment. There are potential implications within a general security application such as liveness and coercion multimodal fusion and autonomous system development and pervasive environments, allowing dynamic security systems to be developed. However, the main focus of this algorithm is to highlight the fusion of liveness and coercion detection and how they can be best applied to specific security scenarios. (C) 2020 Elsevier Ltd. All rights reserved.
Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a m...
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Consistent, cross-mission retrievals of near-surface concentration of chlorophyll-a (Chla) in various aquatic ecosystems with broad ranges of trophic levels have long been a complex undertaking. Here, we introduce a machine-learning model, the Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in inland and coastal waters. The model is trained and validated using a sizeable database of co-located Chla measurements (n = 2943) and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively. Our performance evaluations of the model, via two-thirds of the in situ dataset with Chla ranging from 0.2 to 1209 mg/m(3) and a mean Chla of 21.7 mg/m(3), suggest significant improvements in Chla retrievals. For both MSI and OLCI, the mean absolute logarithmic error (MAE) and logarithmic bias (Bias) across the entire range reduced by 40-60%, whereas the root mean squared logarithmic error (RMSLE) and the median absolute percentage error (MAPE) improved two-to-three times over those from the state-of-the-art algorithms. Using independent Chla matchups (n < 800) for Sentinel-2A/B and -3A, we show that the MDN model provides most accurate products from recorded images processed via three different atmospheric correction processors, namely the SeaWiFS Data Analysis System (SeaDAS), POLYMER, and ACOLITE, though the model is found to be sensitive to uncertainties in remote-sensing reflectance products. This manuscript serves as a preliminary study on a machine-learning algorithm with potential utility in seamless construction of Chla data records in inland and coastal waters, i.e., harmonized, comparable products via a single algorithm for MSI and OLCI data processing. The model performance is anticipated to enhance by improving the global representativeness of the training data as
Multi-objective optimisation problems (MOOPs) consider multiple objectives simultaneously. Solving these problems does not render one unique solution but instead a set of equally optimal solutions, i.e., the Pareto fr...
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Multi-objective optimisation problems (MOOPs) consider multiple objectives simultaneously. Solving these problems does not render one unique solution but instead a set of equally optimal solutions, i.e., the Pareto front. The goal of solving a MOOP is to accurately and efficiently approximate the Pareto front. The use of evolutionary optimisation algorithms is widespread in this discipline. During each iteration, parent solutions are combined and mutated to create new offspring solutions. Both populations are subsequently combined and sorted. Only the N fittest solutions of the combined set are selected as the parent solutions for the subsequent iteration. The fitness of a solution is defined by its convergence to the Pareto front and its contribution to the overall solution diversity. Widely used evolutionary algorithms, like NSGA-II (Deb et al., 2002), use non-dominated sorting to assess the convergence of solutions and the concept of crowding distance to ensure a high solution diversity. Both concepts, however, require that all N solutions of the population are compared with all other ( N — 1) solutions for both aspects, and this for all M objectives. This results in a computational complexity of O(MN 2 ). In this contribution, a novel evolutionary algorithm is presented, boasting a significantly lower computational complexity of O(N log (N)). This is achieved by subdividing the feasible space into angular sections. Solutions are scored based on their distance from the current Utopia point and the overall crowdedness of their respective section. Sorting the population based on the attributed scores allows the selection of the N fittest solutions, without having to mutually compare them.
Evolutionary optimization algorithms have been recently introduced as nonimaging optics design techniques. Unlike optimization of imaging systems, non sequential ray tracing simulations and complex non centred systems...
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ISBN:
(数字)9781510629349
ISBN:
(纸本)9781510629349
Evolutionary optimization algorithms have been recently introduced as nonimaging optics design techniques. Unlike optimization of imaging systems, non sequential ray tracing simulations and complex non centred systems design must be considered, adding complexity to the problem. The Merit Function (MF) is a key element in the automatic optimization algorithm, nevertheless the selection of each objective's weight, {w(i)}, inside merit function needs a previous trial and error process for each optimization. The problem then is to determine appropriate weights value for each objective. In this paper we propose a new Dynamic Merit Function, DMF, with variable weight factors {w(i)(n)}. The proposed algorithm, automatically adapts weight factors, during the evolution of the optimization process. This dynamic merit function avoids the previous trial and error procedure selecting the right merit function and provides better results than conventional merit functions (CMF). Also we analyse the Multistart optimization algorithm applied in the flowline nonimaging design technique.
Common requirements for cloud detection methods including the adjustability with respect to incorrect results are clarified, and a method is proposed that satisfies the requirements by applying the support vector mach...
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Common requirements for cloud detection methods including the adjustability with respect to incorrect results are clarified, and a method is proposed that satisfies the requirements by applying the support vector machine (SVM). Because the conditions of clouds and Earth's surfaces vary widely, incorrect results in actual cloud detection operations are unavoidable. Cloud detection methods therefore should be adjustable to easily reduce the frequency of incorrect results under certain conditions, without causing new incorrect results under other conditions. Cloud detection methods are also required to resolve a characteristic issue: the boundary between clear-sky and cloudy-sky areas in nature is vague, because the density of the cloud particles continuously varies. This vagueness makes the cloud definition subjective. Furthermore, the training dataset preparation for machine learning should avoid circular arguments. The SVM learning is generally less likely to result in overfitting: this study suggests that only typical data are sufficient for the SVM training dataset. By incorporating the discriminant analysis (DA), it is possible to subjectively determine the definition of typical cloudy and clear sky and to obtain typical cloud data without direct cloud detection. In an approach to adjust the classifier, data typical of certain conditions that lead to incorrect results are added to the training dataset. In this study, an adjustment procedure is proposed, which quantitatively judges, whether an addition is actually effective for reduction of the frequency of incorrect results. Another approach for the adjustment is improving feature space used for cloud detection. Indices as quantitative guidance to estimate whether an addition or elimination of a feature actually reduces the frequency of incorrect results can be obtained from the analysis of the support vectors. The cloud detection method incorporating the SVM is therefore able to integrate practical adjustment pr
KMeans is one of most popular algorithms in data mining (ranking number 2) and has be widely used in many fields. KMeans uses Euclidean distance to compare two data. However Euclidean distance is sensitive to linear t...
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ISBN:
(纸本)9781510626423
KMeans is one of most popular algorithms in data mining (ranking number 2) and has be widely used in many fields. KMeans uses Euclidean distance to compare two data. However Euclidean distance is sensitive to linear transform in data collection process. Due to these linear transforms, the distance between two data points for the same class (intra-class distance) may larger than those for different classes (inter-class distance) that may cause low clustering performance for KMeans algorithm. In this paper, we propose simple linear regression approach for data clustering. Instead of using Euclidean distance to measure the difference, we recommend using the goodness of fitting (or normalized cross correlation) to measure the similarity and compare two data points. Using this new data comparison technique, we introduce linear regression approach for data clustering and demonstrate that the proposed method has higher performance and low computational cost than KMeans methods.
A persistent concern of control engineering is the performance of systems in the presence of uncertainty. In this paper, we consider uncertainties affecting systems as stochastic processes of independent stationary in...
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ISBN:
(数字)9781510627086
ISBN:
(纸本)9781510627086
A persistent concern of control engineering is the performance of systems in the presence of uncertainty. In this paper, we consider uncertainties affecting systems as stochastic processes of independent stationary increments. We show that in many situations the performance of an uncertain system can be measured by a function of a parametric stopping time and associated values of stochastic processes. Under some mild regularity conditions, we demonstrate that the performance measure is governed by stochastic functional central limit theorems as the parameters of the stopping time tend to certain values. Such results can be applied to the analysis and design of control systems affected by uncertainties.
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