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Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical...
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical...
Retrievals of surface soil moisture (SSM) from remotely sensed satellites have become important in agriculture, meteorology. However, any models have been presented for different satellite imagery, which are complex with data processing cumbersome and inconvenient. This paper introduces the one model of CBERS-02B and two models of Landsat TM image. Comparing and analyzing the accuracy of the models...
We present in this paper the analysis results of prominent educational characteristics differentiating people from the two regions in the world: advanced economies versus east Asia and the pacific countries. The automatic multivariate analysis of classification trends has been demonstrated through the visual data mining tool called KNIME. We found from the empirical studies that from the years 1950...
In this case study we investigate software reliability models and their applicability to process improvement at an IT help desk. We propose a model selection framework and demonstrate its success using real help desk incident data from a portfolio of 156 desktop software applications. Incidents are predicted at five intervals and measured against actual numbers of submitted incidents. We analyze incident...
Prediction of protein special structural plays a significant role to better recognize the protein folding patterns. Multiple prediction methods may be used to predict the structures based on the information of sequences and biostatistics. The accuracy, nevertheless, is strongly affected by the efficiency of classification, the robustness of model and other factors. In our research, flexible neutral...
The worldwide increase in the integration of photovoltaic generation has necessitated improvements in the forecasting approaches. Two models are proposed to cater for PV generation forecasts for few minutes to several hours look-ahead times. A very fast and accurate prediction model based on extreme learning machine is deployed for day-ahead prediction. Moreover, an adaptive and sequential model is...
Nonlinearity of power flow equations is one of the major underlying factors in a power systems operation complexity. The need for a robust and less complex models rises in a volatile, dynamic and real time scenario. This paper introduces new empirical models using multivariate linear regression (MLR) methods with least squares for both real and reactive branch flows. The models do not make prior assumptions...
Handwritten signature recognition is one important component of biometric authentication. This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions. Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information. Although many successful methods...
In recent years, water quality prediction has attracted many attentions of governments and researchers. The safety of water quality seriously affects the human health, fishery economy and agricultural activities. If an early prediction to the water quality with an acceptable accuracy can be achieved, the negative impacts will be minimized or even be avoided. Many researchers have applied artificial...
The distribution grid is changing to become an active resource with complex modeling needs. The new active distribution grid will, within the next ten years, contain a complex mix of load, generation, storage and automated resources all operating with different objectives on different time scales from each other and requiring detailed analysis. Electrical analysis tools that are used to perform capacity...
This study presents an approach to integrate Landsat satellite imagery and forest monitoring field plots, to produce forest attribute maps across the state of Victoria, Australia. Over 450 field plots, sampled from a stratified systematic random framework, were measured to characterise woody and non-woody forest attributes. Field plot data were applied in various k-NN procedures using Landsat8 data...
Transportation and land use planners and modelers often use zone systems to take advantage of readily available socio-economic data in their modelling exercises. The most important issue is that analysis zone size is usually large, and therefore, homogeneous or single-type land uses cannot be achieved in many cases. Subsequently, intra-zone travel and mixed activities distribution cannot be captured...
Support vector machine (SVM) and its derivative algorithms have been increasingly used to predict algal blooms recently. However, its computation complexity remains an annoying problem. To improve the time cost of SVM, a hybrid approach is proposed in this paper based on Partial Least Square (PLS) feature extraction and Core Vector Machine Regression (CVR) algorithm. We describe the principle of our...
Advances in protein three-dimensional structure prediction depend strongly on the ability to measure the quality of a protein model. One of the best single measures of the model quality is the area under the graph of the so-called “GDT function” that assigns to each distance cutoff θ the percentage of residues in the model structure that can be superimposed at distance ≤ θ from the corresponding residues...
This paper proposes a dynamic nonlinear autoregressive model based algorithm for gene regulatory networks (GRNs) identification with biological stage change detection using the L1-regularization. This allows subtle variations in the same state to be penalized and prominent changes across adjacent states to be captured. Furthermore, by assuming local-stationarity within each detected biological state,...
Developed a method for computer analysis of the microparticles motion parameters along trajectories, based on the proposed A. A. Vavilov principle consistent disclosure of structural, parametrical and signal uncertainty. Signal uncertainty is caused by instrumental and methodological errors in the localization of microparticles position in the trajectories resulting from image processing. The difference...
Forecasting can be used for helping the decision-makers to determine the next business strategy to improve the quality of Indonesia tourism such as the improvement of the accommodation facility like transportation and lodging, public services, and promotion to introduce Indonesia tourism objects. This research compared the forecasting performance between GM (1,1) and ARIMA models to determine the...
The most successful approach to speech and speaker recognition is to treat the speech signal as a stochastic pattern and to use a statistical pattern recognition technique for matching utterances. This paper attempts to study the performance of Text dependent speaker verification system using Delta-Delta Mel Frequency Cepstral Coefficients (MFCC-Δ-Δ) feature vector and Fuzzy C means (FCM) speaker...
In order to implement high precision time synchronization autonomously in the absence of any external time source, in this paper, the author introduced the synchronize model of fireflies into UAV formation network, and proposed a kind of distributed time synchronization method base on broadcast, and conducted some computer simulation experiments and built one test platform to prove the feasibility...
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