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The oscillometric method still challenges accurate blood pressure measurement due to its difficulty compensating for pregnancy, age, hypo-, and hypertension. Global sensitivity analysis methods were used to develop a simplified model that is useful for directing and optimizing the design of an automated oscillometric blood pressure measurement system. The most influential biological and design parameters...
Although atlas-based methods simplify the segmentation process by making it more automated, such methods are often very sensitive to the computationally expensive image registration step. Also, existing methods based on a parametric deformation model may fail when the transformation between the atlas and target images can not be properly described with this model. This paper presents a novel and efficient...
In this paper we discuss verification and validation of simulation models. Four different approaches to deciding model validity are described, a graphical paradigm that relates verification and validation to the model development process is presented, and various validation techniques are defined. Conceptual model validity, model verification, operational validity, and data validity are discussed...
In dealing with robustness of specific areas,such as automatic speech recognition (ASR).this paper proposes some new ideas. The idea of using named entity recognition(NER), which is domain-specific is based on the conditional random field(CRF).NE are used to establish the context, leading the speech recognition process' pronunciation element into the post-treatment of speech recognition, Speech recognition...
Regarding information technologies, transnational education has to face several challenges in order to offer a suitable education for computer science students worldwide. Software tools, and specially open source ones, give to the students the possibility of experiment with the most known techniques in the area. Among them, the KEEL software tool can be highlighted as a versatile framework for understanding...
This paper is aimed at exploring the potential of online words to perform biometric writer recognition. Most of the scientific literature dealing with online writer recognition has focused on signature and somehow disregarded handwritten text. Using a novel recognition system based on stroke categorization and dynamic time warping, it is shown that short sequences of online text (words and combinations...
Most predictive modeling techniques utilize all available data to build global models. This is despite the wellknown fact that for many problems, the targeted relationship varies greatly over the input space, thus suggesting that localized models may improve predictive performance. In this paper, we suggest and evaluate a technique inducing one predictive model for each test instance, using only neighboring...
In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive...
Insurance premium is a very important index for the developing situations of insurance. Based on the temporal series data of insurance premium over the past ten years, this article probes into the predicting methods for insurance premium based on grey system theory, sets up grey information renewal GM??i1??C1??j models pointing against the demerits of the traditional GM??i1??C1??jmodels, and then...
Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined...
The level of certain or set of physico-chemical parameter(s) determinates the optimal living condition for the organism. These thresholds in biology are express using categories of classes. One such category which consists from five classes is water quality (WQ) category based on Saturated Oxygen. Due to natural or human-made pressure on the ecosystem, bottom line as that we have to monitor the levels...
This paper presents a novel hybrid approach to the estimation of biophysical parameters from remotely sensed data. This approach integrates theoretical analytical models and empirical models based on field reference samples to increase the reliability and the accuracy of the estimation. The estimation process is modeled by two terms: the first one expresses the relationship between the input features...
Land cover classification accuracy assessments are frequently limited to an error matrix, which derived from location-independent measures and consequently doesn't provide any information about the spatial distribution of the error. The objective of this work is to present a methodology for mapping the spatial distribution of classification errors based on stochastic simulation and that takes into...
Diameter distribution is used to predict stand stock, timber volume and stand yield in most forest management. In the paper, opulus shelterbelts in Boai County were analyzed. A model to predict stand diameter distribution was constructed with artificial neural network(ANN) approach by using the average stand diameter, the coefficient of variation of diameter as well as relative diameter as input variables,...
The applications of remote sensing data are maturing and the study of resources and environmental sciences greatly requires the spatialized data sets of socio-economic data which are always obtained from administrative regions at county or province level. Based on the summary and analysis of previous gross domestic product (GDP) spatialization approaches, with the regional differences of China's economic...
Dynamic Bayesian Belief networks (DBNs) have been commonly used to represent temporal data in several domains, however, an ideal representation requires a near perfect mapping between the process being modeled and the DBN. Furthermore, DBNs assume a full set of observations collected at a fixed frequency. Bayesian model selection has arisen to address biased inference and underlying assumptions about...
Identification of irrigated farm land is critical to a variety of environmental models. For this work, it is needed to model the impacts of irrigation on groundwater quality and flow rates. As the ground cover appearance for many land uses is quite dynamic, we take a multi-temporal remote sensing approach and classify each location as likely irrigated if it appears irrigated at any stage. Our model...
Wetland ecosystems have strong ability for carbon storage and fixation, and play important roles in the global carbon cycle. The accurate estimation of the leaf chlorophyll content is a significant foundation for researching wetland ecosystem cycle models. In this paper, the inversion of the leaf chlorophyll content was based on PROSPECT model from the physical mechanism angle. Leaf spectrum was simulated...
In this paper, the effects of zero abundance data on fish habitat modelling using a genetic Takagi-Sugeno fuzzy system were assessed with specific focus on habitat preference curves (HPCs) and model performance. Three independent data sets were prepared from a series of fish habitat surveys conducted in an agricultural canal in Japan. To quantify the effects of zero abundance data, two kinds of data...
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