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Parameter selection is a critical task in scientific workflows in order to maintain the accuracy of the simulation in an environment where physical conditions change dynamically such as in the case of weather research and forecast (WRF) simulations. Considering the large number of simulation parameters, the size of the configuration search space becomes prohibitive for rapidly evaluating and identifying...
In this paper we propose a new adaptive learning framework that classifies learner's based on individual preferences in terms of understanding and processing information. This framework generates learner's learning style based on Felder Silverman learning style model and suggest learning content based on learning style. The paper outlines how the system allows instructors to monitor learner's learning...
Today's buildings provide continuously growing amounts of data monitored from diverse sensors. With regard to the similarly increasing energy needs of buildings, accurate evaluation and analysis of these data can be used in order to improve energy efficiency and reduce overall energy consumption of buildings. Hence, this work introduces a comprehensive framework based on well-known data analytics...
To solve the problem of satellite communication down without preventive alarm, a predict method based on data mining is proposed. By quantitative analysis of history data the monitor records, the tendency of parameter which has a high relationship with the communication quality could be predicted. Thus, the ability of early warning for satellite communication is promoted efficiently.
Prediction of scholar popularity has become an important research topic for a number of reasons. In this paper, we tackle the problem of predicting the popularity trend of scholars by concentrating on making predictions both as earlier and accurate as possible. In order to perform the prediction task, we first extract the popularity trends of scholars from a training set. To that end, we apply a time...
Trust model has been suggested as an effective security mechanism in distributed network environment. Considerable researches have been done on trust evaluation and trust prediction. Traditional methods take the historical behavior data into consideration to predict the trust value of the network entity. However, the context of the network entity is seldom taken into account. It is obvious that the...
Traffic flow prediction has become a hot spot in the intelligent transportation system study. In this paper, novel methods are proposed to predict traffic flow. We divide 24 hours into 4 stages according to the bimodal distribution of traffic flow, and integrate topology features of urban traffic network into 4 typical machine learning methods. Experiments on the traffic flow of Qinhuangdao city demonstrate...
Trust quantification methods depends on the deployment region, network applications, level of security required. Considering the environment of mobile ad hoc networks, in this study we propose a new trust model. This new model is divided into two parts: trust assessment and trust prediction. The process of node trust assessment is based on node's historical behaviors, in which the trust decision factors...
Considering the hysteresis of switch timing control system used in the industrial farming of pleurotus eryngii, respiration control experiments were carried out with given temperature, humidity and light in a factory farm. Planting environment was monitored by a remote monitor system with a radio frequency module. A CO2 concentration prediction model is developed based on BP neural network and a cycle...
Under real driving conditions, the fatigue monitoring system based on drivers' video is highly affected by light environment, which deteriorates the registration of facial information and thus the accuracy of surveillance. This paper, on the basis of AAM (Active Appearance Model)-based face registration method, analyzes the reasons of its failure when lighting conditions change and proposes an improved...
Network bandwidth is a critical resource for tenants in cloud datacenter. Early researches assume that tenants are aware of bandwidth demands of the virtual machines, and a fixed bandwidth capacity can be satisfying. However, bandwidth demands of different virtual machines are complex and time-varying and it is insufficient to re-allocate the resource when congestion or low utilization occurs. Our...
As an emerging technique, the similarity-based residual life prediction (SbRLP) method is a significant method for residual useful life (RUL) prediction. However, related researches on the SbRLP method with multiple degradation variables are rare. Hence, a framework of the SbRLP method with multiple degradation indicators is advanced. Within the framework, two solutions (e.g. solution A and B) are...
A model for monitoring the wind turbine gearbox based on Supervisory Control and Data Acquisition (SCADA) data is developed. A deep neural network (DNN) is trained with the data of normal gearboxes to predict its performance. The developed DNN model is next tested with data of the normal and abnormal gearboxes. The abnormal behavior of the gearbox can be detected by the statistical process control...
The study aimed to develop a universal method to monitor soil APC by hyperspectral data. The correlations between soil APC and the hyperspectrum reflectivity and its mathematical transformations were analyzed. The feature bands and its transformations were screened to develop the optimizing model of monitoring soil APC based on the method of multiple linear regression. The in-situ testing samples...
This paper considers the problems of condition monitoring and fault detection in an existing solar photovoltaic (PV) plant in Australia. A PV prediction model is proposed to accurately model the PV plant output. This model is then used with three condition monitoring and fault detection methods. The considered methods involve comparison of measured and modeled voltage and current ratios with appropriate...
Land subsidence is one of the important geological hazards in urban construction in Beijing, which has received the attention of academia and government departments. Ground settlement have caused damage of the buildings, railways, bridges and other infrastructure and have caused huge losses of economic, urban construction and people's lives. In this study, we monitor the temporal and spatial distribution...
This study attempted to establish the relationship between ground-level PM2.5 concentrations and satellite-retrieved aerosol optical depth (AOD) using the Vegetation Adjusted NTL Urban Index (VANUI), over the New England region for the year 2013. A geographically weighted regression (GWR) model was used to predict ground-level PM2.5 on a daily basis. The study demonstrates that DMSP/OLS NTL data has...
An automated approach for near-real time simulation and geo-visualization of flooding, including estimation of affected infrastructures, in Lake Mainit, considered the Philippines' deepest lake is presented. Perennial flooding in several areas around the lake due to increase in the lake's water level during the rainy season and during the passing of tropical storms exemplified the need for rapid determination...
In this paper, models are created to predict the levels of ground level Ozone at particular locations based on the cross-correlation and spatial-correlation of different air pollutants whose readings are obtained from several different air quality monitoring stations in Gauteng province, South Africa, including the City of Johannesburg which is on the cusp of being one of the world's megacities and...
A range of load prediction techniques has largely been used for energy management at various levels. However, the data used for the prediction are cumulative energy data, which reveal the activities of consumers and not individual consumer, on the distribution power network. Individual consumer data is essential for real time prediction, monitoring and detect of electricity theft. A new approach of...
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