Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.     

RETRACTED ARTICLE: Multimedia based intelligent network big data optimization model

Multimedia Tools and Applications - The data of a city space is very large. In the city, there are tens of the thousands of sensors of video, audio and image at the same time. Real-time...     

RETRACTED ARTICLE: Research on image classification method based on convolutional neural network

Neural Computing and Applications - Image classification method is currently the more popular image technology, but it still has certain problems in practice. In order to improve the image...     

RETRACTED ARTICLE: A novel large-scale multimedia image data classification algorithm based on mapping assisted deep neural network

Multimedia Tools and Applications - With the increasing number of the images, how to effectively manage and use these images becomes an urgent problem to be solved. The classification of the images...     

RETRACTED ARTICLE: Intelligent traffic monitoring and traffic diagnosis analysis based on neural network algorithm

Traffic sign recognition and lane detection play an important role in traffic flow planning, avoiding traffic accidents, and alleviating traffic chaos. At present, the traffic intelligent recognition rate still needs to be improved. In view of this, based on the neural network algorithm, this study constructs an intelligent transportation system based on neural network algorithm, and combines machine vision technology to carry out intelligent monitoring and intelligent diagnosis of traffic system. In addition, this study discusses in detail the core of the monitoring system: multi-target tracking algorithm, and introduces the complete implementation process and details of the system, and highlights the implementation and tracking effect of the multi-target tracker. Finally, this study uses case identification to analyze the effectiveness of the algorithm proposed by this paper. The research results show that the proposed method has certain practical effects and can be used as a reference for subsequent system construction.

     

RETRACTED ARTICLE: Designing statistical assessment healthcare information system for diabetics analysis using big data

Health services research provides a multi-disciplinary area of scientific exploration in relation to financial systems, social factors, organizational processes, and health technologies. With the help of big data, the huge amount of data can well be stored and handled effectively for diagnosis and also proper treatment of diseases can be monitored with these emerging technologies. In recent years, Diabetes Mellitus is non-transmittable illnesses that are a matter of concern in most of the developing countries. This paper proposes a model of a statistical assessment, healthcare information system for Diabetes Analysis employing big data. The performance metric such as accuracy and F-measure for the proposed statistical assessment model is evaluated by Hadoop framework, the results are comparatively higher than existing methods.

     

Image-based fluid data assimilation with deep neural network

Structural and Multidisciplinary Optimization - Estimating unknown parameters or conditions based on observation in a numerical model is a problem considered in data assimilation. In this study, we...     

RETRACTED ARTICLE: Nonlinear backstepping control design of LSM drive system using adaptive modified recurrent Laguerre orthogonal polynomial neural network

The good control performance of the permanent magnet linear synchronous motor (LSM) drive system is very difficult to achieve using linear controller because of uncertainty effects, such as ending-fictitious force. A backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. Although favorable tracking responses can be obtained by the backstepping control system, the chattering in the control effort is critical because of the large control gain. Because there are many nonlinear and time-varying uncertainties in the LSM drive systems, the nonlinear backsteping control system, which an adaptive modified recurrent Laguerre orthogonal polynomial neural network (NN) is used to estimate uncertainty, is thus proposed to reduce the chattering in the control effort and thereby enhance the robustness of the LSM drive system. In addition, the on-line parameter training methodology of the modified recurrent Laguerre orthogonal polynomial NN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre orthogonal polynomial NN are derived to accelerate parameter convergence. Finally, comparison of the experimental results of the present study demonstrates the high control performance of the proposed control scheme.

     

RETRACTED ARTICLE: The big data visualization technology based ecosystem cycle on high speed network

Multimedia Tools and Applications - Big Data is becoming a key strategy in the business sector, with the increasing number of corporate customer data tracking and collection practices, and the...     

基于BP神经网络的多传感器数据融合技术优化

传统的数据融合算法要求获得比较精确的对象数学模型,对于复杂的难于建立模型的场合无法适用。为解决上述问题,提出了一种基于BP神经网络算法的多传感器数据融合方法,对对象的先验要求不高,具有较强的自适应能力。仿真结果表明,采用BP神经网络对传感器数据进行融合处理大大提高了传感器的稳定性及其精度,效果良好。     

RETRACTED ARTICLE: Dense stereovision using mono-CCD color cameras

Most stereo algorithms are based only on an analysis of the luminance information. However, with advances in camera technology, in addition to the fact that color information can robustly improve matching, color stereovision is receiving more and more attention. Color stereovision setups are usually based on single-sensor cameras which provide color filter array (CFA) images. In these images, a single color component is sampled at each pixel rather than the three required components red, green, and blue (RGB). We show that standard demosaicing techniques, which are used to interpolate the missing components, are not well adapted when the resulting color pixels are matched in order to estimate image disparities. In order to avoid this problem while exploiting color information, we propose a new matching system designed for dense stereovision based on pairs of CFA images.     

A region-based multi-sensor image fusion scheme using pulse-coupled neural network

For most image fusion algorithms split relationship among pixels and treat them more or less independently, this paper proposes a region-based image fusion scheme using pulse-coupled neural network (PCNN), which combines aspects of feature and pixel-level fusion. The basic idea is to segment all different input images by PCNN and to use this segmentation to guide the fusion process. In order to determine PCNN parameters adaptively, this paper brings forward an adaptive segmentation algorithm based on a modified PCNN with the multi-thresholds determined by a novel water region area method. Experimental results demonstrate that the proposed fusion scheme has extensive application scope and it outperforms the multi-scale decomposition based fusion approaches, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images.     

RETRACTED ARTICLE: Smart communication using tri-spectral sign recognition for hearing-impaired people

The Journal of Supercomputing - In recent years, new technology developments have been proposed and implemented to support people with hearing impairment and speech loss. It is a severe...     

RETRACTED ARTICLE: Chinese regional economic cooperative development model based on network analysis and multimedia data visualization

Since the reform and opening up, China’s economic development has been accelerating. In the current world economic system, China occupies a very important position. However, there is a phenomenon of uneven economic growth among different regions, namely, there are large differences in economic growth rates and economic development levels between different provinces and cities. In recent years, a large number of studies have shown that China’s regional economic growth has obvious spatial correlation. In this paper, we adopt the method of network analysis to study and explain the spatial correlation of regional economic growth. Multimedia mining is a combination of data mining technology and multimedia technology. It is a cross-disciplinary field of knowledge discovery, data mining, artificial intelligence, machine learning, database technology, and multimedia technology. Therefore, data visualization technology can be used to study the coordinated development model of regional economy. Multimedia data visualization is an evolving concept whose boundaries are constantly expanding, mainly referring to technologically advanced technical methods that allow the use of graphics, image processing, computer vision, and user interfaces. Visualize data by expressing, modeling, and displaying stereo, surface, attributes, and animations. Compared with special technical methods such as stereo modeling, the technical methods covered by data visualization are much broader. The simulation results prove that the propose model can obtain the better overall perforamcne.