General Network Statistical Decision Fusion

2012 ◽  
pp. 59-162
Author(s):  
Yunmin Zhu ◽  
Jie Zhou ◽  
Xiaojing Shen ◽  
Enbin Song ◽  
Yingting Luo
Keyword(s):  
Decision Fusion ◽  
Statistical Decision ◽  
General Network

Soft Statistical Decision Fusion for Distributed Medical Data on Grids

2011 ◽  
pp. 59-75
Author(s):  
Yu Tang ◽  
Yan-Qing Zhang
Keyword(s):  
Single Case ◽  
Decision Fusion ◽  
Medical Data ◽  
Distributed Data ◽  
Soft Data ◽  
Fusion Algorithm ◽  
High Quality ◽  
Statistical Decision ◽  
Geographical Locations ◽  
Fusion Approach

This chapter introduces the decision fusion as means of exploring information from distributed medical data. It proposes a new method of applying soft data fusion algorithm on gird to analyze massive data and discover meaningful and valuable information. It could potentially help to better understand and process medical data and to provide high quality services in patient diagnosis and treatment. It allows incorporation of multiple physicians into one single case to recover and resolve problem, and integration of distributed data sources overcome some of limitations of geographical locations to share knowledge and experience based on soft data and decision fusion approach.

Soft Statistical Decision Fusion for Distributed Medical Data on Grids

2011 ◽  
pp. 2484-2497
Author(s):  
Yu Tang
Keyword(s):  
Single Case ◽  
Decision Fusion ◽  
Medical Data ◽  
Distributed Data ◽  
Soft Data ◽  
Fusion Algorithm ◽  
High Quality ◽  
Statistical Decision ◽  
Geographical Locations ◽  
Fusion Approach

This chapter introduces the decision fusion as a means of exploring information from distributed medical data. It proposes a new method of applying soft data fusion algorithm on the grid to analyze massive data and discover meaningful and valuable information. It could potentially help to better understand and process medical data and provide high-quality services in patient diagnosis and treatment. It allows incorporation of multiple physicians into one single case to recover and resolve problems, and integration of distributed data sources overcome some limitations of geographical locations to share knowledge and experience based on the soft data and decision fusion approach.

A novel approach to white liquor control in a continuous digester using the Box-Jenkins methodology to predict chip bulk density

TAPPI Journal ◽  
2015 ◽  
Vol 14 (6) ◽  
pp. 395-402
Author(s):  
FLÁVIO MARCELO CORREIA ◽  
JOSÉ VICENTE HALLAK D’ANGELO ◽  
SUELI APARECIDA MINGOTI
Keyword(s):  
Bulk Density ◽  
Moving Average ◽  
Pulp Mill ◽  
Statistical Decision ◽  
White Liquor ◽  
Novel Approach ◽  
Cooking Process ◽  
Eucalyptus Kraft Pulp ◽  
Relevant Variables ◽  
Kraft Cooking

Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948 observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.

scholarly journals Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion

2020 ◽  
Vol E103.D (2) ◽  
pp. 450-453
Author(s):  
Guizhong ZHANG ◽  
Baoxian WANG ◽  
Zhaobo YAN ◽  
Yiqiang LI ◽  
Huaizhi YANG
Keyword(s):  
Sparse Representation ◽  
Steel Structure ◽  
Decision Fusion ◽  
One Class Classification

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

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