scholarly journals Early Warning Evaluation of Tuberculosis and Meteorological Factors in Shanxi Province Based on Dynamic Bayesian Network

2021 ◽  
Author(s):  
Meichen Li ◽  
Zhuang Zhang ◽  
Hao Ren ◽  
Yueling Fan ◽  
Weimei Song ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Early Warning ◽  
Meteorological Factors ◽  
Recognition Performance ◽  
Dynamic Bayesian Network ◽  
Shanxi Province ◽  
Monthly Precipitation ◽  
Cross Correlation Analysis ◽  
Warning Model ◽  
Better Than

Abstract Background: Tuberculosis is a major global public health problem. However, it is still in the exploratory stage that the study of the meteorological factors related to the incidence of tuberculosis in Shanxi Province. Therefore, it is very urgent to establish an early warning system that easily operate of tuberculosis. Method: The epidemiological characteristics of tuberculosis in Shanxi Province were described, and the Dynamic Bayesian Network early warning model was established by time series cross-correlation analysis and Bayesian Network.Results: 1. The reported incidence of tuberculosis in Shanxi Province showed an overall downward trend from 2008 to 2017, showing a phenomenon of high in the middle and low at both ends each year, with certain seasonal characteristics. 2. Based on the results of cross-correlation analysis, it is reasonable to use dynamic Bayesian model fitting with meteorological factors lagging for 2 months; the monthly average temperature and monthly precipitation are positively correlated with the incidence of tuberculosis, but the monthly mean air pressure is negatively. 3. Comparison of classification and recognition performance of the three models shows that DBN has the highest classification accuracy in the two regions, which indicates that DBN is better than the other two models in reflecting the performance of minority classes, and better for the comprehensive classification of minority classes and majority classes. Conclusion: 1. Shanxi Province has tuberculosis clustering in time, space and time and space. Incidence peak is in spring and early summer. March is the highest month in the year. Seven meteorological factors such as monthly precipitation are the main factors affecting the incidence of tuberculosis in Shanxi Province. 2. The classification and recognition performance of the Dynamic Bayesian Network early warning model of tuberculosis-meteorological factors established in this study is significantly better than that of static Bayesian Network and support vector machine model, and can better predict the future.

scholarly journals Early Warning Evaluation of Tuberculosis and Meteorological Factors in Shanxi Province based on Dynamic Bayesian Network

2021 ◽  
Author(s):  
Mei-Chen Li ◽  
Zhuang Zhang ◽  
Hao Ren ◽  
Yue-Ling Fan ◽  
Wei-Mei Song ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Early Warning ◽  
Cross Correlation ◽  
Meteorological Factors ◽  
Recognition Performance ◽  
Dynamic Bayesian Network ◽  
Shanxi Province ◽  
Early Warning Model ◽  
Cross Correlation Analysis ◽  
Warning Model

Abstract Tuberculosis is a major global public health problem. However, there haven’t been reported the study of meteorological factors related to the incidence of tuberculosis in Shanxi Province. Therefore, it is very urgent to establish an early warning system that easily operate of tuberculosis. The epidemiological characteristics of tuberculosis in Shanxi Province were described, and the Dynamic Bayesian Network early warning model was established by time series cross-correlation analysis and Bayesian Network. The incidence showed an overall downward trend from 2008 to 2017 with certain seasonal characteristics. Based on cross-correlation analysis, it is reasonable to use Dynamic Bayesian model fitting with meteorological factors lagging for 2 months. Comparison of classification and recognition performance of the Dynamic Bayesian Network, Bayesian Network and support vector machine model shows that Dynamic Bayesian Network has the highest classification accuracy in the two regions. In Shanxi Province, tuberculosis cluster in time, space and time and space, and incidence peak is in spring and early summer, seven meteorological factors are the main factors affecting the incidence of tuberculosis. The classification and recognition performance of the Dynamic Bayesian Network early warning model of tuberculosis-meteorological factors is significantly better than the others, and can better predict the future.

scholarly journals Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guannan Wang ◽  
Pei Yang ◽  
Jiayi Chen
Keyword(s):  
Early Warning ◽  
Business Models ◽  
Load Forecasting ◽  
Value Added ◽  
Load Prediction ◽  
Industrial Parks ◽  
Forecasting Method ◽  
Warning Model ◽  
Technical Products ◽  
Better Than

This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show that the accuracy of our prediction model and early warning model is better than that of the baseline and can reach the standard of application in practice; this model can also be used for early warning of local sudden large loads and identification of enterprise power demand. Therefore, the validity of the method proposed in this paper is verified using the historical dataset of industrial parks, and relevant technical products and business models are formed to provide value-added services to users by combining existing practical cases for the specific scenario of industrial parks.

scholarly journals Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091155
Author(s):  
Zhiqiang Liu ◽  
Wenbo Zhu ◽  
Hongzhou Zhang ◽  
Shengjin Wang ◽  
Lu Fang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Bayesian Network ◽  
System Reliability ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Dynamic Bayesian Network ◽  
Practical Significance ◽  
Identification Rate ◽  
Fuzzy C Means ◽  
Recognition Systems

The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use.

scholarly journals The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

2021 ◽  
Vol 13 (2) ◽  
pp. 566
Author(s):  
Nelly Florida Riama ◽  
Riri Fitri Sari ◽  
Henita Rahmayanti ◽  
Widada Sulistya ◽  
Mohamad Husein Nurrahmat
Keyword(s):  
Path Analysis ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Coastal Flooding ◽  
Coastal Areas ◽  
Public Acceptance ◽  
Community Attitudes ◽  
Early Warning Model ◽  
Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

scholarly journals Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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