scholarly journals Big Data Spatio-Temporal Correlation Analysis and LRIM Model Based Targeted Poverty Alleviation through Education

2021 ◽  
Vol 10 (12) ◽  
pp. 837
Author(s):  
Yue Han ◽  
Lin Liu ◽  
Qiaoli Sui ◽  
Jiaxing Zhou
Keyword(s):  
Poverty Alleviation ◽  
Principal Component ◽  
Temporal Correlation ◽  
Significant Negative Correlation ◽  
Integration Model ◽  
Factors Affecting ◽  
National Education ◽  
Education Levels ◽  
Spatio Temporal ◽  
Poverty Elimination

There are many factors affecting poverty, among which education is an important one. Firstly, from the perspective of digital statistics, this research quantitatively analyzes the correlation between average education years (AEY) and Gross Domestic Product per capita (GDP/C), and finds that there is a significant positive correlation between AEY and GDP/C in provinces of China. Furthermore, from the perspective of spatial distribution and geostatistics, this research analyzes the correlation between AEY and the distribution of poor counties, revealing the inherent connection between education and poverty. Based on the data processing of nighttime light remote sensing images, this research adopts the machine learning method of random forest to extract the distribution status of spatio-temporal sequences for poor counties. Through the analysis, it is found that poor counties are characterized by centralized distribution and spatial autocorrelation spatially, and the number of poor counties decreases year by year in temporal evolution. On this basis, we analyze the correlation between education levels and the distribution of poor counties. It is found that, on the spatial scale, AEY in poor counties is relatively low, while AEY in non-poor counties is relatively high, showing a significant negative correlation between the two. On the temporal scale, the number of poor counties gradually decreased from 2000 to 2010, and at the same time, the education levels of poor counties also gradually improved. Finally, from the perspective of improving education levels to promote poverty elimination, we analyze the main factors affecting education using Principal Component Analysis (PCA) and other methods and obtain a regression model. This research proposes the Linear and Residual Integration Model (LRIM) to more accurately predict AEY in each province in 2020 based on historical data, and identifies the regions with low AEY as key regions for targeted poverty alleviation through education (TPAE) in the future. This research provides a decision-making basis to achieve TPAE means, helping to achieve the victory of the national education poverty elimination battle.

Learning spatio-temporal correlation filter for visual tracking

2021 ◽  
Vol 436 ◽  
pp. 273-282
Author(s):  
Youmin Yan ◽  
Xixian Guo ◽  
Jin Tang ◽  
Chenglong Li ◽  
Xin Wang
Keyword(s):  
Visual Tracking ◽  
Temporal Correlation ◽  
Correlation Filter ◽  
Spatio Temporal

scholarly journals A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Temporal Correlation ◽  
Local Association ◽  
Sensing Data ◽  
Multi Scale ◽  
Correlation Networks ◽  
Efficient Integration ◽  
Spatio Temporal ◽  
Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

scholarly journals Spatio-Temporal Characteristics and Obstacle Factors of the Urban-Rural Integration of China’s Shrinking Cities in the Context of Sustainable Development

2021 ◽  
Vol 13 (8) ◽  
pp. 4203
Author(s):  
Bin Du ◽  
Ying Wang ◽  
Jiaxin He ◽  
Wai Li ◽  
Xiaohong Chen
Keyword(s):  
Sustainable Development ◽  
Economic Efficiency ◽  
Formation Mechanisms ◽  
Shrinking Cities ◽  
The Sustainable Development ◽  
Factors Affecting ◽  
Temporal Characteristics ◽  
The North ◽  
Urban Rural ◽  
Spatio Temporal

Based on the fundamental concept of sustainable development, this study empirically analyzes the spatio-temporal characteristics, formation mechanisms and obstacle factors of the urban-rural integration of shrinking cities in China, from 2008 to 2018. The conclusions are as follows: the overall level of the urban-rural integration of shrinking cities in China is low; the internal differences of urban-rural integration are also small, and the changes are slow. Next, the space difference is high in the east and low in the west, high in the south and low in the north. Moreover, differences exist among different levels of urban agglomerations. Urban economic efficiency, urban resources and environment, urban social equity and rural economic efficiency are the main factors affecting the urban-rural integration of shrinking cities in China. Urban and rural economic efficiency are the two most prominent shortcomings that restrict the urban-rural integration of shrinking cities. The spatial resistance mode of each city is more than the two-system resistance; the main resistance of shrinking cities with a higher level of urban-rural integration also comes from the non-economic field. This study expands the research scope that up till now has ignored the discussion of urban-rural issues in the research of shrinking cities at home and abroad, and provides practical guidance for the sustainable development of shrinking cities in China.

scholarly journals What Affects Chinese Residents’ Perceptions of Climate Change?

2018 ◽  
Vol 10 (12) ◽  
pp. 4712 ◽  
Author(s):  
Jinjia Wu ◽  
Jiansheng Qu ◽  
Hengji Li ◽  
Li Xu ◽  
Hongfen Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Public Awareness ◽  
Public Understanding ◽  
Related Factors ◽  
Climate Change Risk ◽  
Climate Governance ◽  
Factors Affecting ◽  
National Education ◽  
The Public ◽  
Mitigation And Adaptation

The theme of global sustainable development has changed from environmental management to climate governance, and relevant policies on climate governance urgently need to be implemented by the public. The public understanding of climate change has become the prerequisite and basis for implementing various climate change policies. In order to explore the affected factors of climate change perception among Chinese residents, this study was conducted across 31 provinces and regions of China through field household surveys and interviews. Combined with the residents’ perception of climate change with the possible affected factors, the related factors affecting Chinese residents’ perception of climate change were explored. The results show that the perceptive level of climate change of Chinese residents is related to the education level and the household size of residents. Improving public awareness of climate change risk in the context of climate change through multiple channels will also help to improve residents’ awareness of climate change. On the premise of improving the level of national education, improving education on climate change in school education and raising awareness of climate change risk among dependents will help to improve the level of Chinese residents’ awareness of climate change, which could be instrumental in promoting public participation in climate change mitigation and adaptation actions.

Reliability Parameters Forecasting for Transmission Lines Based on Principal Component Regression

2013 ◽  
Vol 291-294 ◽  
pp. 2381-2386 ◽  
Author(s):  
Wen Xia Liu ◽  
Ji Kai Xu ◽  
Hong Yuan Jiang ◽  
Yong Tao Shen
Keyword(s):  
Principal Component Analysis ◽  
Transmission Lines ◽  
Principal Component Regression ◽  
Principal Component ◽  
Forecasting Accuracy ◽  
Factors Affecting ◽  
Reliability Parameters ◽  
Impacting Factors ◽  
Simulation Results

It is the foundation for evaluating the reliability of transmission lines to obtain and analyze the original reliability parameters. However, these parameters depend on long- term statistic and calculation. In the case of lacking such parameters in a new project , this paper proposes a method of Principal Component Analysis to obtain the principal component of the impacting factors ,in which various factors affecting reliability parameters are taken into account. Through this method, we can use PCR to obtain the failure rate of the unknown transmission lines on the base of the known credible lines’ rates. The simulation results show that the proposed approach possesses higher forecasting accuracy and provides references for the power system dispatching departments and transmission lines maintenance departments.

Technology Research on the Fault Diagnosis of Wireless Sensor Network System

2013 ◽  
Vol 846-847 ◽  
pp. 442-445
Author(s):  
Chun Lin He
Keyword(s):  
Fault Diagnosis ◽  
Sensor Network ◽  
Simulation Experiment ◽  
Temporal Correlation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network System ◽  
Technology Research ◽  
Spatio Temporal ◽  
Network Fault

The fault diagnosis technology have emerged and developed rapidly with the development of wireless sensor networks and requirements of applications improve. This paper describes two commonly used sensor network fault modeling. What is more, in order to solve this problem that sensor nodes are vulnerable and therefore produce wrong data, the paper proposes a distributed fault detecting algorithm based on spatio-temporal correlation among data of adjacent nodes. The simulation experiment shows that the algorithm can efficiently detect errors in the network and very few errors are introduced.

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