scholarly journals Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China

2021 ◽  
Vol 40 (3) ◽  
Author(s):  
Zhiyu Wang ◽  
Jingyu Wu ◽  
Guang Yu ◽  
Zhiping Song
Keyword(s):  
Data Analysis ◽  
Qing Dynasty ◽  
Text Analysis ◽  
Large Scale ◽  
Liaoning Province ◽  
Support Vector ◽  
Historical Documents ◽  
The Qing Dynasty ◽  
Historical Materials ◽  
The Government

In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective. This technology solves the problem of large-scale historical data analysis that is difficult for humans to read and intuitively understand. In this study, we use the historical documents of the Qing Dynasty Hetu Dangse,preserved in the Archives of Liaoning Province, as data analysis samples. China’s Hetu Dangse is the largest Qing Dynasty thematic archive with Manchu and Chinese characters in the world. Through word frequency analysis, correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of historical archives, improve the efficient use of historical materials as well as build connections between historical knowledge. Through this, archivists can be guided practically in historical materials’ management and compilation.

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

The Qing Imperial Academy of Medicine: Its Institutions and the Physicians Shaped by Them

2015 ◽  
Vol 41 (1) ◽  
pp. 63-92
Author(s):  
Che-chia Chang
Keyword(s):  
Medical Education ◽  
Qing Dynasty ◽  
Medical Service ◽  
The State ◽  
The Qing Dynasty ◽  
Medical Academy ◽  
Imperial Academy ◽  
The Common ◽  
The Government ◽  
First Dynasty

This paper is intended to explain the changes in the activities of the Imperial Academy of Medicine during the Qing dynasty (1644-1911). By tracing its precedents and comparing their functions, I will explain its role during the Qing dynasty. Furthermore, the seemingly hidebound institutional codes in fact reveal interesting information about the dynamics of the Academy. Through examining the impacts of the regulations on personnel and their careers, we are able to explain the very different requirements of the Qing rulers for their medical service. Up until the Ming period (1368-1644) there was an institutional boundary between medical services for the palace and those for the state, even though they shared the same personnel. The Qing was the first dynasty in which even this unclear line disappeared. In this sense, the Qing Academy did not simply copy the tradition of its predecessors. Instead, the services for the emperor’s individual needs became more and more central to its mission. Thus, the common people’s rather critical perceptions of the bureau were largely true. In spite of its increased emphasis on serving the imperial household, the Qing Academy retained its connections with the government. As an alien regime, the Manchu court’s concern for the security of its rulers was much higher than during the previous dynasty. To meet the needs of the new regime, the device of the Qing Academy emphasized fostering elites rather than selecting them. Now the Academy not only provided medical education to the junior members as in earlier periods, but also shaped them in behavior. This affected both the organization of the Imperial Medical Academy, and the strategies of the physicians employed in it.

Fast and scalable support vector clustering for large-scale data analysis

2014 ◽  
Vol 43 (2) ◽  
pp. 281-310 ◽  
Author(s):  
Yuan Ping ◽  
Yun Feng Chang ◽  
Yajian Zhou ◽  
Ying Jie Tian ◽  
Yi Xian Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Support Vector ◽  
Support Vector Clustering ◽  
Large Scale Data ◽  
Vector Clustering ◽  
Scale Data

scholarly journals Kebijakan Pemerintah dalam Kegiatan Shalat Berjamaah di Masa Pandemi Covid-19

2021 ◽  
Vol 1 (1) ◽  
pp. 48-55
Author(s):  
Zihan Fahiza ◽  
Siti Nur Zalikha
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Qualitative Data ◽  
Disease Outbreak ◽  
Policy Formulation ◽  
Social Activities ◽  
Qualitative Data Analysis ◽  
Analysis Techniques ◽  
Pros And Cons ◽  
The Government

The Covid-19 pandemic is the largest disease outbreak that occurred from 2019 to 2020, therefore the government itself issued policies related to policies. one of which is related to congregational prayer activities during the pandemic. The emergence of policies related to congregational activities inevitably raises pros and cons among the community. When a region carries out large-scale social activities (PSBB), it disturbs every activity related to religion. The method used here is juridical normative and uses qualitative data analysis techniques. each policy formulation that is responsible for membership registered in a meeting arranged hierarchically.

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

FROM THE HISTORY OF THE FOUNDATION OF THE IMPERIAL ACADEMY OF PAINTING DURING THE QING DYNASTY

2021 ◽  
pp. 133-153
Author(s):  
Natallia G. Surayeva ◽  
Keyword(s):  
Qing Dynasty ◽  
Historical Documents ◽  
The Qing Dynasty ◽  
History Of ◽  
Imperial Academy ◽  
Three Stages ◽  
Infor Mation

At present, there is a lot of information about the court artists and the court art of the Qing dynasty (1636–1912), but there is fragmentary infor- mation only about the art academy at the court, in which artists and craftsmen worked. The article attempts to systematize information about the functioning of the court art structure during the 276 years of the dynasty’s existence, since there is no mentioning in the historical documents about a single permanent art organization as such; with the change of ruler, the place of activity of the court artists also changed, information about those structures is very scant. The article identifies three stages in the functioning of the court art structure, makes an attempt to describe the principles of admission to the academy, the differences in ranks and remuneration, it also describes the character of the remuneration and punishment of court artists and besides introduces a new terminology.

scholarly journals HADOOP-BASED DISTRIBUTED SYSTEM FOR ONLINE PREDICTION OF AIR POLLUTION BASED ON SUPPORT VECTOR MACHINE

2015 ◽  
Vol XL-1-W5 ◽  
pp. 215-219 ◽  
Author(s):  
Z. Ghaemi ◽  
M. Farnaghi ◽  
A. Alimohammadi
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Data Analysis ◽  
Air Quality ◽  
Large Scale ◽  
Processing Time ◽  
Air Pollutant ◽  
Pollutant Concentration ◽  
Support Vector ◽  
Prediction Problems

The critical impact of air pollution on human health and environment in one hand and the complexity of pollutant concentration behavior in the other hand lead the scientists to look for advance techniques for monitoring and predicting the urban air quality. Additionally, recent developments in data measurement techniques have led to collection of various types of data about air quality. Such data is extremely voluminous and to be useful it must be processed at high velocity. Due to the complexity of big data analysis especially for dynamic applications, online forecasting of pollutant concentration trends within a reasonable processing time is still an open problem. The purpose of this paper is to present an online forecasting approach based on Support Vector Machine (SVM) to predict the air quality one day in advance. In order to overcome the computational requirements for large-scale data analysis, distributed computing based on the Hadoop platform has been employed to leverage the processing power of multiple processing units. The MapReduce programming model is adopted for massive parallel processing in this study. Based on the online algorithm and Hadoop framework, an online forecasting system is designed to predict the air pollution of Tehran for the next 24 hours. The results have been assessed on the basis of Processing Time and Efficiency. Quite accurate predictions of air pollutant indicator levels within an acceptable processing time prove that the presented approach is very suitable to tackle large scale air pollution prediction problems.

The remains of the ancient bridges on the Wei River in Xi’an City

2015 ◽  
Vol 15 (1) ◽  
Keyword(s):  
Qing Dynasty ◽  
Tang Dynasty ◽  
Large Scale ◽  
Warring States Period ◽  
The Qing Dynasty ◽  
Warring States ◽  
Western Han Dynasty ◽  
The North ◽  
Eastern Han ◽  
Wei River

AbstractThe remains of seven bridges in three groups across the Wei River to the north and northeast of the Chang’an City of the Western Han Dynasty discovered successively since 2012 filled in the blank of the archaeology of the bridges across the Wei River. Among the seven bridges, the Chucheng Gate Bridge No. 1 had huge size, which was the largest wooden structure bridge of the same period known to date. The 14C data showed that the Chucheng Gate Bridge No. 4 was built in the late Warring-States Period; the Chucheng Gate Bridge No. 1 was built in the Western Han and at least rebuilt for two times in the Eastern Han and the Three-Kingdoms Period and Western Jin Dynasty; the Luocheng Gate Bridge was built around the late Western Han through the early Eastern Han and the Chucheng Gate Bridge No. 3 was built in the Tang Dynasty. The coins of the Qing Dynasty unearthed in the excavated area of the Chucheng Gate bridges hinted that at latest in the Kangxi Era of the Qing Dynasty, the Wei River was still in the course of the Han through the Tang Dynasties, and the large-scale northward moving of the course of the Wei River would be after that period.

The Government of the Natural in the Qing Dynasty

2019 ◽  
Vol 60 (4) ◽  
pp. 483-486
Author(s):  
Q. S. Tong ◽  
Liu Chen
Keyword(s):  
Qing Dynasty ◽  
The Qing Dynasty ◽  
The Government

Data Mining and its Application in Animal Husbandry Management System

2014 ◽  
Vol 926-930 ◽  
pp. 2525-2528
Author(s):  
Hui Wang ◽  
Xiao Ge Li ◽  
Tai Yu Liu ◽  
Qian Wu
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Large Scale ◽  
New Technologies ◽  
Rapid Development ◽  
Animal Husbandry ◽  
Management Tools ◽  
Management Level ◽  
The Government

Data will be an important resource, including Animal Husbandry, including many of the industry, and even determine the success or failure of an industry. Animal Husbandry management is the behavior of large amount of data, improve the management level of all walks of life requires a lot of long-term data analysis and preparation. The rapid development of China's Animal Husbandry ,but the level of information to stay in the primary stage, the further development of Animal Husbandry, the information necessary to overcome this problem. In this paper, data mining, data warehouse and other new technologies in Animal Husbandry management, large-scale farming, and the government has brought the latest regional regulatory and other management tools and data analysis for the industry to promote mining.

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