Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development...Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development".The selection of the target location of resettlement sites for poverty-stricken villages is of critical importance to the success of resettlement projects,yet the selection process is challenged by the need for analyzing a variety of contributing factors,and the need for many rounds of tedious data processing.So in this paper we present an in-depth analysis of the major factors and data processing model concerning mountainous povertystricken villages,which also takes a major part of China's poor villages.Our analysis shows the following factors bear the most importance in resettlement selection:1) topography:candidate areas should have slope less than 25 degrees and altitude less than 2400 meters.2) accessibility:close to market conventions places and transportation facilities.3) farming resources:with abundant land and water resources.4) non-intrusiveness:interests of receiving villages should be considered and negative impact minimized.A simple measure could be having the candidate area 1000 m away from the receiving residents.5) Minimal ecological and political footprint:candidate areas shall not conflict with nature conservation areas or nationally planned key land use projects.6) Social and cultural compatibility:residents will better off if relocated in the same county,considering language,religion,ethnic culture and other factors.Taking Makuadi,Lushui County of Nujiang Prefecture as a case study,we demonstrate how GIS analysis and modeling tools can be used in the selection process of resettlement projects in mountainous areas.展开更多
The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation ...The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.展开更多
In this paper, we conduct research on the development of Daur ethnic education under the influence of big data. Life in the river valley of Daur ethnic which is a nationality has a long history, splendid culture and t...In this paper, we conduct research on the development of Daur ethnic education under the influence of big data. Life in the river valley of Daur ethnic which is a nationality has a long history, splendid culture and the national spirit of national self-confidence, the foundation of national culture has a strong self-confidence. Combining the concept of big data and data analysis technique to the Daur ethnic education will largely enhance the result of the current education pattern. The survey of the research indicated that we should pay attention to the precious data captured.展开更多
Failure mode and effects analysis （FMEA） offers a quick and easy way for identifying ranking-order for all failure modes in a system or a product. In FMEA the ranking methods is so called risk priority number （RPN...Failure mode and effects analysis （FMEA） offers a quick and easy way for identifying ranking-order for all failure modes in a system or a product. In FMEA the ranking methods is so called risk priority number （RPN）, which is a mathematical product of severity （S）, occurrence （0）, and detection （D）. One of major disadvantages of this ranking-order is that the failure mode with different combination of SODs may generate same RPN resulting in difficult decision-making. Another shortfall of FMEA is lacking of discerning contribution factors, which lead to insufficient information about scaling of improving effort. Through data envelopment analysis （DEA） technique and its extension, the proposed approach evolves the current rankings for failure modes by exclusively investigating SOD in lieu of RPN and to furnish with improving sca.les for SOD. The purpose of present study is to propose a state-of-the-art new approach to enhance assessment capabilities of failure mode and effects analysis （FMEA）. The paper proposes a state-of-the-art new approach, robust, structured and useful in practice, for failure analysis.展开更多
This document reviews the development of adult education towards Hong Kong, Macao and the Overseas over the past 30 years in Jinan University, and puts forward ideas for the development under the background of＂The Be...This document reviews the development of adult education towards Hong Kong, Macao and the Overseas over the past 30 years in Jinan University, and puts forward ideas for the development under the background of＂The Belt and Road＂. Such as： Increase policy support and enhance the level of management services; Based on big data analytics, optimize the mode of talent training; Develop short-term training projects and promote online education; Establish brand awareness and improve teaching quality monitoring mechanisms.展开更多
The aim of this paper is to present works performed in HTC （Heat-Tech Center）, Research ＆ Development Centre of Veolia Group located in Warsaw regarding assessment of probability of failure in DHN （district heatin...The aim of this paper is to present works performed in HTC （Heat-Tech Center）, Research ＆ Development Centre of Veolia Group located in Warsaw regarding assessment of probability of failure in DHN （district heating network）. This work is a part of a project dedicated to develop a software which objective is to increase reliability of DHN. The research methods consisted of three approaches. First, using database of failures which happened in Warsaw DHN and repairing protocols from past 10 years, a statistics approach was applied to perform first analysis. The result was that pipelines with nominal diameter DN （nominal diameter） ≤ 150 had higher failure rate per km, than pipelines with DN 〉 150. The next step of research was to study influence of internal （corrosion caused by heat carrier, quality of materials） and external （stray currents） factor in order to assess its individual influence on failure rate of pipe and explain reasons of differences in failure rate. To end a FMEA （failure mode and＇effects analysis） will aim to identify the main failures modes appearing on DHN, to estimate the main causes of these failures and to propose the best solutions regarding the causes, the costs and the means available.展开更多
We present the basic idea of abstract principal component analysis(APCA)as a general approach that extends various popular data analysis techniques such as PCA and GPCA.We describe the mathematical theory behind APCA ...We present the basic idea of abstract principal component analysis(APCA)as a general approach that extends various popular data analysis techniques such as PCA and GPCA.We describe the mathematical theory behind APCA and focus on a particular application to mode extractions from a data set of mixed temporal and spatial signals.For illustration,algorithmic implementation details and numerical examples are presented for the extraction of a number of basic types of wave modes including,in particular,dynamic modes involving spatial shifts.展开更多
Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on f...Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.展开更多
The FIFA World Cup^(TM) is the most profitable worldwide event.The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition.This work is focused...The FIFA World Cup^(TM) is the most profitable worldwide event.The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition.This work is focused on the extraction of behavioural patterns for both,players and teams strategies,through the automated analysis of this dataset.The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting.However,the main contribution is related to the study on several automatic knowledge extraction techniques,such as clustering methods,and how these techniques can be used to obtain useful behavioural models from a global statistics dataset.The information provided by the clustering algorithms shows similar properties which have been combined to define the models,making the human interpretation of these statistics easier.Finally,the most successful teams strategies have been analysed and compared.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.40761019)National Natural Science Foundation of Yunnan (Grant No.2007D157M)
文摘Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development".The selection of the target location of resettlement sites for poverty-stricken villages is of critical importance to the success of resettlement projects,yet the selection process is challenged by the need for analyzing a variety of contributing factors,and the need for many rounds of tedious data processing.So in this paper we present an in-depth analysis of the major factors and data processing model concerning mountainous povertystricken villages,which also takes a major part of China's poor villages.Our analysis shows the following factors bear the most importance in resettlement selection:1) topography:candidate areas should have slope less than 25 degrees and altitude less than 2400 meters.2) accessibility:close to market conventions places and transportation facilities.3) farming resources:with abundant land and water resources.4) non-intrusiveness:interests of receiving villages should be considered and negative impact minimized.A simple measure could be having the candidate area 1000 m away from the receiving residents.5) Minimal ecological and political footprint:candidate areas shall not conflict with nature conservation areas or nationally planned key land use projects.6) Social and cultural compatibility:residents will better off if relocated in the same county,considering language,religion,ethnic culture and other factors.Taking Makuadi,Lushui County of Nujiang Prefecture as a case study,we demonstrate how GIS analysis and modeling tools can be used in the selection process of resettlement projects in mountainous areas.
基金supported by the National Natural Science Foundation of China(Nos.42130812,42174151,and 41874155).
文摘The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.
文摘In this paper, we conduct research on the development of Daur ethnic education under the influence of big data. Life in the river valley of Daur ethnic which is a nationality has a long history, splendid culture and the national spirit of national self-confidence, the foundation of national culture has a strong self-confidence. Combining the concept of big data and data analysis technique to the Daur ethnic education will largely enhance the result of the current education pattern. The survey of the research indicated that we should pay attention to the precious data captured.
文摘Failure mode and effects analysis （FMEA） offers a quick and easy way for identifying ranking-order for all failure modes in a system or a product. In FMEA the ranking methods is so called risk priority number （RPN）, which is a mathematical product of severity （S）, occurrence （0）, and detection （D）. One of major disadvantages of this ranking-order is that the failure mode with different combination of SODs may generate same RPN resulting in difficult decision-making. Another shortfall of FMEA is lacking of discerning contribution factors, which lead to insufficient information about scaling of improving effort. Through data envelopment analysis （DEA） technique and its extension, the proposed approach evolves the current rankings for failure modes by exclusively investigating SOD in lieu of RPN and to furnish with improving sca.les for SOD. The purpose of present study is to propose a state-of-the-art new approach to enhance assessment capabilities of failure mode and effects analysis （FMEA）. The paper proposes a state-of-the-art new approach, robust, structured and useful in practice, for failure analysis.
文摘This document reviews the development of adult education towards Hong Kong, Macao and the Overseas over the past 30 years in Jinan University, and puts forward ideas for the development under the background of＂The Belt and Road＂. Such as： Increase policy support and enhance the level of management services; Based on big data analytics, optimize the mode of talent training; Develop short-term training projects and promote online education; Establish brand awareness and improve teaching quality monitoring mechanisms.
文摘The aim of this paper is to present works performed in HTC （Heat-Tech Center）, Research ＆ Development Centre of Veolia Group located in Warsaw regarding assessment of probability of failure in DHN （district heating network）. This work is a part of a project dedicated to develop a software which objective is to increase reliability of DHN. The research methods consisted of three approaches. First, using database of failures which happened in Warsaw DHN and repairing protocols from past 10 years, a statistics approach was applied to perform first analysis. The result was that pipelines with nominal diameter DN （nominal diameter） ≤ 150 had higher failure rate per km, than pipelines with DN 〉 150. The next step of research was to study influence of internal （corrosion caused by heat carrier, quality of materials） and external （stray currents） factor in order to assess its individual influence on failure rate of pipe and explain reasons of differences in failure rate. To end a FMEA （failure mode and＇effects analysis） will aim to identify the main failures modes appearing on DHN, to estimate the main causes of these failures and to propose the best solutions regarding the causes, the costs and the means available.
基金supported by National Science Foundation of USA(Grant No.DMS101607)
文摘We present the basic idea of abstract principal component analysis(APCA)as a general approach that extends various popular data analysis techniques such as PCA and GPCA.We describe the mathematical theory behind APCA and focus on a particular application to mode extractions from a data set of mixed temporal and spatial signals.For illustration,algorithmic implementation details and numerical examples are presented for the extraction of a number of basic types of wave modes including,in particular,dynamic modes involving spatial shifts.
基金supported by the Major State Basic Research Development of China (Grant No. 2011CB706803)National Natural Science Foundation of China (Grant No. 50875098)Important National Science & Technology Specific Projects of China (Grant No. 2009ZX04014-024)
文摘Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
基金partly supported by:Spanish Ministry of Science and Education under project TIN201019872the grant BES-2011-049875 from the same MinistryJobssy.com company under project FUAM076913
文摘The FIFA World Cup^(TM) is the most profitable worldwide event.The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition.This work is focused on the extraction of behavioural patterns for both,players and teams strategies,through the automated analysis of this dataset.The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting.However,the main contribution is related to the study on several automatic knowledge extraction techniques,such as clustering methods,and how these techniques can be used to obtain useful behavioural models from a global statistics dataset.The information provided by the clustering algorithms shows similar properties which have been combined to define the models,making the human interpretation of these statistics easier.Finally,the most successful teams strategies have been analysed and compared.
