Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both cost...Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining （DM） techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks （ANNs） to naive Bayesian classifiers. The aim was to predict the type of rockburst-that is, the rockburst level-based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.展开更多
OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and a...OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and analysis of data on 3970 sub-mentally healthy individuals selected from 13385 relevant question naires.METHODS: The strategic tree algorithm was used to identify the main mani festations of the state of sub-mental health. The backpropogation artificial neural network was used to analyze the main mani festations of sub-healthy mental states of three different degrees. A sub-mental health evaluation model was then established to achieve predictive evaluationresults.RESULTS: Using classifications from the Scale of Chinese Sub-healthy State, the main manifestations of sub-mental health selected using the strate gictree were F1101(Do you lack peace of mind?),F1102(Are you easily nervous when something comes up?), and F1002(Do you often sigh?). The relative intensity of manifestations of sub-mental health was highest for F1101, followed by F1102,and then F1002. Through study of the neural network, better differentiation could be made between moderate and severe and between mild and severe states of sub-mental health. The differentiation between mild and moderate sub-mental health states was less apparent. Additionally, the sub-mental health state evaluation model, which could be used to predict states of sub-mental health of different individuals, was established using F1101, F1102, F1002, and the mental self-assessment totals core.CONCLUSION: The main manifestations of the state of sub-mental health can be discovered using data mining methods to research and analyze the latent laws and knowledge hidden in research evidence on the state of sub-mental health. The state of sub-mental health of different individuals can be rapidly predicted using the model established here.This can provide a basis for assessment and intervention for sub-mental health. It can also replace the relatively outdated approaches to research on sub-health in the technical era of information and digitization by combining the study of states of sub-mental health with information techniques and by further quantifying the relevant information.展开更多
Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, qua...Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.展开更多
文摘Rockburst is an important phenomenon that has affected many deep underground mines around the world. An understanding of this phenomenon is relevant to the management of such events, which can lead to saving both costs and lives. Laboratory experiments are one way to obtain a deeper and better understanding of the mechanisms of rockburst. In a previous study by these authors, a database of rockburst laboratory tests was created; in addition, with the use of data mining （DM） techniques, models to predict rockburst maximum stress and rockburst risk indexes were developed. In this paper, we focus on the analysis of a database of in situ cases of rockburst in order to build influence diagrams, list the factors that interact in the occurrence of rockburst, and understand the relationships between these factors. The in situ rockburst database was further analyzed using different DM techniques ranging from artificial neural networks （ANNs） to naive Bayesian classifiers. The aim was to predict the type of rockburst-that is, the rockburst level-based on geologic and construction characteristics of the mine or tunnel. Conclusions are drawn at the end of the paper.
基金Supported by Chinese"Disease"Sub-health Medicine Research and Intervention of the Eleventh Five-Year Science and Technology Support Project of China(No.2006BAI13B01)Financial Support Case Studies of Traditional Chinese Medicine Treatment of Disease and Health Management Ideas of Shanghai Health Bureau(No.2010227)+2 种基金Scientific Innovation Research Funds of Shanghai Municipal Education Commission(No.14YZ061)Teacher Academic Community Fund of Shanghai University of Traditional Chinese Medicine(No.2013JXG03)Chinese Culture and Its Core Value System Modernization Transformation of the National Social Science Funds(No.12AZD094)
文摘OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and analysis of data on 3970 sub-mentally healthy individuals selected from 13385 relevant question naires.METHODS: The strategic tree algorithm was used to identify the main mani festations of the state of sub-mental health. The backpropogation artificial neural network was used to analyze the main mani festations of sub-healthy mental states of three different degrees. A sub-mental health evaluation model was then established to achieve predictive evaluationresults.RESULTS: Using classifications from the Scale of Chinese Sub-healthy State, the main manifestations of sub-mental health selected using the strate gictree were F1101(Do you lack peace of mind?),F1102(Are you easily nervous when something comes up?), and F1002(Do you often sigh?). The relative intensity of manifestations of sub-mental health was highest for F1101, followed by F1102,and then F1002. Through study of the neural network, better differentiation could be made between moderate and severe and between mild and severe states of sub-mental health. The differentiation between mild and moderate sub-mental health states was less apparent. Additionally, the sub-mental health state evaluation model, which could be used to predict states of sub-mental health of different individuals, was established using F1101, F1102, F1002, and the mental self-assessment totals core.CONCLUSION: The main manifestations of the state of sub-mental health can be discovered using data mining methods to research and analyze the latent laws and knowledge hidden in research evidence on the state of sub-mental health. The state of sub-mental health of different individuals can be rapidly predicted using the model established here.This can provide a basis for assessment and intervention for sub-mental health. It can also replace the relatively outdated approaches to research on sub-health in the technical era of information and digitization by combining the study of states of sub-mental health with information techniques and by further quantifying the relevant information.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Recent emergence of diverse services have led to explosive traffic growth in cellular data networks. Understanding the service dynamics in large cellular networks is important for network design, trouble shooting, quality of service(Qo E) support, and resource allocation. In this paper, we present our study to reveal the distributions and temporal patterns of different services in cellular data network from two different perspectives, namely service request times and service duration. Our study is based on big traffic data, which is parsed to readable records by our Hadoop-based packet parsing platform, captured over a week-long period from a tier-1 mobile operator's network in China. We propose a Zipf's ranked model to characterize the distributions of traffic volume, packet, request times and duration of cellular services. Two-stage method(Self-Organizing Map combined with kmeans) is first used to cluster time series of service into four request patterns and three duration patterns. These seven patterns are combined together to better understand the fine-grained temporal patterns of service in cellular network. Results of our distribution models and temporal patterns present cellular network operators with a better understanding of the request and duration characteristics of service, which of great importance in network design, service generation and resource allocation.
