PS-G-GERT Effectiveness Evaluation Model of Link-Based GEO Satellite Communication Constellation under Poor Information Background

The effectiveness evaluation of GEO satellite communication constellation not only characterizes the communication capability of the constellation but also provides a basis for optimizing the constellation structure.Whether due to information poverty or the complexity of the system,the impact of uncertain information on the effectiveness evaluation needs to be considered to ensure the accuracy of the evaluation results.To address this issue,this paper develops a model for evaluating the GEO satellite communication constellation’s effectiveness in the context of poor information.Firstly,it analyses the GEO satellite communication constellation plus system based on communication links with an in-depth analysis of the constellation structure.Secondly,an equivalent transfer function algorithm based on the characteristic function and transfer probability is proposed with the help of graphical evaluation and review technique.Then,by analyzing the communication link importance connotation,the algorithm formula of communication link effectiveness and its importance is derived,and the constellation effectiveness and variance are found.Finally,the model constructed in this paper is used to evaluate the effectiveness of a GEO satellite communication constellation,further verifying the accuracy and validity of the model.Through comparative analysis,it is shown that the model can not only solving the effectiveness of the constellation but also analyzing the variation of its effectiveness.It lays a theoretical foundation for the analysis and optimization of the GEO satellite communication constellation effectiveness.

社会信息化发展中的信息分化?

信息分化是一种特殊的社会分化类型,它是指在当代社会信息化发展的过程中,由于信息技术的迅速发展与有效应用而导出的一种人类社会的不同信息活动主体之间的信息差距及其不断扩大的社会分化现象。信息分化虽然有其正面的功能,但其社会危害不小,主要是:造成贫富分化的加剧,导致社会结构的失衡,引发社会关系的冲突。面对社会信息化发展过程中的信息分化,主要的对策思路是:发展是真正的硬道理,应积极推进信息化进程;消除贫困是重要的策略,应广泛开展信息扶贫;缩小知能差别是根本措施,应大力发展教育事业。

Grey Relation between Nonlinear Characteristic and Dynamic Uncertainty of Rolling Bearing Friction Torque

The rolling bearing friction torque which is characterized by its uncertainty and nonlinearity affects heavily the dynamic performance of a system such as missiles, spacecrafts and radars, etc. It is difficult to use the classical statistical theory to evaluate the dynamic evaluation of the rolling bearing friction torque for the lack of prior information about both probability distribution and trends. For this reason, based on the information poor system theory and combined with the correlation dimension in chaos theory, the concepts about the mean of the dynamic fluctuant range （MDFR） and the grey relation are proposed to resolve the problem about evaluating the nonlinear characteristic and the dynamic uncertainty of the rolling bearing friction torque. Friction torque experiments are done for three types of the rolling bearings marked with HKTA, HKTB and HKTC separately; meantime, the correlation dimension and MDFR are calculated to describe the nonlinear characteristic and the dynamic uncertainty of the friction torque, respectively. And the experiments reveal that there is a certain grey relation between the nonlinear characteristic and the dynamic uncertainty of the rolling bearing friction torque, viz. MDFR will become the nonlinear increasing trend with the correlation dimension increasing. Under the condition of fewer characteristic data and the lack of prior information about both probability distribution and trends, the unitive evaluation for the nonlinear characteristic and the dynamic uncertainty of the rolling bearing friction torque is realized with the grey confidence level of 87.7%-96.3%.

Development of an ANN-based building energy model for information-poor buildings using transfer learning

Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility.In recent years,the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems(BASs),which automatically collect and store real-time building operational data.For new buildings and most existing buildings without installing advanced BASs,there is a lack of sufficient data to train data-driven predictive models.Transfer learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other buildings.Few studies focused on the influences of source building datasets,pre-training data volume,and training data volume on the performance of the transfer learning method.The present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research gap.Around 400 non-residential buildings’data from the open-source Building Genome Project are used to test the proposed method.Extensive analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training data.The most influential building features which influence the effectiveness of transfer learning are found to be building usage and industry.The research outcomes can provide guidance for implementation of transfer learning,especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.

宁夏中南部贫困村农民科技信息需求分析——基于45个贫困村325位农民的调查

通过对宁夏中南部地区45个贫困村325位农民进行的科技信息需求问卷调查显示,目前宁夏中南部贫困村农民对科技信息类型、获取渠道、服务方式等方面的选择上均带有较为明显的倾向,为此,需要从信息源、传播途径、服务方式等方面加强科技信息服务水平,以更好地满足宁夏中南部地区贫困村农民的需求,切实发挥科技信息助推扶贫开发的积极作用.