Traditionally governance structures are classified into ＂hierarchy or market＂ or ＂equity or non-equity.＂ However,such classifications may not be effective in characterizing all governance structures of research an...Traditionally governance structures are classified into ＂hierarchy or market＂ or ＂equity or non-equity.＂ However,such classifications may not be effective in characterizing all governance structures of research and development（R D） alliances.Therefore,the first objective of this study is to investigate why there exist different organizational governance structures in managing R D alliances;the second objective of this study is to give strategic advice in choosing appropriate forms with respect to various characteristics of R D alliances.Through the theoretical lens that integrate both transaction cost economics（TCE） and the resource-based view（RBV）,a model that focuses on six major factors is developed for determining governance structure choices,namely,technological uncertainty,cultural difference,asset specificity,technology complementarity,appropriability of the individual firm＇s know-how,and trust.An R D alliance with higher technological uncertainty,larger cultural differences,and greater concerns for protecting an individual＇s know-how is more likely to adopt non-integrated alliances as the governing structure.An R D alliance with a higher degree of asset-specificity,greater technology complementarity and greater trust among partnering organizations is more likely to adopt integrated alliances as the governing structure;an R D alliance in the face of lower technological uncertainty will tend to adopt integrated alliances.The more aligned the choice of the governance structure with its determinants,the better the R D alliance will perform,and vice versa.展开更多
由于无线局域网(wireless local area network, WLAN)的广泛部署和智能终端对WLAN协议的普遍支持,本文提出一种基于自适应深度射线树的WLAN室内目标入侵检测算法,其利用现有WLAN基础设施即可实现未携带任何信号收发设备的室内目标的入...由于无线局域网(wireless local area network, WLAN)的广泛部署和智能终端对WLAN协议的普遍支持,本文提出一种基于自适应深度射线树的WLAN室内目标入侵检测算法,其利用现有WLAN基础设施即可实现未携带任何信号收发设备的室内目标的入侵检测.为此,首先建立基于自适应深度射线树的准三维射线追踪模型,对室内静默和入侵状态下的接收信号强度(received signalstrength, RSS)传播特性进行建模;其次,联合RSS均值、方差、最大值、最小值、极差值和中位值6种信号特征构建概率神经网络(probabilistic neural network, PNN)的训练数据库;最后,利用训练得到的PNN对新采集RSS数据进行多分类判决,进而实现对室内目标的入侵检测与区域定位.实验结果表明,本文所提算法具有较高的入侵检测概率和较低的数据库构建开销.展开更多
基金The Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Traditionally governance structures are classified into ＂hierarchy or market＂ or ＂equity or non-equity.＂ However,such classifications may not be effective in characterizing all governance structures of research and development（R D） alliances.Therefore,the first objective of this study is to investigate why there exist different organizational governance structures in managing R D alliances;the second objective of this study is to give strategic advice in choosing appropriate forms with respect to various characteristics of R D alliances.Through the theoretical lens that integrate both transaction cost economics（TCE） and the resource-based view（RBV）,a model that focuses on six major factors is developed for determining governance structure choices,namely,technological uncertainty,cultural difference,asset specificity,technology complementarity,appropriability of the individual firm＇s know-how,and trust.An R D alliance with higher technological uncertainty,larger cultural differences,and greater concerns for protecting an individual＇s know-how is more likely to adopt non-integrated alliances as the governing structure.An R D alliance with a higher degree of asset-specificity,greater technology complementarity and greater trust among partnering organizations is more likely to adopt integrated alliances as the governing structure;an R D alliance in the face of lower technological uncertainty will tend to adopt integrated alliances.The more aligned the choice of the governance structure with its determinants,the better the R D alliance will perform,and vice versa.
文摘由于无线局域网(wireless local area network, WLAN)的广泛部署和智能终端对WLAN协议的普遍支持,本文提出一种基于自适应深度射线树的WLAN室内目标入侵检测算法,其利用现有WLAN基础设施即可实现未携带任何信号收发设备的室内目标的入侵检测.为此,首先建立基于自适应深度射线树的准三维射线追踪模型,对室内静默和入侵状态下的接收信号强度(received signalstrength, RSS)传播特性进行建模;其次,联合RSS均值、方差、最大值、最小值、极差值和中位值6种信号特征构建概率神经网络(probabilistic neural network, PNN)的训练数据库;最后,利用训练得到的PNN对新采集RSS数据进行多分类判决,进而实现对室内目标的入侵检测与区域定位.实验结果表明,本文所提算法具有较高的入侵检测概率和较低的数据库构建开销.
