梳理国外知识隐藏相关文献,揭示知识隐藏的影响因素。基于Web of Science数据库,获取紧密相关的159篇文献,采用内容分析法,从能力、机会、动机、行为等4个方面归纳知识隐藏的研究内容。研究结果发现:知识隐藏行为受到能力因素、机会因...梳理国外知识隐藏相关文献,揭示知识隐藏的影响因素。基于Web of Science数据库,获取紧密相关的159篇文献,采用内容分析法,从能力、机会、动机、行为等4个方面归纳知识隐藏的研究内容。研究结果发现:知识隐藏行为受到能力因素、机会因素、动机因素等的影响,符合COM-B模型的规律;促进知识隐藏的关键环境因素主要包括环境压力、负面关系等;抑制知识隐藏的环境因素主要包括支持性环境、心理保障、积极动机等。研究结果证实COM-B模型可用于分析知识隐藏的行为轨迹,并提出实践启示及未来研究启示。展开更多
加密域可逆数据隐藏(reversible data hiding in encrypted domain,RDHED)技术可在保护载体隐私的同时嵌入秘密信息,但当前针对3D网格模型的RDHED方法普遍面临嵌入容量低的难题.针对这一问题,提出了一种基于八叉树分块和顶点划分策略的...加密域可逆数据隐藏(reversible data hiding in encrypted domain,RDHED)技术可在保护载体隐私的同时嵌入秘密信息,但当前针对3D网格模型的RDHED方法普遍面临嵌入容量低的难题.针对这一问题,提出了一种基于八叉树分块和顶点划分策略的加密3D网格模型可逆数据隐藏方法.首先,采用八叉树结构将模型自适应地划分为不重叠子块,保留块内空间相关性;其次,设计基于顶点熵的划分策略,精确选取参考顶点以提升预测精度;最后,采用自适应MSB(most significant bit)预测方法,最大化每个顶点的可嵌入空间,从而显著提升嵌入容量.实验结果表明,该方法在提高3D网格模型嵌入容量的同时,确保了数据的可逆性与可分离性,为3D模型的可逆数据隐藏提供了一种有效的解决方案.展开更多
In order to track ground moving target, a variable structure interacting multiple model （VS-IMM） using mean shift unscented particle filter （MS-UPF） is proposed in this paper. In model-conditioned filtering, sampl...In order to track ground moving target, a variable structure interacting multiple model （VS-IMM） using mean shift unscented particle filter （MS-UPF） is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication （GMTI） radar is employed in three common simulation scenarios of ground target： entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error （RMSE） and average normalized estimation error squared （ANEES）, are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.展开更多
文摘梳理国外知识隐藏相关文献,揭示知识隐藏的影响因素。基于Web of Science数据库,获取紧密相关的159篇文献,采用内容分析法,从能力、机会、动机、行为等4个方面归纳知识隐藏的研究内容。研究结果发现:知识隐藏行为受到能力因素、机会因素、动机因素等的影响,符合COM-B模型的规律;促进知识隐藏的关键环境因素主要包括环境压力、负面关系等;抑制知识隐藏的环境因素主要包括支持性环境、心理保障、积极动机等。研究结果证实COM-B模型可用于分析知识隐藏的行为轨迹,并提出实践启示及未来研究启示。
文摘加密域可逆数据隐藏(reversible data hiding in encrypted domain,RDHED)技术可在保护载体隐私的同时嵌入秘密信息,但当前针对3D网格模型的RDHED方法普遍面临嵌入容量低的难题.针对这一问题,提出了一种基于八叉树分块和顶点划分策略的加密3D网格模型可逆数据隐藏方法.首先,采用八叉树结构将模型自适应地划分为不重叠子块,保留块内空间相关性;其次,设计基于顶点熵的划分策略,精确选取参考顶点以提升预测精度;最后,采用自适应MSB(most significant bit)预测方法,最大化每个顶点的可嵌入空间,从而显著提升嵌入容量.实验结果表明,该方法在提高3D网格模型嵌入容量的同时,确保了数据的可逆性与可分离性,为3D模型的可逆数据隐藏提供了一种有效的解决方案.
文摘In order to track ground moving target, a variable structure interacting multiple model （VS-IMM） using mean shift unscented particle filter （MS-UPF） is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication （GMTI） radar is employed in three common simulation scenarios of ground target： entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error （RMSE） and average normalized estimation error squared （ANEES）, are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.
