随着社交网络的快速发展,利用人类行为实现信息隐藏成为当前隐写研究热点.本文提出一种基于0-1背包算法的社交网络下一对多行为隐写术.该算法将信息的传递方式由一对一变为一对多,通过引入0-1背包算法,提高了传输效率和传输方式的灵活性...随着社交网络的快速发展,利用人类行为实现信息隐藏成为当前隐写研究热点.本文提出一种基于0-1背包算法的社交网络下一对多行为隐写术.该算法将信息的传递方式由一对一变为一对多,通过引入0-1背包算法,提高了传输效率和传输方式的灵活性;加入CMI(Coded Mark Inversion)编码预处理,解决了传输大量连续相同比特秘密信息时有较高误码率的问题;发送者和每个接收者的有效共同好友数量得到降低,从而减少数据冗余.实验表明,该方案提高了社交网络下行为隐写的实用价值,有较高的安全性.展开更多
In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF n...In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.展开更多
文摘随着社交网络的快速发展,利用人类行为实现信息隐藏成为当前隐写研究热点.本文提出一种基于0-1背包算法的社交网络下一对多行为隐写术.该算法将信息的传递方式由一对一变为一对多,通过引入0-1背包算法,提高了传输效率和传输方式的灵活性;加入CMI(Coded Mark Inversion)编码预处理,解决了传输大量连续相同比特秘密信息时有较高误码率的问题;发送者和每个接收者的有效共同好友数量得到降低,从而减少数据冗余.实验表明,该方案提高了社交网络下行为隐写的实用价值,有较高的安全性.
文摘In this paper, we construct two models for the searching task for a lost plane. Model 1 determines the searching area. We predict the trajectory of floats generated after the disintegration of the plane by using RBF neural network model, and then determine the searching area according to the trajectory. With the pass of time, the searching area will also be constantly moving along the trajectory. Model 2 develops a maritime search plan to achieve the purpose of completing the search in the shortest time. We optimize the searching time and transform the problem into the 0-1 knapsack problem. Solving this problem by improved genetic algorithm, we can get the shortest searching time and the best choice for the search power.
