An identity-based cryptographic model for discrete logarithm and integer factoring based cryptosystem

In 1984, Shamir proposed the concept of the identity-based (ID-based) cryptosystem. Instead of generating and publishing a public key for each user, the ID-based scheme permits each user to choose his name or network address as his public key. This is advantageous to public-key cryptosystems because the public-key verification is so easy and direct. In such a way, a large publickey file is not required. Since new cryptographic schemes always face security challenges and many discrete logarithm and integer factorization problem-based cryptographic systems have been deployed, therefore, the purpose of this paper is to design a transformation process that can transfer all the discrete logarithm and integer factorization based cryptosystems into the ID-based systems rather than re-invent a new system. In addition, no modification of the original discrete logarithm and integer factorization based cryptosystems is necessary.     

公钥密码体制的现状与发展

文中对公钥密码体制的现状与发展进行了介绍，其中着重讨论了几个比较重要的公钥密码体制M-H背包算法、RSA、ECC、量子密码、NTRU密码体制和基于辫群上的密码体制。     

公钥密码体制的现状与发展

文中对公钥密码体制的现状与发展进行了介绍,其中着重讨论了几个比较重要的公钥密码体制M-H背包算法、RSA、ECC、量子密码、NTRU密码体制和基于辫群上的密码体制。     

对一个基于离散对数代理盲签名方案的分析与改进

通过对何俊杰等人提出的一个基于离散对数代理盲签名方案进行分析, 从中发现该方案会引起原始签名人的公钥替换攻击。为了解决该方案的安全缺陷, 提出了一种新的改进方案。分析表明, 改进后的新方案可以抵抗原始签名人的公钥替换攻击, 并且在基于离散对数问题下保证了代理盲签名的所有安全性要求, 而且提高了效率。     

基于免疫的粗糙集属性约简算法及应用

地下施工中影响施工的风险影响因素十分繁杂,为了从中剔除不必要或不重要的因素,提出一种新的基于免疫的粗糙集属性约简算法--IRSAR.该算法在求出决策表的相对核core的基础上初始化抗体群,并定义了新的亲和度计算函数和克隆增殖函数,有效地提高了亲和度的收敛速度.利用记忆数组存放每一代中满足条件的抗体,制定了记忆数组更新策略,从而得到最优抗体.实验结果表明,IRSAR算法能够较快地得出合理有效的约简结果.     

基于差别矩阵的属性约简算法及其应用

铝电解过程中存在着各种相互影响的工艺参数,如何从中选择一部分参数进行分析,对铝电解生产有着重要的意义。提出了一种改进的基于差别矩阵的属性约简算法,避免了普通差别矩阵中的重复元素。用真实的铝电解生产数据对提出的算法进行了验证,效果良好。     

改进的非线性数据降维方法及其应用

局部线性嵌入算法（Locally Linear Embedding,LLE）是基于流形学习的非线性降维方法之一。LLE利用样本点的近邻点的线性组合对每个样本点进行局部重构,而不同近邻个数的选取会产生不同的重构误差,从而影响整体算法的实施。提出了一种LLE的改进算法,算法有效地降低了近邻点个数对算法的影响,并很好地学习了高维数据的流形结构。所提方法的有效性在人造和真实数据的对比实验中得到了证实。     

The granular extension of Sugeno-type fuzzy models based on optimal allocation of information granularity and its application to forecasting of time series

The Sugeno-type fuzzy models are used frequently in system modeling. The idea of information granulation inherently arises in the design process of Sugeno-type fuzzy model, whereas information granulation is closely related with the developed information granules. In this paper, the design method of Sugeno-type granular model is proposed on a basis of an optimal allocation of information granularity. The overall design process initiates with a well-established Sugeno-type numeric fuzzy model (the original Sugeno-type model). Through assigning soundly information granularity to the related parameters of the antecedents and the conclusions of fuzzy rules of the original Sugeno-type model (i.e. granulate these parameters in the way of optimal allocation of information granularity becomes realized), the original Sugeno-type model is extended to its granular counterpart (granular model). Several protocols of optimal allocation of information granularity are also discussed. The obtained granular model is applied to forecast three real-world time series. The experimental results show that the method of designing Sugeno-type granular model offers some advantages yielding models of good prediction capabilities. Furthermore, those also show merits of the Sugeno-type granular model: (1) the output of the model is an information granule (interval granule) rather than the specific numeric entity, which facilitates further interpretation; (2) the model can provide much more flexibility than the original Sugeno-type model; (3) the constructing approach of the model is of general nature as it could be applied to various fuzzy models and realized by invoking different formalisms of information granules.     

新型灰狼算法的粗糙集属性约简及应用

粗糙集理论是模式识别和机器学习的重要内容,属性约简是粗糙集理论中核心步骤。然而传统的粗糙集理论对数据集进行属性约简,计算复杂度高,容易陷入局部最优解。提出了一种新型灰狼优化算法的粗糙集属性约简技术,可以很好地解决传统粗糙集理论出现的弊端。同时为了验证算法的可行性,采用国际通用UCI数据库进行验证,与两种传统的属性约简方法进行对比分析。实验结果表明,该方法属性约简个数少,识别精度高,证明该方法切实可行,操作简单。     

Analysis and design of a secure key exchange scheme

We propose a new key exchange scheme where the secret key is obtained by multiplying the powers of block upper triangular matrices. After studying the cryptographic properties of these block matrices, the theoretical aspects of this scheme are analyzed, concluding that common ciphertext attacks are not applicable to this cryptosystem. Moreover, our proposal is compared with the Diffie-Hellman scheme achieving satisfactory results.     

粒的位表示及其在属性约简中的应用

粒计算是一种新的软计算思想,它涵盖了所有和粒度相关的理论、方法和技术.提出了一种信息粒的位表示方法,从而将繁琐的集合运算转化为更适于计算机运算的二进制数的逻辑运算,并基于此提出了一种新的属性约简算法.实验结果表明,该算法在时间上较当前的其它同类算法具有更高的效率.     

基于遗传算法的粗糙集属性约简及其应用

属性约简是粗糙集合研究的重要内容之一。为了能够有效地获取决策表中属性最小相对约简,提出一种基于遗传算法的属性约简算法。依据条件属性的重要度,把条件属性加入到相对核属性集中,将相对核加入遗传算法的初始种群中以加快算法收敛。并应用该算法对水上事故模型预测进行分析,做出推理和决策。     

自适应和声搜索算法及在粗糙集属性约简中的应用

针对改进和声搜索算法（ＩＨＳ）存在的不足,提出了自适应和声搜索算法（ＡＨＳ）．该算法利用和声库中变量函数的最大差值来调节ＰＡＲ 和ｂｗ,从而提高了对多维问题的搜索效率．利用５个标准测试函数对ＡＨＳ算法进行测试,并应用于粗糙集的属性约简中．仿真结果表明了该算法的有效性和实用性．     

混合属性约简方法在电力负荷预测中的应用

影响电力短期负荷预测精度的因素众多,为了找到负荷值与各种外在因素之间的关系,提出了一种基于粗糙集理论的混合属性约简算法,并对与预测日相似性数据进行快速约简,讨论了基于混合属性约简和BP神经网络相结合的预测模型。实验结果表明,这种方法提高了短期电力负荷预测精度。     

粗糙集属性约简方法及其在医疗中的应用研究

针对基于可辨识矩阵核求取属性约简存在的空间与时间都不理想的问题,提出一种新的基于粗糙集的属性约简启发式算法。该方法不直接构造及存储可辨识矩阵,而且在核不存在的情况下,也能取得较好的起点核心集,将获取矩阵元素及得到核心元素同步进行,并加入了对属性集频率的综合考虑。同时,将此方法应用于医疗诊断决策,并对属性约简前后的决策性能进行了分析。实验结果表明,利用约简后的属性集,计算复杂性降低,同时保持高的决策准确率,算法是有效的。     

随机信息系统属性相关性及在知识约简中的应用

引入随机信息系统的属性的信息熵,利用属性的信息熵来刻画随机信息系统的属性之间的相关性。提出一种利用属性相关性来进行随机信息系统知识约简的方法并给出基于该方法进行知识约简的算法。     

改进的属性约简算法及其在肝癌微血管侵犯预测中的应用

基于邻域粗糙集的属性约简算法在进行属性约简时只考虑单一属性对决策属性的影响,未能考虑各属性间的相关性,针对这个问题,提出了一种基于卡方检验的邻域粗糙集属性约简算法（ChiS-NRS）。首先,利用卡方检验计算相关性,在筛选重要属性时考虑相关属性之间的影响,在降低时间复杂度的同时提高了分类准确率;然后,将改进的算法与梯度提升决策树（GBDT）算法组合以建立分类模型,并在UCI数据集上对模型进行验证;最后,将该模型应用于预测肝癌微血管侵犯的发生。实验结果表明,与未约简、邻域粗糙集约简等几种约简算法相比,改进算法在一些UCI数据集上的分类准确率最高;在肝癌微血管侵犯预测中,与卷积神经网络（CNN）、支持向量机（SVM）、随机森林（RF）等预测模型相比,提出的模型在测试集上的预测准确率达到了88.13%,其灵敏度、特异度和受试者操作曲线（ROC）的曲线下面积（AUC）分别为87.10%、89.29%和0.90,各指标都达到了最好。因此,所提模型能更好地预测肝癌微血管侵犯的发生,能辅助医生进行更精确的诊断。     

Dimensional reduction of 3D building models using graph theory and its application in building energy simulation

Since the various people involved in the design process for a building project tend to hold conflicting views, this inevitably leads to a range of disparate models for planning and calculation purposes. In order to interpret the relevant geometrical, topological and semantical data for any given building model, we identify a structural component graph, a graph of room faces, a room graph and a relational object graph as aids and explain algorithms to derive these relations. We start with a building model by transferring its geometrical, topological and semantical data into a volume model, decomposing the latter into a so-called connection model and then extracting all air volume bodies and hulls of the model by means of further decomposition into elementary cyclic connection components. The technique is demonstrated within the scope of building energy simulation by deriving both a dimensionally reduced object model required for setting up a thermal multizone model and a geometrical model for defining single or multiple computational fluid dynamic domains in a building together with incidence matrices correlating these models. The algorithm is basically applicable to any building energy simulation tool.