基于Java点对点通信技术的DES密钥交换

介绍了使用Java的点对点通信技术,基于Diffie-Hellman规则,给出了IBM DES密钥交换的总体方案、算法和应用程序,详细说明了其中涉及的主要技术和方法,同时给出了在PC机上用二进制指数分解法实现大数模运算的算法分析和实现方案。     

弱电工程施工和管理研究与探讨

弱电工程项目的质量控制的关键是对工程的设计、施工和管理过程进行严格的控制和把关,并按规范步骤进行操作和实施。通过工程的前期沟通、技术选择、需求确定、配置清单和施工图纸等措施可规范设计过程。通过施工前会议、施工要点、材料和设备采购、工程进度、方案设计等措施可规范施工过程。通过制定施工组织和协调管理等措施可规范施工管理。通过规范的设计、施工和管理可以提高工程质量和工程效益。     

基于关键闸坝优选的区域水网水量调控

为实现区域水网水量的高效调控,从水量、水生态、水质和闸坝管理等4个方面构建包含10个指标的闸坝优选评价指标体系,以廊坊市凤河-永定河区域水网为研究对象,计算区域内32座闸坝的综合关键指数并给出影响程度排序,采用水动力数学模型验证关键闸坝选取的合理性。结果表明：优选出的永丰闸和东张务闸是区域水网的关键闸坝,通过调节这两座闸坝的开度即可满足区域水网的生态需水和防洪排涝调度目标,保持区域水网较优的水力连通能力,提高区域水网水量调控效率。     

Group codes on certain algebraic curves with many rational points

We construct a series of algebraic geometric codes using a class of curves which have many rational points. We obtain codes of lengthq ² over _q , whereq = 2q ₀ ² andq ₀ = 2 ⁿ , such that dimension + minimal distance q ² + 1 – q ₀ (q – 1). The codes are ideals in the group algebra _q [S], whereS is a Sylow-2-subgroup of orderq ² of the Suzuki-group of orderq ² (q ² + 1)(q – 1). The curves used for construction have in relation to their genera the maximal number of GF _q -rational points. This maximal number is determined by the explicit formulas of Weil and is effectively smaller than the Hasse—Weil bound.Supported by Deutsche Forschungsgemeinschaft while visiting Essen University     

融合关键对象识别与深层自注意力的Bi-LSTM情感分析模型

在线评论文本通常涉及多个评价对象,对象的表达方式有显式和隐式之分,针对不同对象的情感倾向可能不会完全一致.关键评价对象是评论中最受关注的对象,其相应的情感语义对整条评论的情感观点起主导作用.本文构建了融合关键对象识别与深层自注意力机制的Bi-LSTM模型,以提升短文本情感分类的效果.使用CNN处理文本,基于卷积层输出结果识别关键评价对象,并在此基础上完成深层自注意力的学习.将对象信息与文本信息进行融合,利用注意力机制强化的Bi-LSTM模型得到评论文本的情感分类结果.在酒店评论数据集上进行实验,与之前基于深度学习的模型相比,本文方法在精确率、召回率和F-score评价指标方面均有更好的表现.     

煤炭企业决策支持信息系统

以龙口矿业集团有限公司的决策支持信息系统为例,详细介绍了煤炭企业决策支持信息系统的总体架构、系统数据来源及处理、关键指标信息、系统功能。该系统通过DTS、中间件等技术获取数据;采用面向主题的数据仓库多维表结构和与业务系统对应的ODS层表结构存储数据;通过分析各种数据指标的类型及属性,建立起一套共包含45个关键指标的企业关键指标体系结构;并采用同比分析、环比分析、趋势分析等数学模型对数据进行分析和展现,从而实现辅助决策。     

Beller-Yacobi协议的研究与模拟实现

本文着重分析了Beller-Yacobi协议及以其中用到的RSA加解密及签名算法,并以RSA加解密算法为例在.NET环境下使用C#语言模拟实现了其详细过程,最终实现了整个Beller-Yacobi密钥传输协议的过程。     

Polynomial approximation of functions of matrices and applications

In solving a mathematical problem numerically, we frequently need to operate on a vector by an operator that can be expressed asf(A), whereA is anN ×N matrix [e.g., exp(A), sin(A), A^–-]. Except for very simple matrices, it is impractical to construct the matrixf (A) explicitly. Usually an approximation to it is used. This paper develops an algorithm based upon a polynomial approximation tof (A). First the problem is reduced to a problem of approximatingf (z) by a polynomial in z, where z belongs to a domainD in the complex plane that includes all the eigenvalues ofA. This approximation problem is treated by interpolatingf (z) in a certain set of points that is known to have some maximal properties. The approximation thus achieved is almost best. Implementing the algorithm to some practical problems is described. Since a solution to a linear systemAx=b isx=A ^–1 b, an iterative solution algorithm can be based upon a polynomial approximation tof (A)=A ^–1. We give special attention to this important problem.     

Continuous process control using neural networks

We present some adaptive control strategies based on neural networks that can be used for designing controllers for continuous process control problems. Specifically, a learning algorithm has been formulated based on reinforcement learning, a weakly supervised learning technique, to solve set-point control and control scheduling for continuous processes where the process cannot be modeled easily. It is shown how reinforcement learning can be used to learn the control strategy adaptively based on exploration of the control space without making assumptions about the process model. A new learning scheme, handicapped learning, was developed to learn a control schedule that specifies a schedule of set points. Applications studied include the control of a nonisothermal continuously stirred tank reactor at its unstable state and the learning of the daily time-temperature schedule for an environment controller. Experimental results demonstrate good learning performance, indicating that the learning algorithm can be used for solving transient startup and boundary value control problems.     

基于视觉的多特征手势识别

手势是一种自然直观的交互方式,基于视觉的手势识别是实现新一代人机交互的关键技术。本文在已有的手势识别技术基础上,从手势分割及手势表示两方面着手,提出了一种单目视觉下的手势识别方法。利用颜色特征检测肤色区域,成功分割出人手;利用人手的轮廓及凸缺陷检测指尖,再利用指尖的数目和方位来表示一个手势,进而结合轮廓长度和面积等几何特征完成手势识别。传统的指尖检测方法需要遍历并扫描手掌外轮廓,计算量大,本文通过凸缺陷检测指尖,减少了计算量,提高了指尖检测的速度。实验结果表明,本文的方法具有很好的鲁棒性及实时性,能适应环境的变化。