多数据项广播调度策略

多数据项广播是移动计算环境中一种重要的数据访问方式.为减少用户的平均访问时间和响应最多事务请求,提出了一种新的多数据项广播调度策略.调度策略分为两个阶段,第一阶段根据各事务包含的数据项数目和对重复申请数据项的处理选择事务请求;第二阶段对已选择的事务请求用QEM算法进行调度.实验结果表明,它比现有的QEM算法有更高事务调度成功率和更短的平均访问时间.     

一种特征加权FCM算法的图像重建技术研究

针对传统FCM(Fuzzy C-means)算法中初始聚类中心选取的随机性以及对初始值敏感的问题,提出一种基于进化策略的色彩空间加权的FCM聚类算法.通过在RGB(Red Green Blue)色彩空间矢量中设置加权矩阵来补偿各色彩的非均匀性,并采用一种类内最小距离最大的统计聚类算法来初始化聚类中心.实验结果表明,该算法能有效减少颜色量化后的均方差值,保持重建图像的整体层次和局部特征细节,对研究图像处理技术有较强的实际意义.

Abstract：

Aiming at a defect on randomness of the initial clustering center choosing and sensitivity of initial value in tradition FCM(fuzzy C-means) algorithm, a clustering algorithm about FCM of weighted color space based on evolutionary strategy is proposed. By interposing weighted matrix in RGB(Red Green Blue) color space, the color's inhomogeneous is compensated. And by using a statistics clustering algorithm of minimal maximal distance, clustering center is initiated. The experimental results show that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction, and has practical value to the study of the image process technology.     

基于模糊理论的非处方药的满意度评价系统

为了帮助顾客从众多对症的非处方药中选出最满意的药品,提出了开发1个基于Web的非处方药满意度评价系统。系统根据顾客提供的典型症状,采用疾病与症状相结合的选药策略,查找出所有对症的非处方药,并以药品安全性、疗效、稳定性、易用性、价格作为评价因素,根据顾客的关注角度和关注程度确定各因素权重,用模型b=W·R对对症药品进行满意度评价,按满意度高低依次推荐。经实际应用,系统推荐准确度在70%以上。     

可验证的多等级门限多秘密共享方案

目前的多等级门限共享方案中,高等级用户的作用可以被若干个低等级用户联合取代,而多秘密门限共享方案中,所有秘密只能在同一级门限下共享。为克服这两个问题,利用Birkhoff插值法和离散对数的困难性,提出了一个可认证的多等级门限多秘密共享方案。该方案可以同时划分多个等级,而每级门限下可以共享多个秘密。每个参与者只需持有一个子秘密,方便管理与使用。     

面向多层次知识表达的贝叶斯分类模型研究

提出多模式贝叶斯分类算法,由变量值之间的条件独立和条件相关性推断因果关系,根据每个完整随机样本而非整个样本空间构造子模式.结合局部计算近似推理进行概率密度和条件概率分布估计,在此基础上采用后离散化策略自动确定连续变量边界.在UCI机器学习数据集上的实验结果证明了该算法的合理性和有效性.     

Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation

Genetic Algorithms (GAs) and other Evolutionary Algorithms (EAs), as powerful and broadly applicable stochastic search and optimization techniques have been successfully applied in the area of management science, operations research and industrial engineering. In the past few years, researchers gave lots of great idea for improvement of evolutionary algorithms, which include population initialization, individual selection, evolution, parameter setting, hybrid approach with conventional heuristics etc. However, though lots of different versions of evolutionary computations have been created, all of them have turned most of its attention to the development of search abilities of approaches. In this paper, for improving the search ability, we focus on how to take a balance between exploration and exploitation of the search space. It is also very difficult to solve problem, because the balance between exploration and exploitation is depending on the characteristic of different problems. The balance also should be changed dynamically depend on the status of evolution process. Purpose of this paper is the design of an effective approach which it can correspond to most optimization problems. In this paper, we propose an auto-tuning strategy by using fuzzy logic control. The main idea is adaptively regulation for taking the balance among the stochastic search and local search probabilities based on the change of the average fitness of parents and offspring which is occurred at each generation. In addition, numerical analyses of different type optimization problems show that the proposed approach has higher search capability that improve quality of solution and enhanced rate of convergence.     

Adaptive prescribed time control for quadrotor formation with stochastic links failure

The formation control for multiple quadrotors subject to maintaining the formation configuration and collision avoidance in the situation of stochastic links failure is investigated in this paper. First, the distributed formation controller is designed, the position controller is developed to manage the desired formation of position, and the attitude controller is developed to control the translation and rotation movements of the quadrotor. Then, in order to avoid the collisions between multiple quadrotors and the obstacles, a potential energy function method is introduced into the quadrotor formation control combined with the nest adaptive control. Inspired by the design of event trigger controller, a communication compensation controller is designed to ensure the stability of quadrotor formation under the condition of random communication interruption and recovery. Moreover, a prescribed time function is designed, which means the convergence time of the formation system can be set in advance. The prescribed time stability of the formation control system is proved by Lyapunov theory. Finally, the simulation results verify the effectiveness and superiority of this method.     

A mathematical model for vehicle routing problem under endogenous uncertainty

In this study, a multistage stochastic programming (SP) model is presented for a variant of single-vehicle routing problem with stochastic demands from a dynamic viewpoint. It is assumed that the actual demand of a customer becomes known only when the customer is visited. This problem falls into the category of SP with endogenous uncertainty and hence, the scenario tree is decision-dependent. Therefore, nonanticipativity of decisions is ensured by conditional constraints making up a large portion of total constraints. Thus, a novel approach is proposed that considerably reduces the problem size without any effect on the solution space. Computational results on some test problems are reported.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.