基于组合—分解的机器发现方法

本提出了一个用以刻划综合与分析的发现过程的机器发现方法，定义了组合－分解算子，组合－分解函数，函数发现和函数维护等概念，给出组合－分解函数发现算法和维护算法。作为应用，本探讨了关于知识发现和知识维护的函数型知识发现过程。     

Systolic算洁和结构求解代数Riccati方程

本文基于矩阵的符号函数法，提出了一种Ｕ－Ｄ分解算法和脉动（Ｓｙｓｔｏｌｉｃ）结构有效地求解代数Ｒｉｃｃａｔｉ方程以及用固定大小的方形阵列解决大型问题的方法．     

逻辑结点函数的优化覆盖*

本文提出多级组合逻辑结构中逻辑结点函数的优化覆盖算法，证明了该算法在多级逻辑优化过程中的有效性．     

概率逻辑公式集分解的合并聚类算法

为使概率逻辑的不确定性推理方法能应用于较大规模的知识库，本文基于一个实际专家系统知识库的开发经验，在概率逻辑公式一致性区间的一般算法基础上，为概率逻辑公式集的分解设计了一种合并聚类算法．对于不同背景的概率逻辑知识库，只要公式集具有一定的分层结构性质，该算法就能保证Ｄａｎｔｚｉｇ－Ｗｏｌｆｅ分解的联合计算模型适用于概率逻辑推理．测试结果表明，该算法对于数１０个变量和子句的实例可收到很好的效果．     

基于优化组合模型的PM2.5浓度预测

为精确预测出具有非线性、非平稳的PM2.5时序数据，提出基于PSO-VMD-LSTM的组合模型。融合机器学习优化算法和深度学习技术，其中利用以包络熵为适应度函数的PSO机器学习算法对VMD分解过程中的惩罚因子和模态个数进行迭代寻优，基于分解处理后的数据进行LSTM深度学习模型预测。构建两种经典分解组合模型作为对照，结果表明，在相同迭代次数情况下，时序分解处理后的LSTM预测比未分解时的预测精度和效果都有明显改善；与经典分解组合模型相比，该模型在准确度和评估指标上均取得最佳预测效果，在PM2.5浓度预测上具有一定普适性。     

单调积之和展开式与布尔函数的单调分解定理

本文给出单调布尔函数的一类范式——单调积之和展开式以及构造它的算法,该算法的实现是很直观的。本文还给出布尔函数的两条单调分解定理,他们在组合逻辑综合中开创了新的途径。     

布尔函数的代数因子分解

本文改进了布尔函数的代数因子分解算法ALG_DIV和QF,采用“分治”方式降低了分解过程的计算时间复杂性,提高了多级逻辑函数分解的效率,能获得近似优化的多级逻辑分解结果.     

一种过程方程组分解方法及其在催化裂化反应——再生模型中的 …

Ｒａｍｉｒｅｚ算法是一种对过程方程组进行分解的算法，此算法将过程方程组分解后，可以使求解计算更快速可靠，本文指出，选择输出变量顺序不同，过程方程组分解过程中形成的平台变量也不同，从而影响决策变量的选择，在确定决策变量时，应优先取平台变量为决策变量，将Ｒａｍｉｒｅｚ算法用于催化裂化反应－再生模型的分解，找出合适的求解策略，验证了提出了观点。     

关于PLA优化设计的一个综合算法

讨论ＰＬＡ优化设计的组合函数分解问题和ＰＬＡ折迭问题，得出对算法有用的两个直接结果，最后将二者联系起来，给出一个综合算法。     

基于数据抽取器的知识发现处理模型

提出了基于数据抽取器的知识发现模型。在模型中，将知识发现过程分成数据预处理、数据抽取、数据挖掘和结果分析四个阶段。该模型利用标准的SQL语言构造数据抽取器，为不同的学习算法准备数据，减少数据挖掘算法对数据库直接调用的次数，避免了直接对大型数据库的数据进行调用，使得对大型数据库进行快速数据挖掘成为可能。可以加快知识发现过程，提高数据挖掘效率，实现对于大型数据库的知识发现。最后设计了SQL-C4．5算法，该算法实现了利用数据抽取器为决策树算法C4．5抽取必要的统计数据，实现了C4．5决策树的构建。     

A novel approach to annotating web service based on interface concept mapping and semantic expansion

With the rapid development of web service technology in these years, traditional standards have been matured during the process of service registry and discovery. However, it is difficult for service requesters to discover satisfactory web services. The reason for this phenomenon is that the traditional service organization mode lacks semantic understanding ability for service function interface. This paper proposes a novel approach to annotating web services. We first adopt domain ontology as a semantic context, and give our general framework of service semantic annotation. Then, interface concept mapping algorithm and service interface expansion algorithm are respectively presented in detail. Finally, the generation process of semantic web service repository is presented based on preceding algorithms. Simulation experiment results demonstrate that annotated web services by the proposed method can more satisfy requirements for service requesters than traditional ones by service matchmaking engine. It can get better service discovery effectiveness.     

IPv6无线传感器网络低功耗路由算法

为满足IPv6无线传感器网络低功耗的要求,采用适配层路由,在LOAD路由协议基础上提出一种简化的按需式路由算法。本算法采用精简的路由控制报文、路由表和路由请求表,简化了路由发现过程,优化了路由维护机制。仿真实验表明,该算法能有效的降低IPv6无线传感器网络的功耗。     

A probabilistic approach to event log completeness

Recently, researchers discovered that the major problems of mining event logs is to discover a simple, sound and complete process model. But since the mining techniques can only reproduce the behaviour recorded in the log, the fitness of the reproduced model is a function of the event log completeness. In this paper, a Fuzzy-Genetic Mining model based on Bayesian Scoring Functions (FGM-BSF) which we called probabilistic approach was developed to tackle problems which emanated from the incomplete event logs. The main motivation of using genetic mining for the process discovery is to benefit from the global search performed by the algorithm. The incompleteness in processes deals with uncertainty and is tackled by using the probabilistic nature of the scoring functions in Bayesian network based on a fuzzy logic value prediction. The global search performed by the genetic approach is panacea to dealing with the population that has both good and bad individuals. Hence, the proposed approach helps to enhance a robust fitness function for the genetic algorithm through highlift traces representing only good individuals not detected by mining model without an intelligent system. The implementation of our approach was carried out on java platform with MySQL for event log parsing and preprocessing while the actual discovery was done in ProM. The results showed that the proposed approach achieved 0.98% fitness when compared with existing schemes.     

A complete chronicle discovery approach: application to activity analysis

Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non‐complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process.     

A decremental algorithm of frequent itemset maintenance for mining updated databases

Data-mining and machine learning must confront the problem of pattern maintenance because data update is a fundamental operation in data management. Most existing data-mining algorithms assume that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new data. While there are many efficient mining techniques for data additions to databases, in this paper, we propose a decremental algorithm for pattern discovery when data is deleted from databases. We conduct extensive experiments for evaluating this approach, and illustrate that the proposed algorithm can well model and capture useful interactions within data when the data is decreasing.     

基于用户满意度的学习服务发现算法

引入用户满意度因子,设计一个学习服务发现算法——eLSDAus,并应用于网络学习系统中。该算法允许用户参与服务发现的过程,对服务发现的效果进行评价。学习服务发现系统将用户评价反馈到学习服务发现算法,利用修正函数修正更新发布服务各属性的匹配度权值,优化反馈给用户的综合匹配度的计算。实验结果表明,在发布的学习服务数量超过1万时,该算法能提高服务发现的查全率4％～5％。网络学习者使用该系统7天后,对学习服务发现结果的总体满意比率可达到93％以上。     

基于服务性能合同模式下单部件系统视情维修策略研究

针对供应商系统维修的低效率以及维修成本参数较难获得的问题,提出了基于服务性能合同模式(PBC)下的单部件系统最优视情维修策略模型。首先,基于Gamma分布,描述单部件系统连续递增的退化过程,依据系统实时检测状态与预防维修阈值、故障阈值之间的关系,实施不同的维修策略;其次,分析单位更新周期内的检测次数和使故障设备恢复如新的维修方式,以供应商利润率最大化为目标函数,以最佳维修阈值与检测间隔时间为决策变量,建立以利润为中心的视情维修优化模型;最后,利用改进灰狼算法求解数学模型,通过算例验证所提出模型的有效性,并进行了各维修费用参数对目标函数以及最优维修策略的灵敏度分析。     

A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs

Process mining is the research domain that is dedicated to the a posteriori analysis of business process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process discovery is one specific subdomain of process mining that entails the discovery of control-flow models from such event logs. Assessing the quality of discovered process models is an essential element, both for conducting process mining research as well as for the use of process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate process discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess process discovery techniques using artificial event logs. In addition, we provide an extensive overview of available process discovery techniques and we describe how discovered process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques.     

A Drug Candidate Design Environment Using Evolutionary Computation

This paper describes the candidate design environment we developed for efficient identification of promising drug candidates. Developing effective drugs from active molecules is a challenging problem which requires the simultaneous satisfaction of many factors. Traditionally, the drug discovery process is conducted by medicinal chemists whose vital expertise is not readily quantifiable. Recently, in silico modeling and virtual screening have been emerging as valuable tools despite their mixed results early on. Our approach combines the capabilities of computational models with human knowledge using a genetic algorithm and interactive evolutionary computation. We enable the chemist's expertise to play a key role in every stage of the discovery process. Our evolved structures are guaranteed to be within the chemistry space specified by the medicinal chemist, thereby making the results plausible. In this paper, we describe our approach, introduce a case study to test our methodology, and present our results.     

基于萤火虫群优化的Ad Hoc网络路由协议

针对Ad Hoc网络拓扑结构多变、网络生存时间受限及数据包分组传输效率低下等问题,本文借鉴萤火虫群优化算法的思想,提出了一种基于萤火虫群优化的Ad Hoc网络路由协议。路由协议用萤火虫优化算法的荧光素强度的更新规则与无线自组网络中的节点移动速度、拥塞程度、节点剩余能量及节点间的距离等因素相互映射,改进萤火虫群优化算法中的搜索萤火虫、驻留萤火虫及回溯萤火虫用于完成Ad Hoc网络中路由协议的路由发现、路由选择及路由维护等过程,整个协议无须传送大量的控制分组,即可实现Ad Hoc网络的稳定。仿真实验结果表明,与AODV及基于蚁群优化的路由算法AntRouting协议相比,本文所提出的路由协议在端到端延时、分组数据传输率及网络生存时间上均有良好的性能。