化LF(X)中任一公式为可归约形式的算法

针对基于归结方法的自动推理过程中的化任一公式为可归约形式这一问题进行研究．简述了LF(X)及其中公式的相关概念，证明了对LF(X)中公式进行转化的若干理论结果，并由此给出化LF(X)中任一公式为可归约形式的理论基础和基本算法．     

基于构件面向体系结构的C4ISR系统设计方法

介绍了C4ISR体系结构描述和设计方法("3A"视图),描述了在其指导下使用结构分析法和面向对象分析法开发体系结构产品的过程,并比较了这两种方法的优缺点.在此基础上,提出基于构件面向体系结构的设计方法来解决系统的互操作和复用问题,给出了支持该方法的迭代开发过程,阐述了该方法与C4ISR体系结构设计方法的对应关系, 为开发可复用、易于互操作的C4ISR系统提供了一种可行的技术途径.     

生产技术准备过程的时间Petri网模型及其推理方法研究

从Petri选择网模型的概念入手,分析了时间Petri网（TPN）模型的特点,研究了时间Petri网中顺序模型、并行模型、模型和循环模型等的推理方法.应用该方法建立了生产技术准备过程的时间Petri网模型,得出了基于时间Petri网的工作流模型复合线性推理方法,并采用一个具体的实例说明该方法的应用.     

民航机务维修差错及其预警专家系统构建

通过对航空机务维修差错控制研究现状的分析,指出存在的问题,提出了以SHEL模型作为维修差错相关信息收集的工具,REASON模型作为分析这些信息的框架,从"人、机、环境、管理"四个方面进一步对维修差错的诱因进行分类的新思路.在此基础上,将Agent技术和基于规则的不确定性推理的预警方法应用于民航机务维修领域,构建了预警专家系统,阐述了其体系结构和系统功能.     

高粘度基质沥青混合料高温抗剪性能试验研究

通过对高粘度基质沥青(AH-30)、重交沥青(AH-70)及SBS改性沥青混合料进行SST剪切和贯入剪切试验,对比研究了3种不同沥青混合料的高温抗剪性能.结果表明:高粘度基质沥青混合料的高温抗剪性能优于重交和改性沥青混合料,抗高温车辙能力明显,可适用于南方湿热地区沥青路面中下面层.     

基于改进D-S证据理论的车辆导航地图匹配

对传统D-S证据理论算法进行了改进,解决了证据的可信度和决策规则问题,使新的组合推理规则能够有效地处理证据中的冲突信息.并结合当前城市道路网日益复杂的实际特点,采用Pignistic概率的决策理论和基于权值D-S证据推理的地图匹配新算法,建立车辆位置信息和方向信息判决规则,有效地利用现有信息扩大各匹配道路之间的信度差异,提高了算法的鲁棒性.通过仿真和试验表明,该算法可以较好地解决城市复杂路网地图匹配问题.     

Chosen ciphertext secure fuzzy identity based encryption without ROM

Two new constructions of chosen-ciphertext secure fuzzy identity-based encryption(fuzzy-IBE) schemes without random oracle are proposed.The first scheme combines the modification of chosen-plaintext secure Sahai and Waters'"large universe"construction and authenticated symmetric encryption, and uses consistency checking to handle with ill-formed ciphertexts to achieve chosen-ciphertext security in the selective ID model.The second scheme improves the effciency of first scheme by eliminating consistency checking.This improved scheme is more effcient than existing chosen-ciphertext secure fuzzy-IBE scheme in the standard model.     

Hepatic CT image query based on threshold-based classification scheme with gabor features

Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme.     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

Modeling design of military equipment operational concepts by MBSE北大核心CSCD

［Objectives］In order to give full play to role of "operational concepts" in the engineering process of military equipment development, it is necessary to understand and form standardized operational concept documents (OCD), and study the modeling design of OCD in order to implement the transformation of the system engineering process from document-centric to model-centric. ［Methods］This study analyzes the US military's definition of operational concepts and introduces its hierarchical operational concepts, especially the bottom level ConOps or OpsCon, usually as a part of equipment acquisition and requirement engineering. Lessons are then drawn from the relevant theories and standards such as IEEE 29148:2018 and ANSI/AIAA G-043B-2018 of the operational concepts document outline in order to design the specifications of the OCD. Finally, key technologies are studied, including SysML-based OpsCon modeling, UML Profile-based OpsCon framework design and MSDL/C-BML-based operational scenario development.［Result］The results indicate the effectiveness of implementing the modeling design of OCDs. ［Conclusions］In the process of equipment requirement engineering, the results of this study can help to improve the efficiency of equipment requirement demonstration through an ambiguous operational concept model and an engineering environment for all stakeholders of the system. © 2022 Journal of Clinical Hepatology. All rights reserved.