Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

Recognizing Expression Variant and Occluded Face Images Based on Nested HMM and Fuzzy Rule Based Approach

The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.

A Rule Based Evolutionary Optimization Approach for the Traveling Salesman Problem

The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many existing algorithms, a traditional genetic algorithm will have limited success with this problem class, particularly as the problem size increases. A rule based genetic algorithm is proposed and demonstrated on sets of traveling salesman problems of increasing size. The solution character as well as the solution efficiency is compared against a simulated annealing technique as well as a standard genetic algorithm. The rule based genetic algorithm is shown to provide superior performance for all problem sizes considered. Furthermore, a post optimal analysis provides insight into which rules were successfully applied during the solution process which allows for rule modification to further enhance performance.

零件加工隐性工艺知识获取方法研究

随着制造业数字化工艺的深入应用,如何高效利用积累的工艺知识,已成为提升工艺设计效率与质量的关键,然而隐性工艺知识却存在难以获取、描述与转化的技术瓶颈,严重阻碍智能化工艺设计模式推广。为此,提出了一种复杂零件加工的隐性工艺知识获取方法。首先,利用等宽法对结构化工艺数据进行离散化处理,构建基于文本挖掘的隐性工艺知识获取流程,并通过产生式规则对隐性工艺知识表达;然后,基于案例推理和规则推理融合的知识推理方法,并采用最近邻算法实现隐性工艺知识的识别;最后,以船用柴油机缸盖类复杂加工零件为验证对象,对加工隐性工艺知识获取方法进行了有效验证。

航行控制算法的智能评估架构设计

控制算法测试验证评估是确保智能船舶航行性能可靠性和安全性的关键。本文梳理了国内外智能评估技术的发展现状,以Transas船舶航行模拟器为船舶智能航行虚拟测试平台,进行航行控制算法测试评估。根据船舶直线段和弯曲段航行特征建立评估指标,提出航行控制算法智能评估系统的设计思路、框架结构和构建流程,初步建立虚拟仿真测试下的船舶航行控制算法智能评估体系,指出该智能评估系统所涉及的关键技术。基于以上研究,对船舶航行控制算法虚拟测试评估技术和未来改进方向进行总结。

基于隐结构模型联合频繁项集的针灸治疗弱精子症不育患者的辨证取穴规律

目的:探讨针灸治疗弱精子症不育患者的辨证取穴规律,为临床治疗弱精子症不育患者提供帮助。方法:检索国家知识基础设施数据库(CNKI)、中国生物医学文献数据库(CBM)、中国学术期刊数据库(CSPD)及中文科技期刊数据库(CCD)等数据库有关针灸治疗弱精子症的相关文献,提取症状、取穴处方信息构建医案数据库,采用隐结构模型、频繁项集的数据挖掘方法,分析针灸治疗弱精子症的辨证取穴规律。结果:纳入文献35篇,涉及症状62种,穴位49个。高频症状包括腰酸、神疲乏力等,高频腧穴包括关元、肾俞等。对症状、腧穴、证型进行频繁项集分析,挖掘出症状-腧穴频繁项集4项,包括耳鸣+头晕+腰酸+关元+肾俞等;证型-症状频繁项集4项,包括肾精亏虚证+健忘+性欲淡漠+神疲乏力+脉细等;证型-症状-腧穴频繁项集4项,包括肾精亏虚证+性欲淡漠+神疲乏力+头发易脱+关元等。结论:针灸治疗弱精子症多以关元、肾俞、足三里为主穴。

Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.

Ad Hoc Network Hybrid Management Protocol Based on Genetic Classifiers

The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communication paths, due to the introduction of Genetic Classifier is demonstrated.

基于改进集成KNN回归算法的风电机组齿轮箱状态监测

风电机组齿轮箱是容易发生故障的重要部件,维修费用高昂,因此有必要对其进行实时状态监测。针对集成K近邻(KNN)算法对随机采样不敏感的问题,提出了一种基于规则采样的改进集成KNN模型。首先利用距离相关系数进行变量选择,然后基于正则化互信息对变量进行排序,将其用于规则采样,构造子训练集,最后基于统计过程控制方法设置预警阈值对实时残差进行分析,根据健康度曲线对风电机组齿轮箱健康度进行监测,并利用某风电机组实际数据对所提方法进行验证。结果表明:所提方法显著提升了模型估计精度,该模型优于常规集成KNN模型,可以实现齿轮箱的早期故障预警。

规则引擎驱动的配电网问题诊断

为提高配电网问题诊断效率,提出一种规则引擎驱动的配电网问题诊断方法。构建配电网问题诊断规则引擎的事实库、规则库和推理机。研究了配电网问题的机理,编写事实库;从配电网规划目标、问题涉及范围和问题表现层级3个维度构建配电网问题诊断规则库;基于Rete算法设计推理机和驱动机制,根据规则库的诊断规则对事实库的配电设备数据进行问题诊断。所提方法实现了诊断规则与输入数据、推理组件的分离,算例验证了所提方法对配电网问题诊断的有效性。

全直流型船舶微电网多时间尺度综合能量管理

为了解决船舶微网在短期不同工况切换时功率波动大和长期运行经济性问题,采用多时间尺度分层控制的综合能量管理策略,在考虑船舶微网运行、负荷需求以及系统运行成本三者之间关系的基础上,对船舶微网系统进行了两层调控,其中针对短期运行下的负荷波动,采用基于规则的能量管理策略,通过对比所搭建的模型在不同模式以及不同运行工况下的仿真,结果表明当负荷处于短期波动时采用该策略能够在平抑功率波动的同时有效提高电能质量;长期运行下考虑可时移负荷对船舶运行经济性的影响,提出改进型的乌鸦搜索算法对燃料和储能电池总运行成本进行优化,通过算例分析表明:该算法考虑负荷时移性,不仅起到了削峰填谷的作用,而且相较于传统的乌鸦搜索算法经济性提高了4.4%。

融合ER和分层BRB的CPU性能分析模型

为解决中央处理器(Central Processing Unit, CPU)性能分析所面临的分析指标复杂、分析过程不具有可解释性、分析结果不可追溯的问题,提出了一种融合ER(Evidence Reasoning)和分层BRB(Belief Rule Base)的CPU性能分析模型.首先,利用ER算法从不同层面对处理器影响因素进行指标评估,其次,通过分层BRB实现对CPU性能的综合分析,最后,采用鲸鱼优化算法(Whale Optimization Algorithm, WOA)对模型参数优化.通过UCI数据库(University of California Irvine, UCI)计算机硬件数据集验证了模型的有效性.整个分析模型建立在ER算法上,保证了模型推理的可解释性,而分层BRB方法解决了传统BRB的组合规则爆炸问题,同时结合优化算法有效的提高模型的准确度.

电网设备故障的智能处置策略规则库构建方法

为了准确处理电网设备故障、提升处置策略规则的智能性,提出电网设备故障的智能处理策略规则库构建方法。采用人工智能技术中的蚁群算法挖掘电网设备故障处置规则,模拟蚂蚁觅食过程构造规则,利用规则剪枝过程去除冗余规则,利用多个决策树形成的随机森林算法构建电网设备故障智能处置策略规则库。实验结果显示,用所提方法构建的电网设备故障智能处置策略规则库能够准确匹配处置策略,解决配电网与输电网设备故障。

自注意力与强化学习耦合的多智能体通信

利用通信规则的多智能体算法通过智能体间交互信息进行协作,而其中信息质量决定了智能体协作能力与完成任务的能力,是提升智能体表现的关键因素.目前通信规则型算法存在通信信息产生方式单一、信息冗余等问题.针对以上问题本文提出了基于自注意力机制的信息处理模块TDU以及适配的信息产生智能体算法.首先,通过改进适配信息生成的强化学习算法作为智能体网络,通过网络学习生成适配信息,提高了智能体间信息产生的适配性并固定了信息选择的范围,降低了信息处理的难度.之后,利用基于自注意力机制的信息处理模块,对生成的信息进行筛选,提取高价值信息进行传输,提高智能体传递信息质量.本文提出的算法在经典智能体通信规则测试环境Swtich Riddle中进行了大量实验,并与主流通信规则型算法进行对比.实验结果表明,本文提出算法有效提高了智能体信息质量,加快了智能体学习速度,增强了智能体通信能力.

基于知识库与规则库的电力系统暂态故障事件诊断方法

为了减少暂态故障诊断误差,扩大诊断范围的同时提升诊断精度,基于知识库与规则库提出一种电力系统暂态故障事件诊断方法。利用小生境遗传算法约简电力系统暂态故障事件决策表图,获取暂态故障事件故障规则集,依据该规则集建立暂态故障事件知识库;通过证据融合理论确定目标暂态故障事件的权重系数,结合关联度得到目标案例和知识库内案例的灰色相似度,获取两个案例间的匹配结果,完成暂态故障事件诊断。实验证明该方法可精准诊断暂态故障事件。当分辨系数取值范围为0.4至1.0时,均方根误差为0;在不同信息缺失比例时,暂态故障事件诊断的精度依旧较高。

液体火箭箭体结构的半定量安全状态评估方法

针对液体火箭箭体结构的安全性问题,提出一种利用置信规则库(belief rule base, BRB)和鲸鱼算法(whale optimization algorithm, WOA)模型来进行安全状态评估的方法。该模型基于BRB方法对液体火箭结构安全进行安全状态评估,最后基于WOA优化算法对构建的BRB模型的参数进行优化。由实验可知,所提出的模型能够有效解决液体火箭箭体结构安全性评估的问题,同时能够充分进行半定量分析,并保持较高的实验精度。

基于规则库的SCD文件虚回路隐患自动核查技术

针对智能变电站在建设、运维、检修、改扩建过程中,因设计不完善、配置不正确、验证测试不到位等造成二次回路配置缺失或参数错误等隐患,为了提升智能变电站安全运行水平,提出了基于规则库的智能变电站全站配置描述文件(Substation Configuration Description,SCD)虚回路自动核查方法。依据相关技术标准、规范,建立隐患规则库;通过对配置文件的解析,获取配置文件中所有虚端子连接关系及装置信息;基于隐患规则库的规则,利用KMP算法对虚回路进行自动核查,判别虚回路是否存在隐患。同时,结合典型案例,验证配置文件自动核查技术的应用效果,提高了虚回路缺失、虚回路连接错误的核查能力,有效提升智能变电站作业现场的工作效率和安全管控水平。

基于模糊规则的热工过程非线性模型的研究

建立精确的热工过程整体模型是对热工过程进行全 局优化控制的基础,而热工过程往往具有非线性和不确定 性,传统的描述热工过程动态数学模型的方法(如传递函 数等)难以建立非线性模型,从而难于精确表达热工过程 及实施整体优化控制。该文提出了一类实用的基于模糊规 则的热工过程非线性建模方法,具体为:首先通过聚类和 竞争学习算法,对热工过程的输入数据空间进行分区,然 后在每个局部的数据子空间上,利用递推的最小二乘辨识 算法(RLS)建立一个基于模糊规则的局部线性动态模型, 这样,一个典型的非线性热工过程可以通过一组基于模糊 规则的线性模型来表示。计算结果表明:基于模糊规则的 非线性模糊模型,不仅能精确地描述过程的非线性,而且 算法简单、实用。

挖掘关联规则的高效ABM算法

提出了一种基于矩阵的挖掘关联规则的高效ABM算法,与经典的挖掘算法相比,该算法只需对数据库扫描一遍,并且存放辅助信息所需要的空间也比较少,实验表明该算法的效率较高。