A Suite of Tools for Debugging Distributed Autonomous Systems

This paper describes a set of tools that enables developers to log and analyze the run-time behavior of distributed control systems. A feature of the tools is that they can be applied to distributed systems. The logging tools enable developers to instrument C or C++ programs so that data indicating state changes can be logged automatically in a variety of formats. In particular, run-time data from distributed systems can be synchronized into a single relational database. Tools are also provided for visualizing the logged data. Analysis to verify correct program behavior is done using a new interval logic that is described in this paper. The logic enables system engineers to express temporal specifications for the autonomous control program that are then checked against the logged data. The data logging, visualization, and interval logic analysis tools are all fully implemented. Results are given from a NASA distributed autonomous control system application.     

考虑风电接入和气电转换的综合能源系统日前区间优化

针对气电综合能源系统优化调度中未充分挖掘可调度因素、未充分考虑风电预测误差不确定性的现状,研究含风电的气电综合能源系统日前区间优化调度方法。首先,基于区间数学对风电出力的不确定性进行区间描述,综合考虑抽水蓄能、气电双向转换以及需求响应等资源和技术,并用区间数学表示优化目标和优化约束,构建含高比例风电的气电综合能源系统日前优化模型,综合优化系统的经济性、环保性。然后,基于区间序关系和可能度的方法将区间优化模型转化为确定性优化模型,继而采用Jaya算法进行求解,保证求解的高效性和解空间的可行性。最后,进行算例分析,仿真结果验证了综合利用抽水蓄能、气电双向转换以及需求响应等要素对系统进行区间优化,能取得优化效果上的优势,并能为系统调度人员提供风电不确定度对系统优化影响的信息。     

Application of neural network interval regression method for minimum zone straightness and flatness

The goal of this paper is to develop an accurate, efficient, and robust algorithm for the minimum zone (MZ) straightness and flatness. In this paper, we use an interval bias adaptive linear neural network (NN) structure together with least mean squares (LMS) learning algorithm, and an appropriate cost function to carry out the interval regression analysis. From the results, we can see that both the straightness and flatness results from the interval regression method by NN can converge closer to the definition of the MZ straightness and flatness, respectively, than that of the least-squares (LSQ) method. The interval regression method by NN developed in this paper is applicable in the linear regression analysis that has a complicated constraint, and where the LSQ method cannot be used.     

一种改进的基于DTOA统计的信号分选算法

脉冲重复间隔(PRI)是雷达辐射源脉冲列信号的重要参数.本文对传统积累到达时间差(DTOA)值直方图算法中统计门限的确定进行了理论分析,通过应用多个统计检测门限能够高效、准确分选出固定和参差调制类型的雷达脉冲列信号.将积累DTOA值直方图改进算法和序列搜索改进算法相结合,设计并分析了分选算法流程,通过严格控制分析流程,...     

属性和专家客观权重未知的区间数群决策方法

针对偏好信息为区间数形式、属性和专家客观权重未知的多属性群决策问题,提出通过属性评价值之间偏离程度的熵值分析和建立目标最小化的非线性规划模型确定属性客观权重,并结合属性主观权重获得属性综合权重;通过灰色关联法分析专家综合评价和群体综合评价之间一致性程度确定专家客观权重,并利用自适应迭代法求得稳定的专家权重;构造了一个新的区间数比较的可能度公式,并基于此公式,给出了方案排序问题的解决方法。通过算例分析及与其他方法对比,验证了所提出方法的可行性和有效性。最后,分析了相关参数对决策结果的影响。     

An optimal stopping time problem with time average cost in a bounded interval

An optimal stopping time problem for a diffusion process stopped at the boundary of a bounded interval is studied. Our cost functional is of time average type which is different from standard long term average cost functional but can be considered as a version of the Gittins index. Both characterization of the, value function and construction of optimal stopping policy will be presented.     

基于后向区间选择偏最小二乘算法的软测量建模

偏最小二乘是在光谱多变量校正中广泛使用的一种算法,现已经发现高效的变量选择不仅能够提高模型的预测能力,也可大大降低模型的复杂度。为了建立具有鲁棒性和低复杂度的基于光谱的在线软测量模型,考虑到光谱变量之间高度相关这一事实,提出一种基于后向区间选择策略的偏最小二乘算法。该算法主要步骤是:先将光谱波长域细分为一定数量的等长子区间;再采用后向淘汰的策略,将各个子区间逐步淘汰,形成一个淘汰序列;最后,再反向选择一定数量的子区间建立最终的模型。通过一个实例以及与传统基于全谱的偏最小二乘算法比较,显示出了该算法的在建立软测量模型方面的优良性能。     

有理B样条曲面的区间隐式化

提出有理B样条曲面的区间隐式化方法,即对一个有理B样条曲面,寻求包含给定的曲面的区间隐式B样条曲面,使得区间隐式B样条曲面的"厚度"尽量小,同时尽量避免出现多余分支.该问题等价于求区间隐式B样条曲面的2个边界曲面.针对该问题建立一个最优化模型并求解.     

基于属性位复用的约束性关联规则挖掘算法

在提取满足用户特定需求的关联规则时,由于现有约束性关联规则挖掘算法存在大量的冗余候选项和重复计算,故提出一种基于属性位复用的约束性关联规则挖掘算法,其适合挖掘任何长度且满足用户特定需求的关联规则。该算法通过属性位的权值组合,将交易事务转换成整数,用属性位复用技术构建候选区间,并利用其端点值双向变化,构建索引候选频繁项,同时也用布尔运算计算其支持数。实验证明其比现有算法更快速,将其应用到客户关系管理系统中分析客户关联信息,可以有效地提高系统效率。     

A Timeframe for SARS-CoV-2 Genomes: A Proof of Concept for Postmortem Interval Estimations

Establishing the timeframe when a particular virus was circulating in a population could be useful in several areas of biomedical research, including microbiology and legal medicine. Using simulations, we demonstrate that the circulation timeframe of an unknown SARS-CoV-2 genome in a population (hereafter, estimated time of a queried genome [QG]; t_E-QG) can be easily predicted using a phylogenetic model based on a robust reference genome database of the virus, and information on their sampling dates. We evaluate several phylogeny-based approaches, including modeling evolutionary (substitution) rates of the SARS-CoV-2 genome (~10⁻³ substitutions/nucleotide/year) and the mutational (substitutions) differences separating the QGs from the reference genomes (RGs) in the database. Owing to the mutational characteristics of the virus, the present Viral Molecular Clock Dating (VMCD) method covers timeframes going backwards from about a month in the past. The method has very low errors associated to the t_E-QG estimates and narrow intervals of t_E-QG, both ranging from a few days to a few weeks regardless of the mathematical model used. The SARS-CoV-2 model represents a proof of concept that can be extrapolated to any other microorganism, provided that a robust genome sequence database is available. Besides obvious applications in epidemiology and microbiology investigations, there are several contexts in forensic casework where estimating t_E-QG could be useful, including estimation of the postmortem intervals (PMI) and the dating of samples stored in hospital settings.