REASONING FOR AUTOMATED MODEL INTEGRATION

This paper investigates some reasoning issues involved in developing an integrated modeling environment called a model management system. A model management system is a computer-based environment designed to support effective development and utilization of quantitative decision models. Since the construction of decision models is a knowledge-intensive process, reasoning plays a critical role. Reasoning is particularly important when automated model integration is needed in a large-scale system. In this case, heuristics are required to reduce the complexity of the process. This paper examines the planning and automated formulation of complex models from smaller building blocks. First, a hierarchy of abstractions that integrates previous contributions in model management is presented. Then, a modeling process is formulated as a planning process by which a set of operators are scheduled to achieve a specific goal. This process involves searches for alternatives that can eliminate the difference between the initial stale and the goal state. Various reasoning strategies and heuristic evaluation Junctions that can be used to improve the efficiency of developing a master plan for model integration are discussed.     

Development and evaluation of an ergonomic software package for predicting multiple-task human performance and mental workload in human–machine interface design and evaluation

Predicting human performance and mental workload in multiple task situations at an early stage of system design can save a significant amount of time and cost. However, existing modeling tools either can only predict human performance or require users of tools to learn a new programming language. Queueing Network-Model Human Processor (QN-MHP) is a new cognitive architecture for modeling both human performance and mental workload in multiple tasks. This paper describes the development of a Visual Basic Application in Excel (VBA) software package and an illustrative case study to evaluate its effectiveness. The software package has an easy-to-use user interface for QN-MHP that assists users of the modeling tool to simulate a dual task including definition of the tasks and interfaces by clicking buttons to select options and filling texts in a table, with no need to learn a simulation language. It allows the model user to intuitively observe the information processing state of the model during simulation, and conveniently compare the simulated human performance and mental workload for different designs. The illustrative case study showed that naïve users without prior simulation language programming experience can model human performance and mental workload in a complex multitask situation within 3 min; and this software package can save 71% of modeling time and reduce 30% of modeling errors. Further developments of the VBA software package of QN-MHP are also discussed on how to make it a comprehensive proactive ergonomic design and analysis tool.     

一种基于灰色马尔柯夫模型的基准地价预测方法

灰色马尔可夫模型综合了灰色模型和马尔可夫预测的优点,对于相对短时序列的数据具有较好的预测效果。论述了该模型的原理和转移概率矩阵的建立方法,并将该模型应用于衡阳市基准地价走势的预测分析,并以实例数据加以验证。结果表明,灰色马尔可夫模型在短时基准地价预测中具有较强的适用性。     

Linear search applied to global motion estimation

Gradient-based algorithms for global motion estimation are effective in many image-processing tasks. However, when analytical estimation of derivatives of objective function is not possible, linear search based algorithms such as Powell perform better than the gradient-based ones. In this paper we propose global motion estimation algorithm that exploits linear search based algorithm, particularly Powell, instead of commonly used gradient-based one. We also introduce a new approach for extracting global motion parameters called Two Step Powell-based GME. Using this approach we further improve the Powell-based GME. The proposed Powell-based GME outperforms Gauss–Newton algorithm (gradient-based) in terms of PSNR. The proposed Two Step Powell GME algorithm outperforms Powell-based GME in terms of PSNR and computational time.     

基于并行计算的大规模外显子芯片数据分析

快速准确地计算出转录组表达水平对转录组研究具有重要的作用。本文针对伽玛分布的概率模型(Gamma model for exon array data, GME)在处理大规模外显子芯 片数据集上效率低下的特点,提出一种充分利用多核处理机或者集群环境来提高效率的并行 计算方法。首先分析GME模型的原理,其次分析模型并行算法的选择,最后在不同规模的数 据集上分析并行计算的效率。通过实验验证了并行计算极大地提高了模型的计算效率。实验结果表 明,与先前的串行计算相比,并行计算使得GME模型更适用于大规模的外显子芯片分析。     

基于非马尔可夫随机Petri网的软件再生建模与分析

软件老化是影响软件系统可靠性的重要潜在因素,软件再生作为一种主动预防性的软件容错技术是解决软件老化问题的主要手段.以往的随机Petri网再生模型假定所有变迁的实施时间服从指数分布.针对变迁的实施时间服从确定性分布或一般性分布的情况,文中提出了一种用非马尔可夫随机Petri网建立软件再生模型的方法.该方法采用马尔可夫再生理论对模型进行分析,并给出模型的瞬态解和稳态解.仿真实验表明:选择合适的软件再生周期,可以有效地降低存在老化的软件系统的平均宕机成本,提高系统的可用性和可靠性.     

基于增量学习的RocksDB键值系统主动缓存机制

由于分层结构的约束,基于日志结构合并（LSM）树的RocksDB键值存储系统面临着读取性能低下的问题。一种有效的解决方法是对热点数据进行主动缓存,但其面临两个挑战：一是如何在数据分布持续动态变化时对热点数据进行预测,二是如何将主动缓存机制与RocksDB存储结构衔接起来。针对这些挑战,基于预测分析技术,构建了由数据采集、系统交互、系统测试等部分组成的面向RocksDB键值系统的主动缓存框架,能够将热点数据缓存在LSM树的较低层级中;并对数据访问模式进行建模,设计并实现了基于增量学习的热点数据预测分析方法,能够有效减少存储介质的I/O访问次数。实验结果表明该机制能有效提升RocksDB在不同动态工作负载下的数据读取性能。     

Fast and accurate global motion estimation algorithm using pixel subsampling

Global motion generally describes the motion of a camera, although it may comprise motions of large objects. Global motions are often modeled by parametric transformations of two-dimensional images. The process of estimating the motions parameters is called global motion estimation (GME). GME is widely employed in many applications such as video coding, image stabilization and super-resolution. To estimate global motion parameters, the Levenburg–Marquardt algorithm (LMA) is typically used to minimize an objective function iteratively. Since the region of support for the global motion representation consists of the entire image frame, the minimization process tends to be very expensive computationally by involving all the pixels within an image frame. In order to significantly reduce the computational complexity of the LMA, we proposed to select only a small subset of the pixels for estimating the motion parameters, based on several subsampling patterns and their combinations. Simulation results demonstrated that the proposed method could speed up the conventional GME approach by over ten times, with only a very slight loss (less than 0.1 dB) in estimation accuracy. The proposed method was also found to outperform several state-of-the-art fast GME methods in terms of the speed/accuracy tradeoffs.     

UTILIZING CONTEXT HISTORY TO PROVIDE DYNAMIC ADAPTATIONS

In order to provide “intimate” and “dynamic” adaptations under Weiser's vision for ubiquitous computing environments, we propose the utilization of context history together with user modeling and machine learning techniques. Our approach supports proactive adaptations by inducing patterns of user behavior. In addition, we support the requirement for enabling the user to receive an explicit and understandable explanation when a proactive adaptation occurs in order to encourage a trust relationship between the user and the context-aware system. In this article, we describe an experiment to examine the feasibility of our approach for supporting proactive adaptations in the domain of an intelligent office environment. The initial results of our experiment are promising and demonstrate how our system could gradually learn the user's preferences for controling his office environment by making inductions from the context history. Based on these initial findings, we believe that context history has a concrete role to play in supporting proactive adaptation in a ubiquitous computing environment.     

导弹多学科设计优化中响应面方法研究

使用响应面方法取代高精度工程模拟可以减少设计周期和成本。目前,有多种响应面模型应用到导弹多学科设计优化中,每种响应面模型有不同优点和不足。本文的目的是通过比较六种响应面模型来帮助设计者选择合适的响应面模型。七种测试函数用来比较响应面模型的三个方面：近似精度、鲁棒性和应用难易度。结果表明,Kriging响应面和增强径向基函数响应面对线性响应、二次响应和高阶非线性响应都有很好的近似效果,而二次多项式响应面和移动最小二乘响应面适合于线性和二次响应,径向基函数响应面适合于高阶非线性响应。神经网络响应面在使用更多的采样点时得到更精确的模型。     

Reducing the verification cost of evolving product families using static analysis techniques

Software product line engineering enables proactive reuse among a set of related products through explicit modeling of commonalities and differences among them. Software product lines are intended to be used in a long period of time. As a result, they evolve over time, due to the changes in the requirements. Having several individual products in a software family, verification of the entire family may take a considerable effort. In this paper we aim to decrease this cost by reducing the number of verified products using static analysis techniques. Furthermore, to reduce model checking costs after product line evolution, we restrict the number of products that should be re-verified by reusing the previous verification result. All proposed techniques are based on static analysis of the product family model with respect to the property and can be automated. To show the effectiveness of these techniques we apply them on a set of case studies and present the results.     

一种反应式SPM及其动态语义XYZ表示

过程支撑环境PSE(process supporting environment)是一种支持软件过程元过程的计算机环境,PSE通过运作一个事先定义好的软件过程模型SPM(software process model)来控制和指导实际软件开发过程.SPM使用的控制方式分为主动式(proactive)和反应式(reactive)两种.由于主动式不能很好地支持软件过程的演化,反应式渐渐受到人们的重视.提出了一种反应式SPM以及建立这种模型所使用的图形化的软件过程建模语言,同时,对于所建立的SPM,提出用时序逻辑语言XYZ/E表示它的行为视图动态语义的方法.这为模型提供了明确的动态语义,为其运作和分析提供了形式化基础.     

分布式网络主动防御系统研究

针对DDOS和蠕虫的特点,提出了一种NeTraMet和QOS相结合的主动防御机制,实现对DDOS和蠕虫经济高效的防治.在蠕虫检测上考虑了无特征蠕虫和有特征蠕虫两种情况;一般基于流量的DDOS检测方法预警时,网络实际已经受到一定程度的攻击而且发生阻塞,为了能够更早预警DDOS攻击,提高网络生存性,在DDOS检测中提出了可疑流量线,当流量达到可疑流量和攻击流量之间时,就启动防御机制,利用路由器的QOS功能,尽量减少攻击流的消耗带宽,维持网络正常服务.最后在NS2中进行模拟验证.     

Variability management in process families through change patterns

Context: The increasing adoption of process-aware information systems together with the high variability in business processes has resulted in collections of process families. These families correspond to a business process model and its variants, which can comprise hundreds or thousands of different ways of realizing this process. Managing process variability in this context can be very challenging, labor-intensive, and error-prone, and new approaches for managing process families are necessary.Objective: We aim to facilitate variability management in process families, ensure process family correctness, and reduce the effort needed for such purposes.Method: We have derived a set of change patterns for process families from variability-specific language constructs identified in the literature. For validation, we have conducted a case study with a safety standard in which we have measured the number of operations needed to model and evolve the variability of the standard with and without the patterns.Results: We present 10 change patterns for managing variability in process families and show how they can be implemented. The patterns support the modeling and evolution of process families and ensure process family correctness by automatically introducing and deleting modeling elements. The case study results show that the application of the defined change patterns can reduce the number of operations when modeling a process family by 34% and when evolving it by 40%.Conclusions: The application of the change patterns can help in effectively modeling and evolving large and highly-variable process families. Their application can also considerably reduce variability management effort.     

How human-robot collaboration impacts construction productivity: An agent-based multi-fidelity modeling approach

Though construction robots have drawn attention in research and practice for decades, human-robot collaboration (HRC) remains important to conduct complex construction tasks. Considering its complexity and uniqueness, it is still unclear how HRC process will impact construction productivity, which is difficult to handle with conventional methods such as field tests, mathematical modeling and physical simulation approaches. To this end, an agent-based (AB) multi-fidelity modeling approach is introduced to simulate and evaluate how HRC influences construction productivity. A high-fidelity model is first proposed for a scenario with one robot. Then, a low-fidelity model is established to extract key parameters that capture the inner relationship among scenarios. The multi-fidelity models work together to simulate complex scenarios. Based on the simulation model, the twofold influence of HRC on productivity, namely the supplement strategy on the worker side, and the design for proactive interaction on the robot side, are fully investigated. Experimental results show that: 1) the proposed approach is feasible and flexible for simulation of complex HRC processes, and can cover multiple collaboration and interaction modes; 2) the influence of the supplement strategy is simple when there is only one robot, where lower Check Interval (CI) and higher Supplement Limit (SL) will improve productivity. But the influence becomes much more complicated when there are more robots due to the internal competition among robots for the limited time of workers; 3) HRC has a scale effect on productivity per robot, which means the productivity improves if there are more robots and workers, even if the human-robot ratio remains the same; 4) introducing proactive interaction between robots and workers could improve productivity significantly, up to 22% in our experiments, which further depends on the supplement strategy and the human-robot ratio. Overall, this research contributes an integrated approach to simulate and evaluate HRC’s impacts on productivity as well as valuable insights on how to optimize HRC for better performance and occupational health. The proposed approach is also useful for the evaluation and development of new robots.     

Genuine multipartite entanglement as the indicator of quantum phase transition in spin systems

In this paper, the genuine multipartite entanglement (GME) and quantum criticality property of spin systems with staggered Dzyaloshinskii–Moriya (DM) interaction are investigated by exploiting quantum renormalization group method. The results show that the GME can indicate quantum phase transitions at critical points after several iterations of the renormalization. Moreover, the DM interaction effectively restores the spoiled GME via creation of quantum fluctuations, while it also changes the critical points. At last, the nonanalytic and scaling behaviors of GME are analyzed in detail.     

A survey of modeling for prognosis and health management of industrial equipment

Prognosis and health management plays an important role in the control of costs associated with operating large industrial equipment, such as wind turbines and aircraft. It is only fair that engineers and scientists have vastly researched modeling approaches to support decision making. Motivated by the growing availability of data and computational power as well as the advances in algorithms and methods, modeling frameworks often merge elements of physics, machine learning, and statistical learning. In this paper, we present a review on modeling in support of prognosis and health management of industrial equipment. This survey complements the existing prognosis and health management literature by discussing how modeling strategies are influenced by industry-specific aspects such as maintenance approaches (e.g., reactive, proactive, and predictive), implementation factors (e.g., industry, business model, purpose, development, and deployment), as well as supporting technologies (sensing, repair, and modeling itself). We use the onshore wind energy and civil aviation industries to illustrate how these aforementioned aspects can influence modeling and implementation of prognosis and health management. The literature review is broad and covers contributions over the past 40 years. We close the paper with few topics that can motive research going forward.     

From event logs to goals: a systematic literature review of goal-oriented process mining

Process mining helps infer valuable insights about business processes using event logs, whereas goal modeling focuses on the representation and analysis of competing goals of stakeholders and systems. Although there are clear benefits in mining the goals of existing processes, goal-oriented approaches that consider logs during model construction are still rare. Process mining techniques, when generalizing large instance-level data into process models, can be considered as a data-driven complement to use case/scenario elicitation. Requirements engineers can exploit process mining techniques to find new system or process requirements in order to align current practices and desired ones. This paper provides a systemic literature review, based on 24 papers rigorously selected from four popular search engines in 2018, to assess the state of goal-oriented process mining. Through two research questions, the review highlights that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining (where goal models are mined) shows a meaningful trace of research. Research about performance indicators measuring goals associated with process mining is also sparse. Although the number of publications in process mining and goal modeling is trending up, goal mining and goal-oriented process mining remain modest research areas. Yet, synergetic effects achievable by combining goals and process mining can potentially augment the precision, rationality and interpretability of mined models and eventually improve opportunities to satisfy system stakeholders.

     

基于区域相关的核函数背景建模算法

传统的背景建模方法都是单独为每个像素建立背景模型,这样就没有考虑像素之间实际存在的相关性,使得在进行前景和背景分割时出现不该出现的空洞和噪声点。为了解决这一问题,提出了一种基于像素邻域信息的核密度估计背景建模算法;给出了相邻像素亮度值分布模型间的相关性描述,并将该相关性引入到背景建模中,得到包含时一空信息的背景模型,并提出背景模型的更新策略;实验结果表明,算法能有效去除前景区域内的空洞和背景区域内的噪声点,能快速准确地更新背景模型,能进行复杂场景的背景建模。     

面向领域知识复用的C4ISR系统需求建模方法

为了准确地获取和复用C4ISR系统需求,提出基于多本体的需求获取与建模框架。详细介绍了在此框架下进行需求建模的过程,首先明确用户的目标模型,然后使用目标对已有的领域知识库进行查询,如果发现匹配的目标,则利用目标-任务的映射关系抽取相关知识,如果没有匹配的目标,则生成概念描述框架引导用户提出需求,最后将两部分需求合并形成最终的需求模型。需求建模过程一方面能够复用已有需求,另一方面能够在本体的引导下提出新的需求,保证了需求模型的质量和建模的效率。