‘Integronsters’, integral and integrated modeling

In many cases model integration treats models as software components only, ignoring the fluid relationship between models and reality, the evolving nature of models and their constant modification and recalibration. As a result, with integrated models we find increased complexity, where changes that used to impact only relatively contained models of subsystems, now propagate throughout the whole integrated system. This makes it harder to keep the overall complexity under control and, in a way, defeats the purpose of modularity, when efficiency is supposed to be gained from independent development of modules. Treating models only as software in solving the integration challenge may give birth to ‘integronsters’ – constructs that are perfectly valid as software products but ugly or even useless as models. We argue that one possible remedy is to learn to use data sets as modules and integrate them into the models. Then the data that are available for module calibration can serve as an intermediate linkage tool, sitting between modules and providing a module-independent baseline dynamics, which is then incremented when scenarios are to be run. In this case it is not the model output that is directed into the next model input, but model output is presented as a variation around the baseline trajectory, and it is this variation that is then fed into the next module down the chain. However still with growing overall complexity, calibration can become an important limiting factor, giving more promise to the integral approach, when the system is modeled and simplified as a whole.     

Automated Composition of Module Chains

We introduce a problem of module composition. Modules are seen as “black boxes” with input and output ports. A compatibility relation models which input ports can connected to which output ports. We are given a set of available modules and a target module. We want to connect available modules into a chain that implements the target module. Constraints may be given on how many copies of each module can or should appear in the solution chain. Costs may be given on modules or ports or connections, so that an optimal solution with respect to these costs is found. We derive an algorithm to solve the above problem automatically. The algorithm transforms the problem into a shortest-path problem in a graph.     

基于状态转移的感知节点能耗模型研究与设计

针对基于理论模型的感知节点能耗研究通常仅考虑部件能耗、缺少从节点整体上分析与评估系统能耗问题的现状,通过分析节点部件在状态运行以及状态变迁时的能耗状况;基于状态转移的矩阵建模方式建立部件能耗模型;基于事件触发机制明确系统主要能耗部件之间的关联,建立节点整体的能耗模型。在OPNET上仿真验证了本模型的可靠性及实用性。仿真结果证明,本模型可为节点选型、协议评测以及网络构建分析提供模型支持和理论指导。     

Incorporating customer personalization preferences in open product architecture design

Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.     

基于时空相似LSTM的空气质量预测模型

由传统机器学习方法组成的空气质量预测模型得到了普遍应用,但是此类模型对于数据有效性,特别是时空相关数据的选取仍旧存在不足.针对深度学习输入数据有效性问题进行研究,提出了一种基于时空相似LSTM的预测模型(spatial-temporal similarity LSTM model,STS-LSTM),以便在时间和空间层面选取更加有效的数据.STS-LSTM分为前序、中序和后序三个模块,前序模块为时空相似选择输入模块,提出了格兰杰因果权重动态时间折叠(Granger causal index weighted dynamic time warping,GCWDTW)算法,用于选取具有更高时空相似性的数据;中序模块使用LSTM作为深度学习网络进行训练;后序模块根据目标站点特征选择不同的输出组合进行集成.STS-LSTM整体模型在空气质量预测误差上较现有算法提升了8％左右,经过有效性选取的数据对于模型精度达到了最高21％的提升.实验结果表明,对于有效数据的选取该算法取得了显著效果,将数据输入输出方法作为应用型深度学习网络的一部分,可以有效提升深度学习网络的最终效果.     

Compositional validation of time-critical systems usingcommunicating time Petri nets

An extended Petri net model which considers modular partitioning along with timing restrictions and environment models is presented. Module constructs permit the specification of a complex system as a set of message passing modules with the timing semantics of time Petri nets. The state space of each individual module can be separately enumerated and assessed under the assumption of a partial specification of the intended module operation environment. State spaces of individual modules can be recursively integrated, to permit the assessment of module clusters and of the overall model, and to check the satisfaction of the assumptions made in the separate analysis of elementary component modules. In the intermediate stages between subsequent integration steps, the state spaces of module and module clusters can be projected onto reduced representations concealing local events that are not essential to the purposes of the analysis. The joint use of incremental enumeration and intermediate concealment of local events allows for a flexible management of state explosion, and permits a scalable approach to the validation of complex systems     

输电塔架参数化三维建模方法研究与实现

快速建立输电塔架三维仿真模型对电网安全运行与信息管理有重要作用。根据输电塔架的结构特点及结构相似性进行模块化划分,并用参数化方法为相同结构型式的模块建立原始三维模型;在此基础上根据塔架型号与呼高,对各模块进行选择与组装,赋值参数驱动各模块的原始模型,实现各种塔型与呼高的塔架三维快速建模。以某酒杯型直线塔为例,输入塔架型号与呼高即实现其三维模型快速建立,验证了该参数化三维建模方法的正确性与实用性。     

分布式系统多星故障诊断任务调度结构研究

多星故障诊断任务调度模型是构建多星任务调度系统的基础,提出分布式的多星故障诊断调度模型,将调度功能分为三个模块——监控模块、任务调度模块和服务器模块。将这些模块结合起来,便于实现多星的故障诊断,便于系统扩展、维护。以多星故障诊断任务调度的禁忌搜索算法为例,分析各模块之间的关系,为未来多星故障诊断任务划分提供了有力支撑。     

基于XML的数据模型在报表模块上的应用

该文针对商业报表工具和报表模块的不足,提出了一个新的报表模块框架,利用基于XML技术定义的数据模型,在报表功能模块与实际数据源之间建立了一个“中间层”。“中间层”的引入,降低了报表模块与实际数据源的耦合度,提高了报表模块的分析能力和可操作性,也为报表模块与其它应用系统之间实现应用集成和数据集成提供了便利。     

基于火电机组性能计算的软件建模与仿真

以电厂热力系统为目标对象,对其特点进行相关分析。以电厂基本设备为最小组成单位,将热力系统划分成若干模块,然后基于此为不同单元构建了相应的仿真模块。融合组态技术以及面向目标的设计理念,设计了能够对火电机组热力性能进行数据处理的计算软件,该软件主要包括以下模块：一、原始数据输入模块;二、仿真计算模块;三、结果输出模块;四、数据管理模块等。设置图形组态环境,同时构建热力设备所对应的仿真模型,能够让软件获得包括可视化和可组态化在内的两大基本功能,如此一来,赋予软件良好的通用性,使其可以满足多个机组、多种状态下的相关计算。     

Modular design to support green life-cycle engineering

The severe competition in the market has driven enterprises to produce a wider variety of products to meet consumers’ needs. However, frequent variation of product specifications causes the assembly and disassembly of components and modules to become more and more complicated. As a result, the issue of product modular design is a problem worthy of concern. In this study, engineering attributes were added to the liaison graph model for the evaluation of part connections. The engineering attributes added, including contact type, combination type, tool type, and accessed direction, serve to offer designers criteria for evaluating the component liaison intensity during the design stage. A grouping genetic algorithm (GGA) is then employed for clustering the components and crossover mechanisms are modified according to the need of modular design. Furthermore, a reasonable green modular design evaluation is conducted using the green material cost analysis. According to the results, adjusted design proposals are suggested and materials that cause less pollution are recommended to replace the components with pollution values higher than those in the module. Finally, the authors use Borland C++ 6.0 to evaluate the system and clustering method. To illustrate the methodology proposed in this study, a table lamp is offered as an example.     

A class decomposition approach for GA-based classifiers

Genetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. In this paper, a new approach using class decomposition is proposed to improve the performance of GA-based classifiers. A classification problem is fully partitioned into several class modules in the output domain and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently and the results obtained are integrated and evolved further for a final solution. A scheme based on Fisher's linear discriminant (FLD) computation is used to estimate the difficulty of separating two classes. Based on the FLD information derived, different integration approaches are proposed and their performance is compared. The experiment results on a benchmark data set show that class decomposition can achieve higher classification rate than the normal GA and FLD-based integration improves the classification accuracy further.     

A Multi-Module Machine Learning Approach to Detect Tax Fraud

Tax fraud is one of the substantial issues affecting governments around the world. It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability. This is done by either reducing sales or increasing purchases. According to recent studies, governments lose over $500 billion annually due to tax fraud. A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies. Most of the work done in tax fraud using machine learning is centered on supervised models. A significant drawback of this approach is that it requires tax returns that have been previously audited, which constitutes a small percentage of the data. Other strategies focus on using unsupervised models that utilize the whole data when they search for patterns, though ignore whether the tax returns are fraudulent or not. Therefore, unsupervised models are limited in their usefulness if they are used independently to detect tax fraud. The work done in this paper focuses on addressing such limitations by proposing a fraud detection framework that utilizes supervised and unsupervised models to exploit the entire set of tax returns. The framework consists of four modules: A supervised module, which utilizes a tree-based model to extract knowledge from the data; an unsupervised module, which calculates anomaly scores; a behavioral module, which assigns a compliance score for each taxpayer; and a prediction module, which utilizes the output of the previous modules to output a probability of fraud for each tax return. We demonstrate the effectiveness of our framework by testing it on existent tax returns provided by the Saudi tax authority.     

基于密集反馈网络的单幅图像超分辨率重建

基于深度学习的单幅图像超分辨率网络模型体积庞大,导致参数利用率低且难以部署,对中间层特征利用不充分。提出一种密集反馈注意力网络（DFAN）模型。在同一特征图中通过多尺度残差注意力模块（MRAB）提取不同尺度的深层特征,以增加特征的多样性。同时将每个MRAB的输出均作为同组中其他残差模块的输入,使各层之间的信息流最大化,从而减小训练难度。实验结果表明,相比VDSR、DRRN、MemNet等模型,DFAN模型具有较优的重建效果,其在重建放大倍数为4的Set5数据集上计算复杂度仅为VDSR模型的0.14倍左右,而峰值信噪比提高了0.57 dB。     

Global context-dependent recurrent neural network language model with sparse feature learning

Recurrent neural network language models (RNNLMs) are an important type of language model. In recent years, context-dependent RNNLMs are the most widely used ones as they apply additional information summarized from other sequences to access the larger context. However, when the sequences are mutually independent or randomly shuffled, these models cannot learn useful additional information, resulting in no larger context taken into account. In order to ensure that the model can obtain more contextual information in any case, a new language model is proposed in this paper. It can capture the global context just by the words within the current sequences, incorporating all the preceding and following words of target, without resorting to additional information summarized from other sequences. This model includes two main modules: a recurrent global context module used for extracting the global contextual information of the target and a sparse feature learning module that learns the sparse features of all the possible output words to distinguish the target word from others at the output layer. The proposed model was tested on three language modeling tasks. Experimental results show that it improves the perplexity of the model, speeds up the convergence of the network and learns better word embeddings compared with other language models.

     

基于Simulink模型确定计算顺序的研究

将Simulink仿真与代码生成相结合具有很大的实用价值。通过分析Simulink模型特点,将模型转变为C语言代码可达到模型仿真和代码生成相结合的目的,而其中亟需解决的问题是在生成代码过程中计算顺序的确定。通过提取Simulink模型文件中的信息来分析Simulink模型中模块的特性,并将模块间的关系以图的形式存储进而得到Simulink模型中模块间的依赖关系。由模块特性和模块间依赖关系得出两种计算顺序:考虑到底层模块的模块化计算顺序和顾及子系统的层次化计算顺序。通过分析比较这两种计算顺序,发现层次化计算顺序优于模块化计算顺序。最后对Simulink自带的f14模型的测试说明了两种计算顺序是可行的。     

Combined circuit/device modeling and simulation of integrated microfluidic systems

A combined circuit/device model for the analysis of integrated microfluidic systems is presented. The complete model of an integrated microfluidic device incorporates modeling of fluidic transport, chemical reaction, reagent mixing, and separation. The fluidic flow is generated by an applied electrical field or by a combined electrical field and pressure gradient. In the proposed circuit/device model, the fluidic network has been represented by a circuit model and the functional units of the /spl mu/-TAS (micro Total Analysis System) have been represented by appropriate device models. We demonstrate the integration of the circuit and the device models by using an example, where the output from the fluidic transport module serves as the input for the other modules such as mixing, chemical reaction and separation. The combined circuit/device model can be used for analysis and design of entire microfluidic systems with very little computational expense, while maintaining the desired level of accuracy.     

Structure and stability analysis of general Mamdani fuzzy dynamic models

Mamdani fuzzy models have always been used as black‐box models. Their structures in relation to the conventional model structures are unknown. Moreover, there exist no theoretical methods for rigorously judging model stability and validity. I attempt to provide solutions to these issues for a general class of fuzzy models. They use arbitrary continuous input fuzzy sets, arbitrary fuzzy rules, arbitrary inference methods, Zadeh or product fuzzy logic AND operator, singleton output fuzzy sets, and the centroid defuzzifier. I first show that the fuzzy models belong to the NARX (nonlinear autoregressive with the extra input) model structure, which is one of the most important and widely used structures in classical modeling. I then divide the NARX model structure into three nonlinear types and investigate how the settings of the fuzzy model components, especially input fuzzy sets, dictate the relations between the fuzzy models and these types. I have found that the fuzzy models become type‐2 models if and only if the input fuzzy sets are linear or piecewise linear (e.g., trapezoidal or triangular), becoming type 3 if and only if at least one input fuzzy set is nonlinear. I have also developed an algorithm to transfer type‐2 fuzzy models into type‐1 models as far as their input–output relationships are concerned, which have some important properties not shared by the type‐2 models. Furthermore, a necessary and sufficient condition has been derived for a part of the general fuzzy models to be linear ARX models. I have established a necessary and sufficient condition for judging local stability of type‐1 and type‐2 fuzzy models. It can be used for model validation and control system design. Three numeric examples are provided. Our new findings provide a theoretical foundation for Mamdani fuzzy modeling and make it more consistent with the conventional modeling theory. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 103–125, 2005.     

基于多模型的能源组件维修性建模与仿真研究

某激光装置能源组件是其关键组件,该组件具有随机劣化失效特点,其部件失效类型多,维修类型和维修策略也比较复杂,现有的基于单一模型和解析方法的系统维修性建模与分析方法难以解决其维修性设计与分析问题。本文采用多模型集成的维修性建模与仿真方法,解决能源组件维修性建模与分析问题。其中,采用故障树模型描述能源组件系统级可靠性,采用SPNAT模型描述各部件的失效过程和维修决策,并基于"Join/Replicate"操作进行模型集成,通过报酬结构定义组件的维修性指标。仿真结果表明,基于多模型集成的维修性建模与仿真方法,是解决能源组件这样的复杂产品维修方案设计与分析的有效途径。     

弧焊机器人离线编程系统标定模块的开发

本文从机器人离线编程技术实用化的角度出发 ,在进行标定模块整体结构分析的基础上 ,针对标定的一般步骤 ,将机器人标定模块分为数据采集和数据处理两个子模块 .然后从机器人标定功能角度出发 ,提出了一种标定模块的总体结构 ,并针对现有的测量设备进行了标定数据采集模块的开发 ,搭建了标定模块的整体框架 ,实现了其中的标定测量数据的自动接收、标定数据的自动处理以及标定结果的输出等功能