基于数据驱动的螺旋式数据仓库开发方法的研究

文章首先分析了数据仓库与数据库技术本质的区别,讨论了软件系统开发的生命周期方法、快速原型法、螺旋法的特点,然后结合数据仓库开发特点,提出了基于数据驱动的螺旋式开发方法,并给出运用该方法开发数据仓库的步骤。     

模型未知LTI系统的数据驱动预测控制

针对含有随机噪声的模型未知线性时不变 (Linear Time Invariant, LTI) 系统模型建立过程复杂且控制律难以得到的问题,提出一种基于数据驱动的预测控制方法。基于系统行为学理论和平衡子系统辨识方法,仅利用测量得到的系统数据构建被控系统的非参数模型,将其和预测控制理论相结合设计出基于数据驱动的预测控制器,对于系统测量数据中存在的有界加性高斯噪声,通过引入数据的松弛变量和L2正则项来降低噪声扰动的影响,采用滚动时域优化策略计算最优控制序列并将其作用于被控系统,实现系统对设定值的轨迹跟踪。将所提控制策略应用于四容水箱系统,仿真结果表明与同样基于数据驱动的子空间预测控制方案相比,所提方法具有更好的动态性能,且该策略在抗噪声扰动方面有明显优势,具有更强的鲁棒性。     

Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review

Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efcient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efcient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can ofer extended support for engineers and researchers in the context of data-driven technology and the SG.     

Artificial intelligence-enhanced seismic response prediction of reinforced concrete frames

Existing physics-based modeling approaches do not have a good compromise between performance and computational efficiency in predicting the seismic response of reinforced concrete (RC) frames, where high-fidelity models (e.g., fiber-based modeling method) have reasonable predictive performance but are computationally demanding, while more simplified models (e.g., shear building model) are the opposite. This paper proposes a novel artificial intelligence (AI)-enhanced computational method for seismic response prediction of RC frames which can remedy these problems. The proposed AI-enhanced method incorporates an AI technique with a shear building model, where the AI technique can directly utilize the real-world experimental data of RC columns to determine the lateral stiffness of each column in the target RC frame while the structural stiffness matrix is efficiently formulated via the shear building model. Therefore, this scheme can enhance prediction accuracy due to the use of real-world data while maintaining high computational efficiency due to the incorporation of the shear building model. Two data-driven seismic response solvers are developed to implement the proposed approach based on a database including 272 RC column specimens. Numerical results demonstrate that compared to the experimental data, the proposed method outperforms the fiber-based modeling approach in both prediction capability and computational efficiency and is a promising tool for accurate and efficient seismic response prediction of structural systems.     

Distributed representation learning and intelligent retrieval of knowledge concepts for conceptual design

Data-driven conceptual design is rapidly emerging as a powerful approach to generate novel and meaningful ideas by leveraging external knowledge especially in the early design phase. Currently, most existing studies focus on the identification and exploration of design knowledge by either using common-sense or building specific-domain ontology databases and semantic networks. However, the overwhelming majority of engineering knowledge is published as highly unstructured and heterogeneous texts, which presents two main challenges for modern conceptual design: (a) how to capture the highly contextual and complex knowledge relationships, (b) how to efficiently retrieve of meaningful and valuable implicit knowledge associations. To this end, in this work, we propose a new data-driven conceptual design approach to represent and retrieve cross-domain knowledge concepts for enhancing design ideation. Specifically, this methodology is divided into three parts. Firstly, engineering design knowledge from the massive body of scientific literature is efficiently learned as information-dense word embeddings, which can encode complex and diverse engineering knowledge concepts into a common distributed vector space. Secondly, we develop a novel semantic association metric to effectively quantify the strength of both explicit and implicit knowledge associations, which further guides the construction of a novel large-scale design knowledge semantic network (DKSN). The resulting DKSN can structure cross-domain engineering knowledge concepts into a weighted directed graph with interconnected nodes. Thirdly, to automatically explore both explicit and implicit knowledge associations of design queries, we further establish an intelligent retrieval framework by applying pathfinding algorithms on the DKSN. Next, the validation results on three benchmarks MTURK-771, TTR and MDEH demonstrate that our constructed DKSN can represent and associate engineering knowledge concepts better than existing state-of-the-art semantic networks. Eventually, two case studies show the effectiveness and practicality of our proposed approach in the real-world engineering conceptual design.     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

数据驱动技术在WRSAFS中的研究与应用

本文以自动化测试过程中应用数据驱动时遇到的问题为切入点,提出了一种更为先进的数据驱动技术并与自动化测试框架WRSAFS(Win Runner Software Automation Framework Support)相结合,重点分析了数据驱动技术在WRSAFS中的应用及相互关系。借助实际被测试软件,应用此技术设计测试用例并取得了很好的效果。     

From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring

Predictive process monitoring (PPM) is a research area that focuses on predicting measures of interest (e.g., the completion time) for running cases based on event logs. State-of-the-art PPM techniques only consider intra-case information that comes from the case whose measures of interest one wishes to predict. However, in many systems, the outcome of a running case depends on the interplay of all cases that are being executed concurrently, or can be derived from the characteristics of cases that are executed in the same period of time. For example, in many situations, running cases compete over scarce resources, and the completion time of a running case can be derived from the number of similar cases running concurrently. In this work, we present a general framework for feature encoding that relies on a bi-dimensional state space representation. The first dimension corresponds to intra-case dependencies and utilizes existing feature encoding techniques. The second dimension encodes inter-case features using two approaches: (1) a knowledge-driven encoding (KDE), which assumes prior knowledge on case types, and (2) a data-driven encoding (DDE), which automatically identifies case types from data using case proximity metrics. Both approaches partition the event log into sets of cases that share common characteristics, and derive features according to these commonalities. We demonstrate the usefulness of the proposed framework with an empirical evaluation carried out against two real-life datasets coming from an outpatient hospital process and a manufacturing process.     

A review of the Expectation Maximization algorithm in data-driven process identification

The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided.