基于数据驱动的故障诊断技术研究现状及展望

对基于数据驱动的过程故障诊断方法进行了总结和划分,其中包含多元统计方法、机器学习方法、流形学习方法等。将各类基于数据驱动的故障诊断方法的原理、研究进展及其在工业过程中的应用进行了描述和分析,最后指出这一领域中需要进一步解决的问题以及近期的研究热点。     

Transfer bounds for linear feature learning

If regression tasks are sampled from a distribution, then the expected error for a future task can be estimated by the average empirical errors on the data of a finite sample of tasks, uniformly over a class of regularizing or pre-processing transformations. The bound is dimension free, justifies optimization of the pre-processing feature-map and explains the circumstances under which learning-to-learn is preferable to single task learning.     

Syllabification rules versus data-driven methods in a language with low syllabic complexity: The case of Italian

Linguistic rules have been assumed to be the best technique for determining the syllabification of unknown words. This has recently been challenged for the English language where data-driven algorithms have been shown to outperform rule-based methods. It may be possible, however, that data-driven methods are only better for languages with complex syllable structures. In this study, three rule-based automatic syllabification systems and two data-driven automatic syllabification systems (Syllabification by Analogy and the Look-Up Procedure) are compared on a language with lower syllabic complexity – Italian. Comparing the performance using a lexicon containing 44,720 words, the best data-driven algorithm (Syllabification by Analogy) achieved 97.70% word accuracy while the best rule set correctly syllabified 89.77% words. These results show that data-driven methods can also outperform rule-based methods on Italian syllabification, a language of low syllabic complexity.     

On the computational complexity and generalization properties of multi-stage and stage-wise coupled scenario programs

We discuss the computational complexity and feasibility properties of scenario sampling techniques for uncertain optimization programs. We propose an alternative way of dealing with a special class of stage-wise coupled programs and compare it with existing methods in the literature in terms of feasibility and computational complexity. We identify trade-offs between different methods depending on the problem structure and the desired probability of constraint satisfaction. To illustrate our results, an example from the area of approximate dynamic programming is considered.     

Leak localization in water distribution networks using a mixed model-based/data-driven approach

This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.     

Data-driven realizations of kernel and image representations and their application to fault detection and control system design

This paper deals with the data-driven design of observer-based fault detection and control systems. We first introduce the definitions of the data-driven forms of kernel and image representations. It is followed by the study of their identification. In the context of a fault-tolerant architecture, the design of observer-based fault detection, feed-forward and feedback control systems are addressed based on the data-driven realization of the kernel and image representations. Finally, the main results are demonstrated on the laboratory continuous stirred tank heater (CSTH) system.     

Action research—Scandinavian experiences

This article focus on paradigms, methods and ethics of action research in the Scandinavian countries. The specific features of the action research paradigm are identified. a historical overview follows of some main action research projects in Norway, Sweden and Denmark. The tendency towards upscale action research projects from organisational or small community projects to large-scale, regional based network approaches are also outlined and discussed. Finally, a synthesised approach of the classical, socio-technical action research approach and the large-scale network and holistic approaches is suggested as a promising approach for the future.     

Hybrid intelligent scenario generator for business strategic planning by using ANFIS

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of experts in scenario planning. The proposed hybrid intelligent scenario generator uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to deal with uncertain inputs. In this methodology, the strengths of expert systems, fuzzy logic and Artificial Neural Networks (ANNs) are joined to generate possible future scenarios. The proposed methodology includes four steps: step 1 defines the scope and internal and external variables and step 2 determines rules from experts. Then, step 3 prepares ANFIS system which is conducted by computer programming in Matlab environment. The Last step is sensitivity analysis to study the effects of variation of inputs on outputs. The applicability of the proposed method has been tested against two different case studies.     

档案数字化思考与研究

档案数字化建设是一项关系档案事业能否稳定、持续、健康发展的战略决策,提出了档案数字化研究方案,以推进档案数字化的健康发展。     

高速公路车道收费系统通用化设计与实现

该文针对目前高速公路收费系统开发周期长、可维护性差等缺点,设计和实现了基于组件和框架复用技术的通用化车道收费系统,开发人员可以在这个系统上进行二次开发,从而大大缩短了开发周期,减少了开发成本。