Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

PTA污水厂COD在线监测结果不合格原因分析及对策

对在线自动监测仪监测结果与人工化验数据存在误差的原因进行分析,以提高在线自动监测仪监测结果的科学性和可靠性,并提出了相对应的建议,有效提升装置出水合格率。     

An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

The development of a single-item Food Choice Questionnaire

Based on the multi-item Food Choice Questionnaire (FCQ) originally developed by Steptoe and colleagues (1995), the current study developed a single-item FCQ that provides an acceptable balance between practical needs and psychometric concerns. Studies 1 (N = 1851) and 2 (2a (N = 3290), 2b (N = 4723), 2c (N = 270)) showed that the single-item FCQ scale has good convergent and discriminant validity. Generally, the results showed the highest correlations with the related multi-item dimensions (>0.40). Study 2 refined the scale. Only the items for convenience (Study 2a), sensory appeal (Study 2b) and mood (Study 2c) needed to be revised (as Study 1 showed a correlation between the multi-item and the single-item below the threshold of 0.60). The results also showed comparable predictive validity. Both methods revealed similar association patterns between food motives and consumption behaviours (Fisher’s z tests revealed agreements of 86.2% for Study 1, 92.9% for Study 2a and 100% for Studies 2b and 2c). Study 3 (N = 6062) showed an example of the added value of a context-specific application for the single-item FCQ. Different motives were shown to be relevant across contexts, and the context-specific motives had additional explained variance beyond the general multi-item FCQ. Studies 2b and 3 also showed the performance of the single-item FCQ in an international context. In sum, the results indicate that the single-item FCQ can be used as a flexible and short substitute for the multi-item FCQ. The study also discusses the conditions that should be considered when using the single-item scale.     

Time-series clustering – A decade review

Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.     

灰局势决策在水利工程开发方案优选中的应用

针对水利工程开发方案优选具有多目标、多层次等特点,建立灰局势决策模型并应用变异系数法确定评价指标权重。利用该模型对灌区改建扩建规划方案进行优选,结果表明这是一种有效的方法。     

提高催化裂化装置(FCCU)柴汽比措施

为了满足市场的需要,中石化天津分公司炼油厂根据FCCU自身特点采取了一系列提高柴汽比的措施,包括:调节反应系统和分馏的操作条件及采用MGD技术,以提高柴油产率。实施后,FCCU的柴汽比从0.5提高到0.9,创造了良好的经济效益。     

油水井措施经济效益分析方法研究

针对油气藏产量下降,经济效益变差的情况,为提高油气井产量,提高油气藏采收率,从经济学的角度出发,确定出了油水井措施效益分析时的各个参数的选定和取法,并建立了措施经济效益评价方法,为实施油水井进一步开发制定相应的决策提供了参考依据.     

CFR辛烷值机测定误差的分析

通过比较现行国家标准与2004年版ASTM方法的主要差异，针对CFR试验机使用中易遇到的具体问题，分析辛烷值测量误差产生的原因，提出消除测量误差应注意的事项，以及校准CFR试验机应采取的措施。指出对CRF试验机一定要严格遵守试验方法及使用维护操作规程，依据实行情况进行校准，特别是检测RON低于80、MON低于60的样品时。     

热镀锌板(卷)工程设计中的环境保护问题

对热镀锌板(卷)的生产工艺、主要生产设备进行了简要介绍，并就其污染源(工序)、生产过程中产生的污染物及其控制措施进行了评述，同时就设计和生产中宜注意的几个环保问题进行了分析。