An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

基于关联规则的网络行为分析

网络用户访问网站的过程中,产生了大量的用户浏览网页的相关记录,隐含着用户在上网过程中的行为习惯。但其中潜在的用户信息难以发现。因此,急需有效的方法提取这些数据中的信息,数据挖掘应用而生。其中,关联规则技术是应用广泛的技术之一。文中利用Apriori算法对Web结构数据进行关联规则挖掘,所得到的规则反映出页面之间的链接关系。分析挖掘结果可得到用户访问的行为规律,为相关网站的安全性和优化改进提供有效的决策依据。     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

基于新标准的俄罗斯釆金业职业风险评估

当前俄罗斯正在进行立法改革以实现治理体系现代化，其中改革的重点是国家管理体系中的劳动保护、工业安全和突发事件的应急处理。过去的立法体系侧重于对受伤员工提供健康补偿和医疗救护，而新的立法体系则以预防为主，以期通过建立完善的风险管理机制和运行规则，提高安全生产水平、普及安全文化教育，保障员工的生命安全。新标准强调企业必须进行职业风险评估，并将职业风险评估纳入企业工作管理体系。论文以采金业为例，选取伊尔库茨克州波戴波区金矿露天采场为研究案例，探讨了现代化职业风险评估的方法，明确了职业风险评估首先要进行危害鉴别，编制了风险源管理档案清单。采用实测员工风险因素的“记分法”和数学模拟“矩阵法”完成了伊尔库茨克州波戴波区金矿露天采场主要职业的风险评估。研究表明: 在钻井爆破作业场所，由碎石的喷射、粉尘的排放和噪声效应引起的危险为不可接受风险，通过实施安全生产制度（即遵守安全标准和技术要求、为工人提供防护装备）,可将高风险和不可接受风险化解为可接受风险；阐述了高风险职业，编制了风险手册，提出了改善工作条件的首要措施是消除影响爆破手和钻机操作员的有害因素，并为员工提供能更有效预防噪音和振动的个人防护用品。     

Efficient closed high-utility pattern fusion model in large-scale databases

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system.     

Learned color taste associations in a repeated brief exposure paradigm

Individuals can learn to associate tastes with odors through repeated exposure. We adapted this paradigm, testing whether individuals can learn to associate tastes with colors, and whether these learned color-taste associations generalize to unconditioned, but qualitatively similar stimuli. Experiment 1 tested if individuals could learn color-taste associations for prototypical tastes, while Experiment 2 tested if individuals could learn to discriminate specific bitter compounds using color-taste associations. Conditioned stimuli in Experiment 1 consisted of solutions representing four different prototypical taste qualities. Conditioned stimuli in Experiment 2 consisted of three different bitter stimuli and a sucrose control. Both experiments consisted of six laboratory visits. On day 1, participants rated intensities of conditioned and unconditioned stimuli. On days 2–5 participants completed triangle tests, where a color was systematically paired with high/low intensity combinations of the conditioned stimuli. On day 6, participants matched colorless solutions containing the conditioned and unconditioned stimuli to one of four colors in a forced choice task. In Experiment 1, group performance was significantly above chance (25%) when matching the conditioned taste stimulus to the previously paired color. Also, learning generalized to unconditioned stimuli with a similar taste quality. In Experiment 2, group performance versus chance (25%) indicated participants learned associated sweetness with the previously paired color, both for sucrose (conditioned stimulus) and aspartame (unconditioned stimulus). However, matching performance did not exceed chance for the three different bitterants. These data suggest that particular color-taste associations can be learned with repeated exposure, even when the exposure period is relatively brief.     

超高回采巷道顶部柔膜袋快速充填技术

针对阳泉煤业集团南庄煤矿8836运输巷局部超高,威胁安全生产的问题,实测了3个充填区域,设计了顶部柔膜袋快速充填方案,并进行了现场试验。结果表明:高水充填材料具备速凝早强特性,2 h强度1.25 MPa,28 d强度3.98 MPa,材料消耗约305 kg/m^3;效果考察表明,在工作面超前区,充填体下方单体柱受力均匀、明显,充填体接顶充分,工作面后方充填体垮落效果良好,未形成悬顶。     

里必井田承压水带压开采评估

煤层底板承压水突出是发生水害事故的原因之一,要减少该类事故的发生,需对底板突水危险性进行准确评价。为保障里必井田承压水带压开采的安全进行,基于煤层底板突水研究理论分析,采用常规突水系数法和等效厚度突水系数法,分别对里必井田内3#煤和15#煤的奥灰水突水危险性进行了研究。研究发现,奥灰水对于3#煤的开采影响不大,但在局部构造及陷落柱发育地段及采区东北部区域仍存在一定的突水可能性;奥灰水对15#煤开采具有较大威胁,大部分区域具有较大突水危险性。等效岩层厚度法计算的突水系数考虑了不同性质岩层的岩石强度和阻水能力,计算更接近生产实际,结果更可靠;且在对突水系数法进一步优化后,其评价的结果可以更精确。     

基于多尺度的贝叶斯模型显著性检测

针对传统基于贝叶斯模型的显著性检测算法存在准确率不理想的问题,提出了一种基于多尺度的贝叶斯模型显著性检测算法。通过超像素分割算法(SLIC)将原图分割成不同尺度的超像素,根据超像素边界信息得到背景种子,进而通过距离计算和多尺度融合得到背景先验;对原图进行颜色增强,采用Harris算子对增强图进行检测角点求得凸包,融合不同尺度下的超像素得到凸包先验;融合背景先验和凸包先验得到最终先验;利用颜色直方图和凸包计算似然概率;将最终先验和似然概率通过贝叶斯模型计算显著图。在公开数据集MSRA1000、ECSSD上与多种传统算法进行准确率和召回率对比,该算法有更好的表现。     

Torii: An aspect-based sentiment analysis system that can mine conditions

Aspect-based sentiment analysis systems are a kind of text-mining systems that specialize in summarizing the sentiment that a collection of reviews convey regarding some aspects of an item. There are many cases in which users write their reviews using conditional sentences; in such cases, mining the conditions so that they can be analyzed is very important not to misinterpret the corresponding sentiment summaries. Unfortunately, current commercial systems or research systems neglect conditions; current frameworks and toolkits do not provide any components to mine them; furthermore, the proposals in the literature are insufficient because they are based on handcrafted patterns that fall short regarding recall or machine learning procedures that are tightly bound with a specific language and require too much configuration. In this article, we present Torii, which is a system that loads a collection of reviews, discovers the aspects on which they report, and summarizes the sentiment that is conveyed on them taking into account the existing conditions, if any. We also describe its architecture, our approach to mine conditions, and our experimental analysis on a large multilingual data set with reviews from multiple categories. To the best of our knowledge, Torii is the first proposal that addresses aspect-based sentiment analysis taking conditions into account.