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Analyzing construction safety through time series methods

Houchen CAO, Yang Miang GOH

《工程管理前沿（英文）》 2019年第6卷第2期页码 262-274 doi: 10.1007/s42524-019-0015-6

摘要： The construction industry produces a large amount of data on a daily basis. However, existing data sets have not been fully exploited in analyzing the safety factors of construction projects. Thus, this work describes how temporal analysis techniques can be applied to improve the safety management of construction data. Various time series (TS) methods were adopted for identifying the leading indicators or predictors of construction accidents. The data set used herein was obtained from a large construction company that is based in Singapore and contains safety inspection scores, accident cases, and project-related data collected from 2008 to 2015. Five projects with complete and sufficient data for temporal analysis were selected from the data set. The filtered data set contained 23 potential leading indicators (predictors or input variables) of accidents (output or dependent variable). TS analyses were used to identify suitable accident predictors for each of the five projects. Subsequently, the selected input variables were used to develop three different TS models for predicting accident occurrences, and the vector error correction model was found to be the best model. It had the lowest root mean squared error value for three of the five projects analyzed. This study provides insights into how construction companies can utilize TS data analysis to identify projects with high risk of accidents.

关键词： time series temporal construction safety leading indicators accident prevention forecasting

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General expression for linear and nonlinear time series models

Ren HUANG, Feiyun XU, Ruwen CHEN

《机械工程前沿（英文）》 2009年第4卷第1期页码 15-24 doi: 10.1007/s11465-009-0015-z

摘要： The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarity of a system and expressed by a linear difference equation; therefore, they are strictly limited to the linear system. However, some nonlinear factors are within the practical system; thus, it is difficult to fit the model for real systems with the above models. This paper proposes a general expression for linear and nonlinear auto-regressive time series models (GNAR). With the gradient optimization method and modified AIC information criteria integrated with the prediction error, the parameter estimation and order determination are achieved. The model simulation and experiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear models applied in academics and engineering. The modeling and prediction accuracy of the GNAR model is superior to the classical time series models. The proposed GNAR model is flexible and effective.

关键词： linear and nonlinear autoregressive model system identification time series analysis

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Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated

《医学前沿（英文）》 2023年第17卷第1期页码 68-74 doi: 10.1007/s11684-022-0955-9

摘要： Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual’s health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique “continuous glucose monitoring” (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.

关键词： complexity of glucose time series continuous glucose monitoring impaired glucose regulation insulin secretion and sensitivity refined composite multi-scale entropy

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Time-series prediction based on global fuzzy measure in social networks

Li-ming YANG,Wei ZHANG,Yun-fang CHEN

《信息与电子工程前沿（英文）》 2015年第16卷第10期页码 805-816 doi: 10.1631/FITEE.1500025

摘要： Social network analysis (SNA) is among the hottest topics of current research. Most measurements of SNA methods are certainty oriented, while in reality, the uncertainties in relationships are widely spread to be overridden. In this paper, fuzzy concept is introduced to model the uncertainty, and a similarity metric is used to build a fuzzy relation model among individuals in the social network. The traditional social network is transformed into a fuzzy network by replacing the traditional relations with fuzzy relation and calculating the global fuzzy measure such as network density and centralization. Finally, the trend of fuzzy network evolution is analyzed and predicted with a fuzzy Markov chain. Experimental results demonstrate that the fuzzy network has more superiority than the traditional network in describing the network evolution process.

关键词： Time-series network Fuzzy network Fuzzy Markov chain

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Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case

Seiya MAKI, Shuichi ASHINA, Minoru FUJII, Tsuyoshi FUJITA, Norio YABE, Kenji UCHIDA, Gito GINTING, Rizaldi BOER, Remi CHANDRAN

《能源前沿（英文）》 2018年第12卷第3期页码 426-439 doi: 10.1007/s11708-018-0560-4

摘要：

The Paris Agreement calls for maintaining a global temperature less than 2°C above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5°C. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas (GHG) emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system (SEMS) and its deployment in households and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time-series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy-prediction models can be used for low-carbon planning.

关键词： electricity monitoring electricity demand prediction multiple-variable time-series modeling time-series cluster analysis Indonesia

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最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

《中国工程科学》 2008年第10卷第11期页码 89-92

摘要：

根据时间序列近期数据较远期数据包含有更多未来信息的思想，对最小二乘支持向量机预测方法进行了扩展，得到了更具一般性的最小二乘支持向量机预测模型，给出了扩展后的预测模型具体算法。两个时间序列的预测实例表明，扩展后的预测方法获得了更好的预测效果，提升了最小二乘支持向量机预测方法的价值。

关键词：最小二乘支持向量机扩展时间序列预测

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Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos

LI Xiaodong, ZENG Guangming, HUANG Guohe, LI Jianbing, JIANG Ru

《环境科学与工程前沿（英文）》 2007年第1卷第3期页码 334-338 doi: 10.1007/s11783-007-0057-6

摘要： By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed, with the assumption that the series was predictable. Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction. Reasonable forecasting results were achieved using this method.

关键词： nonlinear reconstruction WWTP influent characteristic Reasonable forecasting

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大型重载支承轴的疲劳裂纹时间序列诊断分析

李学军,宾光富,王裕清

《中国工程科学》 2006年第8卷第4期页码 50-53

摘要：

大型重载支承轴隐蔽部位由于发生不可观测的突发性疲劳断裂，严重影响正常生产，给企业带来重大经济损失；分析这类支承轴的结构特点与振动信号特征之间的关系，运用时序分析方法对振动信号进行建模，并采用残差 $σ_{a}^{2}$ 和归一化残差平方和NRSS作为识别疲劳裂纹状态的特征指标，有效诊断出了支承轴的疲劳裂纹程度。实验结果表明，采用 $σ_{a}^{2}$ 和NRSS作为特征指标的时序分析方法对大型重载支承轴隐蔽部位的疲劳裂纹状态进行诊断，比常规的时频幅值特征分析法更为敏感有效、简便易行，且具备很强的实用性。

关键词：大型重载支承轴隐蔽部位疲劳裂纹时间序列

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Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

《环境科学与工程前沿（英文）》 2023年第17卷第2期 doi: 10.1007/s11783-023-1621-4

摘要：

● Used a double-stage attention mechanism model to predict ozone.

关键词： Ozone prediction Deep learning Time series Attention Volatile organic compounds

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Symbolic representation based on trend features for knowledge discovery in long time series

Hong YIN,Shu-qiang YANG,Xiao-qian ZHU,Shao-dong MA,Lu-min ZHANG

《信息与电子工程前沿（英文）》 2015年第16卷第9期页码 744-758 doi: 10.1631/FITEE.1400376

摘要： The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.

关键词： Long time series Segmentation Trend features Symbolic Knowledge discovery

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一种基于多因素分析和多模型集成的海洋溶解氧浓度时间序列预测混合神经网络模型 Article

刘辉, 杨睿, 段铸, 吴海平

《工程（英文）》 2021年第7卷第12期页码 1751-1765 doi: 10.1016/j.eng.2020.10.023

摘要：

溶解氧是水产养殖的重要指标，准确预测溶解氧浓度可有效提高水产品质量。本文提出了一种新的溶解氧混合预测模型，该模型包括多因素分析、自适应分解和优化集成三个阶段。首先，考虑到影响溶解氧浓度的因素复杂繁多，采用灰色关联度法筛选出与溶解氧关系最密切的环境因素，多因素的考虑使得模型融合更加有效。其次，运用经验小波变换方法自适应地将溶解氧、水温、盐度和氧饱和度等序列分解为子序列。然后，利用5个基准模型对经验小波变换分解出的子序列进行预测，这五个子预测模型的集成权重通过粒子群优化和引力搜索算法计算得出。最后，通过加权分配得到溶解氧多因素集成模型。来自太平洋岛屿海洋观测系统希洛WQB04站收集的时间序列数据验证了该模型的性能。实验的评价指标包括Nash-Sutcliffe效率系数、Kling-Gupta效率系数、平均绝对百分比误差、误差标准差和决定系数。实例分析表明：①所提出的模型能够获得优异的溶解氧预测结果；②该模型优于文中其他对比模型；③预测模型可用于分析溶解氧变化趋势，便于管理者能够做出更好的决策。

关键词：溶解氧浓度预测时间序列多步预测多因素分析经验小波变化分解多模型优化集成

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Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

《机械工程前沿（英文）》 2010年第5卷第2期页码 149-156 doi: 10.1007/s11465-010-0008-y

摘要： With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.

关键词： statistical process control (SPC) fuzzy adaptive resonance theory (ART) histogram control chart time series analysis

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一种模拟疏散流程的疏散时间计算方法

祝佳琰,张和平

《中国工程科学》 2006年第8卷第8期页码 73-76

摘要：

在工程实际中，通过人员安全疏散所需要的时间与人员安全疏散可用的时间进行比较来判断建筑的疏散设施能否满足突发情况下人员安全疏散的要求。将建筑的疏散设施抽象成网络的节点，从而将人员在建筑中的疏散流程简化成节点的串联系统模型，并联系统模型或者是串、并联系统组成的复杂模型，并给出了计算的方法。

关键词：人员疏散疏散设施串联模型并联模型

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification

《能源前沿（英文）》 2023年第17卷第4期页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection unary classification self-supervised representation learning multivariate nonlinear time series