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Analyzing construction safety through time series methods

Houchen CAO, Yang Miang GOH

Frontiers of Engineering Management 2019, Volume 6, Issue 2,   Pages 262-274 doi: 10.1007/s42524-019-0015-6

Abstract: Various time series (TS) methods were adopted for identifying the leading indicators or predictors of

Keywords： 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

Frontiers of Mechanical Engineering 2009, Volume 4, Issue 1,   Pages 15-24 doi: 10.1007/s11465-009-0015-z

Abstract: The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarityThis paper proposes a general expression for linear and nonlinear auto-regressive time series modelsThe modeling and prediction accuracy of the GNAR model is superior to the classical time series models

Keywords： 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

Frontiers of Medicine 2023, Volume 17, Issue 1,   Pages 68-74 doi: 10.1007/s11684-022-0955-9

Abstract: Most information used to evaluate diabetic statuses is collected at a special time-point, such as takingBy calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropyanalysis of the CGM data, the study showed for the first time that the complexity of glucose time series

Keywords： complexity of glucose time series     continuous glucose monitoring     impaired glucose regulation     insulin    

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

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

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 10,   Pages 805-816 doi: 10.1631/FITEE.1500025

Abstract: 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.

Keywords： 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

Frontiers in Energy 2018, Volume 12, Issue 3,   Pages 426-439 doi: 10.1007/s11708-018-0560-4

Abstract: innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-seriesThe real-time SEMS data and time-series clustering to explore similarities in electricity consumption

Keywords： electricity monitoring     electricity demand prediction     multiple-variable time-series modeling     time-series    

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Generalization and application in time series forecasting of the least square support vector machine

Xiang Xiaodong

Strategic Study of CAE 2008, Volume 10, Issue 11,   Pages 89-92

Abstract: According to the theory that the present data contains more future information than historical data in time-seriessupport vector machine,and develops algorithm of the extended prediction model.Prediction examples of two time-series

Keywords： least square support vector machine     generalization     time series     forecasting    

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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

Frontiers of Environmental Science & Engineering 2007, Volume 1, Issue 3,   Pages 334-338 doi: 10.1007/s11783-007-0057-6

Abstract: The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed, with the assumptionthat the series was predictable.

Keywords： nonlinear     reconstruction     WWTP influent     characteristic     Reasonable forecasting    

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Time Series Diagnosing Analysis for the Fatigue Crack of Large-scale and Heavyburden Supporting Shafts

Li Xuejun,Bin Guangfu,Wang Yuqing

Strategic Study of CAE 2006, Volume 8, Issue 4,   Pages 50-53

Abstract: vibration signal characteristic, the model of the vibration signal is established by the method of timeseries.Residual (σtime-frequency

Keywords： large-scale and heavyburden machine     supporting shaft     concealment part     fatigue crack     time series    

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

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 2, doi: 10.1007/s11783-023-1621-4

Abstract:

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

Keywords： 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

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 9,   Pages 744-758 doi: 10.1631/FITEE.1400376

Abstract: 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 longerIn this paper, we propose a new symbolic representation method for long time series based on trend featuresThe method uses a two-step mechanism to segment long time series rapidly.A time series is represented by trend symbols, which are also suitable for use in knowledge discovery

Keywords： Long time series     Segmentation     Trend features     Symbolic     Knowledge discovery    

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A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based Article

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Engineering 2021, Volume 7, Issue 12,   Pages 1751-1765 doi: 10.1016/j.eng.2020.10.023

Abstract: Second, the series of DO, water temperature, salinity, and oxygen saturation are decomposed adaptivelyinto sub-series by means of the empirical wavelet transform (EWT) method.Then, five benchmark models are utilized to forecast the sub-series of EWT decomposition.The performance of the proposed model is verified by time-series data collected by the pacific islands

Keywords： Dissolved oxygen concentrations forecasting     Time-series multi-step forecasting     Multi-factor analysis    

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Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

Frontiers of Mechanical Engineering 2010, Volume 5, Issue 2,   Pages 149-156 doi: 10.1007/s11465-010-0008-y

Abstract: At the same time, combined with spectrum analysis of the autoregressive model of quality parameters,

Keywords： statistical process control (SPC)     fuzzy adaptive resonance theory (ART)     histogram     control chart     timeseries analysis    

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An Algorithm of Evacuation Time Which Simulates the Evacuation Course

Zhu Jiaying,Zhang Heping

Strategic Study of CAE 2006, Volume 8, Issue 8,   Pages 73-76

Abstract:

In practice, if available safety egress time is longer than required safety egress time, people canfacilities such as doors were abstracted as units of the net, and the evacuation course was simplified as series-wound

Keywords： evacuation arithmetic     room unit     series-wound model     shunt-wound model    

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

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: 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.

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear timeseries    

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Title Author Date Type Operation

Analyzing construction safety through time series methods

Houchen CAO, Yang Miang GOH

Journal Article

General expression for linear and nonlinear time series models

Ren HUANG, Feiyun XU, Ruwen CHEN

Journal Article

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

Journal Article

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

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

Journal Article

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

Journal Article

Generalization and application in time series forecasting of the least square support vector machine

Xiang Xiaodong

Journal Article

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

Journal Article

Time Series Diagnosing Analysis for the Fatigue Crack of Large-scale and Heavyburden Supporting Shafts

Li Xuejun,Bin Guangfu,Wang Yuqing

Journal Article

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

Journal Article

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

Journal Article

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

Journal Article

Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

Journal Article

An Algorithm of Evacuation Time Which Simulates the Evacuation Course

Zhu Jiaying,Zhang Heping

Journal Article

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

Journal Article

Si Jingjian: High Frequency Time Series Data Reconstruction of Energy Finance Based on Compressed Sensing

10 Jun 2022

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