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A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Basedon Multi-Factor Analysis and a Multi-Model Ensemble 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: In this paper, a new DO hybrid forecasting model is proposed that includes three stages: multi-factorinto 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.Finally, a multi-factor ensemble model for DO is obtained by weighted allocation.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     Empirical wavelet transform decomposition     Multi-model optimization ensemble    

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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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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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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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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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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     insulinsecretion and sensitivity     refined composite multi-scale entropy    

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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.Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was builtReasonable forecasting results were achieved using this method.

Keywords： nonlinear     reconstruction     WWTP influent     characteristic     Reasonable 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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Decomposition analysis of energy-related carbon dioxide emissions in the iron and steel industry in China

Wenqiang SUN, Jiuju CAI, Hai YU, Lei DAI

Frontiers of Environmental Science & Engineering 2012, Volume 6, Issue 2,   Pages 265-270 doi: 10.1007/s11783-011-0284-8

Abstract: The logarithmic mean divisia index (LMDI) technique was applied with period-wise analysis and time-series

Keywords： dioxide (CO2) emissions     decomposition analysis     logarithmic mean divisia index (LMDI) technique     time-series    

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

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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The Establishment of a New Air Health Index Integrating the Mortality Risks Due to Ambient Air Pollution and Non-Optimum Temperature Article

Qingli Zhang, Renjie Chen, Guanjin Yin, Xihao Du, Xia Meng, Yang Qiu, Haidong Kan, Maigeng Zhou

Engineering 2022, Volume 14, Issue 7,   Pages 156-162 doi: 10.1016/j.eng.2021.05.006

Abstract: Based on the exposure-response (E-R) coefficients obtained from time-series models, the new AHI was constructed

Keywords： Air pollution     Temperature     Air Health Index     Mortality     Time-series     Risk communication    

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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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Multi-time scale dynamics in power electronics-dominated power systems

Xiaoming YUAN, Jiabing HU, Shijie CHENG

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 303-311 doi: 10.1007/s11465-017-0428-z

Abstract:

Electric power infrastructure has recently undergone a comprehensive transformation from electromagnetics to semiconductors. Such a development is attributed to the rapid growth of power electronic converter applications in the load side to realize energy conservation and on the supply side for renewable generations and power transmissions using high voltage direct current transmission. This transformation has altered the fundamental mechanism of power system dynamics, which demands the establishment of a new theory for power system control and protection. This paper presents thoughts on a theoretical framework for the coming semiconducting power systems.

Keywords： power electronics     power systems     multi-time scale dynamics     mass-spring-damping model     self-stabilizingand en-stabilizing property     multi-time scale power system stabilizer    

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Optimal dispatch of multi energy system using power-to-gas technology considering flexible load on user

Zi LING, Xiu YANG, Zilin LI

Frontiers in Energy 2018, Volume 12, Issue 4,   Pages 569-581 doi: 10.1007/s11708-018-0595-6

Abstract: Based on the concept of energy hub, according to its series characteristic, this paper established ageneric multi-energy system model using the P2G technology.Finally, a concrete analysis was made on the optimal dispatch result of the multi-energy system usingThe results indicate that cooperative dispatch of multi-energy system using the P2G technology considering

Keywords： multi-energy system     energy hub     series characteristic     optimal dispatch     flexible load    

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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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Title Author Date Type Operation

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Basedon Multi-Factor Analysis and a Multi-Model Ensemble

Hui Liu, Rui Yang, Zhu Duan, Haiping Wu

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

Analyzing construction safety through time series methods

Houchen CAO, Yang Miang GOH

Journal Article

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

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

Journal Article

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

Xiang Xiaodong

Journal Article

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

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

General expression for linear and nonlinear time series models

Ren HUANG, Feiyun XU, Ruwen CHEN

Journal Article

Decomposition analysis of energy-related carbon dioxide emissions in the iron and steel industry in China

Wenqiang SUN, Jiuju CAI, Hai YU, Lei DAI

Journal Article

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

Journal Article

The Establishment of a New Air Health Index Integrating the Mortality Risks Due to Ambient Air Pollution and Non-Optimum Temperature

Qingli Zhang, Renjie Chen, Guanjin Yin, Xihao Du, Xia Meng, Yang Qiu, Haidong Kan, Maigeng Zhou

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

Multi-time scale dynamics in power electronics-dominated power systems

Xiaoming YUAN, Jiabing HU, Shijie CHENG

Journal Article

Optimal dispatch of multi energy system using power-to-gas technology considering flexible load on user

Zi LING, Xiu YANG, Zilin LI

Journal Article

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

Journal Article