Article
Construction & Building Technology
Xiaolei Chu, Wei Cui, Shengyi Xu, Lin Zhao, Hua Guan, Yaojun Ge
Summary: This article proposes a time series decomposition methodology to divide the structural dynamic properties into long-term parts, multiscale periodic parts, holiday parts, and error parts. By extracting the dynamic properties of a long-span bridge, the article discovers the rules of structural deterioration and the relationships with periodically varying ambient conditions. This methodology can provide references for damage detection and safety assessment for similar bridges.
STRUCTURAL CONTROL & HEALTH MONITORING
(2023)
Article
Computer Science, Artificial Intelligence
Yue Cheng, Weiwei Xing, Witold Pedrycz, Sidong Xian, Weibin Liu
Summary: This article proposes a long-term time-series forecasting method based on the nonlinear fuzzy information granule series, which improves the long-term performance of predictors. The method represents information granules with nonlinear time-dependent curves, and introduces a temporal window splitting algorithm based on curvature equations and weighted directed graphs. Nonlinear trend fuzzy granulation is used as a data preprocessing module to achieve better long-term forecasting performance. The proposed method achieves superior performance in traffic flow forecasting compared to existing models.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Hiroshi Okamura, Yutaka Osada, Shota Nishijima, Shinto Eguchi
Summary: Nonlinear phenomena in ecology pose challenges for inference and prediction due to autocorrelation and outliers. Traditional least squares and least absolute deviations methods have limitations, leading to the development of a new robust regression approach that accurately estimates autocorrelation while reducing the influence of outliers. Simulations and real data analysis demonstrate that the new method outperforms existing methods in long-term and short-term prediction of nonlinear estimation problems in spawner-recruitment data.
SCIENTIFIC REPORTS
(2021)
Article
Meteorology & Atmospheric Sciences
Bowen Yan, Pak Wai Chan, Qiusheng Li, Yuncheng He, Zhenru Shu
Summary: The chaotic nature of many meteorological systems limits its predictability, and accurate prediction of meteorological variables relies on diagnosing the complex underlying dynamics properly. The study shows that wind speed exhibits a higher level of complexity in its underlying dynamics compared to pressure and temperature, leading to more irregular time-dependent behavior and lower predictability. Additionally, seasonal variability in the dynamics of meteorological time series is evident, especially for wind speed and temperature.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Economics
Lukasz T. T. Gatarek, Aleksander Welfe
Summary: The paper finds that the original time series can be transformed into a sequence of jumps measured by time distances and represented as a compound Poisson process, which has significant implications. Firstly, the jump-generating process is stationary, unlike the one generating the original data. Secondly, the dynamics of a variable can be determined using solely the properties of the derived stationary counterpart. Thirdly, the proposed methodology provides advantages in prediction, allowing for forecasting the number of periods needed to achieve a desired level and decomposing the path into jumps of different sizes. It also offers insights into the trajectory shape that traditional approaches to forecasting nonstationary time series lack.
JOURNAL OF FORECASTING
(2023)
Article
Chemistry, Multidisciplinary
Tonny Okedi, Adrian C. Fisher
Summary: Biophotovoltaics (BPVs) have the potential to generate low-carbon electricity and chemicals using photosynthetic microorganisms, but understanding the electron path within microorganisms is a key challenge in developing commercial devices. This study applies STL to decompose the current density profile and uses LSTM network to predict the light-controlled seasonal component, opening up possibilities for faster optimization and control software development for biophotovoltaic devices.
ENERGY & ENVIRONMENTAL SCIENCE
(2021)
Article
Statistics & Probability
Sumanta Basu, Suhasini Subba Rao
Summary: We propose NonStGM, a framework for studying dynamic associations among the components of a nonstationary multivariate time series. It captures conditional noncorrelations and nonstationarity/stationarity using graphical models and recovers sparsity patterns from finite-length time series using discrete Fourier transforms.
ANNALS OF STATISTICS
(2023)
Article
Statistics & Probability
Jonathan Embleton, Marina I. Knight, Hernando Ombao
Summary: This article introduces a new method (MULT-LSW) for analyzing brain signals and demonstrates its applicability and the new insights it provides through a study on experimental data. The method can handle both the nonstationary behavior within signals and the evolution across multiple trials.
ANNALS OF APPLIED STATISTICS
(2022)
Article
Environmental Studies
Luca Salvati
Summary: European cities experienced a long-term shift from centralized demographic growth to de-concentration, with Athens as a case study showing non-linear population growth patterns and coexisting development stages since World War II, shedding light on long-term mechanisms of metropolitan development and informing urban growth management policies.
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
(2022)
Article
Mathematics, Interdisciplinary Applications
Xinyao Wang, Huanwen Jiang, Guosheng Han
Summary: This article introduces a method for multifractal analysis of nonlinear time series and applies it to the multifractal analysis of urban and suburban areas. The study finds that both urban and suburban systems exhibit multifractality, with the urban system showing stronger multifractality, particularly in spring and winter.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Statistics & Probability
Zeda Li, Ori Rosen, Fabio Ferrarelli, Robert T. Krafty
Summary: This article introduces a nonparametric approach to spectral analysis of high-dimensional multivariate nonstationary time series, using a novel frequency-domain factor model for flexible representation of spectral matrices in a fully Bayesian framework with adaptive segmentation and efficient sampling within segments.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Silvio Fernando Alves Xavier Junior, Erika Fialho Morais Xavier, Jader Silva Jale, Tatijana Stosic, Carlos Antonio Costa dos Santos
Summary: This study explored the complexity of monthly rainfall temporal series recorded from 1962 to 2012 at 69 meteorological stations in Paraiba state, northeastern Brazil, using the Modified Multiscale Entropy Method. By comparing results across different regions, the study distinguished rainfall patterns and contributed to the use of multiscale approaches in climatological studies.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics, Interdisciplinary Applications
Jorge A. A. Romero-Bustamante, Oscar Velazquez-Camilo, Angeles Garcia-Hernandez, Victor M. M. Rivera, Eliseo Hernandez-Martinez
Summary: This study proposes a method for indirect monitoring of cane sugar crystallization using multiscale analysis of temperature, pH, and torque time series. The results suggest that multiscale time series analysis has the potential for low-cost online monitoring of cane sugar crystallization.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Physics, Multidisciplinary
Li Wan, Guang Ling, Zhi-Hong Guan, Qingju Fan, Yu-Han Tong
Summary: This paper proposes a new complexity measurement algorithm called multiscale weighted phase permutation entropy (MWPPE) to improve permutation entropy (PE) by utilizing phase transformation, weight influence, and multiscale information for a better understanding of the complexity of nonlinear time series. The method is further extended to fractional order to obtain fractional multiscale phase permutation entropy (FMPPE). The effectiveness of the proposed algorithms is discussed based on simulation sequences, and the results show that they can effectively amplify the detection effect of dynamic changes. Additionally, the FMPPE strategy is found to be more effective than the MWPPE method in distinguishing developed country stock indices from emerging country stock indices.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Economics
Francesco Bravo, Degui Li, Dag Tjostheim
Summary: This article studies parametric robust estimation in nonlinear regression models with regressors generated by a class of non-stationary and null recurrent Markov processes. Consistency and limit distribution results for general robust estimators (including nonlinear least squares, least absolute deviation, and Huber's M-estimators) are derived under regularity conditions. Monte-Carlo simulation studies are conducted to examine numerical performance and verify established asymptotic properties, with empirical applications illustrating the usefulness of the proposed method.
JOURNAL OF ECONOMETRICS
(2021)
Article
Engineering, Manufacturing
Ruimin Chen, Prahalada Rao, Yan Lu, Edward W. Reutzel, Hui Yang
Summary: Powder bed fusion additive manufacturing offers design flexibility for metal products, but controlling quality becomes challenging with complex designs. This study explores advanced imaging for improved quality control and introduces a novel generalized recurrence network for analyzing the interaction between design parameters and quality characteristics in thin-wall builds. The results demonstrate sensitivity of network features to build orientations, width, height, and contour space, providing insights for optimizing engineering design and enhancing build quality.
ADDITIVE MANUFACTURING
(2021)
Article
Engineering, Industrial
Zhenggeng Ye, Hui Yang, Zhiqiang Cai, Shubin Si, Fuli Zhou
Summary: This study introduces a new mixture degradation model to assess the reliability of manufacturing machines by considering the impact of feedstocks with different qualities. The model establishes an interacting chain for quality and reliability in serial-parallel manufacturing systems.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Electrical & Electronic
Hui Yang, Prahalad Rao, Timothy Simpson, Yan Lu, Paul Witherell, Abdalla R. Nassar, Edward Reutzel, Soundar Kumara
Summary: Quality is crucial in deploying new processes, products, or services, and the emergence of additive manufacturing (AM) has the potential to revolutionize enterprise functions. However, technical challenges currently hinder the widespread application of AM. This article proposes designing, developing, and implementing a new DMAIC methodology for 6S quality management in AM systems.
PROCEEDINGS OF THE IEEE
(2021)
Article
Engineering, Manufacturing
Shenli Pei, Kaifeng Wang, Cheng-Bang Chen, Jingjing Li, Yang Li, Danielle Zeng, Xuming Su, Hui Yang
Summary: This study investigated the inherent variabilities in the fused filament fabrication of short carbon-fiber-reinforced Nylon-6 composites under different process conditions, quantifying the sources of uncertainty and their effects on microstructures and Young's modulus. Microstructural characteristics were extracted and uncertainties quantified via image-based data analytics and analysis of variance. A modified Halpin-Tsai model with consideration of fiber and void distributions was developed to quantify uncertainties on Young's modulus and validated through tensile tests.
JOURNAL OF MANUFACTURING PROCESSES
(2021)
Article
Computer Science, Information Systems
Alexander Krall, Daniel Finke, Hui Yang
Summary: The Internet of Things has advanced medical sensing technologies, leading to new data-rich environments in healthcare. However, this also poses risks of data breaches and model inversion attacks. Innovative approaches such as Mosaic Gradient Perturbation are needed to protect patient privacy and minimize such risks.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Robotics
Ruimin Chen, Yan Lu, Paul Witherell, Timothy W. Simpson, Soundar Kumara, Hui Yang
Summary: An ontology-based Bayesian network model is proposed in the study to represent the causal relationships between additive manufacturing parameters and quality assurance/quality control requirements, which enables engineering interpretations and advances the monitoring and control of additive manufacturing processes.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2021)
Article
Robotics
Runsang Liu, Hui Yang
Summary: This study develops new tessellation algorithms for decision support in epidemic resource allocation and management. The methodology estimates resource locations and coverage based on the spatial analysis of heterogeneous infection distribution. The proposed algorithms have strong potentials for epidemic decision support in infection modelling and resource allocation.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Automation & Control Systems
Bing Yao, Yun Chen, Hui Yang
Summary: The article introduces a constrained Markov decision process (CMDP) framework to optimize mobile electrocardiography (ECG) sensing under the constraint of the energy budget, and evaluates its performance in energy-constrained sensing of cardiac events, showing superior results compared to traditional policies.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Adam Meyers, Hui Yang
Summary: This article develops a fault-tolerance model for time-varying networks, considering stochastic switching of nodes and/or edges between active and inactive states, and analyzes fault tolerance from a global connectivity perspective using a Markov chain framework for quantitative measures.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Zhenggeng Ye, Shubin Si, Hui Yang, Zhiqiang Cai, Fuli Zhou
Summary: The paper investigates the impact of low-quality feedstocks on the performance of manufacturing systems and proposes an effective method for computing the performance of networked manufacturing systems. The method takes into account the interdependence between machines and low-quality feedstocks and evaluates the operational performance using models and algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Robotics
Hui Yang, Siqi Zhang, Yan Lu, Paul Witherell, Soundar Kumara
Summary: This paper presents a stochastic modeling framework for monitoring melt-pool variations in the metal-based additive manufacturing process. Experimental results show the effectiveness of tensor decomposition for spatiotemporal monitoring of melt-pool variations.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Industrial
Alexander Krall, Daniel Finke, Hui Yang
Summary: This article presents a new virtual sensing approach that uses imaginary sensors placed at different locations in signaling trajectories to monitor evolving dynamics within the signal space. It proposes self-organizing principles to optimize the placement of these sensors and develops a network model to represent real-time flux dynamics among them. The establishment of the network model, along with the concept of transition uncertainty, enables a fine-grained view into system dynamics and introduces a new Flux Rank algorithm for process monitoring.
Article
Engineering, Manufacturing
Runsang Liu, Hui Yang
Summary: This research proposes a new method to simulate photon emission and the generation of melt pool images, and establishes a multimodal probability distribution function model for the geometric variations of melt pools. Experimental results demonstrate that the proposed method effectively builds a multimodal distribution model for melt pool geometric variations, and has the potential to be generally applicable for different types of melt pool images and AM processes.
ADDITIVE MANUFACTURING
(2023)
Article
Engineering, Industrial
Zhenggeng Ye, Zhiqiang Cai, Hui Yang, Shubin Si, Fuli Zhou
Summary: This study proposes a reinforcement learning-based method to solve the joint optimization problem of preventive maintenance and work-in-process quality inspection in manufacturing networks. By introducing dynamic reliability and quality models, and employing the Deep Deterministic Policy Gradient algorithm, the optimal joint control of reliability and quality in manufacturing networks is achieved.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Biochemical Research Methods
Cheng-Bang Chen, Yujie Wang, Xuanya Fu, Hui Yang
Summary: The paper presents a novel recurrence analysis methodology for automatic image-guided detection of Invasive ductal carcinoma in breast cancer histopathological images. The authors utilize wavelet decomposition and a weighted recurrence network approach to extract recurrence features and develop automated IDC detection models leveraging machine learning methods. The developed models successfully characterize the complex microstructures of histopathological images and achieve high IDC detection performances.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Bo Li, Tian Huang
Summary: This paper proposes an approximate optimal strategy based on a piecewise parameterization and optimization (PPAO) method for solving optimization problems in stochastic control systems. The method obtains a piecewise parameter control by solving first-order differential equations, which simplifies the control form and ensures a small model error.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Guram Mikaberidze, Sayantan Nag Chowdhury, Alan Hastings, Raissa M. D'Souza
Summary: This study explores the collective behavior of interacting entities, focusing on the co-evolution of diverse mobile agents in a heterogeneous environment network. Increasing agent density, introducing heterogeneity, and designing the network structure intelligently can promote agent cohesion.
CHAOS SOLITONS & FRACTALS
(2024)
Article
Mathematics, Interdisciplinary Applications
Gengxiang Wang, Yang Liu, Caishan Liu
Summary: This investigation studies the impact behavior of a contact body in a fluidic environment. A dissipated coefficient is introduced to describe the energy dissipation caused by hydrodynamic forces. A new fluid damping factor is derived to depict the coupling between liquid and solid, as well as the coupling between solid and solid. A new coefficient of restitution (CoR) is proposed to determine the actual physical impact. A new contact force model with a fluid damping factor tailored for immersed collision events is proposed.
CHAOS SOLITONS & FRACTALS
(2024)