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Event-Triggered data-Driven Distributed LFC Using controller-Dynamic-Linearization Method

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION processING OVER NETWORKS 2025年 11卷 85-96页

作者： Bu, Xuhui Zhang, Yan Zeng, Yiming Hou, Zhongsheng Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

This paper is concerned with an event-triggered distributed load frequency control method for multi-area interconnected power systems. Firstly, because of high dimension, nonlinearity and uncertainty of the power system, the relevant model information cannot be fully obtained. To realize the design of LFC algorithm under the condition that the model information is unknown, the equivalent functional relationship between the control signal and the area-control-error signal is established by using a dynamic linearization technique. Secondly, a novel distributed load frequency control algorithm is proposed based on controller dynamic-linearization method and the controller parameters are tuned online by constructing a radial basis function neural network. In addition, to reduce the computation and communication burden on the system, an event-triggered mechanism is also designed, in which whether the data is transmitted at the current instant is completely determined by a triggering condition. Rigorous analysis shows that the proposed method can render the frequency deviation of the power system to converge to a bounded value. Finally, simulation results in a four-area power system verify the effectiveness of the proposed algorithm.

关键词： Power systems Power system stability Power system dynamics Frequency control Mathematical models Event detection Information processing Load modeling Nonlinear dynamical systems Uncertainty Multi-area power systems load frequency control distributed controller-dynamic-linearization method event-triggered strategy data-driven

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What Time Is It? Finding Which Temporal Features is More Useful for Next Activity Prediction

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2025年第1期6卷 261-271页

作者： Aversano, Lerina Iammarino, Martina Madau, Antonella Pirlo, Giuseppe Semeraro, Gianfranco Univ Foggia Dept Agr Sci Food Nat Resources & Engn I-71122 Foggia Italy European Digital Innovat Hub Digital Transformat E I-70124 Bari Italy Pegaso Univ Dept Informat Sci & Technol I-80143 Naples Italy Univ Sannio Dept Engn I-82100 Benevento Italy Univ Bari Aldo Moro Dept Comp Sci I-70121 Bari Italy Univ Sch Adv Studies IUSS Pavia I-27100 Pavia Italy

process Mining merges data science and process science that allows for the analysis of recorded process data by capturing activities within event-logs. It finds more and more applications for the optimization of the production and administrative processes of private companies and public administrations. This field consists of several areas: process discovery, compliance monitoring, process improvement, and predictive process monitoring. Considering predictive process monitoring, the subarea of next activity prediction helps to obtain a prediction about the next activity performed using control flow data, event data with no attributes other than the timestamp, activity label, and case identifier. A popular approach in this subarea is to use sub-sequences of events, called prefixes and extracted with a sliding window, to predict the next activity. In the literature, several features are added to increase performance. Specifically, this article addresses the problem of predicting the next activity in predictive process monitoring, focusing on the usefulness of temporal features. While past research has explored a variety of features to improve prediction accuracy, the contribution of temporal information remains unclear. In this article it is proposed a comparative analysis of temporal features, such as differences in timestamp, time of day, and day of week, extracted for each event in a prefix. Using both k-fold cross-validation for robust benchmarking and a 75/25 split to simulate real scenarios in which new process events are predicted based on past data, it is shown that timestamp differences within the same prefix consistently outperform other temporal features. Our results are further validated by Shapley's value analysis, highlighting the importance of timestamp differences in improving the accuracy of next activity prediction.

关键词： Predictive models Feature extraction process monitoring process mining Accuracy Machine learning algorithms Distance measurement Deep learning data science Context modeling next activity prediction temporal information classification predictive process monitoring

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Statistical batch-based bearing fault detection

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JOURNAL OF MATHEMATICS IN INDUSTRY 2025年第1期15卷 1-20页

作者： Jorry, Victoria Duma, Zina-Sabrina Sihvonen, Tuomas Reinikainen, Satu-Pia Roininen, Lassi LUT Univ Yliopistonkatu 34 FI-53850 Lappeenranta Finland

In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning methods in diagnosing these faults. Based on the complex working conditions of rotary machines, multivariate statistical process control charts such as Hotelling's T2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$T<^>process$\end{document} and Squared Prediction Error are useful for providing early warnings. However, these methods are rarely applied to condition monitoring of rotating machinery due to the univariate nature of the datasets. In the present paper, we propose a multivariate statistical process control-based fault detection method that utilizes multivariate data composed of Fourier transform features that are extracted for fixed-time batches. Our approach makes use of the multidimensional nature of Fourier transform characteristics, which record more detailed information about the machine's status, in an effort to enhance early defect detection and diagnosis. Experiments with varying vibration measurement locations (Fan End, Drive End), fault types (ball, inner, and outer race faults), and motor loads (0-3 horsepower) are used to validate the suggested approach. The outcomes illustrate our method's effectiveness in fault detection and point to possible wider uses in industrial maintenance.

关键词： Principal component analysis Fault detection Multivariate statistical process control Fourier transformation Rolling-element bearing Vibration signal

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analysis of Cohort Stepped Wedge Cluster-Randomized Trials With Nonignorable Dropout via Joint modeling

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STATISTICS IN MEDICINE 2025年第5期44卷 e10347页

作者： Gasparini, Alessandro Crowther, Michael J. Hoogendijk, Emiel O. Li, Fan Harhay, Michael O. Red Door Analyt AB Stockholm Sweden Karolinska Inst Dept Med Epidemiol & Biostat Stockholm Sweden Locat VU Univ Amsterdam Publ Hlth Res Inst Dept Epidemiol & Data Sci Amsterdam UMCMed Ctr Amsterdam Netherlands Yale Sch Publ Hlth Dept Biostat New Haven CT USA Yale Sch Publ Hlth Ctr Methods Implementat & Prevent Sci New Haven CT USA Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA USA UCL MRC Clin Trials Unit London England

Stepped wedge cluster-randomized trial (CRTs) designs randomize clusters of individuals to intervention sequences, ensuring that every cluster eventually transitions from a control period to receive the intervention under study by the end of the study period. The analysis of stepped wedge CRTs is usually more complex than parallel-arm CRTs due to more complex intra-cluster correlation structures. A further challenge in the analysis of closed-cohort stepped wedge CRTs, which follow groups of individuals enrolled in each period longitudinally, is the occurrence of dropout. This is particularly problematic in studies of individuals at high risk for mortality, which causes nonignorable missing outcomes. If not appropriately addressed, missing outcomes from death will erode statistical power, at best, and bias treatment effect estimates, at worst. Joint longitudinal-survival models can accommodate informative dropout and missingness patterns in longitudinal studies. Specifically, within the joint longitudinal-survival modeling framework, one directly models the dropout process via a time-to-event submodel together with the longitudinal outcome of interest. The two submodels are then linked using a variety of possible association structures. This work extends linear mixed-effects models by jointly modeling the dropout process to accommodate informative missing outcome data in closed-cohort stepped wedge CRTs. We focus on constant intervention and general time-on-treatment effect parametrizations for the longitudinal submodel and study the performance of the proposed methodology using Monte Carlo simulation under several data-generating scenarios. We illustrate the joint modeling methodology in practice by reanalyzing data from the "Frail Older Adults: Care in Transition" (ACT) trial, a stepped wedge CRT of a multifaceted geriatric care model versus usual care in 35 primary care practices in the Netherlands.

关键词： closed-cohort designs informative attrition joint longitudinal-survival modeling Monte Carlo simulation stepped wedge designs time-varying treatment effect

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Extraction of silica and biochar from biomass waste for the synthesis of aerogel and its application for the removal of dye

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BIOMASS CONVERSION AND BIOREFINERY 2025年 1-19页

作者： Samanta, Ashmita Ghosh, Subhasis Das, Papita Jadavpur Univ Dept Chem Engn 188 Raja SC Mullick Rd Kolkata 700032 West Bengal India Jadavpur Univ Sch Adv Studies Ind Pollut Control Engn 188 Raja Subodh Chandra Mallick Rd Kolkata 700032 West Bengal India

Contamination o f wastewater is one of the rising global issues, and several studies are being conducted to perform efficient treatment processes with cost-effective methods. In this study, sugarcane bagasse was used as biomass to extract silica (Si) and biochar (BC) and was used further as fillers for aerogel formation with polyvinyl alcohol (PVA) polymer to eliminate Congo red dye from the solution. A batch study was conducted in each case, and the efficiency of dye removal, by different aerogel PVA, PVA-Si, and PVA-BC was studied. Experimental parameters were varied to analyze the effect of the various parameters in each case. The highest removal was 94.98% using PVA-Si aerogel at 300 min with 10 mg/L initial concentration of Congo red, pH 7, and temperature 30 degrees C. The adsorption capacity of Congo red was 29.49 mg/g by PVA-Si aerogel. The characterization of the aerogel showed the occupancy of various functional groups, modification in crystallographic structure, and surface alteration of aerogels. The analysis for the study of kinetics exhibited that the process of adsorption fitted the pseudo-second-order reaction for all the aerogels and also the thermodynamics data demonstrated that the adsorption process was spontaneous and endothermic. Also, from the isotherm modeling, it was observed that the process best fitted with the Freundlich isotherm model.

关键词： Aerogel Silica Biochar PVA Congo red Adsorption modeling Sugarcane bagasse

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Simultaneous Fault Diagnosis for control Valve Using Feature Fusion and Multilabel Classification Framework Considering Fault Similarity

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Dai, Wei Fan, Ruipeng Nan, Jing China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

With the increasing complexity of industrial systems, the frequency of simultaneous faults within these systems is also on the rise, posing a challenge to traditional fault diagnosis methods that rely solely on sensor data. To this end, a novel simultaneous fault diagnosis method for the whole process from data feature extraction to diagnosis model construction is proposed in this article. First, a multi-feature fusion feature extraction method integrating cloud model (CM) and dynamic-inner principal component analysis (DiPCA) is proposed, termed CM-DiPCA, which effectively captures the hidden uncertainties and dynamic characteristics embedded in the fault signals, thereby improving the ability of the diagnosis model to identify and distinguish various fault modes. Subsequently, a dual stochastic configuration network (dualSCN) framework is constructed to identify the fault category while determining the number of faults, significantly enhancing the self-adaptive capabilities and modeling efficiency of the diagnostic model. Moreover, considering the potential correlation between single and simultaneous faults, the CM-based overlap degree (OD) calculation is introduced into dualSCN, thereby reducing the adverse effects predicted only from a probabilistic perspective. The experimental results show that the proposed method exhibits advantages in identifying simultaneous faults and modeling efficiency compared with the existing methods.

关键词： Cloud model (CM) control valve fault diagnosis fault diagnosis potential correlation potential correlation simultaneous fault simultaneous fault stochastic configuration network (SCN) stochastic configuration network (SCN) stochastic configuration network (SCN)

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Object-centered heuristic A* conformance checking method 2

Object-centered heuristic A* conformance checking method

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2nd International conference on Big data, Computational Intelligence, and Applications, BDCIA 2024

作者： Yang, Xiaolong Wang, Lili College of Mathematics and Big Data Anhui University of Science＆Technology Anhui Huainan232001 China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and Technology Anhui Huainan232001 China

ISBN: (纸本)9781510689053

Traditional approaches typically flatten the process when checking the conformance of complex processes. However, this flattening approach can result in the loss of dependencies between objects, reducing the accuracy of the conformance check. This paper proposes an object-centered alignment optimization method based on the heuristic A* algorithm to address this issue. This method effectively improves the efficiency of object-centered alignment in complex scenarios by considering both heuristic search and actual cost. The experimental results demonstrate that, in comparison to existing approaches, the proposed method presented in this paper observably enhances the efficiency of alignment in complex graph structures, exhibits superior adaptability, and gains a better understanding of the relationship between object-centric process behavior and data. These results provide new ideas and methods for improving conformance checking, which are of great theoretical and practical significance, especially in dynamic and complex environments where the potential for application has been further validated. © 2025 SPIE.

关键词： Detection and tracking algorithms Mining Alignment modeling process modeling Algorithm development Beam propagation method Mathematical optimization Connectors Medicine Transportation

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An integrated modeling framework for groundwater contamination risk assessment in arid, data-scarce environments

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ACTA GEOPHYSICA 2025年第2期73卷 1865-1889页

作者： Rafiei-Sardooi, Elham Azareh, Ali Ghazanfarpour, Hossein Parteli, Eric Josef Ribeiro Faryabi, Mohammad Barkhori, Saeed Univ Jiroft Fac Nat Resources Dept Ecol Engn Kerman Iran Univ Jiroft Dept Geog Kerman Iran Shahid Bahonar Univ Kerman Dept Geog Kerman Iran Univ Duisburg Essen Fac Phys D-47057 Duisburg Germany

Groundwater contamination risk mapping constitutes an important component of groundwater management and quality control. In the present study, we describe a method for such mapping that is more suitable for arid regions than other methods developed in previous work. Specifically, we integrate machine learning tools, interpolation and process-based models with a modified version of DRASTIC-AHP to evaluate groundwater vulnerability to nitrate contamination, and to map this contamination in Jiroft plain, Iran. The DRASTIC model provides a tool for evaluating aquifer vulnerability by using seven parameters related to the hydrogeological setting (depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity), while the criteria ratings and weights of these parameters are evaluated by means of an analytic hierarchy process (AHP). However, to obtain the risk map, the model predictions related to groundwater vulnerability are combined here with a contamination hazard map, which we estimate by applying ensemble modeling. This modeling builds on the occurrence probability predicted by means of a modeling framework that is based on generalized linear modeling (GLM), flexible discriminant analysis (FDA) and support vector machine (SVM). We find that the application of our ensemble modeling to predicting groundwater contamination in Jiroft plain leads to better results (AUC = 0.916, Kappa = 0.89, MSE = 0.18 and RMSE = 0.11) compared to the separated employment of the various machine learning (ML) methods, i.e., either SVM (AUC = 0.847, Kappa = 0.86, MSE = 0.19 and RMSE = 0.29), GLM (AUC = 0.829, Kappa = 0.81, MSE = 0.23 and RMSE = 0.37) or FDA (AUC = 0.816, Kappa = 0.8, MSE = 0.26 and RMSE = 0.42). Our integrated modeling framework provides an assessment of both regional patterns of groundwater contamination and an estimate of contamination impacts based on socio-environmental variables, being particularly suitable for appl

关键词： Groundwater Contamination Hazard Risk Vulnerability Iran

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Real-Time Auto controlling of Viable Cell Density in Perfusion Cultivation Aided by In-Line Dielectric Spectroscopy With Segmented Adaptive PLS Model

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BIOTECHNOLOGY AND BIOENGINEERING 2025年第4期122卷 858-869页

作者： Sun, Yunpeng Zhang, Qiongqiong He, Yunfei Chen, Dongliang Wang, Zheyu Zheng, Xiang Fang, Mingyue Zhou, Hang WuXi Biol Cell Culture Proc Dev CCPD Shanghai Peoples R China WuXi Biol NonGMP Pilot Plant NPP Shanghai Peoples R China WuXi Biol Bioproc Res & Dev BRD Shanghai Peoples R China

Serving as a dedicated process analytical technology (PAT) tool for biomass monitoring and control, the capacitance probe, or dielectric spectroscopy, is showing great potential in robust pharmaceutical manufacturing, especially with the growing interest in integrated continuous bioprocessing. Despite its potential, challenges still exist in terms of its accuracy and applicability, particularly when it is used to monitor cells during stationary and decline phases. In this study, data pre-processing methods were first evaluated through cross-validation, where the first-order derivative emerged as the most effective method to diminish variability in prediction accuracy across different training datasets. Subsequently, a segmented adaptive partial least squares (SA-PLS) model was developed, and its accuracy and universality were demonstrated through several validation studies using different clones and culture processes. Furthermore, a real-time viable cell density (VCD) auto-control system in perfusion culture was established, where the VCD was maintained around the target with notable precision and robustness. This model enhanced the monitoring capabilities of capacitance-based PAT tools throughout the cultivation, expanded their application in cell-specific automatic control strategies, and contributed vitally to the advancement of continuous manufacturing paradigms.

关键词： biomass capacitance modeling multivariate data analysis perfusion

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Optimization of process parameters in metal injection molding for turbine blade using response surface methodology

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第11-12期137卷 5899-5912页

作者： Rosli, Mohd Uzair Khor, Chu Yee Zailani, Zainal Abidin Univ Malaysia Perlis Fac Mech Engn & Technol Arau 02600 Perlis Malaysia

Manufacturing turbine blades using metal injection molding (MIM) is a complex process that requires precise control over parameters to achieve high dimensional accuracy. Inadequate management of shrinkage, frozen volume, and volume filled leads to dimensional deviations, resulting in defects or reduced performance of turbine blades in operation. Optimizing these response factors ensures reliable production and high-quality turbine blades. This study investigates the influence of process parameters in metal injection molding by evaluating their significance and interaction. A three-level central composite design (CCD) approach-based response surface methodology analysis was applied to statistically specify the effect of important numerical and categorical process variables: mold temperature, melt temperature, injection time, flow rate on the critical response process output variables concerning product quality, namely shrinkage, frozen volume, and volume filled. By using a face-centered design, a total of 30 simulation data was fitted. analysis of variance (ANOVA) was then performed to assess the significance of factors and their interactions at a 95% confidence level (p < 0.05). Subsequently, empirical models were developed and rigorously validated against the simulation results. The optimum process parameters of the metal part were characterized as follows: mold temperature of 15 degrees C, 138 degrees C of melt temperature, 2.5 s of injection time, and 94 cm3/s flow rate. The results are expected to advance the metal injection molding industry by providing valuable references and enhancing the understanding of the optimization process.

关键词： Metal injection molding Response surface methodology Simulation and modeling Optimization

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