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Federated learning empowered Time-Sensitive dynamic data modeling for applied computational problem of supply chain sector

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2025年第1期19卷 167-190页

作者： George, Melbin Baskar, S. Karpagam Acad Higher Educ Coimbatore India Karpagam Acad Higher Educ Dept ECE Coimbatore India

Federated learning (FL) improves the product movement from raw material transformation to consumer goods. The optical network controls the flow of goods and services to achieve low-cost operation for data modelling with less energy usage. data handling and protection are significant drawbacks in optical communication and applied computational problems in the supply chain sector. This article proposes a Time-Sensitive dynamic data modeling (TSD2M) method for addressing the network-to-network synchronization error. The synchronization error is identified through network switching and goods delivery. The squirrel search algorithm includes A seasonal monitoring condition to avoid becoming stuck on a subset of optimum solutions. The network utilization and data models are revamped based on the best-afford demands. Global best solution update retains the best-performing solution with less time implementation through the learning process. Therefore, the proposed method maximizes delivery precision, data acquisition, and demand identification.

关键词： Optical Network Supply Chain Management (SCM) dynamic data modeling data protection

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A novel dynamic PCA algorithm for dynamic data modeling and process monitoring

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JOURNAL OF PROCESS CONTROL 2018年 67卷 1-11页

作者： Dong, Yining Qin, S. Joe Univ Southern Calif Ming Hsieh Dept Elect Engn Los Angeles CA 90089 USA Univ Southern Calif Mork Family Dept Chem Engn & Mat Sci Los Angeles CA 90089 USA Chinese Univ Hong Kong Sch Sci & Engn 2001 Longxiang Blvd Shenzhen Guangdong Peoples R China

Principal component analysis (PCA) has been widely applied for data modeling and process monitoring. However, it is not appropriate to directly apply PCA to data from a dynamic process, since PCA focuses on variance maximization only and pays no attention to whether the components contain dynamics or not. In this paper, a novel dynamic PCA (DiPCA) algorithm is proposed to extract explicitly a set of dynamic latent variables with which to capture the most dynamic variations in the data. After the dynamic variations are extracted, the residuals are essentially uncorrelated in time and static PCA can be applied. The new models generate a subspace of principal time series that are most predictable from their past data. Geometric properties are explored to give insight into the new dynamic model structure. For the purpose of process monitoring, fault detection indices based on DiPCA are developed based on the proposed model. Case studies on simulation data, data from an industrial boiler process, and the Tennessee Eastman process are presented to illustrate the effectiveness of the proposed dynamic models and fault detection methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： dynamic PCA dynamic data modeling Process monitoring Fault detection

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dynamic-Inner White Component Analysis: A New Latent Variable Model for dynamic data Analytics and Process Monitoring

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5614-5626页

作者： Huo, Kaida Huang, Dexian Yang, Fan Shang, Chao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Recently, dynamic latent variable (DLV) models have been prevalent in dynamic data modeling and process monitoring. They maximize the covariance or canonical correlation between latent components and one-step ahead prediction thereof;however, auto-correlations may still be present in residuals, resulting in unmodeled dynamics and hence compromising the efficacy of generic variability-based monitoring indices. In this work, a novel dynamic-inner white component analysis (DiWCA) model is put forth. Its salient feature lies in that residuals of DLVs are enforced to be as "white" as possible to suppress unmodeled dynamics such that process dynamics can be adequately described. The whiteness index is used to quantify the closeness of finite-length innovation time series to white noise, based on which a bi-level program is formulated to extract DLVs. Thanks to the enforced whiteness, two new monitoring statistics can be constructed in DiWCA to effectively detect whether static variations become "colore" and thus yield interpretable information that complements generic variability-based indices. Finally, case studies on the Tennessee Eastman process and a real-world vacuum furnace system show that DiWCA is capable of adequately capturing process dynamics from data and providing deep insight into process operating status via two whiteness-based monitoring statistics. Note to Practitioners-Interpretability is a crucial aspect of statistic design in process monitoring schemes. Generic test statistics in DLV models are mostly constructed based upon variability of residuals, which are only a partial indicator of the existence of temporal dynamics. In the proposed DiWCA model, residuals are endowed with desirable noise-like properties so that new whiteness-based monitoring statistics can be constructed to testify whether the residuals are "colored". This offers meaningful information about the existence of temporal dynamics in an interpretable way that well complements generic v

关键词： Latent dynamic model dynamic data modeling process monitoring Latent dynamic model dynamic data modeling process monitoring

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An active fault tolerance framework for uncertain nonlinear high-order fully-actuated systems

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AUTOMATICA 2023年第1期152卷

作者： Cai, Miao He, Xiao Zhou, Donghua Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

This paper presents a novel active fault tolerance framework for uncertain high-order fully-actuated systems (HOFASs). Starting from the uncertain faulty HOFAS model with nonlinear measurement, the advantages of this description are revealed via Lie Derivatives. A HOFAS adaptive observer and controller integration is established and further a closed-loop HOFAS dynamic data modeling structure is derived. This structure uncovers the dynamic characteristics including fault features, and naturally yields a fault detectability condition. Based on the fault information from the dynamic data model, an active fault tolerant control strategy for HOFASs is proposed, which enriches HOFAS control theory. The uniformly bounded stability of the HOFAS model is proved theoretically and illustrated experimentally. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： High -order fully -actuated systems Observer and controller integration framework dynamic data modeling Active fault tolerant control

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A dynamic-inner convolutional autoencoder for process monitoring

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COMPUTERS & CHEMICAL ENGINEERING 2022年 158卷 107654-107654页

作者： Zhang, Shuyuan Qiu, Tong Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China Tsinghua Univ Beijing Key Lab Ind Big Data Syst & Applicat Beijing 100084 Peoples R China

Modern manufacturing industries are urgently demanding intelligent process monitoring systems for plant maintenance and accident prevention in the Industry 4.0 era. With the rapid development of deep learning, data-driven process monitoring methods are attracting wide attention and have been applied to many processes. However, most deep learning methods do not model process latent dynamics and are deficient to detect dynamic variations. In this work, a novel dynamic-inner convolutional autoencoder (DiCAE) is proposed. Unlike previous autoencoders that only focus on input reconstruction, DiCAE innovatively integrates a vector autoregressive model into a 1-dimensional convolutional autoencoder to monitor nonlinear processes, as well as capture process dynamics. When applied to a numerical simulation, DiCAE could detect the dynamic variation and distinguish different process data into separate clusters with an intuitive visualization, while other conventional methods cannot. The effectiveness of DiCAE is also demonstrated on the benchmark Tennessee Eastman process. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： dynamic data modeling 1D convolutional neural network Vector autoregressive model Process monitoring Fault detection

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Synthetic dataset Generation for Optimizing Multimodal Drone Delivery Systems

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DRONES 2024年第12期8卷 724-724页

作者： Altinses, Diyar Torres, David Orlando Salazar Gobachew, Asrat Mekonnen Lier, Stefan Schwung, Andreas South Westphalia Univ Appl Sci Dept Automat Technol & Learning Syst D-59494 Soest Germany South Westphalia Univ Appl Sci Dept Logist & Supply Chain Management D-59872 Meschede Germany

Street delivery faces significant challenges due to outdated road infrastructure, which was not designed to handle current vehicle volumes, leading to congestion and inefficiencies, especially in last-mile delivery. Integrating drones into the delivery system offers a promising solution by bypassing congested roads, thereby enhancing delivery speed and reducing infrastructure strain. However, optimizing this multimodal delivery system is complex and data-driven, with real-world data often being costly and restricted. To address this, we propose a synthetic dataset generator that creates diverse and realistic delivery scenarios, incorporating environmental variables, customer profiles, and vehicle characteristics. The key contribution of our work is the development of a dynamic generator for multiple optimization problems with diverse complexities or even combinations of optimization problems. This generator allows for the incorporation of real-world factors such as traffic congestion and synthetically generated factors such as wind conditions and communication constraints, as well as others. The primary objective is to establish a foundation for creating benchmark scenarios that enable the comparison of existing and new approaches. We evaluate the generated dataset by applying it to three optimization problems, including facility location, vehicle routing, and path planning, using different techniques to demonstrate the dataset's effectiveness and operational viability.

关键词： multimodal delivery system synthetic dataset optimization problems dynamic data modeling

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Supervised Nonlinear dynamic System for Soft Sensor Application Aided by Variational Auto-Encoder

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2020年第9期69卷 6132-6142页

作者： Shen, Bingbing Ge, Zhiqiang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Inst Frontier Technol Hangzhou 310027 Peoples R China

dynamic data modeling has been attracting much attention from researchers and has been introduced into the probabilistic latent variable model in the process industry. It is a huge challenge to extend these dynamic probabilistic latent variable models to nonlinear forms. In this article, a supervised nonlinear dynamic system (NDS) based on variational auto-encoder (VAE) is introduced for processes with dynamic behaviors and nonlinear characteristics. Based on the framework of VAE, which has a probabilistic data representation and a high fitting ability, the supervised NDS can extract effective nonlinear features for latent variable regression. The feasibility of the proposed supervised NDS is tested on two numerical examples and an industrial case. Detailed comparisons verify the effectiveness and superiority of the proposed model.

关键词： data models Nonlinear dynamical systems Probabilistic logic Feature extraction Numerical models Neural networks Principal component analysis dynamic data modeling nonlinear features probabilistic latent variable model supervised nonlinear dynamic system (NDS) variational autoencoder (VAE)

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dynamic process monitoring based on orthogonal dynamic inner neighborhood preserving embedding model

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 193卷 103812-000页

作者： Chen, Xiaoxia Tong, Chudong Lan, Ting Luo, Lijia Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Zhejiang Peoples R China Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310014 Zhejiang Peoples R China

A novel dynamic data modeling algorithm named orthogonal dynamic inner neighborhood preserving embedding (ODiNPE) is proposed for dynamic process monitoring. The formulation of the ODiNPE algorithm attempts to optimize a dual objective, which integrates together the maximization of the auto-covariance of the latent factors and the minimization of reconstruction error from the neighborhood, while an orthogonal constraint on the projecting directions is also satisfied. Therefore, the proposed algorithm is expected to extract highly auto-correlated dynamic latent factors with intrinsic neighborhood information embedded. The application of the ODiNPE in dynamic process monitoring has demonstrated its effectiveness and superiority over other state-of-art dynamic process monitoring approaches.

关键词： dynamic data modeling Neighborhood preserving embedding Statistical process monitoring Principal component analysis

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dynamic-Inner Canonical Correlation and Causality Analysis for High Dimensional Time Series data

Dynamic-Inner Canonical Correlation and Causality Analysis f...

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10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Dong, Yining Qin, S. Joe Univ Southern Calif Ming Hsieh Dept Elect Engn Los Angeles CA 90089 USA Chinese Univ Hong Kong 2001 Longxiang Rd Shenzhen Guangdong Peoples R China Univ Southern Calif Mork Family Dept Chem Engn & Mat Sci Los Angeles CA 90089 USA

In this paper, a novel dynamic-inner canonical correlation analysis (DiCCA) algorithm is proposed to extract dynamic components from high dimensional dynamic data. DiCCA extracts latent variables with descending dynamics, which are referred to as principal time series. Since DiCCA enables the principal time series to have maximal predictability, the most important dynamic features in the data are guaranteed to be extracted first. Therefore, usually a lower dimensional principal time series are able to provide good representation of the dynamic features, leading to the ease of interpretation and visualization. A case study on the Eastman plant-wide oscillating dataset demonstrates the effectiveness of the proposed method. Combined with Granger causality analysis, major oscillatory latent dynamics are analyzed, identified, and localized to equipment malfunctions. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： latent dynamic model dynamic data modeling Granger causality analysis root cause diagnosis

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Research of BGP Routing Instability Detection and Analysis

Research of BGP Routing Instability Detection and Analysis

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2016 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics(AMEII 2016)

作者： Tang Qi-jie Zhao Yu Zhou Yuan Zhou Yun-ting Luo Qi-fu Ji Feng-zhu Xichang Satellite Launch Center

ISBN: (纸本)9781510823808

In this paper,we propose an BGP routing instability detection algorithm that is based on statistics and pattern recognition through deeply learning BGP-4 and seriously analyzing the cause,properties,Manifestations of BGP routing instability,and verify its correctness and effectiveness by using SSFNet simulation *** routing instability detection algorithm can accurately detect change points in time series and what type of instability sub-time series with reasonable values of parameters.

关键词： Border Gateway Protocol BGP routing instability simulation experiment dynamic data modeling hypothesis test

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