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A General Framework for Closed Loop Negative Feedback Multivariable Physiological control Systems: Existence, Uniqueness, and Stability of Homeostatic Equilibrium Points

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IEEE ACCESS 2025年 13卷 17630-17651页

作者： Procopio, Anna Tedesco, Annarita Regio, Fabrizio Lo Cesarelli, Giuseppe Donisi, Leandro Ricciardi, Carlo Merola, Alessio Ponsiglione, Alfonso Maria Romano, Maria Montefusco, Francesco Cosentino, Carlo Amato, Francesco Univ Magna Graeciadi Catanzaro Dept Expt & Clin Med I-88100 Catanzaro Italy Univ Naples Federico II Dept Publ Hlth I-80131 Naples Italy Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy Univ Naples Parthenope Engn Dept Naples Italy Univ Campania Luigi Vanvitelli Dept Adv Med & Surg Sci I-80138 Naples Italy Univ Napoli Parthenope Dept Econ Law Cybersecur & Sports Sci Naples Italy

The study of homeostatic equilibrium is a key concern in several fields, from physiology and biology to medicine and biomedical engineering. control theory approaches can provide effective strategies to model physiological control systems, helping in understanding the dynamics of bio- and physio-logical regulation processes. However, the intrinsic complexity of living systems makes it difficult to identify unified biomodels that can represent a wide variety of physiological systems. In this context, the present work proposes a general framework to model the dynamics and describe the behavior of a wide class of multivariable physiological control systems, from the molecular to the whole-organ scale. The framework adopts a structure based on a closed-loop topology taking into account multiple inputs and outputs and with the negative feedback action intrinsically embedded within the model. The development of such a general model has at least three important repercussions: the first concerns the possibility of better understanding the basic mechanisms common to many physiological systems;the second is to develop a common theoretical framework to enable effective approaches to the analysis and design of synthetic biological control systems;finally, the investigation of the structural properties of the model in a general context, allows a guided and simplified application to specific cases. To this regard, in this paper, the existence, possible uniqueness and stability properties of the homeostatic equilibrium points of the general model are investigated;the theoretical framework is then illustrated through two real-world case-studies: (i) the PI3K/AKT/mTOR pathway nonlinear dynamics, a critical regulator of cellular growth, proliferation, and survival;(ii) the control mechanism of the neuromuscular stretch reflex, among the prime triggers implicated in postural control. Results proved the capability of the proposed framework to capture the intricate dynamics of multivari

关键词： Physiology Biological system modeling control systems Biology Negative feedback Regulation Homeostasis process control Integrated circuit modeling Delays Multivariable physiological control systems neuromuscular stretch reflex cellular growth dynamics stability of homeostatic equilibrium

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data-driven Energy Consumption modeling and Simulation Method of Discrete Manufacturing Workshop Based on Simplified Power Curve 9

Data-driven Energy Consumption Modeling and Simulation Metho...

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9th International conference on Automation, control and Robotics Engineering (CACRE)

作者： Yao, Ming Chang, Puikuan Shao, Zhufeng Zhang, Qi Niu, Pengfei Tsinghua Univ State Key Lab Tribol Beijing Peoples R China Tsinghua Univ Beijing Key Lab Precis Ultraprecis Mfg Equipments Beijing Peoples R China Tsinghua Univ Zhili Coll Beijing Peoples R China Instrumentat Technol & Econ Inst Beijing Peoples R China

ISBN: (纸本)9798350350319;9798350350302

As a core part of industrial production, the management of energy consumption (EC) in discrete manufacturing workshops (DMWs) is critical to improving productivity, reducing costs, and minimizing environmental impact. This paper provides an in-depth study of EC in DMWs, which is categorized into two main groups: work EC and public EC. The work EC includes basic energy, manufacturing energy and transportation energy, and each EC is analyzed and modeled in detail. Different analysis methods are proposed for the two levels of concern, process level and technological level. Based on these analyses, a shop-floor level energy calculation method based on simplified power curves and a data-driven power modeling approach are proposed for constructing accurate shop-floor energy models. Taking typical manufacturing and transportation equipment such as NC machine tools and AGVs as examples, the various power characteristics in the work EC are analyzed and the corresponding models are established. The experimental results show that a better power modeling accuracy can be obtained by using the data-driven method.

关键词： energy consumption modeling power modeling discrete manufacturing workshop data-driven intelligent algorithm

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Multilayer Networks: For modeling and analysis of Big data 16

Multilayer Networks: For Modeling and Analysis of Big Data

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16th International conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Santra, Abhishek Billah, Hafsa Chakravarthy, Sharma Information Technology Lab. CSE Department University of Texas ArlingtonTX United States

ISBN: (纸本)9789897587160

In this position paper, we make a case for the appropriateness, utility, and effectiveness of graph models for big data analysis focusing on Multilayer Networks (or MLNs) – a specific type of graph. MLNs have been shown to be more appropriate for modeling complex data compared to their traditional counterparts. MLNs have also been shown to be useful for diverse data types, such as videos and information integration. Further, MLNs have been shown to be flexible for computing analysis objectives from diverse application domains using extant and new algorithms. There is research for automating the modeling of MLNs using widely used EER (Enhanced/Extended Entity Relationship) or Unified modeling Language (UML) approaches. We start by discussing different graph models and their benefits and limitations. We demonstrate how MLNs can be effectively used to model applications with complex data. We also summarize the work on the use of EER models to generate MLNs in a principled manner. We elaborate on analysis alternatives provided by MLNs and their ability to match analysis needs. We show the use of MLNs for - i) traditional data analysis, ii) video content analysis, iii) complex data analysis, and iv) propose the use of MLNs for information integration or fusion. We show examples drawn from the literature of their modeling and analysis usage. We conclude that graphs, specifically MLNs provide a rich alternative to model and analyze big data. Of course, this certainly does not preclude newer data models that are likely to come along. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： data consistency

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Causality analysis and modeling of fuel ratio influencing factors in blast furnace ironmaking process 14

Causality analysis and modeling of fuel ratio influencing fa...

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14th Asian control conference (ASCC)

作者： Qian, Binjie Chen, Qifu Wu, Min Cao, Weihua China Univ Geosci Sch Automat Wuhan 430074 Peoples R China China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China

ISBN: (纸本)9798331540845;9789887581598

Blast furnace ironmaking is a key process in iron and steel production, and the fuel ratio has an important impact on the smelting efficiency, cost and environmental impact of the blast furnace. This study aims to provide new ideas and methodological support for the optimized management and control of the blast furnace ironmaking process by exploring the effects of different factors on the fuel ratio. We firstly sorted out the factors affecting the fuel ratio based on the blast furnace process mechanism, and then deeply analyzed the causal relationship of each parameter on the fuel ratio based on the transfer entropy value, and screened out the parameters that did not have a significant effect on the fuel ratio. Taking the remaining key parameters as inputs, the LSTM and GRU models, which can capture time series information, are used to predict the mean and variance of the fuel ratio, respectively, and the two are combined to compute the final fuel ratio, which overcomes the problem of poor model generalization brought by small fluctuations in the training data of fuel ratio. The experimental results show that the convergence speed of the model is significantly improved after parameter screening, and the fuel ratio can be effectively predicted within a certain error range.

关键词： Fuel ratio causal analysis transfer entropy data forecasts LSTM GRU

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modeling and control of Continuous Countercurrent Tangential Chromatography

Modeling and Control of Continuous Countercurrent Tangential...

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American control conference (ACC)

作者： Lu, Amos E. Dighe, Anish Braatz, Richard D. MIT 77 Massachusetts Ave Cambridge MD 02139 USA

ISBN: (纸本)9798350382662;9798350382655

A model-based control system is developed for continuous countercurrent tangential chromatography. The mechanistic model is formulated as a distributed parameter system. The computational cost of the mechanistic model is reduced by reformulating the partial differential equations as ordinary differential equations via the method of characteristics. The model parameters are fit to experimental data for the capture of a monoclonal antibody from clarified bioreactor material. An control problem is formulated for the objective of maximizing system productivity subject to a constraint on the protein recovery, and analyzed to provide insight into the process parameters that strongly affect the closed-loop performance.

关键词： Chromatographic analysis

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Transferred Bosting Learning for Transient Stability analysis of Power Systems 24

Transferred Bosting Learning for Transient Stability Analysi...

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International conference on Power Electronics and Artificial Intelligence (PEAI)

作者： Zhang, Qiqi State Grid Shanghai Municipal Elect Power Co Shanghai Peoples R China

ISBN: (纸本)9798400716638

The power system holds a crucial role in the energy supply system, being strategically important for national economic development and social activities. Transient stability analysis and preventive control are pivotal in ensuring the secure and stable operation of power systems. In the face of the intricate and ever-changing power system operation modes, the robust adaptive capabilities of online transient stability analysis and preventive control are essential cornerstones for ensuring the system's safety and stability. The rapid advancements in power big data and artificial intelligence technology present new opportunities for online transient stability analysis. This paper focuses on data-driven analysis, particularly in scenarios involving changes in power system topology. We present an online transient stability analysis method using transfer boosting learning, incorporating the concept of data transfer learning into the transient stability analysis modeling process. By effectively leveraging information from existing transient stability analysis data, we facilitate the construction and updating of transient stability analysis models under new fault and topology scenarios. This method achieves the model's easy scalability and updateability. Experimental validation on power data confirms the method's effectiveness.

关键词： power system transfer bosting learning online transient stability analysis

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Enhancing Grid Resilience: Islanding control in a Digital Twin of Real Electrical Distribution System 6

Enhancing Grid Resilience: Islanding Control in a Digital Tw...

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6th Global Power, Energy and Communication conference (GPECOM)

作者： Sundhu, Anosh Arshad Saadatmandi, Soheil Arnone, Maurizio Links Fdn Turin Italy Politecn Torino Dipartimento Energia Galileo Ferraris Turin Italy

ISBN: (纸本)9798350351088;9798350351095

Digital Twin (DT) technology creates a digital counterpart of a physical system or process, facilitating instantaneous monitoring, analysis, and simulation. When applied to an Electrical Distribution System (EDS), DT enables continuous analysis by merging static and dynamic data, which illustrates the present condition of the grid and forecasts future scenarios for the Distribution System Operator (DSO), as well as for producers and consumers. It also provides the ability to simulate various hypothetical situations for DSOs. The current work is an extension of a previously published journal paper that discusses the architecture of the Smart Grid controller (SGC) application and the developed tools within it for Smart Recursive Load Flow (SRLF) calculation, aiming to estimate the loads in Medium Voltage (MV) feeders. The SGC application was designed to be integrated with the Supervisory control and data Acquisition System (SCADA) of any EDS to facilitate the development of DT. This paper focuses on the development of the Islanding control tool within the SGC application, presenting a control scheme for robust management and control of split grids. The islanding trigger entails specific instructions from the mainland system operator, deliberate disconnection maneuvers, and handling cascading failures in response to potential risks. This paper demonstrates the effectiveness of the developed Islanding control tool, which was evaluated on an actual EDS.

关键词： Digital Twin Distribution System Operator Electrical Distribution System Smart Grid controller Supervisory control and data Acquisition System Smart Recursive Load Flow

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Q-Learning-Based Multi-Rate Optimal control for process Industries

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS ii-EXPRESS BRIEFS 2023年第6期70卷 2006-2010页

作者： Xia, Zhenxing Hu, Mengjie Dai, Wei Yan, Huaicheng Ma, Xiaoping China Univ Min & Technol Engn Res Ctr Intelligent Control Underground Space Sch Informat & Control Engn Xuzhou 221116 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

This brief studies the multi-rate optimal control problem for a class of industrial processes, whose controlling rate will be set faster than the sampling rate sometimes. This multi-rate phenomenon makes the accurate modeling of control systems challenging and difficult. In this brief, we present a model-free self-learning control scheme for the real-time solution of this problem, combining the lifting technology and Q-learning. For the asynchronous periods, the lifting system is established first to reconstruct the input and output by stacking the control and sampling signals to a frame period, maintaining the original dynamic information. Then, Q-learning is adopted to learn the optimal control policy with the real-time data and the convergence analysis of the proposed algorithm is derived. In this way, the control actions are executed at a faster rate to obtain the better dynamic performance. Finally, a hardware-in-loop (HIL) simulation study for process industries is carried out, showing that the proposed approach has high tracking and real-time performance.

关键词： Q-learning Optimal control process control control systems Performance analysis Industries Heuristic algorithms Multi-rate optimal control lifting system

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Knowledge modeling and intelligent analysis of road traffic accidents 9

Knowledge modeling and intelligent analysis of road traffic ...

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9th International conference on Electromechanical control Technology and Transportation, ICECTT 2024

作者： Liu, Xiaojia Chen, Yunjie Peng, Wanli Yu, Dexin School of Navigation Jimei University 1 Jiageng Road Fujian Xiamen361021 China

ISBN: (纸本)9781510682399

This paper proposes a method for efficiently organizing, managing, and visualizing road traffic accident data through knowledge graph technology. It begins by outlining the construction process of the road traffic accident knowledge graph based on the logical and technical architecture of knowledge graph. Then, it performs knowledge modeling and extraction using road traffic safety guidelines and accident data, storing the extracted knowledge in a Neo4j graph database. Finally, it conducts accident portrait, classification, statistics, and correlation analysis using the constructed knowledge graph. The research demonstrates that knowledge graph technology facilitates rapid querying and analysis of accident data, visualizes accident knowledge, and identifies associations between accident elements. © 2024 SPIE.

关键词： Knowledge graph

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Modelling and Execution of data-Driven processes with JSON-Nets 24th

Modelling and Execution of Data-Driven Processes with JSON-N...

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24th International Working conference on Business process modeling, Development, and Support (BPMDS) / 28th International Working conference on Exploring modeling Methods for Systems analysis and Development (EMMSAD)

作者： Fritsch, Andreas Schuler, Selina Forell, Martin Oberweis, Andreas Karlsruhe Inst Technol KIT Inst Appl Informat & Formal Descript Methods AIFB Kaiserstr 89 D-76133 Karlsruhe Germany

ISBN: (纸本)9783031342400;9783031342417

Modern business environments are governed by a wide range of data in various data formats. Despite the importance of integrating the data and control-flow perspective, existing business process modelling languages have only limited capability to precisely describe data-driven processes. In this paper, we propose a new approach called JSON-Nets, a variant of high-level Petri nets, that utilizes JSON technologies to integrate complex data objects in executable process models. We introduce JSON-Nets using an illustrative example and provide a formal specification, as well as a prototypical implementation of a modelling tool to evaluate our conception.

关键词： Business process modeling Petri nets JSON

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