IFAC Journal of Systems and Control最新文献

{"title":"Automatic control of reactive brain computer interfaces","authors":"Pex Tufvesson ,&nbsp;Frida Heskebeck","doi":"10.1016/j.ifacsc.2024.100251","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100251","url":null,"abstract":"<div><p>This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the performance of the brain computer interface. We use a reactive brain computer interface based on a visual oddball paradigm for the investigation and improvement of the performance of automatic control and feedback algorithms used in the system. By using automatic control for selection of the stimuli for the visual oddball experiment, the target stimulus is identified faster than if no automatic control is used. Finally, we introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100251"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000129/pdfft?md5=121b6676b9693cead323163b827332bb&pid=1-s2.0-S2468601824000129-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140042122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ubiquity of models describing inspiratory effort dynamics in patients on pressure support ventilation","authors":"Jennifer L. Knopp ,&nbsp;Yeong Shiong Chiew ,&nbsp;Dimitrios Georgopoulos ,&nbsp;Geoffrey M. Shaw ,&nbsp;J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2024.100250","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100250","url":null,"abstract":"<div><p>Mechanical Ventilation (MV) is an important therapy in the intensive care unit (ICU). Assisted spontaneous breathing (ASB) MV modes are a key and growing part of MV care, as they require less sedation and help avoid muscle atrophy. Equally, a lack of standardised approaches to MV care has led to the rise of model-based methods, which typically cannot estimate spontaneous breathing (SB) efforts, and are thus not able to be used for ASB MV. To address this issue, several models of SB effort have been created, though they require specialised added sensors and/or maneuvers. ►This research utilises a unique observational clinical dataset, which includes esophageal and gastric pressure measurements not typically taken in the ICU for N=6 patients. These measurements enable more direct model-based estimation of muscular pressure effort in SB MV patients. The data is analysed for all 6 patients for 3 models which include dynamics common to the current models. Models are assessed based on model fit. ►The results show all 3 models are unable to capture dynamics in 2 patients due to added muscular dynamics in their breathing, violating assumptions in the model dynamics or constraints. These results indicate the need for more flexible models and associated identification methods to better capture these dynamics.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100250"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140024358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable H∞ fuzzy control for fault-tolerant actuator failures of active suspension system","authors":"Jamal Mrazgua ,&nbsp;El Houssaine Tissir ,&nbsp;Mohamed Ouahi ,&nbsp;Fernando Tadeo","doi":"10.1016/j.ifacsc.2024.100258","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100258","url":null,"abstract":"<div><p>A new methodology for fault tolerant control (FTC) is proposed to compensate actuator failures using Takagi–Sugeno systems. This makes possible to design the controller that represents actuator failures using a scaling factor by solving a family of linear matrix inequalities (LMIs). The resulting control system guarantees asymptotic stability, compensates the effect of actuator faults and ensures certain an <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span> performance level. This methodology is applied to the active suspension systems that motivated this research, where in the context of active suspension (AS) systems, the guaranteed performance correspond to ride comfort in the presence of road disturbances. Thus, a controller is developed for a quarter-car model with active suspension. The simulated results illustrate the effectiveness of the proposed approach.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100258"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140549185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Human Activity Recognition by graph engineered ensemble deep learning model","authors":"Mamta Ghalan,&nbsp;Rajesh Kumar Aggarwal","doi":"10.1016/j.ifacsc.2024.100253","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100253","url":null,"abstract":"<div><p>This research delves into the domain of Human Activity Recognition (HAR) through sensor data analysis, offering a comprehensive exploration of three diverse datasets: UniMiB-SHAR, Motion Sense, and WISDM Actitracker. The UniMiB-SHAR dataset encompasses a diverse array of linear as well as non-linear and complex activities which involve the movement of more than one joint or muscle (for example Hitting Obstacles, jogging and falling with face down). This motion generates highly correlated sensor readings over a certain period of time. In this case, Convolution Neural Networks (CNNs) are effective in feature extraction as well as classification of HAR activities, but they may not fully grasp the combined features of spatial as well as temporal aspects in the HAR datasets and heavily rely on labelled data. Whereas, Graph convolution networks (GCN), with their capacity to model complex interactions through graph structure, complement CNN’s capabilities in classifying non-linear activities in the HAR dataset. By leveraging the Knowledge graph structure and acquiring the feature embeddings from the GCN model, in this study, a Noval ensemble CNN model is proposed for the classification of activities. The novel HAR pipeline is termed as Graph Engineered EnsemCNN HAR (GE-EnsemCNN-HAR) and its performance is evaluated on HAR datasets. Proposed model demonstrated a noteworthy accuracy of 93.5% on UniMiB-SHAR dataset, surpassing the Shallow CNN model with GNN with an improvement of 20.14%. The proposed model achieved a notable accuracy rate of 96.18% and 98% when evaluated on the Motion Sense and WISDM Actitracker dataset.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100253"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel adaptive observer for HVDC transmission line: A new power management approach for renewable energy sources involving Vienna rectifier","authors":"Adil Mansouri ,&nbsp;Abdelmounime El Magri ,&nbsp;Rachid Lajouad ,&nbsp;Fouad Giri","doi":"10.1016/j.ifacsc.2024.100255","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100255","url":null,"abstract":"<div><p>This paper proposes a novel approach to control and manage energy production based on grid requirements. The main challenge is the distance between the load and production area, making it difficult to quantify energy requirements in real time at the production area. To overcome this challenge, the proposed approach relies on an adaptive observer design that provides accurate and reliable estimates of multiple signals without expensive and unreliable sensors. In this study, a renewable energy source that consists of a wind turbine coupled to a permanent magnet synchronous generator is actuated with a Vienna power converter. However, it is crucial to emphasize that this approach can be implemented at any production site, regardless of its nature. The paper’s main contribution is the design of a novel adaptive high-gain observer that estimates the grid energy requirement based on the voltage value at the endpoint of the high-voltage direct current line. Moreover, the system load parameters are unknown and come non-linear in the system model. Simulations and analysis demonstrate the effectiveness of the proposed observer, showing convergence to the origin under the well-established condition of persistent excitation (PE).</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100255"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140141847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the control properties on the energetic and economic performance of Heat-Integrated Distillation Columns under variable feed composition","authors":"R. Gutiérrez-Guerra ,&nbsp;J.G. Segovia-Hernández","doi":"10.1016/j.ifacsc.2024.100256","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100256","url":null,"abstract":"<div><p>Heat Integrated Distillation Columns (HIDiC) are highly energy-efficient technologies whose performance has been validated through robust optimization algorithms and practical tests. Despite these configurations are dynamically controllable technologies, the simultaneous relationship between dynamics and optimal energetic and economic performance under variable feed composition has not been analyzed. Thus, this paper tackles this gap in literature. Five binary mixtures and three feed composition were examined in this study. The optimization of these configurations was firstly achieved using a Boltzmann-based optimizer while the control properties were obtained through the closed-loop process analysis using the IAE criterion and rigorous simulations in Aspen Dynamics in a second stage. Results showed that the HIDiC configurations with the best dynamic behavior do not match with the HIDiC columns with the best energetic and economic performance. However, suboptimal HIDiC configurations experienced only slightly less energetic and economic benefits but better dynamic properties that the best HIDiC configurations. Particularly, the best suboptimal HIDiC columns to separate the mixtures with relative volatility (<span><math><mi>α</mi></math></span>) lower than 1.4 were determined for a feed composition of 25 mol% for the light component. Nevertheless, the most adequate HIDiC columns to separate mixtures with <span><math><mrow><mi>α</mi><mo>&gt;</mo><mn>1</mn><mo>.</mo><mn>4</mn></mrow></math></span> were obtained for equimolar feed composition and feed composition of 75% for the light component.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100256"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140296135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated covariate modeling using efficient simulation of pharmacokinetics","authors":"Ylva Wahlquist ,&nbsp;Kristian Soltesz","doi":"10.1016/j.ifacsc.2024.100252","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100252","url":null,"abstract":"<div><p>Pharmacometric modeling plays an important role in drug development and personalized medicine. Pharmacometric covariate models can be used to describe the relationships between patient characteristics (such as age and weight) and pharmacokinetic (PK) parameters. Traditionally, the functional structure of these relationships are obtained manually. This is a time-consuming task, and consequently limits the search space of covariate relationships. The use of data-driven machine learning (ML) in pharmacometrics has the potential to automate the search for adequate model structures, which can speed up the modeling process and enable the evaluation of a wider range of model candidates. Even with moderately sized data sets, ML approaches require millions of simulations of pharmacokinetic (PK) models, which dictates the need for an efficient simulator. In this paper, we demonstrate how to automate covariate modeling using neural networks (NNs), that are trained using efficient PK simulation techniques. We apply the methodology to a propofol data set with 1031 individuals and compare the results to previously published covariate models for propofol. We use the NN as a function approximator that relates covariates to the parameters of a three-compartment PK model, and train it on dose and plasma concentration time series. Our study demonstrates that NN-based covariate modeling allows for automation of the otherwise time-consuming task of identifying which of available covariates to include in the model, and what functional mappings from these covariates to PK model parameters to consider in the model search. Additional to this saving in modeler effort, the NN-based model obtained in our clinical data set example has PK parameters within a clinically reasonable range, and slightly enhanced predictive precision than a previously published state-of-the-art covariate models for propofol model.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100252"},"PeriodicalIF":1.9,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000130/pdfft?md5=e0ca790bb32973869e3ad07a61739e6f&pid=1-s2.0-S2468601824000130-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abdominal multi-organ segmentation using multi-scale and context-aware neural networks","authors":"Yuhan Song,&nbsp;Armagan Elibol ,&nbsp;Nak Young Chong","doi":"10.1016/j.ifacsc.2024.100249","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100249","url":null,"abstract":"<div><p>Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end-to-end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multi-scale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network (FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network (SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100249"},"PeriodicalIF":1.9,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139986389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voltage-based separation of respiration and cardiac activity by harmonic analysis in electrical impedance tomography","authors":"Erik Stein ,&nbsp;Rongqing Chen ,&nbsp;Alberto Battistel ,&nbsp;Sabine Krueger-Ziolek ,&nbsp;Knut Moeller","doi":"10.1016/j.ifacsc.2024.100248","DOIUrl":"https://doi.org/10.1016/j.ifacsc.2024.100248","url":null,"abstract":"<div><p>This study aims to improve the accuracy of Electrical Impedance Tomography (EIT) measurements for monitoring ventilation and cardiac signal in medical imaging by proposing a new signal separation approach that does not require contrast agents. Conventionally, contrast agents like high-conductive saline solutions are used for signal separation in EIT measurements. This study uses a harmonic analysis on EIT raw voltage data to separate the ventilation- and cardiac-related signals (early separation). It evaluates its efficacy with a simulation model at low (1%) and high (10%) superimposed additive noise levels against the already published harmonic analysis at pixel level after EIT image reconstruction (late separation). The findings indicate that the voltage-based harmonic analysis approach, i.e., early separation, provides reliable signal separation, especially under high noise conditions, compared to the late separation. This method enables the possibility of incorporating independent cardiac-specific or ventilation-specific prior knowledge into the image reconstruction process, potentially improving the resulting images.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100248"},"PeriodicalIF":1.9,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000099/pdfft?md5=884ea48403ab5ce22c593036379e1e22&pid=1-s2.0-S2468601824000099-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139935528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PK/PD model based design of PID control for closed-loop anesthesia","authors":"Nicola Paolino ,&nbsp;Michele Schiavo ,&nbsp;Nicola Latronico ,&nbsp;Massimiliano Paltenghi ,&nbsp;Antonio Visioli","doi":"10.1016/j.ifacsc.2024.100247","DOIUrl":"10.1016/j.ifacsc.2024.100247","url":null,"abstract":"<div><p>This paper investigates the use of a recently developed pharmacokinetic/pharmacodynamic model for the design of a Proportional–Integral–Derivative controller for total intravenous anesthesia. In particular, we consider the administration of propofol as manipulated variable and the BIS signal as the process variable, and we propose a personalized approach to tune the controller by using the Eleveld model. Simulation results show that the personalized controller outperforms the population-based one, which fails to provide the required clinical performance for elderly people. Thus, the development of a pharmacokinetic/pharmacodynamic model specifically devised for control design purposes would be beneficial to provide a truly personalized control law and to increase the overall performance.</p></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"27 ","pages":"Article 100247"},"PeriodicalIF":1.9,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468601824000087/pdfft?md5=2b0e8ad78f8accc945934dc9d0f8f6ca&pid=1-s2.0-S2468601824000087-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}