IFAC Journal of Systems and Control最新文献_第7页

Identification of passive respiratory mechanics using Rapid Expiratory Occlusions (REOs) 利用快速呼气闭塞法（REOs）识别被动呼吸力学

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.ifacsc.2025.100345

Ella F.S. Guy , Jennifer L. Knopp , Lui R. Holder-Pearson , J. Geoffrey Chase

{"title":"Identification of passive respiratory mechanics using Rapid Expiratory Occlusions (REOs)","authors":"Ella F.S. Guy , Jennifer L. Knopp , Lui R. Holder-Pearson , J. Geoffrey Chase","doi":"10.1016/j.ifacsc.2025.100345","DOIUrl":"10.1016/j.ifacsc.2025.100345","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Feasible methods to assess respiratory compliance and airway resistance without requiring clinical effort or interrupting normal breathing patterns would decrease the high burden of respiratory testing on healthcare systems. This study aims to provide proof of concept of a novel rapid expiratory occlusion (REO) test in a healthy adult population.</div></div><div><h3>Methods:</h3><div>REO test data was collected for unassisted spontaneous breaths and a PEEP challenge in N=80 healthy adults. Model-identified compliance and resistance values are compared to physiological expectations and literature.</div></div><div><h3>Results:</h3><div>Median [min, max] compliance was 0.506 [0.199, 1.562] cmH <sub>2</sub>O<sup>−1</sup>L, and resistance was 1.777 [0.811 2.478] cmH <sub>2</sub>OL<sup>−1</sup>s in initial spontaneous breathing, matching expectations. When PEEP was applied compliance decreased (independent of PEEP level) and resistance increased (proportional to set PEEP).</div></div><div><h3>Conclusions:</h3><div>This study established proof-of-concept efficacy for a model-based REO method identifying compliance and resistance, and informs device development and testing for clinical populations.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100345"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268212","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}

引用次数: 0

Advancements in Electrical Impedance Tomography: Addressing electrode displacement with artificial neural networks 电阻抗断层扫描的进展：用人工神经网络定位电极位移

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.ifacsc.2025.100335

Guilherme C. Duran, Edson K. Ueda, André K. Sato, Thiago C. Martins, Marcos S.G. Tsuzuki

{"title":"Advancements in Electrical Impedance Tomography: Addressing electrode displacement with artificial neural networks","authors":"Guilherme C. Duran, Edson K. Ueda, André K. Sato, Thiago C. Martins, Marcos S.G. Tsuzuki","doi":"10.1016/j.ifacsc.2025.100335","DOIUrl":"10.1016/j.ifacsc.2025.100335","url":null,"abstract":"<div><div>Electrode displacement is a common source of error in Electrical Impedance Tomography (EIT), particularly in long-term or dynamic measurements where stable electrode contact is difficult to maintain. This study proposes a comprehensive machine learning framework to detect, classify, and correct electrode displacements prior to image reconstruction. The approach combines tree-based classifiers—such as XGBoost and LightGBM—and Convolutional Neural Networks (CNNs) to identify both the presence and location of displaced electrodes. These models were evaluated across a series of classification tasks with increasing complexity, demonstrating high accuracy even in scenarios involving multiple simultaneous displacements with different magnitudes. For the rectification of distorted voltage measurements, several Denoising Autoencoder (DAE) configurations were investigated. Electrode-specific DAEs trained on all displacement magnitudes achieved an average Mean Squared Error (MSE) reduction of 84.5%, while shift-based DAEs offered computational efficiency for coarse corrections. A hybrid strategy combining fast shift-based and high-accuracy electrode-specific models proved effective and scalable. The pipeline was validated using both synthetic datasets and real EIT measurements, confirming its robustness and generalization capabilities. These results support the use of learning-based correction schemes to improve the reliability and consistency of EIT in practical applications affected by electrode movement.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100335"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061274","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}

引用次数: 0

Exploring the nexus of surface heat and influencing factors in Hyderabad and Bangalore, India 探讨印度海得拉巴和班加罗尔地区地表热的关系及其影响因素

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-19 DOI: 10.1016/j.ifacsc.2025.100340

K.S. Arunab, Aneesh Mathew

{"title":"Exploring the nexus of surface heat and influencing factors in Hyderabad and Bangalore, India","authors":"K.S. Arunab, Aneesh Mathew","doi":"10.1016/j.ifacsc.2025.100340","DOIUrl":"10.1016/j.ifacsc.2025.100340","url":null,"abstract":"<div><div>This study examined the relationship between Land Surface Temperature (LST) and various controllable, partially controllable, and uncontrollable factors in the cities of Bangalore and Hyderabad. LST showed significant correlations with geographical coordinates in both cities. Despite these directional differences, both cities exhibited consistent correlations with key environmental factors, including Enhanced Vegetation Index (EVI), Normalized Difference Built-up Index (NDBI), Land Cover (LC), Modified Bareness Index (MBI), slope and Modified Normalized Difference Water Index (MNDWI), highlighting the influence of vegetation and built-up areas on urban heat dynamics. The study further compared continuous and grouped LST representations, revealing that grouped LST data exhibited stronger and more consistent correlations with environmental factors, suggesting the presence of non-linear relationships. Factors such as EVI, LC, MBI, MNDWI, Distance to Bare soil (DBS), and Distance to Built-up (DBU) exhibited stronger correlations with grouped LST, highlighting the complexity of LST interactions across different temperature intervals. Grouped LST in Bangalore showed high correlations with LC (0.95), MBI (−0.941), and EVI (−0.938), while in Hyderabad, the strongest associations were with EVI (−0.965), LC (0.929), and DBS (0.918). The study highlights the importance of selecting appropriate LST representations in model development, as stronger correlations with grouped LST suggest non-linearities and potential threshold effects. The study underscores the critical role of vegetation, water bodies, and urban form in shaping LST patterns, offering valuable insights for urban heat mitigation. The study provides valuable insights for policymakers and climate resilience planners, supporting sustainable urban development and enhanced thermal comfort.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100340"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221229","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}

引用次数: 0

Stability analysis of mixed logit dynamics with internal/external conformity biases and committed minority 具有内外一致性偏差和承诺少数的混合logit动力学稳定性分析

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-10-03 DOI: 10.1016/j.ifacsc.2025.100343

Tatsuya Miyano , Yuji Ito , Daisuke Inoue , Takeshi Hatanaka

引用次数: 0

WBML-PV: Window-based machine learning for ultra-short-term photovoltaic power forecasting WBML-PV：基于窗口的超短期光伏功率预测机器学习

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-26 DOI: 10.1016/j.ifacsc.2025.100342

Syed Kumail Hussain Naqvi , Kil To Chong , Hilal Tayara

{"title":"WBML-PV: Window-based machine learning for ultra-short-term photovoltaic power forecasting","authors":"Syed Kumail Hussain Naqvi , Kil To Chong , Hilal Tayara","doi":"10.1016/j.ifacsc.2025.100342","DOIUrl":"10.1016/j.ifacsc.2025.100342","url":null,"abstract":"<div><div>Accurate ultra-short-term photovoltaic (PV) power forecasting is essential for grid management and the integration of renewable energy. However, the stochastic and volatile nature of PV power, along with inherent uncertainty, challenges stable grid operation as PV penetration grows. Currently, deep learning (DL) and reinforcement learning (RL) models often struggle to generalize under new conditions, manage computational demands, and address the uncertainty in PV forecasting. To address these issues, a window-based machine learning (WBML) approach is proposed, utilizing light gradient boosting machine (WB-LGBM) and extreme gradient boosting (WB-XGBoost) models. These proposed models outperform attention-based and non-attention-based RL and DL baselines in deterministic metrics like mean absolute error (MAE) and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, while significantly reducing training time. Optimized via Optuna and evaluated using fuzzy C-means clustering, their performance is validated by the Diebold–Mariano test. Uncertainty is assessed using non-parametric kernel density estimation (NPKDE) and confidence intervals (CIs) at 99%, 95%, 90%, and 80% confidence levels within the WBML framework, demonstrating robust and conservative forecast uncertainty quantification. Amplitude and phase errors are analyzed with standard deviation error, bias, dispersion, skewness, and kurtosis. The models demonstrate reduced imbalance penalties and enhanced revenue through improved forecasting accuracy.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100342"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221231","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}

引用次数: 0

Deep Koopman-based reachability analysis for data-driven predictive control of unknown nonlinear systems 基于深度koopman的未知非线性系统数据驱动预测控制可达性分析

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1016/j.ifacsc.2025.100339

Teketel Ketema , Surafel Luleseged Tilahun , Simon D. Zawka , Abebe Geletu

引用次数: 0

Optimizing electric vehicle charging in smart parking lots using particle swarm optimization: A comparative study in Morocco, France, and Tunisia 基于粒子群算法的智能停车场电动汽车充电优化：摩洛哥、法国和突尼斯的比较研究

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-09 DOI: 10.1016/j.ifacsc.2025.100338

Khadija El Harouri , Soumia El Hani , Nisrine Naseri , Imade Aboudrar , Amina Daghouri

{"title":"Optimizing electric vehicle charging in smart parking lots using particle swarm optimization: A comparative study in Morocco, France, and Tunisia","authors":"Khadija El Harouri , Soumia El Hani , Nisrine Naseri , Imade Aboudrar , Amina Daghouri","doi":"10.1016/j.ifacsc.2025.100338","DOIUrl":"10.1016/j.ifacsc.2025.100338","url":null,"abstract":"<div><div>Electric vehicles (EVs) are becoming a basis of sustainable mobility, requiring efficient charging management to minimize costs, balance grid demand, and optimize renewable energy utilization. In workplace parking lots, integrating solar energy and vehicle-to-grid (V2G) technology offers new opportunities for smart energy management. This paper presents an optimization-based charging strategy using Particle Swarm Optimization (PSO) to minimize total energy costs while reducing peak power drawn from the grid, maximizing the use of photovoltaic (PV) energy and ensure that all vehicles reach their target State of Charge (SOC) before leaving the parking lot. Additionally, The proposed approach leverages advantage of V2G technology, enabling EVs to return energy to the grid during peak demand hours, which enhances grid stability and reducing overall energy expenses. A key contribution of this work is the comparative analysis of EV charging management in three different geographical contexts: Morocco, France, and Tunisia. Each country provides distinct energy cost structures, solar availability. A dynamic electricity pricing model is incorporated to adapt the charging strategy based on daily and seasonal tariff variations. The optimization strategy considers multiple constraints like EV arriving and leaving periods, initial and target SOC, PV energy production, and dynamic electricity pricing. Results from simulations indicate that the suggested PSO-based charging strategy achieves significant cost savings can reach up to 65% compared to a conventional unmanaged scenario, reduces peak power coming from the grid, and maximize PV power utilization via self-consumption. Additionally, the findings highlight the benefits of multi-objective optimization in smart parking energy management.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100338"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061273","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}

引用次数: 0

A comparative analysis of PPO and SAC algorithms for energy optimization with country-level energy consumption insights 能源优化的PPO和SAC算法与国家级能源消耗洞察的比较分析

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-10-03 DOI: 10.1016/j.ifacsc.2025.100344

Enes Bajrami, Andrea Kulakov, Eftim Zdravevski, Petre Lameski

{"title":"A comparative analysis of PPO and SAC algorithms for energy optimization with country-level energy consumption insights","authors":"Enes Bajrami, Andrea Kulakov, Eftim Zdravevski, Petre Lameski","doi":"10.1016/j.ifacsc.2025.100344","DOIUrl":"10.1016/j.ifacsc.2025.100344","url":null,"abstract":"<div><h3>Background:</h3><div>This study addresses national-scale energy optimization using deep reinforcement learning. Unlike prior works that rely on simulated environments or synthetic datasets, this research integrates real-world energy indicators, including electricity generation, greenhouse gas emissions, renewable energy share, fossil fuel dependency, and oil consumption. These indicators, sourced from the World Energy Consumption dataset, capture both developed and developing energy systems, enabling the evaluation of intelligent control policies across diverse contexts.</div></div><div><h3>Methodology:</h3><div>Two advanced algorithms, Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC), were implemented and trained using PyTorch across multi-phase evaluation runs (300–3000 episodes). Comparative performance analysis was conducted on key metrics: execution speed, action consistency, and reward optimization. A secondary regional analysis focused on contrasting the Balkan and Nordic countries to evaluate algorithm adaptability between highly developed and developing energy infrastructures.</div></div><div><h3>Significant findings:</h3><div>SAC demonstrated superior computational throughput and policy stability, making it suitable for real-time and resource-constrained environments. PPO exhibited stronger action magnitudes, enabling more assertive control signals for high-impact interventions. Both agents significantly outperformed a rule-based baseline in responsiveness and adaptability. The proposed framework represents a novel contribution by combining deep reinforcement learning with interpretable, country-level energy indicators. Future work will extend the evaluation to additional continents, including Asia, Africa, and South America, to assess global scalability and applicability.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100344"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221234","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}

引用次数: 0

Application of LiDAR and neuromorphic vision in Ambient Assisted Living environments 激光雷达和神经形态视觉在环境辅助生活环境中的应用

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-09-30 DOI: 10.1016/j.ifacsc.2025.100347

Niklas Huhs, Niloofar Kalashtari , Jens Kraitl, Christoph Hornberger, Olaf Simanski

{"title":"Application of LiDAR and neuromorphic vision in Ambient Assisted Living environments","authors":"Niklas Huhs, Niloofar Kalashtari , Jens Kraitl, Christoph Hornberger, Olaf Simanski","doi":"10.1016/j.ifacsc.2025.100347","DOIUrl":"10.1016/j.ifacsc.2025.100347","url":null,"abstract":"<div><div>Continuous and non-invasive patient monitoring is essential in healthcare, particularly within Ambient Assisted Living (AAL) environments, to enhance safety and acceptance while preserving privacy. This work investigates two complementary approaches for patient monitoring. In the first approach, a Light Detection and Ranging (LiDAR)-based system was developed to detect and track human subjects in a room using a fine-tuned You Only Look Once, version 5 (YOLOv5) deep learning model. Thanks to LiDAR’s precision and depth sensing capabilities, the system enables live tracking of multiple individuals under varying lighting conditions while safeguarding patient privacy. When the position of the patients in the room is known, the second approach is relevant. A neuromorphic camera, which has a more limited field of view in the room, was employed to measure vital signs such as respiration rate and heart rate by capturing subtle chest movements and micro-vibrations induced by blood circulation. A study involving 26 participants was conducted, with measurements taken at distances ranging from 0.5 metres to 2 metres as well as before and after exercise tasks, consisting of light jogging on a treadmill. Reference data were collected using a Powerlab 15T system equipped with a three-point ECG and a respiration belt. The neuromorphic camera-based measurements demonstrated promising accuracy, validating the feasibility of the approach. Overall, these combined systems offer a contact-free, privacy-preserving solution for continuous patient monitoring, addressing challenges such as limited healthcare staffing, infection control, and the need for vital parameter online tracking in AAL environments.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100347"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268211","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}

引用次数: 0

A physics-informed LSTM framework with lag compensation for coupled vibration signal modeling 耦合振动信号建模的时滞补偿LSTM框架

IF 1.8

IFAC Journal of Systems and Control Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.ifacsc.2025.100348

Xinwei Sun , Lei Zhang

{"title":"A physics-informed LSTM framework with lag compensation for coupled vibration signal modeling","authors":"Xinwei Sun , Lei Zhang","doi":"10.1016/j.ifacsc.2025.100348","DOIUrl":"10.1016/j.ifacsc.2025.100348","url":null,"abstract":"<div><div>Investigating vibration signals in complex electromechanical systems is essential for improving system stability and control performance. This study proposes a data–physics dual-driven framework to model the dynamic coupling between suspension current and levitation gap in maglev systems. A joint time–frequency analysis is first conducted using Fourier transform, ripple coefficient evaluation, and hysteresis correlation to quantify nonlinear coupling strength and identify a positively lagged relationship between current and gap. To capture this effect, we develop a physics-informed neural network (PINN) that integrates a lag compensation module, embeds electromagnetic equations as physical constraints, and employs an LSTM architecture for end-to-end vibration signal prediction. Unlike conventional approaches that design neural controllers from a control perspective, our method focuses on learning intrinsic coupling patterns directly from real-world operational data. This data-informed modeling approach, enhanced with time-delay compensation and physical consistency, enables accurate prediction of dynamic responses under realistic disturbances. Experiments on data from the Changsha medium-low-speed maglev train show that our model achieves the lowest MAE and RMSE compared to standard PINNs and purely data-driven baselines. It also responds rapidly to gap changes, with a response time of 0.167 ms, making it suitable for real-time maglev control applications. The implementation code is available at: <span><span>https://github.com/sunning2024/RPinn</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":29926,"journal":{"name":"IFAC Journal of Systems and Control","volume":"34 ","pages":"Article 100348"},"PeriodicalIF":1.8,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568440","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}

引用次数: 0