IFAC Journal of Systems and Control最新文献_第5页

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 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-30 DOI: 10.1016/j.ifacsc.2025.100347

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

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.

在医疗保健中，特别是在环境辅助生活（AAL）环境中，持续和非侵入性的患者监测对于提高安全性和接受度，同时保护隐私至关重要。这项工作调查了两种互补的病人监测方法。在第一种方法中，开发了基于光探测和测距（LiDAR）的系统，使用经过微调的You Only Look Once， version 5 （YOLOv5）深度学习模型来检测和跟踪房间中的人类受试者。由于激光雷达的精度和深度传感能力，该系统可以在不同的照明条件下实时跟踪多个个体，同时保护患者的隐私。当病人在房间里的位置是已知的，第二种方法是相关的。神经形态相机在房间内的视野更有限，通过捕捉微妙的胸部运动和血液循环引起的微振动来测量呼吸率和心率等生命体征。研究人员对26名参与者进行了研究，测量了他们在0.5米到2米之间的距离，以及在锻炼任务（包括在跑步机上慢跑）之前和之后的运动量。参考数据的收集使用配备有三点心电图和呼吸带的Powerlab 15T系统。基于神经形态相机的测量显示出良好的准确性，验证了该方法的可行性。总的来说，这些组合系统为患者的持续监测提供了一种无接触、保护隐私的解决方案，解决了诸如有限的医疗人员、感染控制以及AAL环境中对重要参数在线跟踪的需求等挑战。

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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 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-26 DOI: 10.1016/j.ifacsc.2025.100342

Syed Kumail Hussain Naqvi , Kil To Chong , Hilal Tayara

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

R^{2}

, 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.

准确的超短期光伏发电功率预测对于电网管理和可再生能源并网至关重要。然而，随着光伏发电的普及，光伏发电的随机性和波动性以及其固有的不确定性给电网的稳定运行带来了挑战。目前，深度学习（DL）和强化学习（RL）模型往往难以在新条件下进行泛化，管理计算需求，并解决PV预测中的不确定性。为了解决这些问题，提出了一种基于窗口的机器学习（WBML）方法，利用光梯度增强机（WB-LGBM）和极端梯度增强（WB-XGBoost）模型。这些模型在平均绝对误差（MAE）和R2等确定性指标上优于基于注意和非基于注意的RL和DL基线，同时显著减少了训练时间。通过Optuna进行优化，使用模糊c均值聚类进行评价，并通过Diebold-Mariano检验验证了其性能。在WBML框架内，使用非参数核密度估计（NPKDE）和99%、95%、90%和80%置信水平的置信区间（ci）评估不确定性，展示了稳健和保守的预测不确定性量化。振幅和相位误差用标准差误差、偏置、色散、偏度和峰度进行分析。该模型表明，通过提高预测精度，减少了不平衡惩罚并增加了收入。

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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 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-19 DOI: 10.1016/j.ifacsc.2025.100340

K.S. Arunab, Aneesh Mathew

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.

本文研究了班加罗尔和海得拉巴的地表温度与各种可控、部分可控和不可控因素的关系。两个城市的地表温度与地理坐标呈显著相关。尽管存在这些方向性差异，但两个城市与增强植被指数（EVI）、归一化建筑差异指数（NDBI）、土地覆盖（LC）、修正光秃指数（MBI）、坡度和修正归一化水差异指数（MNDWI）等关键环境因子的相关性一致，突出了植被和建成区对城市热动态的影响。研究进一步比较了连续和分组的地表温度表示，发现分组的地表温度数据与环境因素表现出更强、更一致的相关性，表明存在非线性关系。EVI、LC、MBI、MNDWI、到裸土距离（DBS）和到建筑距离（DBU）等因子与分组LST的相关性较强，凸显了不同温度区间LST相互作用的复杂性。分组LST在班加罗尔与LC（0.95）、MBI（- 0.941）和EVI（- 0.938）呈正相关，而在海得拉巴与EVI（- 0.965）、LC（0.929）和DBS（0.918）呈正相关。该研究强调了在模型开发中选择适当的LST表示的重要性，因为与分组LST的较强相关性表明非线性和潜在的阈值效应。该研究强调了植被、水体和城市形态在形成地表温度模式中的关键作用，为城市热缓解提供了有价值的见解。该研究为政策制定者和气候适应能力规划者提供了有价值的见解，支持可持续城市发展和增强热舒适。

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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 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-16 DOI: 10.1016/j.ifacsc.2025.100339

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

This paper proposes a deep Koopman-based reachability analysis technique for a data-driven control of unknown nonlinear systems subject to process and measurement noises. An intelligent approach combining a neural network and Q-learning algorithm is employed. In particular, the power of the Long Short-Term Memory (LSTM) neural network is leveraged to lift the original nonlinear system into a higher-dimensional space, where the nonlinear dynamics can be approximated linearly, relying solely on the input–output data. The LSTM is set to draw learning insights from Extended Dynamic Mode Decomposition (EDMD) and Information-Theoretic Metric Function (ITMF) results. The Q-learning algorithm is employed to compute adaptive input–output references in the implementation of an adaptive nonlinear zonotopic predictive control technique to compute a robust control input of the system. We also introduced controllability and observability criteria in the presence of noisy data. Finally, a numerical example is given to verify the proposed approach.

本文提出了一种基于深度koopman的可达性分析技术，用于受过程和测量噪声影响的未知非线性系统的数据驱动控制。采用了神经网络和q -学习算法相结合的智能方法。特别是，利用长短期记忆（LSTM）神经网络的力量将原始非线性系统提升到高维空间，在高维空间中，非线性动力学可以线性近似，仅依赖于输入输出数据。LSTM旨在从扩展动态模式分解（EDMD）和信息论度量函数（ITMF）结果中获得学习见解。在实现自适应非线性分区预测控制技术中，采用q -学习算法计算自适应输入输出参考，计算系统的鲁棒控制输入。我们还介绍了在存在噪声数据的情况下的可控性和可观测性准则。最后，给出了一个数值算例来验证所提出的方法。

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 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-09 DOI: 10.1016/j.ifacsc.2025.100338

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

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.

电动汽车（ev）正在成为可持续出行的基础，需要有效的充电管理来最大限度地降低成本，平衡电网需求，并优化可再生能源的利用。在工作场所停车场，集成太阳能和车辆到电网（V2G）技术为智能能源管理提供了新的机会。本文提出了一种基于粒子群优化（PSO）的优化充电策略，以最大限度地降低总能源成本，同时减少从电网获取的峰值功率，最大限度地利用光伏（PV）能源，并确保所有车辆在离开停车场之前达到目标充电状态（SOC）。此外，所提出的方法利用了V2G技术的优势，使电动汽车能够在高峰需求时段将能量回馈给电网，从而增强了电网的稳定性并降低了总体能源支出。这项工作的一个关键贡献是对三种不同地理环境下的电动汽车充电管理进行了比较分析：摩洛哥、法国和突尼斯。每个国家提供不同的能源成本结构，太阳能的可用性。采用动态电价模型来适应基于日和季节电价变化的收费策略。优化策略考虑了电动汽车到达和离开时间、初始和目标SOC、光伏发电和动态电价等多个约束条件。仿真结果表明，与传统的无管理充电方案相比，基于pso的充电策略可节省高达65%的成本，减少来自电网的峰值功率，并通过自我消耗最大化光伏电力利用率。此外，研究结果强调了智能停车能源管理中多目标优化的好处。

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引用次数: 0

3D-Scan hole detection for robot-assisted laparoscopic surgery 机器人辅助腹腔镜手术的3d扫描孔检测

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-09 DOI: 10.1016/j.ifacsc.2025.100336

Birthe Göbel , Alexander Richter , Stefan J. Rupitsch , Alexander Reiterer , Knut Möller

Minimally invasive laparoscopic surgery, where endoscopes and instruments are inserted through small incisions, has advanced to the next stage of development: robot-assisted minimally invasive surgery. These systems use a camera and robotic manipulators operated by human surgeons through human-in-the-loop control. To further improve surgical precision and autonomy, data-driven assistance must be expanded. One promising approach is 3D reconstruction based on endoscopic images. A 30°endoscope tip enhances the field of view by enabling rotational motion around the instrument’s axis. However, when performing a 3D scan with such an endoscope, a blind spot inherently forms along the shaft axis, creating a region that cannot be captured during rotation. Additional missing data may arise due to occlusions from anatomical geometry and the specific endoscope pose during a scan. These limitations result in incomplete 3D reconstructions, which can negatively impact surgical navigation and decision-making. This paper presents a method tailored to medical applications for detecting and characterizing holes in laparoscopic 3D scans. The proposed method uses geometric analysis of the point cloud to identify regions of sparse or missing data and correlates these gaps with endoscope positioning and anatomical visibility. It is designed to operate robustly on high-density point clouds generated by advanced laparoscopic 3D reconstruction systems. By integrating robotic control, our method provides a foundation for adaptive endoscope repositioning to recover missing views and improve reconstruction completeness. The proposed method paves the way towards fast (

\sim

5 s) feedback for optimized 3D scanning in laparoscopic environments.

微创腹腔镜手术，内窥镜和器械通过小切口插入，已经进入下一个发展阶段：机器人辅助微创手术。这些系统使用一个摄像头和由人类外科医生通过人在环控制操作的机器人操纵器。为了进一步提高手术的精确性和自主性，必须扩大数据驱动的辅助。一种很有前途的方法是基于内窥镜图像的3D重建。30°内窥镜尖端通过围绕仪器轴的旋转运动来增强视野。然而，当使用这种内窥镜进行3D扫描时，固有的盲点沿着轴轴形成，在旋转过程中产生无法捕获的区域。由于解剖几何和扫描期间特定内窥镜姿势的闭塞，可能会出现额外的丢失数据。这些限制导致三维重建不完整，对手术导航和决策产生负面影响。本文提出了一种适合于医学应用的方法，用于检测和表征腹腔镜3D扫描中的孔。该方法利用点云的几何分析来识别数据稀疏或缺失的区域，并将这些空白与内窥镜定位和解剖可见性相关联。它被设计成在由先进的腹腔镜三维重建系统产生的高密度点云上稳健地运行。该方法结合机器人控制，为内窥镜自适应定位提供了基础，以恢复缺失视图，提高重建的完整性。所提出的方法为在腹腔镜环境中优化3D扫描的快速（~ 5秒）反馈铺平了道路。

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引用次数: 0

Reducing exercise-related hypoglycemia in automated insulin delivery with reinforcement learning 通过强化学习减少自动胰岛素输送中运动相关的低血糖

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-04 DOI: 10.1016/j.ifacsc.2025.100337

Dana Zimmermann, Hans-Michael Kaltenbach

Exercise is an important component for glucose management in type 1 diabetes, but remains challenging for automated insulin delivery systems as altered glucose dynamics are difficult to model explicitly. Glucose monitoring data might enable data-driven approaches for learning these dynamics implicitly. We propose combining model predictive control with a reinforcement learning component to adjust basal insulin infusion rates for exercise. We train our model on a variety of exercise scenarios and demonstrate improved glucose control using two different frameworks. We evaluate how generalizable both frameworks are by personalizing a trained model with a small number of additional individual-specific training episodes.

运动是1型糖尿病血糖管理的重要组成部分，但由于改变的葡萄糖动力学难以明确建模，因此对自动胰岛素输送系统仍然具有挑战性。葡萄糖监测数据可能使数据驱动的方法能够隐式地学习这些动态。我们建议将模型预测控制与强化学习组件相结合，以调整运动的基础胰岛素输注速率。我们在各种运动场景中训练我们的模型，并使用两种不同的框架演示改善的血糖控制。我们通过使用少量额外的个人特定训练集来个性化训练模型来评估这两个框架的泛化程度。

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 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub 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

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.

电极位移是电阻抗断层扫描（EIT）中常见的误差来源，特别是在长期或动态测量中，难以保持稳定的电极接触。本研究提出了一个全面的机器学习框架，用于在图像重建之前检测、分类和纠正电极位移。该方法结合了基于树的分类器（如XGBoost和lightgbm）和卷积神经网络（cnn）来识别移位电极的存在和位置。这些模型通过一系列越来越复杂的分类任务进行评估，即使在涉及多个不同震级同时发生的位移的情况下也显示出很高的准确性。为了校正失真的电压测量，研究了几种去噪自编码器（DAE）的配置。在所有位移量级上训练的电极特异性DAEs平均均方误差（MSE）降低了84.5%，而基于位移的DAEs在粗校正方面提供了计算效率。结合快速移位和高精度电极特定模型的混合策略被证明是有效的和可扩展的。利用合成数据集和实际EIT测量数据对该管道进行了验证，证实了其鲁棒性和泛化能力。这些结果支持使用基于学习的校正方案来提高实际应用中受电极运动影响的EIT的可靠性和一致性。

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引用次数: 0

Validation of the mePAP: A low-cost, high-quality, open-source PAP device for research and increasing equity in respiratory care mePAP的验证：一种低成本、高质量、开源的PAP设备，用于研究和增加呼吸保健的公平性

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-01 DOI: 10.1016/j.ifacsc.2025.100333

Jordan F. Hill, Samuel Jackson, Mia Uluilelata, Samrath Sood, Jaimey A. Clifton, Ella F.S. Guy, J. Geoffrey Chase

Respiratory diseases affect 14% of New Zealand’s population, with over 100,000 individuals suffering from sleep apnoea, which causes breathing disruptions due to airway blockages. Positive airway pressure (PAP) devices are the gold standard treatment, typically operating in continuous (CPAP), bilevel (BiPAP), or automatic (APAP) modes. However, current PAP devices cost between NZ$800–$2500, creating a financial barrier for users, particularly those from lower socio-economic backgrounds. The mePAP was developed as a low-cost, open-source PAP device, constructed for NZ$250, capable of delivering CPAP, BiPAP, and APAP therapies with an airway pressure sensor for more precise control. Validation against a Fisher & Paykel CPAP was performed through benchtop testing, mechanical lung simulations, and a clinical trial with 40 healthy subjects. The mePAP was preferred by 42.5% of subjects, with 25% reporting no difference between the devices. A mean comfort rating of 6.36 for the mePAP compared to 5.92 for the Fisher & Paykel CPAP confirmed the two devices were comparable, with pressure fluctuations from the mePAP’s low-cost motor imperceptible to users. The airway sensor feedback loop enabled accurate pressure delivery, with BiPAP and APAP algorithms dynamically adjusting therapy pressure in response to breathing patterns. These results validate the mePAP as a low-cost alternative to commercial PAP devices, with comparable performance and comfort. Its affordability and open-source design have the potential to improve healthcare accessibility and reduce inequities, making respiratory therapy more accessible to underserved populations while enabling further research into respiratory treatments.

呼吸系统疾病影响了新西兰14%的人口，超过10万人患有睡眠呼吸暂停症，这种疾病由于呼吸道阻塞而导致呼吸中断。气道正压（PAP）设备是金标准治疗，通常在连续（CPAP），双水平（BiPAP）或自动（APAP）模式下操作。然而，目前PAP设备的价格在800至2500新西兰元之间，这对用户造成了经济障碍，特别是那些社会经济背景较低的用户。mePAP是一种低成本、开源的PAP设备，售价250新西兰元，能够提供CPAP、BiPAP和APAP治疗，并带有气道压力传感器，以实现更精确的控制。通过台式测试、机械肺模拟和40名健康受试者的临床试验，对Fisher & Paykel CPAP进行了验证。42.5%的受试者更喜欢mePAP， 25%的受试者报告两种设备之间没有差异。mePAP的平均舒适评分为6.36，而Fisher & Paykel CPAP的平均舒适评分为5.92，这证实了这两种设备的可比性，mePAP的低成本马达产生的压力波动对用户来说是难以察觉的。气道传感器反馈回路实现了准确的压力传递，BiPAP和APAP算法根据呼吸模式动态调整治疗压力。这些结果验证了mePAP作为商用PAP设备的低成本替代品，具有相当的性能和舒适性。其可负担性和开源设计有可能改善医疗保健可及性并减少不公平现象，使服务不足的人群更容易获得呼吸治疗，同时促进对呼吸治疗的进一步研究。

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引用次数: 0

Stochastic virtual patient-guided mechanical ventilation treatment: A virtual patient study with mechanical power consideration 随机虚拟患者引导的机械通气治疗：考虑机械功率的虚拟患者研究

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-08-21 DOI: 10.1016/j.ifacsc.2025.100334

Christopher Yew Shuen Ang , Yeong Shiong Chiew , Xin Wang , Ean Hin Ooi , Mohd Basri Mat Nor , Matthew E. Cove , Cong Zhou , J. Geoffrey Chase

Background and Objective

: Computerised decision support systems (CDSS) in mechanical ventilation (MV) provide individualised, closed-loop treatment but often require extensive input parameters, which are challenging to obtain continuously in clinical settings. Many also fail to incorporate mechanical power (MP) and MP ratio — recently identified as significant predictors of patient outcomes. This study introduces the Stochastic Virtual Patient Ventilation Protocol (SVP VENT), a model-based CDSS addressing these limitations.

Methods

: The SVP VENT Protocol integrates a stochastic virtual patient model to predict temporal lung elastance,

E_{r s}

, trends and deliver closed-loop, lung protective ventilation minimising MP ratio and driving pressure. The protocol was validated against the VENT and SiVENT protocols using an established virtual patient platform comprising over 1229 h of both volume control (VC) and pressure control (PC) retrospective MV data. Patient responses were monitored to ensure adherence to accepted clinical safety guidelines.

Results

: The SVP VENT protocol consistently outperformed retrospective clinical data, VENT and SiVENT protocols in ensuring adherence to clinical safety metrics, achieving an all-adherence rate of

\sim

57% and

\sim

67% for the VC and PC cohorts, respectively. Across cohorts, the protocol maintained MP and MP ratio levels below safety thresholds (12 J/min and 4.5, respectively), and extended intervention intervals up to 3 h, potentially reducing clinical workload.

Conclusion

: Overall, the virtual trial demonstrates the SVP VENT protocol’s potential to enhance MV management by extending intervention intervals, while maintaining patient safety. These findings support initial clinical trials to evaluate the protocol’s impact on clinical workload and patient safety over prolonged monitoring periods, facilitating its integration into standard clinical practices.

背景与目的：机械通气（MV）中的计算机化决策支持系统（CDSS）提供个性化的闭环治疗，但通常需要大量的输入参数，这在临床环境中很难连续获得。许多也没有纳入机械功率（MP）和MP比率-最近被确定为患者预后的重要预测因素。本研究介绍了随机虚拟患者通气协议（SVP VENT），这是一种基于模型的CDSS，解决了这些局限性。方法：SVP VENT方案集成了一个随机虚拟患者模型来预测时间肺弹性、er、趋势，并提供闭环、肺保护性通气最小化MP比和驱动压力。使用已建立的虚拟患者平台，包括超过1229小时的音量控制（VC）和压力控制（PC）回顾性MV数据，根据VENT和SiVENT协议验证该方案。监测患者的反应，以确保遵守公认的临床安全指南。结果：SVP VENT方案在确保临床安全指标的依从性方面始终优于回顾性临床数据、VENT和SiVENT方案，VC和PC队列的全依从率分别达到57%和67%。在整个队列中，该方案将MP和MP比率维持在安全阈值以下（分别为12 J/min和4.5 J/min），并将干预间隔延长至3小时，从而可能减少临床工作量。结论：总的来说，虚拟试验证明了SVP VENT方案通过延长干预间隔来增强中压管理的潜力，同时保持患者安全。这些发现支持初步临床试验，以评估该方案在长期监测期间对临床工作量和患者安全的影响，促进其融入标准临床实践。

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引用次数: 0