Output Estimation Research Articles

Comparison between invasive cardiac output and left ventricular assist device flow parameter.

Evaluate the correlation between left ventricular assist device flow parameter and invasive cardiac output measurements. We retrospectively evaluated right heart catheterization examinations performed in left ventricular assist device patients from 2 tertiary medical centers. We evaluated the correlation between cardiac output measurement methods (indirect Fick and thermodilution) and pump flow parameter using linear regression, agreement was graphically displayed using Bland-Altman plot technique. Clinical, echocardiographic, pump and haemodynamic parameters were compared between patients with and without discordance, defined as at least 20% difference between measurements. The study population consisted of 102 patients (median age 58 [51-64], 86% males, 17 ± 12 months post left ventricular assist device implantation) with a total of 544 measurement compared. Discordance between measurements were present in 102 of 226 (45%) comparisons between indirect Fick and pump flow and in 72 of 161 (48%) between thermodilution and pump flow. A comparison of indirect Fick and left ventricular assist device exhibited a statistical correlation of R = 0.751, and that of thermodilution and left ventricular assist device of R = 0.789. Parameters associated with the presence of discordance between cardiac output measurements included a higher rate of aortic valve opening, lower indirect Fick and higher thermodilution cardiac output. After excluding the lowest tertile of indirect Fick cardiac output values, the correlation between measurements improved (thermodilution: R = 0.879 and indirect Fick: R = 0.843, p < 0.001). The current left ventricular assist device flow parameter provides an estimation of cardiac output that correlates well with indirect Fick and exhibits the strongest correlation with thermodilution. This correlation was stronger after excluding lower cardiac output values.

Poor correlation of venous lactate with systemic oxygen saturation in the paediatric cardiac ICU: a pilot study.

Cardiac intensive care providers require a comprehensive understanding of cardiac output and oxygen delivery. The estimation of cardiac output in clinical practice often relies on thermodilution and the Fick principle. Central venous saturation and lactate levels are commonly used indicators for cardiac output assessment. However, the relationship between venous lactate levels and venous oxygen saturation in paediatric cardiac intensive care patients remains unclear. This is a single-centre retrospective pilot study aimed to investigate the correlation between venous lactate and venous oxygen saturation in paediatric patients. Data collected included venous saturation, heart rate, mean arterial blood pressure, arterial saturation by pulse oximetry, cerebral and renal near-infra-red spectroscopy values, and the presence of a functionally univentricular heart. Statistical analyses included Bayesian Pearson correlation and regression analyses. A total of 203 data points from 37 unique patients were included in the analysis. There was no significant correlation between serum lactate and venous saturation (correlation coefficient = -0.01; Bayes factor 10 = 0.06). Serum lactate also did not correlate with other haemodynamic metrics. Venous saturation showed correlations with arterial saturation and cerebral and renal near-infra-red spectroscopy. Regression analysis revealed that parallel circulation, arterial saturation, and cerebral near-infra-red spectroscopy were predictive of venous saturation. The following equation resulted from the regression analysis: 68.0 - (12.7 x parallel circulation) - (0.8 x arterial saturation) + (0.3 x cerebral near-infra-red spectroscopy). This model had a Bayes factor 10 of 0.03 and adjusted R-squared was 0.29. In paediatric cardiac intensive care patients, there is no significant correlation between venous lactate and venous saturation, suggesting that lactate may not be a reliable marker for assessing the adequacy of oxygen delivery in this population. Only a weak correlation could be identified once the venous saturation was 70% or lower. Additional research is needed to explore alternative markers for monitoring oxygen delivery in critically ill paediatric patients.

Stubborn State and Disturbance Observer Co‐Design for Nonlinear Descriptor Systems With δQC$$ \delta \mathrm{QC} $$ via a Dynamic Event‐Triggered Mechanism

ABSTRACTThis article studies the state and disturbance simultaneous estimation problem for a class of nonlinear descriptor systems with using a dynamic event‐triggered mechanism. refers to incremental quadratic constraints, which can provide a unified description of many types of common nonlinear functions. To reduce the negative impact of measurement outliers on the identification estimation, a stubborn state and disturbance observer co‐design scheme is proposed for the first time by embedding dynamic saturation output estimation errors. Meanwhile, a dynamic event‐triggered mechanism is introduced to avoid the need for continuously available output information, which can reduce the pressure on communication resources. By constructing a Lyapunov function, existence conditions of the stubborn state and disturbance observer are obtained in the form of a convex optimization problem so that the error estimation maintains an acceptable estimation performance. Finally, simulation examples illustrate the universality and stubbornness of the proposed observer.

Analyzing the effects of shading on power output in curved photovoltaic array on airship

This study investigates the use of photovoltaic (PV) arrays on airships, focusing on challenges due to the curved structure that affects solar radiation distribution. A novel Photo-Thermal-Electric Model (PTEM), developed in MATLAB/Simulink, is introduced to simulate PV array behavior under various conditions, including the impact of solar altitude, laying angle, and airship flight attitude on power generation. PTEM's effectiveness is demonstrated through detailed power output graphs, revealing that radiation gradients significantly affect power generation, often leading to overestimations in the Photo-Thermal Model (PTM) model by 3 %–30 %. Comparative analysis shows that PTEM provides more accurate energy output estimates for individual PV modules, especially in areas with pronounced radiation gradients, differing from PTM estimates by hundreds of watts. These findings offer valuable insights for enhancing PV module lifespan and optimizing energy system designs, contributing to more efficient renewable energy solutions for long-endurance aircraft.

Pore Size Classification and Prediction Based on Distribution of Reservoir Fluid Volumes Utilizing Well Logs and Deep Learning Algorithm in a Complex Lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids. Traditional methods for predicting pore size distribution (PSD), relying on drilling cores or thin sections, face limitations associated with depth specificity. In this study, we introduce an innovative framework that leverages nuclear magnetic resonance (NMR) log data, encompassing clay-bound water (CBW), bound volume irreducible (BVI), and free fluid volume (FFV), to determine three PSDs (micropores, mesopores, and macropores). Moreover, we establish a robust pore size classification (PSC) system utilizing ternary plots, derived from the PSDs.Within the three studied wells, NMR log data is exclusive to one well (well-A), while conventional well logs are accessible for all three wells (well-A, well-B, and well-C). This distinction enables PSD predictions for the remaining two wells (B and C). To prognosticate NMR outputs (CBW, BVI, FFV) for these wells, a two-step deep learning (DL) algorithm is implemented. Initially, three feature selection algorithms (f-classif, f-regression, and mutual-info-regression) identify the conventional well logs most correlated to NMR outputs in well-A. The three feature selection algorithms utilize statistical computations. These algorithms are utilized to systematically identify and optimize pertinent input features, thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors. So, all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs. Subsequently, the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM), belonging to the category of DL algorithms and harnessing the computational power of GPUs, is employed for the prediction of CBW, BVI, and FFV logs. This prediction leverages the optimal logs identified in the preceding step. Estimation of NMR outputs was done first in well-A (80% of data as training and 20% as testing). The correlation coefficient (CC) between the actual and estimated data for the three outputs CBW, BVI and FFV are 95%, 94%, and 97%, respectively, as well as root mean square error (RMSE) was obtained 0.0081, 0.098, and 0.0089, respectively. To assess the effectiveness of the proposed algorithm, we compared it with two traditional methods for log estimation: multiple regression and multi-resolution graph-based clustering methods. The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches. This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs. Seven distinct PSCs within well-A employing actual NMR logs (CBW, BVI, FFV), in conjunction with an equivalent count within wells B and C utilizing three predicted logs, are harmoniously categorized leading to the identification of seven distinct pore size classification facies (PSCF). this research introduces an advanced approach to pore size classification and prediction, fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log. The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.

A Knowledge Distillation Method Based on Evidence Theory to Prevent Catastrophic Forgetting

Abstract Incremental learning is an emerging machine-learning approach designed to prevent catastrophic forgetting while learning a new task with knowledge retained from previous tasks. However, existing methods often must fully utilize the distributional information of the old task model’s outputs. To alleviate this, we propose a class incremental learning method grounded in evidence theory to leverage the distributional information of outputs from the old task model, which integrates uncertainty estimation into the knowledge distillation process to ensure the new task model effectively learns from the old task model’s distribution information. Specifically, we incorporate uncertainty estimation based on evidence theory to calculate knowledge distillation loss during training. We compute the uncertainty estimates of outputs from both the new and old task models, and then they are used in the knowledge distillation loss calculation. We propose a novel classification strategy that considers outputs’ probability and uncertainty estimates, which could determine the sample category without requiring an additional training phase or supplementary models. Experiments on the CIFAR-100 and ImageNet datasets demonstrate the effectiveness of the proposed method, showing that it outperforms existing methods by improving accuracy by 1%-2%. Results suggest that leveraging the distribution information of model outputs can effectively mitigate catastrophic forgetting in deep learning models within incremental learning scenarios.

Computation-Efficient Fault Detection Framework for Partially Known Nonlinear Distributed Parameter Systems.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Adaptive distributed unknown input observer for linear systems

This paper studies the adaptive distributed unknown input observer (ADUIO) for linear systems with local outputs, which contains a group of local observers under directed graph. The difficulty is the adaptive estimation of global output for the systems with unknown inputs. To solve the problem, disturbance decoupling principle and leader-following consensus strategy are integrated to estimate local outputs of other observers, which can be accumulated to recover the global outputs. Based on the estimated global outputs, an ADUIO is constructed to estimate the full state which avoids using local output matrices of other observers and global information of the graph. Different from the extensive joint detectability assumption in existing results, a detectability assumption is given to make the estimation errors converge to zero asymptotically.

Adaptive distributed fault estimation observer design for nonlinear time-delay multi-agent systems with directed graphs

This paper focuses the development of an adaptive observer-based distributed fault estimation observer (DFEO) for multi-agent nonlinear time-delay systems under a directed communication topology. The process involves constructing a fault estimation observer for each agent based on their relative output estimation errors. An adaptive DFEO design is proposed for multi-agent systems with a directed communication topology. Furthermore, investigating the effect of faulty agents on the criteria of fault detection on healthy agents. The proposed design is represented with Linear Matrix Inequalities (LMIs) to establish a systematic design procedure. The study also includes simulation results to demonstrate the effectiveness of the proposed design techniques.

Evaluating tissue hypoxia and the response to fluid administration in septic shock patients: a metabolic cluster analysis

BackgroundThe selection of adequate indicators of tissue hypoxia for guiding the resuscitation process of septic patients is a highly relevant issue. Current guidelines advocate for the use of lactate as sole metabolic marker, which may be markedly limited, and the integration of different variables seems more adequate. In this study, we explored the metabolic profile and its implications in the response to the administration of a fluid challenge in early septic shock patients.MethodsObservational study including septic shock patients within 24 h of ICU admission, monitored with a cardiac output estimation system, with ongoing resuscitation. Hemodynamic and metabolic variables were measured before and after a fluid challenge (FC). A two-step cluster analysis was used to define the baseline metabolic profile, including lactate, central venous oxygen saturation (ScvO2), central venous-to-arterial carbon dioxide difference (PcvaCO2), and PcvaCO2 corrected by the difference in arterial-to-venous oxygen content (PcvaCO2/CavO2).ResultsSeventy-seven fluid challenges were analyzed. Cluster analysis revealed two distinct metabolic profiles at baseline. Cluster A exhibited lower ScvO2, higher PcvaCO2, and lower PcvaCO2/CavO2. Increases in cardiac output (CO) were associated with increases in VO2 exclusively in cluster A. Baseline isolated metabolic variables did not correlate with VO2 response, and changes in ScvO2 and PcvaCO2 were associated to VO2 increase only in cluster A.ConclusionsIn a population of early septic shock patients, two distinct metabolic profiles were identified, suggesting tissue hypoxia or dysoxia. Integrating metabolic variables enhances the ability to detect those patients whose VO2 might increase as results of fluid administration.

State Reconstruction of Remote Robotic System Using Delayed Output and Torque Estimation

State Reconstruction of Remote Robotic System Using Delayed Output and Torque Estimation

How black hole activity may influence exoplanetary evolution in our Galaxy

ABSTRACT An increasing number of exoplanets have been discovered in the Milky Way galaxy, which is also known to harbour a super-massive black hole (Sagittarius A*) at its centre. Here, we investigate how the central black hole (BH) activity may affect the evolution of exoplanets in our Galaxy. Accreting BHs emit high-energy radiation – extreme ultraviolet and X-rays – which can lead to XUV photoevaporation of the planetary atmospheres. We evaluate the atmospheric mass-loss using both theoretical estimates of the BH radiative output and observational constraints on the past activity history of Sgr A*. The resulting mass-loss is analysed as a function of the galactocentric distance. For the first time, we compute the exoplanet atmospheric evolution under BH irradiation by explicitly including the temporal evolution of the central luminosity output (i.e. the BH activity history). We obtain that Sgr A* could have a major impact on exoplanets located in the inner region of the Galaxy (e.g. Galactic bulge); a significant fraction of the atmospheric mass can be removed by BH irradiation; and in extreme cases, the initial atmosphere may be completely stripped away. Such mass-loss can have important consequences on the atmospheric chemistry and potential biological evolution. We discuss the physical implications for planetary habitability, and we also briefly consider the case of stellar-mass BHs. Overall, accreting black holes may play a significant role in the evolution of exoplanets in our Galaxy across cosmic time.

Predictive Modeling of Solar PV Panel Operating Temperature over Water Bodies: Comparative Performance Analysis with Ground-Mounted Installations

Solar panel efficiency is significantly influenced by its operating temperature. Recent advancements in emerging renewable energy alternatives have enabled photovoltaic (PV) module installation over water bodies, leveraging their increased efficiency and associated benefits. This paper examines the operational performance of solar panels placed over water bodies, comparing them to ground-mounted solar PV installations. Regression models for panel temperature are developed based on experimental setups at BITS Pilani, India. Developed regression models, including linear, quadratic, and exponential, are utilized to predict the operating temperature of solar PV installations above water bodies. These models incorporated parameters such as ambient temperature, solar insolation, wind velocity, water temperature, and humidity. Among these, the one-degree regression models with three parameters outperformed the models with four or five parameters with a prediction error of 5.5 °C. Notably, the study found that the annual energy output estimates from the best model had an error margin of less than 0.2% compared to recorded data. Research indicates that solar PV panels over water bodies produce approximately 2.59% more annual energy output than ground-mounted systems. The newly developed regression models provide a predictive tool for estimating the operating temperature of solar PV installations above water bodies, using only three meteorological parameters: ambient temperature, solar insolation, and wind velocity, for accurate temperature prediction.

Shallow Shadows: Expectation Estimation Using Low-Depth Random Clifford Circuits.

We provide practical and powerful schemes for learning properties of a quantum state using a small number of measurements. Specifically, we present a randomized measurement scheme modulated by the depth of a random quantum circuit in one spatial dimension. This scheme interpolates between two known classical shadows schemes based on random Pauli measurements and random Clifford measurements. We focus on the regime where depth scales logarithmically in the system size and provide evidence that this retains the desirable sample complexity properties of both extremal schemes while also being experimentally feasible. We present methods for two key tasks; estimating expectation values of certain observables from generated classical shadows and, computing upper bounds on the depth-modulated shadow norm, thus providing rigorous guarantees on the accuracy of the output estimates. We achieve our findings by bringing together tools from shadow estimation, random circuits, and tensor networks.

Intermediate estimator based finite-time fault-tolerant control for nonlinear switched stochastic systems

The finite time fault-tolerant control problem for switched stochastic nonlinear systems with actuator and sensor faults is studied in this paper. Firstly, the finite-time intermediate estimator is considered to reconstructed the system state, sensor and actuator faults. The proposed observer involves the output estimation error feedback to improve the estimation accuracy. According to the estimator mentioned above, the fault-tolerant controller is proposed to reduce the fault impact. Using the average dwell time and piecewise Lyapunov functions, sufficient conditions for the nonlinear switched stochastic systems to achieve finite-time stochastic boundedness are obtained. Finally, two examples are proposed to illustrate the feasibility of above method.

Efficient fuzzy methodology for congestion control in wireless sensor networks

In wireless sensor networks (WSNs), ensuring reliable and efficient communication between nodes requires effective congestion control. This study presents an innovative approach, called FSFG, which combines FCM fuzzy clustering, Fuzzy Inference System (FIS), Support Vector Regression (SVR), and Genetic Algorithm (GA) to address congestion in WSNs. FSFG consists of two phases: Structure Identification and Parameter Identification. In the first phase, fuzzy clustering techniques are employed to determine the desirability of outputs in different classes. Subsequently, support vector regressions are used to determine membership functions with a minimal error rate. In the parameter identification phase, the obtained rules from the FCM method are used to design the desired system, enabling the estimation of necessary outputs based on relevant inputs. Through simulations, the results demonstrate that the FSFG algorithm excels in congestion control, exhibiting a lower error rate and outperforming other algorithms in terms of delay and packet delivery rate.

Turbine-Level Possible Power Prediction using Farm-Wide Spatial Information through Similarity-Based Multivariate Gaussians

Wind farms usually comprise several turbines of the same type in proximity to one another. Therefore, similarities exist between the power production of specific turbines within the wind farm over time. Considering this, it is possible to find a way to express the similarity between turbines and exploit their properties to find a formulation of the expected behavior of a turbine. With this estimation of possible power output, one can analyze the losses generated by curtailments or transients with a higher precision. Based on this, a probabilistic model is proposed that can be used to calculate energy losses due to maintenance or environmental reasons. On top of that, heavy deviations in the behavior of specific wind turbines can be detected on high-frequency (1-second) data. Overall, the goal of this work is to predict possible power on high-frequency SCADA data using a statistical white-box modeling approach. The presented method is based on a probabilistic framework, constructing a system of linear combinations that permits analytically tracking the expected behavior of a turbine, even if it is non-operational for a specific amount of time. The methodology includes two parts, the first one is the use of data-driven power curves, and the second one consists of an inferential framework based on the environmental conditions of the farm. Results show that the method presented performs better than the manufacturer’s power curves under specific wind speeds and wind directions.

Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling

In agrivoltaic systems combining solar photovoltaic and agricultural activities, ground albedo is mainly characterized by the crop and its seasonal variations. This study examines the effects of using fixed, satellite-derived, and hourly measured albedo on the performance of a vertical bifacial system and a 1-axis tracking system using a bifacial photovoltaic model (AgriOptiCE). The model is developed with Matlab® and partially based on the open-source package pvlib. AgriOptiCE is firstly validated by comparing estimated front and rear irradiances with on-site measurements for specific periods from a 1-axis tracker site in Golden, USA and a vertical agrivoltaic system in Västerås, Sweden. Furthermore, photovoltaic system power output estimations using AgriOptiCE are also validated for the vertical agrivoltaic system and the conventional ground-mounted fixed-tilt system at the same location. The validations demonstrate the high accuracy of the proposed model in estimating front and rear irradiances and power output, obtaining R2 &gt; 0.85 for all the studied cases. The study results indicate that measured albedo provides the highest accuracy, while satellite-derived albedo has poorer results due to the broader spatial, temporal, and spectral resolution. Fixed albedo is not recommended for yearly assessment of bifacial PV systems because it cannot account for snow events and daily variations, resulting in lower overall accuracy.

Multi-objective design of parametric equivalent-input-disturbance approaches with application to vehicle suspension systems

A multi-objective parametric design method is developed for an equivalent-input-disturbance-based control system with mismatched disturbances. Different from the existing design methods that calculated control parameters by stability conditions, multiple performance objectives including unmeasurable output estimation, disturbance rejection, noise suppression, input refinement, and robustness, are simultaneously optimized in the developed design. First, for the sake of low computation complexity, the closed-loop system is represented. Then, the parameterization of the design conditions for multiple performance objectives is given. Further, a nonlinear optimization method is used to perform the performance-oriented system design. Lastly, the efficacy and benefits of the developed methods are demonstrated through simulations of a quarter-vehicle suspension system and comparisons with other alternative methods.

Evaluation of protocols to determine urine output and urinary urea nitrogen excretion in dairy cows with and without dietary salt supplementation

Urine output and urinary urea-N excretion (UUNe) excretion are critical measures to accurately evaluate N metabolism in lactating dairy cows and environmental concerns related to manure N. The objectives of this study were: (1) to compare estimates of UUNe, urine output, and related variables from 3 pre-established measurement protocols (bladder catheterization, external collection cup, and spot sampling) and from dietary salt supplementation, (2) to study temporal variation in UUNe, urine output, and related variables as affected by measurement protocol, and (3) to evaluate urine specific gravity as a predictor of urine output. Twelve multiparous Holstein cows were used in a split-plot, Latin square design. Cows were randomly assigned to a diet (main plot) containing either 0.7% or 1.6% NaCl (DM basis) and then assigned to a sequence of 3 protocols (subplot) in a balanced 3 × 3 Latin square with 14-d period. For each protocol, measurements were conducted every 4 h for 3 consecutive days. Urine output was determined gravimetrically for bladder catheterization and external collection cup or based on measured cow BW, measured urinary creatinine concentration, and the assumed creatinine excretion of 29 mg/kg BW per day for spot sampling. Urine specific gravity was measured by refractometry. When averaged over a 3-d measurement period and compared with bladder catheterization, spot sampling underestimated urine output (6.8 kg/d; 20%) and UUNe (26 g/d; 13%) but exhibited greater concentration of urinary urea-N (+58 mg/dL; 10%). There were no differences in any measurements determined via bladder catheterization or external cup device protocols, except for urine output that tended to be 3.7 kg/d lower for collection cup compared with bladder catheterization. The 2 gravimetric protocols yielded lower urinary creatinine concentration than spot sampling (64.7 vs. 88.1 mg/dL) and lower creatinine excretion (25.3 mg/kg BW per day) than the value of 29 mg/kg BW per day generally assumed in the spot sampling protocol. Salt supplementation tended to increase urine output (+5.2 kg/d) and decrease urinary urea-N concentration (-93 mg/dL), urinary creatinine concentration (-9.5 mg/dL), milk protein concentration (-0.19 percentage unit) and milk protein yield (-70 g/d). There was greater temporal variation of urine output when measured via the collection cup compared with bladder catheterization in the first 2 d but not the third day of sampling, suggesting that an extended period of adaptation might have improved data quality of the collection cup protocol. The R2 of the linear regression to predict urine output with urine specific gravity was 67%, 73%, and 32% for bladder catheterization, collection cup, and spot sampling, respectively. In this study, spot sampling underestimated both urine output and UUNe, but UUNe determination did not differ between external collection cup and bladder catheterization. However, our data suggested the need to investigate the adaptation protocol, required days of measurements and the conversion of urine mass to urine volume to improve accuracy and precision of urine collection protocols.