Error Index Research Articles

The research on the applicability of different typhoon wind fields in the simulation of typhoon waves in China’s coastal waters

Typhoon waves possess significant destructive potential, and their numerical simulation relies on accurate sea surface wind fields. An evaluation of different combinations of the radial air pressure distribution coefficient B and the radius of maximum wind speed (Rmax) in the Holland wind field (HWF) model was conducted to determine the optimal configuration. The HWF and the ERA5 wind field (EWF) were used as input wind fields to drive the typhoon wave model for China’s coastal waters. Validation results indicated that neither wind field accurately reflected real conditions; therefore, a hybrid wind field (HBWF) was created by combining HWF and EWF using weighting coefficients that vary with the radius of wind speed to enhance accuracy. Simulation results showed that the HBWF improved the accuracy of significant wave heights (SWHs), with a mean relative error of 25.29%, compared to 32.48% for HWF and 27.94% for EWF. Additionally, HBWF also demonstrated the best performance in terms of root mean square error (RMSE) and consistency index. Overall, the HBWF enhances the simulation accuracy of typhoon waves in China's coastal waters.

Predicting Ride-Hailing Demand with Consideration of Social Equity: A Case Study of Chengdu

In the realm of shared autonomous vehicle ride-sharing, precise demand prediction is vital for optimizing resource allocation, improving travel efficiency, and promoting sustainable transport solutions. However, existing studies tend to overlook social attributes and demographic characteristics across various regions, resulting in disparities in prediction fairness between areas with plentiful and limited transportation resources. In order to achieve more accurate and fair prediction, an innovative Social Graph Convolution Long Short-Term Memory framework is proposed, incorporating demographic, spatial, and transportation accessibility information into multiple functional graphs, including functional similarity, population structure, and historical demand graphs. Furthermore, Mean Percentage Error indicators are employed in the loss function to balance prediction accuracy and fairness. The findings indicate that there is an enhancement in both prediction accuracy and fairness by at least 8.9% and 12.9%, respectively, compared to base models. Additionally, the predictions for rush hours in both privileged and underprivileged regions exhibit greater precision and rationality, supporting sustainable transport practices. The proposed framework effectively captures the demands of diverse social groups, thereby contributing to the advancement of social equity and long-term sustainability in urban mobility.

Enhancing the Rainfall Forecasting Accuracy of Ensemble Numerical Prediction Systems via Convolutional Neural Networks

Abstract Ensemble prediction systems are commonly used to demonstrate the potential uncertainty of weather forecasts. These systems also help provide weather predictions to prepare for disasters. Specifically, a type of prediction called a quantitative precipitation forecast (QPF) is calculated from the average of multiple forecasts. The probability-matched mean (PMM) method is used to improve these QPF predictions when they underestimate rainfall. However, the PMM method can exhibit limitations when dealing with certain types of rain patterns. To address this issue, this study focuses on improving short-term heavy rainfall predictions using an artificial intelligence (AI) algorithm that combines deep neural networks (DNNs), including convolutional neural networks (CNNs), with a space-based attention mechanism (convolutional block attention module [CBAM]). Four experiments were conducted to evaluate the new method, including those performed to optimize the timing of a 24-hour QPF model, adjust the training dataset, and refine the training algorithm. Two specific weather events were assessed: rainfall influenced by southwest winds after typhoons and afternoon thunderstorm rainfall. A comparison of the results obtained via the root mean square error (RMSE) and structural similarity index measure (SSIM) reveals that the accuracy and distribution of the QPF predictions significantly improved over those of the PMM method. Compared with the PMM method, our approach can reduce the RMSE from 77.51 to 19.52 in the Mei-yu case, an improvement of approximately 74.82%. The SSIM also increased from 0.33 to 0.56, indicating a 70.9% enhancement. Overall, the new approach successfully enhances rainfall prediction accuracy via AI techniques and has the potential to be applied in disaster preparedness operations.

Long‐Term Estimation of SoH Using Cascaded LSTM‐RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation

ABSTRACTAccurate estimation of state of health (SoH) of the battery over long‐term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short‐term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet‐based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM‐RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM‐RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

Application of deep forest algorithm incorporating seasonality and temporal correlation for wind speed prediction in offshore wind farm

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation.

A multiple linear regression model for predicting characteristic frequencies in biological tissues

This research introduces a novel mathematical methodology for identifying the distinctive frequency of human tissue. The model has been formulated using bioelectrical impedance analysis. The developed model can be utilized to detect a range of ailments, including those associated with the cardiovascular system, cancer, and dengue fever. A total of 3813 data points, including both males and females, were utilized. Data from a sample of both male and female individuals, including their age, height, bioelectrical impedance at frequencies ranging from 5 kHz to 1 MHz (for the Fc model), body mass index, and an impedance index of 2000, were utilized to create mathematical models. To validate the suggested models, data from a total of 1813 individuals (both male and female) were utilized. The statistical analysis of the proposed model (Fc) reveals a significant correlation (Pearson coefficient = 0.997, p < 0.001) between both male and female subjects, with a positive covariance. The model’s 95% limits of agreement, ranging from −1.28 to 1.98 L for both males and females, are sufficiently minimal. All errors fall within this limit. In addition, the suggested model has undergone validation in terms of various types of error analysis, such as bias and root mean square (RMSE). The bias and RMSE values, which are indicators of error, reach a maximum of 0.32 and 0.38 L (for both male and female), respectively. These values are within the predicted range and can be considered minimal.

ANALYSIS OF THE INFLUENCE OF MONETARY INSTRUMENTS ON THE SIZE OF THE DOMESTIC PUBLIC DEBT

The article is devoted to highlighting the results of research aimed at improving existing approaches to conducting a comprehensive analysis of the nature of the relationships between the parameters of the state's monetary and debt policy. Among other things, such indicators as the size of the domestic public debt, the size of international reserves, the level of inflation, the exchange rate and the discount rate are taken into account.The proposed approach to the analysis of the influence of managed monetary indicators on the size of the internal state debt proved that the use of the ARDL model gives slightly better results than the VECM in terms of the adequacy of the description and forecasting of the studied process. At the same time, both models proved their ability to describe the change in the size of the internal state debt, and have high values of coefficients of determination and low values of the indicator of the average absolute percentage error. In the future, it is recommended to use it for the analysis of the causality of the influence, discriminant analysis and the expediency of changing monetary instruments independently of other instruments.On the basis of the developed models, the results were obtained, which allow us to draw conclusions that the system continues to remain stable after fluctuations of key factors capable of destabilizing it and will be able to return to its previous state after a shock for some time. This is confirmed by the fact that, on the basis of statistical tests, the existence of a long-term relationship between the investigated indicators and the impulse response function together with the decomposition of the variance of the VAR model was proved.

Time series forecasting of rain-induced attenuation using coupled ARIMA-SARIMA models for radio propagation applications over subtropical locations

This present research aimed to identify the optimum model for predicting the rain-induced attenuation along the terrestrial and satellite communication networks and employed the time series forecasting models of Autoregressive integrated moving average (ARIMA) and Seasonal Autoregressive integrated moving average (SARIMA) to examine and contrast several techniques for predicting rain-induced attenuation across a sub-tropical area of South Africa. In this research, the ARIMA was developed using the Box and Jenkins technique to predict long-term rain-induced attenuation in the following South African provinces: Kwazulu-Natal, Eastern Cape, Gauteng, and Northern Cape. The datasets used for the research were obtained from the South African Weather Station from 1994–2023 were used to build and check the model after generating rain-induced attenuation using synthetic storm techniques (SST). The forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model and that of autoregressive integrated moving average (ARIMA) were compared with four forecast performance measures: - Mean Squared Error (MSE), Mean Absolute Error (MAE), R- squared or the coefficient of determination (R2) and Root Mean Squared Error (RMSE). The results of the study showed that due to its lower forecast performance error indicators, SARIMA performed better than ARIMA in forecasting rain-induced attenuation in South Africa. There is no statistically significant difference between the two projected values, according to the results of a Ljung-box test for significant difference. The authors draw the inference that the two approaches can effectively be employed in their proper locations. Rain-attenuation prediction data indicates that this long-term projection can help decision makers to improve the performance of microwave networks in the face of random fluctuations and avoiding unexpected signal loss with efficient installation of communications infrastructure. It also has applications in prediction of floods, urban planning, and crop management.

Carving a niche in building energy using modern vernacular house with passive wall materials - A multi-criteria decision-making framework

The building sector, one of the world's largest energy consumers, includes vernacular houses that offer good living conditions. However, increasing population and energy demands have caused vernacular houses to impact the ecosystem negatively, thus losing popularity. Passive design features can alleviate this problem but entail several factors and alternatives. This study aims to develop a fuzzy-based multi-criteria decision-making framework for selecting an optimal wall material for vernacular house that satisfies the technical, economic, environmental and social factors. Seven different wall materials, along with governing factors like room temperature, humidity, cooling and heating loads, cost, comfort index, CO2 emission, job creation and social acceptability, are considered. Four fuzzy-based decision-making tools are used, followed by sensitivity analysis to examine the results. A test cell of an established 3BHK vernacular house is made using design-builder and is simulated with Energy plus software. The error indices confirmed the design-builder model's accuracy, with NMBE and CV-RMSE values for room temperature and relative humidity both under 1 %, well within ASHRAE 14's threshold limits. From the results, cinder concrete is found as optimal passive wall material, resulting in lower cooling and heating loads and CO2 emission by 0.68 %, 9.64 %, and 2.27 %, respectively, than traditional vernacular house.

A robust multi-model framework for groundwater level prediction: The BFSA-MVMD-GRU-RVM model

Groundwater level prediction is critical for environmental protection and agricultural planning. Accurate predictions help manage risks associated with excessive groundwater extraction and land subsidence. This study introduces a novel model combining multivariate variational mode decomposition (MVMD), gated recurrent unit (GRU), and relevance vector machine (RVM), along with the Boruta feature selection algorithm (BFSA), for precise groundwater level forecasting. The model operates in several stages. First, BFSA selects the most informative features as input data. Next, the MVMD method decomposes the time series of these features. The GRU model then extracts crucial information from these decompositions. Finally, the extracted data is fed into the RVM model to predict groundwater levels one month ahead in Iran's Bastam Plain. To assess the model's accuracy, multiple error indices were employed. The MVMD-GRU-RVM model outperformed other models, improving the testing Nash–Sutcliffe Efficiency and mean absolute error by 24–31 % and 6–61 %, respectively. Additionally, it enhanced R2 values of the MVMD-CNN, MVMD-RNN, MVMD-MLP, and MVMD-RBFNN models by 1.0–3.33 %. These results demonstrate that the MVMD-GRU-RVM model significantly improves groundwater level prediction accuracy. The key points of the paper are the successful effectiveness of MVMD in processing non-stationary time series and improving the predictive performance of the models, as well as the high ability of the GRU model in data feature extraction. The study also shows that the novel model can provide outputs with lower uncertainty.

A frequency-independent absorption function surrogate for perfectly matched layer in exterior acoustics

In many engineering applications, the solution of acoustic wave problems in the infinite domain is required over a broad frequency range with densely sampled increments. In order to achieve efficient numerical simulations via a spatial discretization, e.g. finite element method, additional artificial absorbing boundaries are necessary to truncate the computational domain into appropriate bounded sizes. One of the most commonly used non-reflecting techniques to attenuate propagating waves is known as the perfectly matched layer. However, the system matrices arising from the finite element treatment of the Helmholtz equation in the absorbing layers are frequency-dependent, implying that they must be formed and inverted at each frequency of interest. Such a procedure is rather troublesome for frequency sweeps. To address this, a surrogate of perfectly matched layers is proposed, which enables the corresponding system matrices to be independent of the frequency. Moreover, it avoids the use of a relatively large computational domain and relatively thick enclosed layers at low frequencies, thus improving the ability of perfectly matched layers across the entire frequency range. After that, an adaptive projection-based model order reduction scheme is further developed to reduce the computational complexity of exterior acoustic systems. A robust error indicator based on the relative error of two constructed reduced order models is accordingly introduced. The performance of the present solution framework is discussed and compared with other implementation strategies, in the context of multi-frequency solution of two-dimensional test models with single or multiple scatterers.

Accelerated cardiac cine with spatio-coil regularized deep learning reconstruction.

To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging. This study proposes a Spatio-Coil Regularized DL (SCR-DL) approach for iterative deep learning reconstruction incorporating multicoil information in data consistency and regularizer. SCR-DL uses shift-invariant convolutional kernels to interpolate missing k-space lines and reconstruct individual coil images, followed by a regularizer that operates simultaneously across spatial and coil dimensions using learned image priors. At 8-fold acceleration, SCR-DL was compared with Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA), sensitivity encoding (SENSE)-based DL and spatio-temporal regularized (STR)-DL reconstruction. In the retrospective undersampled cine, images were quantitatively evaluated using normalized mean square error (NMSE) and structural similarity index measure (SSIM). Additionally, agreement for left-ventricular ejection fraction and left-ventricular mass were assessed using prospectively accelerated cine images at 2-fold and 8-fold accelerations. The SCR-DL algorithm successfully reconstructed highly accelerated cine images. SCR-DL had significant improvements in NMSE (0.03 ± 0.02) and SSIM (91.4% ± 2.7%) compared with GRAPPA (NMSE: 0.09 ± 0.04, SSIM: 69.9% ± 11.1%; p < 0.001), SENSE-DL (NMSE: 0.07 ± 0.04, SSIM: 86.9% ± 3.2%; p < 0.001), and STR-DL (NMSE: 0.04 ± 0.03, SSIM: 90.0% ± 2.5%; p < 0.001) with retrospective undersampled cine. Despite the 3-fold reduction in scan time, there was no difference between left-ventricular ejection fraction (59.8 ± 4.5 vs. 60.8 ± 4.8, p = 0.46) or left-ventricular mass (73.6 ± 19.4 g vs. 73.2 ± 19.7 g, p = 0.95) between R = 2 and R = 8 prospectively accelerated cine images. SCR-DL enabled highly accelerated cardiac cine imaging, significantly reducing breath-hold time. Compared with GRAPPA or SENSE-DL, images reconstructed with SCR-DL showed superior NMSE and SSIM.

Ground settlement prediction for highway subgrades with sparse data using regression Kriging

Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies. To resolve it, this paper proposes a regression Kriging (RK)—based method for ground settlement prediction with sparse data. Under the framework of RK, the stationarity of sample residual and trend structure are key factors for prediction accuracy. It is found that the use of Box–Cox transformation, which can help to achieve stationarity of sample residual, leads to significant increase of the prediction accuracy with sparse data. Specifically, the various evaluation metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), mean arctangent absolute percent error (MAAPE) and scatter index (SCI)) are significantly decreased when the Box–Cox transformation is incorporated. In addition, the first-order polynomial trend structure is found to be more appropriate than those with higher orders for predicting settlements resulting from primary consolidation. Moreover, comparative study is conducted among the proposed RK method, classical prediction methods and back propagation neural network (BPNN). It is found that the evaluation metrics obtained by the RK method are significantly smaller than those obtained by the other methods, indicating its highest accuracy. By contrast, BPNN has the worst performance among the various methods, because the sparse data are inadequate to establish a satisfactory BPNN model.

Feasibility of trauma video review for the collection of medication-related information for research and quality improvement.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Trauma video review (TVR) involves reviewing video recordings of team-based trauma response to evaluate team performance, identify improvement opportunities, and assess procedures, but the feasibility of using TVR to gather medication-related information is unknown. We aimed to assess the feasibility of using TVR for data collection of medication-related variables for research and quality improvement by comparing and describing differences between TVR and electronic medical record (EMR) review. This was an observational study of level I/II trauma patients treated between November 2022 and March 2023. Patients with video recording started within 1 minute of arrival, at least 1 medication administered, and with pharmacist participation in care were included. The number of variables able to be collected by TVR or EMR review were compared and reported in the categories of medication administration, indicators of adverse drug events (ADEs), medication errors, and communication. The numbers and types of discrepancies between data collection modalities were quantified and described. Agreement between TVR and EMR review was assessed and reported as an intraclass correlation coefficient (ICC). Twenty-five patients were included; 758 and 1,011 variables collected by TVR and EMR review, respectively. In total, 689 variables were collected by both methods, and data collection modalities matched exactly in 4 of 25 patients (16%); ICC, 0.677 (moderate level of agreement). There were 46 (6.7%) discrepancies; 84% involved communication related variables. TVR missed more variables than EMR review, mostly medication errors and inability to assess ADEs but captured more communication-related variables. TVR and EMR review together offer a greater source of medication-related information for data collection compared to either alone. EMR review collected medication administration, ADE, and medication errors variables more often than TVR and TVR was better able to collect communication-related variables. When designing studies/quality improvement efforts related to medication use during trauma resuscitation (e.g., pharmacist impact on time to administration), combined data collection modalities should be used, when available.

Anisotropic error estimator for the Stokes–Biot system

This paper presents an a posteriori error analysis for the problem defining the interaction between a free fluid and poroelastic structure approximated by finite element methods on anisotropic meshes in Rd, d=2 or 3. Korn’s inequality for piecewise linear vector fields on anisotropic meshes is established and is applied to nonconforming finite element method. Then the existence and uniqueness of the approximation solution are deduced for conforming and nonconforming cases. With the obtained finite element solutions, local error indicators and a global estimator are generated, demonstrating reliability and efficiency. The efficiency is proved by means of bubble functions and the corresponding anisotropic inverse inequalities. In order to prove the reliability, it is vital that an anisotropic mesh corresponds to the anisotropic function under consideration. To measure this correspondence, a so-called matching function is defined, and its discussion shows it to be useful tool. With its help, the upper error bound is shown by means of the corresponding anisotropic interpolation estimates and a special Helmholtz decomposition in both media.

Adaptative scheme for diffuse field response of multilayered structures

The computation of the diffuse field response of multilayered homogeneous structures leads to integrals which should be computed efficiently. This is all the more true as the frequency increases, since the integrand has localized peaks. The objective of this communication is to propose an adaptive integration scheme that aims to limit the number of resolutions of the physical problem and to maximize the reuse of previous evaluations in the course of the method. After discussing the usual integration methods, the proposed method will be presented. A first approximation of the integral is performed with two nested integration schemes with a coarse mesh of the initial integration domain. A local error indicator is derived and leads to a first partition. On each set of the latter, and depending on the local error estimated previously, an adequate numerical scheme is chosen. The error is controlled during all the process and it is iterated until convergence. The method is tested first on academic cases and then on industrial cases. The performance of the method is discussed, taking into account not only the number of evaluations of the physical function, but also the additional numerical cost generated by the adaptation process.

Proposal of a facet-to-facet multiple flow direction algorithm based on geometrical and mathematical analysis of physical dispersion over triangle facet

Flow direction algorithms have been widely used to extract crucial terrain attributes of great hydrological and geomorphological significance. However, essential distinctions between the empirically-designed strategies of typical algorithms and the natural rules of physical dispersions bring various problems (e.g. parallel channel, artificial dispersion), leading to the low size and extent precisions of estimated results. In this work, geometrical and mathematical analysis is conducted on the inherent characteristics of physical dispersions along slope lines on local terrains. On each 3 × 3 window of digital elevation model (DEM), center pixel is divided into eight non-overlapping sub-facets. Necessary and sufficient (NS) conditions of size relationships between the elevations of adjacent pixels are summarized to directly identify the receiving facets of a sub-facet. Then, strict mathematical relations are derived between slope direction of a sub-facet and flow proportions allocated to receiving facets. A strategy is designed to re-adjust receiving facets and flow proportions for the boundary flow of adjacent facets. Lastly, a multiple-flow-direction algorithm called TFGA is proposed with the NS condition of size relationships, mathematical relation of slope direction with flow proportion, and adjustment strategy of boundary flow. Case studies are conducted for investigating the total contributing areas (TCA) and specific contributing areas (SCA) estimated by TFGA. Results reveal all-side superiorities of TFGA to typical algorithms in spatial patterns, error indicators and statistic characteristics of estimated TCAs and SCAs. Particularly, TFGA improves size precision of estimated results by approximately one order of magnitude. In a conclusion, we highly recommend TFGA for digital elevation analysis.

Deep learning-based multi-frequency denoising for myocardial perfusion SPECT

BackgroundDeep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.MethodsFifty anonymized patients who underwent routine 99mTc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.ResultsAttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.ConclusionsAttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.

Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals

Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.

Accurate evaluation on peak shaving capacity of combined-heat-and-power thermal power units based on physical information neural network

The high proportion of new energy requires the power system to have sufficient flexibility and peak shaving capacity. The combined-heat-and-power thermal power unit is one of the main flexibility resources. Accurately evaluating the peak shaving capability from thermal power units is of great significance for power system scheduling. However, there are some problems in the previous evaluation models. Fully mechanistic models may exhibit poor accuracy due to component degradation and other reasons, while fully data-driven models may exhibit poor generalization performance due to limited training conditions. To address these issues, this paper first uses physical information neural network to fuse thermal power mechanisms and operational data, and constructs a high-precision and strong-generalization power output evaluation model. Then, error indicators and generalization indicators are used to validate the effectiveness and superiority of the model. In the test set, the mean absolute error, root mean square error and Pearson correlation coefficient of the model are1.633 MW, 1.971 MW and 0.9643, respectively. Based on this model, the peak shaving capacity range of the target thermal power plant under different heating demands is calculated. Finally, the experiment analyzed the real-time peak shaving capability and peak shaving margin.