Error Curve Research Articles

Radiomic and Clinical Model in the Prognostic Evaluation of Adenoid Cystic Carcinoma of the Head and Neck

Background/Objectives: Adenoid Cystic Carcinoma (AdCC) is a rare malignant salivary gland tumor, with high rates of recurrence and distant metastasis. This study aims to stratify patients Relapse-Free Survival (RFS) using a combined model of clinical and radiomic features from preoperative MRI. Methods: This retrospective study included patients with primary AdCC who underwent surgery and adjuvant radiotherapy. Segmentations were manually performed by two head and neck radiologists. Radiomic features were extracted using the 3D Slicer software. Descriptive statistics was performed. A Survival Random Forest model was employed to select which radiological feature predict RFS. Cox proportional hazards models were constructed using clinical, radiological variables or both. Synthetic data augmentation was applied to address the small sample size and improve model robustness. Models were validated on real data and compared using the C-index and Prediction Error Curves (PEC). Results: Three Cox models were developed: one with clinical features (C-index = 0.67), one with radiomic features (C-index = 0.68), and one combining both (C-index = 0.77). The combined clinical-radiomic model had the highest predictive accuracy and outperformed models based on clinical or radiomic features. The combined model also exhibited the lowest mean Brier score in PEC analysis, indicating better predictive performance. Conclusions: This study demonstrate that a combined radiomic-clinical model can predict RFS in AdCC patients. This model may provide clinicians a valuable tool in patient’s management and may aid in personalized treatment planning.

BIOS-01. MACHINE LEARNING-BASED PREDICTION OF INPATIENT CARE COSTS FOR PATIENTS WITH BRAIN METASTASES: A REAL-WORLD ANALYSIS

Abstract INTRODUCTION Brain metastases (BMs) are increasingly a cause of significant cancer-related morbidity, mortality, and economic burden. While BM-related healthcare costs have been previously described, predictive analytics based on contemporary cohorts, particularly for inpatient care, remain unavailable. This study sought to evaluate inpatient care costs (IPCC) and associated reimbursements for patients with BMs and perform predictive modeling through supervised machine learning (sML) using a contemporary, commercial US claims database. METHODS This retrospective study, reported following STROBE and TRIPOD+AI reporting guidelines, included adult patients (aged 18-65 years) in the United States (US) with BMs included in the MarketScan Commercial Claims and Encounters Database who received inpatient care during January 2016 to December 2021. Eligible patients were identified using ICD-10 codes. IPCC measures were adjusted using the Medical Care component of the 2023 Consumer Price Index. The primary study outcome was total IPCC, represented by adjusted gross total payments. After log-transformation performed due to data skew, sML was carried out using Random Forest algorithm with 1000 iterations with a 70:30 training-test data split, followed by residual analysis and evaluation of predictive performance using root-mean-squared error (RMSE), out-of-bag error (OOBE), and calibration curve. Multivariable linear regression was carried out for sensitivity analyses. RESULTS A total of 25117 unique inpatient admissions across 13477 BM patients were included, with median (IQR) age of 57 years (51-61). Gross median (IQR) CPI-medical-care-adjusted values per admission were: total payments $28,119.51 (16847.45-55111.67); hospital payments $24,678.74 (14601.66-47864.84); and payments to principal physician $884.80 (441.07-2434.15), being 3.6% (IQR 1.9-6.8) of total payment. Median inpatient length of stay was 4 days (IQR 2-8). ML model had an OOBE of 0.61 and, using test data, RMSE of 0.85, indicating robust performance. No major deviations were found on residual analysis. CONCLUSIONS Contemporary IPCCs for BM patients in US are substantial but driven minimally by payments to principal physician. sML may be utilized to successfully predict IPCC for BM patients.

Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data.

Due to the heterogeneity in the effectiveness of immunotherapy for lung cancer, identifying predictors is crucial. This study aimed to develop a machine learning model to identify predictors of overall survival in lung cancer patients treated with immune checkpoint inhibitors (ICIs). A retrospective analysis was performed on data from 1314 lung cancer patients at the Chongqing University Cancer Hospital from September 2018 to September 2022. We used the random survival forest (RSF) model to identify survival-influencing factors, using backward elimination for variable selection. A Cox proportional hazards (CPH) model was constructed using the most significant predictors. We assessed model performance and generalizability using time-dependent receiver operating characteristics (ROC) and predictive error curves. The RSF model demonstrated better predictive accuracy than the CPH (IBS 0.17 vs. 0.17; C-index 0.91 vs. 0.68), with better discrimination and prediction performance. The influential variables identified included D-dimer, Karnofsky performance status, albumin, surgery, TNM stage, platelet count, and age. The RSF model, which incorporated these variables, achieved area under the curve (AUC) scores of 0.95, 0.94, and 0.98 for 1-, 3-, and 5-year survival predictions, respectively, in the training set. The validation set showed AUCs of 0.94, 0.90, and 0.95, respectively, exceeding the performance of the CPH model. The study successfully developed a machine learning model that accurately predicted the survival benefits of ICI therapy in lung cancer patients, supporting clinical decision-making in lung cancer treatment.

Inverse problem-solving method applied to the acoustic characterisation of a thin film via the Debye series method (DSM)

ABSTRACT The study of the ultrasonic acoustic response of materials is of increasing interest for the characterisation of high-frequency applications. In this work, some elastic properties (acoustic velocity and acoustic attenuation) and some physical properties (thickness and density), together with the phase shift of the transducer, are estimated via one method. Scanning acoustic microscopy was performed via an innovative and different method using a high-frequency (50 MHz) planar wave transducer. In addition, the Debye series model (DSM) was used to model the acoustic response of the bilayer structure of the glass substrate and the resin‒epoxy sample. The inverse problem of estimating the acoustic parameters that results in a DSM reflected echo that matches the experimental signal is solved by proposing an innovative step-by-step curve fitting algorithm. The algorithm for solving the inverse problem is then optimised via relative mean square error (RMSE) curves, resulting in a robust method for assessing the uniqueness of the multiparameter solution.

Microwave Coincidence Imaging with Phase-Coded Stochastic Radiation Field

Microwave coincidence imaging (MCI) represents a novel forward-looking radar imaging method with high-resolution capabilities. Most MCI methods rely on random frequency modulation to generate stochastic radiation fields, which introduces the complexity of radar systems and imposes limitations on imaging quality under noisy conditions. In this paper, microwave coincidence imaging with phase-coded stochastic radiation fields is proposed, which generates spatio-temporally uncorrelated stochastic radiation fields with phase coding. Firstly, the radiation field characteristics are analyzed, and the coding sequences are designed. Then, pulse compression is applied to achieve a one-dimensional range image. Furthermore, an azimuthal imaging model is built, and a reference matrix is derived from the frequency domain. Finally, sparse Bayesian learning (SBL) and alternating direction method of multipliers (ADMM)-based total variation are implemented to reconstruct targets. The methods have better imaging performance at low signal-to-noise ratios (SNRs), as shown by the imaging results and mean square error (MSE) curves. In addition, compared with the SBL and ADMM-based total variation methods based on the direct frequency-domain solution, the proposed method’s computational complexity is reduced, giving it great potential in forward-looking high-resolution scenarios, such as autonomous obstacle avoidance with vehicle-mounted radar and terminal guidance.

Simulation and validation of the transmission error, meshing stiffness, and load sharing of planetary spur gear transmissions

Although the load sharing between planets of sequentially phased planetary gear transmissions has been studied in the past, the required solving techniques based on the Finite Element Method result in long time consuming and high computational cost. This limits the possibilities of undertaking extensive studies that take into consideration a high number of cases allowing optimal solutions to be sought or general conclusions drawn. In addition, the determination of the curves of transmission error, time-varying mesh stiffness, and load sharing among tooth pairs in simultaneous contact are also complicated. In this work an analytical model has been developed for the simulation of the time-varying mesh stiffness, quasi-static transmission error, and load sharing ratio between planets and tooth pairs of planetary spur gear transmissions. It is based on similar models for external and internal spur gears previously developed and has been validated by comparison with a hybrid model based on the Finite Element Method and theoretic-experimental correlation.

Simulation of Motion Nonlinear Error Compensation of CNC Machine Tools With Multi-Axis Linkage

In order to solve the problem of nonlinear error for a dual rotary table five-axis CNC machine tool due to the linkage of rotary and translational axes, the simulation of motion nonlinear error compensation for a multi-axis linkage CNC machine tool is proposed. The adjacent points in the tool position file are selected as the tool position points for building the model, and then the nonlinear error model resolved by the harmonic function is established according to the error distribution in the classical post-processing. The nonlinear error between the two tool position points is quickly predicted by the analytical expression of this model, and the real-time error compensation of the intermediate interpolation points is realized. Finally, MALTLAB simulation analysis is performed on the tool position file of an impeller part machining to verify the effectiveness of the proposed algorithm. The experimental results show that it can be seen from the distribution curve of the nonlinear error that it is about 10% after compensation as before compensation, thus verifying the effectiveness of the nonlinear error compensation mechanism. The correctness of the nonlinear error analysis and compensation method and the effectiveness of post-processing are verified.

Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach.

Objective: Health recommendation systems suggest behavioral modifications to improve quality of life. However, current approaches do not facilitate the generation or examination of such recommendations considering the multifeature longitudinal evolution of behaviors. This paper proposes the use of a deep learning transformer-based model that allows the analysis of recommendations for behavior changes. Methods: We adapted a prediction approach, namely Behavior Sequence Transformer (BST), which analyzes temporal human routines and patterns, generating inductive outcomes. The evaluation relied on a case study that employed the behavioral history and profile of the English Longitudinal Study of Ageing (ELSA) participants (n = 2682), predicting their psychological mood (normal, pre-depressed, depressed) according to input recommendations for behavioral changes. Root mean squared error (RMSE) and learning curves were used to track the recommendation accuracy evolution and possible overfitting problems. Results: Experiments demonstrated lower RMSE values for the multifeature model (0.28/0.03) when compared to its single-feature versions (marital status, 0.59/0.001), (high pressure, 0.357/0.04), (diabetes, 0.36/0.01), (sleep quality, 0.57/0.02), (level of physical activity, 0.57/0.01). Conclusions: The results demonstrate the architecture's capability to analyze multifeatured longitudinal data, supporting the generation of suggestions for concurrent modifications across multiple input features. Moreover, these suggestions align with findings in specialized literature.

ПРИМЕНЕНИЕ КОЛИЧЕСТВЕННОГО МЕТОДА В ИССЛЕДОВАНИИ ОПОЛЗНЕВОЙ ВОСПРИИМЧИВОСТИ СЕЛЕОПАСНОГО БАССЕЙНА РЕКИ АГСУЧАЙ

The article uses a quantitative method to analyze dangerous landslide processes occurring in the mudflow-prone Agsuchay River basin, taking into account the active development of tourism and recreational activities in the Shamakhi-Ismayilli region. In order to identify landslide susceptibility and the potential manifestation of landslides, the “weight” of 9 factors associated with landslides was determined, including hypsometry, slope angles (slope steepness), slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion networks, distance to roads and land use. By summing up all the landslide formation factors without exception and multiplying them by their “weight”, a map of landslide susceptibility of the mudflow-prone Agsuchay River basin was compiled. The reliability of the obtained models was assessed using the AUC ROC (area under the error curve) analysis, which showed a fairly high efficiency (up to 72 %) of the applied method.

Kinetic analysis and calculation correction methods for moisture evaporation rate in pine lignocellulosic biomass

This study investigates the kinetics of moisture evaporation in pine wood during the drying stage, focusing on analyzing thermogravimetric (TG) curves. The research is based on the kinetic rate model and TG curves obtained at various heating rates, specifically 5, 10, 15, and 20 K/min for the heat release rate, within a temperature range from room temperature to 473 K. Utilizing thermogravimetric experiments and the kinetic rate model, the apparent activation energy and pre-exponential factor are calculated. Further refinement of these calculations is achieved using the Nelder-Mead algorithm. The results show good agreement between the average values of the apparent activation energy (37.88 kJ/mol) and pre-exponential factor (4458.20 s−1) and experimental measurements in the initial and later drying stages, with discrepancies noted in the middle stage. Establishing a relationship between the correction coefficients of these parameters and the heating rate leads to a reduction in calculation errors. Notably, the pre-exponential factor is found to be influenced by heating rates, while the apparent activation energy remains relatively stable. Moreover, the errors are significantly reduced after correction, and the error curve of 10 K/min is the most significant. Overall, the proposed correction method holds promise for accurately calculating moisture evaporation rates in lignocellulosic biomass, offering valuable insights for biomass drying applications.

Development and validation of accelerated failure time model for cause-specific survival and prognostication of oral squamous cell carcinoma: SEER data analysis.

Oral Squamous Cell Carcinoma is the most prevalent malignancies affecting the oral cavity. Despite progress in studies and treatment options its outlook remains grim with survival prospects greatly affected by demographic and clinical factors. Precisely predicting survival rates and prognosis plays a role in making treatment choices for the best achievable overall health outcomes. To develop and validate an accelerated failure time model as a predictive model for cause-specific survival and prognosis of Oral Squamous Cell Carcinoma patients and compare its results to the traditional Cox proportional hazard model. We screened Oral cancer patients diagnosed with Squamous Cell Carcinoma from the Surveillance Epidemiology and End Results (SEER) database between 2010 and 2020. An accelerated failure time model using the Type I generalized half logistic distribution was used to determine independent prognostic factors affecting the survival time of patients with oral squamous carcinoma. In addition, accelerated factors were estimated to assess how some variables influence the survival times of the patients. We used the Akaike Information Criterion, Bayesian Information Criterion to evaluate the model fit, the area under the curve for discriminability, Concordance Index (C-index) and Root Mean Square Error and calibration curve for predictability, to compare the type I generalized half logistic survival model to other common classical survival models. All tests are conducted at a 0.05 level of significance. The accelerated failure time models demonstrated superior effectiveness in modeling (fit and predictive accuracy) the cause-specific survival (CSS) of oral squamous cell carcinoma compared to the Cox model. Among the accelerated failure time models considered, the Type I generalized half logistic distribution exhibited the most robust model fit, as evidenced by the lowest Akaike Information Criterion (AIC = 27370) and Bayesian Information Criterion (BIC = 27415) values. This outperformed other parametric models and the Cox Model (AIC = 47019, BIC = 47177). The TIGHLD displayed an AUC of 0.642 for discrimination, surpassing the Cox model (AUC = 0.544). In terms of predictive accuracy, the model achieved the highest concordance index (C-index = 0.780) and the lowest root mean square error (RMSE = 1.209), a notable performance over the Cox model (C-index = 0.336, RMSE = 6.482). All variables under consideration in this study demonstrated significance at the 0.05 level for CSS, except for race and the time span from diagnosis to treatment, in the TIGHLD AFT model. However, differences emerged regarding the significant variations in survival times among subgroups. Finally, the results derived from the model revealed that all significant variables except chemotherapy, all TNM stages and patients with Grade II and III tumor presentations contributed to the deceleration of time to cause-specific deaths. The accelerated failure time model provides a relatively accurate method to predict the prognosis of oral squamous cell carcinoma patients and is recommended over the Cox PH model for its superior predictive capabilities. This study also underscores the importance of using advanced statistical models to improve survival predictions and outcomes for cancer patients.

Analysis of countermeasures for improving the quality of college students’ entrepreneurship by deep learning and blockchain technology

This study seeks to enhance the entrepreneurial quality of college students by introducing advanced technologies such as deep learning and blockchain. A student majoring in electronic information at a university is the research object. Based on the background of the Internet of Things and blockchain technology, the innovation and entrepreneurship information data platform for college students is constructed. The results show that the α values of students’ background, students’ professional ability, students’ practical ability, and students’ development ability are greater than 0.7. When determining the number of neurons in the hidden layer, the training error curve decreases continuously. The test error rises and then decreases when the number of neurons in the hidden layer is 10, 11, and 12. The Levenberg-Marquardt algorithm, selected as the training function, exhibits optimal performance with a training step count of 1,000 and achieves a performance score of 4.9. The actual values of the enhanced BPNN align closely with the expected values, demonstrating minimal deviation. The findings emphasize the importance for students in electronic information majors to actively engage in diverse social practices. This study serves as a valuable reference for enhancing the entrepreneurial quality of college students.

Suppression of vortex electromagnetic wave divergence by modulating the wave front dislocation line

Abstract When the beam is transmitted in the atmosphere, under the influence of atmospheric turbulence, the beam will occur energy attenuation, wavefront distortion and other phenomena, which will seriously affect the transmission and reception of information. Based on the PB phase principle enhances the focusing capability of the auto-focusing Airy Gaussian vortex beam by regulating the wavefront error curvature of the vortex beam, and then enhances the resistance of the beam against atmospheric turbulence. The results reveal the curvature of the wavefront error curve and the peak intensity of the light field of the beam can be changed by changing the beam polarization state parameters n and m in the focal plane. The auto-focusing Airy Gaussian vortex beam with the polarization state parameters m=1, n=4 increases the peak magnitude of the light field intensity in the focal plane of the auto-focusing Airy Gaussian vortex beam with the polarization state parameters m=0, n=0 to 1.99 times. Demonstrate that geometric phase tuning the auto-focusing Airy Gaussian vortex beams can enhance resistance to atmospheric turbulence. Furthermore, this paper extends this method to the field of millimeter wave. Under the transmission distance of 1km, the peak intensity of the initial vortex electromagnetic wave after the geometric phase adjustment is increased to 3.33 times, and the receiving radius of the target surface is reduced to 0.24 of the initial vortex electromagnetic wave. It shows that geometric phase-regulated vortex electromagnetic wave can suppress vortex electromagnetic wave divergence and have potential applications in wireless communication.

Nonlinear modeling of measurement errors in gateway energy meters

In order to clarify the quantitative relationship between grid parameters and measurement errors of gateway energy meters, and accurately predict the dynamic measurement errors of gateway energy meters, the author proposes a nonlinear modeling of measurement errors of gateway energy meters. Firstly, elaborate on the NARX prediction model to clarify the basic structure of the nonlinear model; Then propose the process of modeling measurement errors; Finally, through testing, identify the main power grid parameters that affect measurement errors and the optimal structure of the model. The experimental results indicate that: The comparison between the true measurement error of two electricity meters and the measurement error calculated by the nonlinear estimator shows that the Hammerstein Weiner estimator has the highest fitting degree to the true measurement error curve, with fitting degrees of 82.21 % and 85.38 % for the measurement errors of 0.2S and 0.5S electricity meters, respectively. The prediction fit of the NRAX model based on the Hammerstein Weiner nonlinear estimator reaches about 81 % under different load conditions. ConclusionThe model determined by this method can accurately predict the dynamic measurement error of the energy meter, and the research results have positive significance for improving the efficiency of gate energy meter calibration and identifying gate energy meter faults.

Quality-preserving multilevel mesh generation for building models

ABSTRACT Three-dimensional (3D) high-fidelity building models are crucial for digital twin cities. Multi-scale modeling is crucial for accurately capturing the complexity of urban environments; however, existing methods often lack the flexibility required to satisfy the varied demands of diverse applications. In this paper, we propose a framework to continuously simplify 3D building models, it dynamically adjusts mesh accuracy to meet diverse needs, enabling seamless transitions from high-poly to medium and low-poly meshes. Our method contains three main stages: First, in the mesh repair stage, we introduce a novel mesh repair framework based on an advanced Marching Cubes 33 algorithm to ensure a watertight and clean topology mesh. Then, for generating medium-poly meshes, we employ a feature-preserving mesh simplification algorithm that combines curvature and area-weighted quadratic error metrics to achieve a high-fidelity simplified mesh. For generating low-poly meshes, we use the concept of carving visual hull to generate low-poly mesh with high visual similarity. Our method has been evaluated on building subsets of the Free3D and Thingi10 K datasets, as well as on various building models generated from different observation scales, and its superiority has been validated through extensive comparisons with state-of-the-art techniques.

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Transistor modeling based onLM‐BPNNandCG‐BPNNfor theGaAs pHEMT

AbstractIn order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (CG) algorithm and the Levenberg–Marquardt (LM) algorithm, the CG‐BPNN and LM‐BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S‐parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the CG‐BPNN model is better than another S‐parameters when predicting ImS12with mean square error (MSE) of 7.6632e‐06, while LM‐BPNN model predicts ImS12with MSE of 2.4672e‐06. Meanwhile, the MSE of CG‐BPNN model is higher than LM‐BPNN model when predicting all the S‐parameters. In addition, it shows a smaller fluctuation range for the error curve of LM‐BPNN model, which is more stable than the CG‐BPNN model. Therefore, the double hidden layer LM‐BPNN model is the better choice to characterize the small signal of GaAs pHEMT.

Boundaries identification of geological structure in lunar Oceanus Procellarum region using full gravity gradient tensor methodology

Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution.

Parameter identification method of information propagation models based on different network structures

In this paper, we create the rumor propagation model with diffusion behavior by considering the state of the rumor in both the time dimension and the space dimension comprehensively. Meanwhile, we demonstrate the reaction–diffusion model using Turing patterns after determining the prerequisites for their occurrence. In order to achieve the purpose of predicting and controlling rumors in time, we choose to utilize the parameter identification technique based on the Barzilai–Borwein (BB) algorithm and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. In the numerical simulation section, we first investigate how the rumor avoidance rate and cross-diffusion coefficients affect the propagation of rumors. Then, based on a continuous spatio-temporal system and complex network system, respectively, we perform parameter identification for the propagation model. We thoroughly examine how the type of algorithm, the quantity of unknown parameters, and the network structure affect the identification outcomes in terms of the cost function, error curve, and program function time. When the model constructed in this paper is used for parameter identification on different network structures, the error gap between the final value and the target value is not significant. However, the cost function and time consumption for parameter identification on complex networks are much smaller than on the continuous medium.

Real-time multi-risk assessment of large scale power system with high-penetration renewable energy

Abstract To accurately assess the impact of real-time uncertainty of high penetration renewable energy sources on the real-time operation of large-scale power systems, this study proposes an innovative method for real-time multi-risk assessment in power systems. Firstly, by utilizing an improved spectral clustering algorithm and mixture density network to analyze historical operational data, effective clustering of different renewable energy generation curves and accurate fitting of prediction errors were achieved. Next, random sampling was performed from the probability density curves of prediction errors to obtain real-time renewable energy outputs. At the same time, a non-sequential Monte Carlo method was used to incorporate interruptions for each component into the scenario simulation. Subsequently, a state assessment was conducted for each sampled scenario by minimizing load shedding and energy curtailment. On this basis, risk indices that comprehensively consider the real-time flexibility of the system and power supply-demand imbalances were established. Finally, validation in a test system verified the accuracy and speed of the proposed multi-risk assessment method.