Model Structure Research Articles

AMIKOMNET: Novel Structure for a Deep Learning Model to Enhance COVID-19 Classification Task Performance

Since early 2020, coronavirus has spread extensively throughout the globe. It was first detected in Wuhan, a province in China. Many researchers have proposed various models to solve problems related to COVID-19 detection. As traditional medical approaches take a lot of time to detect the virus and require specific laboratory tests, the adoption of artificial intelligence (AI), including machine learning, might play an important role in handling the problem. A great deal of research has seen the adoption of AI succeed in the early detection of COVID-19 using X-ray images. Unfortunately, the majority of deep learning adoption for COVID-19 detection has the shortcomings of high error detection and high computation costs. In this study, we employed a hybrid model using an auto-encoder (AE) and a convolutional neural network (CNN) (named AMIKOMNET) with a small number of layers and parameters. We implemented an ensemble learning mechanism in the AMIKOMNET model using Adaboost with the aim of reducing error detection in COVID-19 classification tasks. The experimental results for the binary class show that our model achieved high effectiveness, with 96.90% accuracy, 95.06% recall, 94.67% F1-score, and 96.03% precision. The experimental result for the multiclass achieved 95.13% accuracy, 94.93% recall, 95.75% F1-score, and 96.19% precision. The adoption of Adaboost in AMIKOMNET for the binary class increased the effectiveness of the model to 98.45% accuracy, 96.16% recall, 95.70% F1-score, and 96.87% precision. The adoption of Adaboost in AMIKOMNET in the multiclass classification task also saw an increase in performance, with an accuracy of 96.65%, a recall of 94.93%, an F1-score of 95.76%, and a precision of 96.19%. The implementation of AE to handle image feature extraction combined with a CNN used to handle dimensional image feature reduction achieved outstanding performance when compared to previous work using a deep learning platform. Exploiting Adaboost also increased the effectiveness of the AMIKOMNET model in detecting COVID-19.

A complete shortest independent loop set algorithm for model structure analysis

AbstractThis article looks at the well‐known shortest independent loop set algorithm, which is used to identify an independent loop set in system dynamics models. It addresses the cases where the algorithm fails to find a complete set due to its limitation to geodetic cycles and introduces a modification that has the potential to identify a complete independent loop set in every case while maintaining the efficiency of the original algorithm. The modified algorithm uses second‐shortest paths to complete the loop set. Special attention is given to the creation of the second‐shortest path matrix required for the modified algorithm, and to the application of the algorithm to a model where the independent loop set created by the shortest independent loop set algorithm was previously shown to be incomplete. © 2024 System Dynamics Society.

Designers' guided site shading strategies: A simplified model for radiative cooling

In the context of rising urban temperatures, the efficacy of cooling strategies, particularly shading installations, has garnered significant attention. However, prevalent issues in thermal radiation literature and assessment tools include challenging usability, insufficient guidance for design, and a lack of scientific data support. This study addresses these challenges by simplifying thermal radiation theory and integrating diverse assessment tools. The outcome unveils a site shading radiation cooling model, a user-friendly, design-guided, and scientifically supported approach for designers evaluating site shading.The research methodology involves conducting initial measurements on summer afternoons using ISO and Rayman to validate ENVI-met simulation accuracy. This study simplifies thermal radiation theory, creating a predictive model structure based on short- and long-wave radiation reception modes and translating scientific parameters into actionable design factors. Model parameters are derived from ENVI-met simulated Tmrt cooling benefits, followed by computations on numerous cases to verify and assess model applicability.Findings indicate a good match between the measured and simulated Tmrt data during summer afternoons. A common 1:1 width-to-height ratio colonnade in Taiwan contributes a 22 °C cooling effect in extremely hot afternoons, with an additional decrease of 5–8 °C if facing west. Each 10 % increase in the roof transmittance resulted in an approximately 3 °C Tmrt reduction benefit, with wood providing the best cooling effect and metal the least. Based on the obtained cooling data, a Site Shading Benefit (SSB) model was developed by incorporating area parameters, allowing for a simple calculation to assess the overall radiative heat reception of outdoor spaces on a site.

The Influence of Visionary Leadership on Work Motivation and Its Impact on Teacher Performance in State Junior High Schools in Central Lombok Regency

The quality of education is influenced by many factors, one of which has the greatest contribution is teacher performance. Research was conducted to explain the impact of visionary leadership on work motivation which then has an impact on teacher performance. The research was carried out on junior high school teachers in Central Lombok using a survey approach using questionnaires distributed randomly to 340 respondents. Determining the research sample from a population of 1,168 people used the Morgan model and then corfirmation to the Warwick and Lininger formula. The collection for data used a 5-choice Likert scale model questionnaire. The analysis is in the form of a model structure using the Partial Least Squares approach. The results are; (1) visionary leadership has a convince direct influence on work motivation in the results of the T-Statistics score analysis is 2.370 and the P-Values score is 0.018 (2) Visionary leadership has a significant positive direct influence on teacher performance in the results of the T-score analysis The statistic is 2.138 also the P-Values score is 0.033 (3) work motivation has a significant positive direct influence on teacher performance in the results of the analysis the T-Statistics score is 8.094 also the P-Values score is 0.000 (4) visionary leadership has a significant indirect influence convince effect on teacher performance through the intermediary of work motivation according to the results of the analysis, the T-Statistics score is 2.190 and the P-Values score is 0.029. Research findings show that visionary leadership and work motivation have a convince impact on teacher performance so they become predictors of influence on teachersperformance.

A Hybrid Sparse-dense Defensive DNN Accelerator Architecture against Adversarial Example Attacks

Understanding how to defend against adversarial attacks is crucial for ensuring the safety and reliability of these systems in real-world applications. Various adversarial defense methods are proposed, which aim to improve the robustness of neural networks against adversarial attacks by changing the model structure, adding detection networks, and adversarial purification network. However, deploying adversarial defense methods in existing DNN accelerators or defensive accelerators leads to many key issues. To address these challenges, this paper proposes sDNNGuard , an elastic heterogeneous DNN accelerator architecture that can efficiently orchestrate the simultaneous execution of original ( target ) DNN networks and the detect algorithm or network. It not only supports for dense DNN detect algorithms, but also allows for sparse DNN defense methods and other mixed dense-sparse (e.g., dense-dense and sparse-dense) workloads to fully exploit the benefits of sparsity. sDNNGuard with a CPU core also supports the non-DNN computing and allows the special layer of the neural network, and used for the conversion for sparse storage format for weights and activation values. To reduce off-chip traffic and improve resources utilization, a new hardware abstraction with elastic on-chip buffer/computing resource management is proposed to achieve dynamical resource scheduling mechanism. We propose an extended AI instruction set for neural networks synchronization, task scheduling and efficient data interaction. Experiment results show that sDNNGuard can effectively validate the legitimacy of the input samples in parallel with the target DNN model, achieving an average 1.42 × speedup compared with the state-of-the-art accelerators.

Design of Ship Trajectory Control Method Integrating Self Disturbance Rejection and Neural Network

To reduce the ship trajectory control difficulty, a ship trajectory control method based on active disturbance rejection control and radial basis function neural network is designed. Firstly, a separated model structure is proposed to model the ship’s navigation system, followed by the introduction of active disturbance rejection control technology to control the ship’s trajectory, and the fusion of radial basis function to improve the parameter adjustment effect. It was found that on the AIS and MSSIS datasets when the system was iterated 25 and 22 times, respectively, the fitness values of the research method were as high as 99.46 and 99.51. In addition, when the accuracy of all algorithms was 0.900, the recall rate of the research method was significantly the highest, at 0.752. When the recall rate was 0.900, the accuracy of the research method was significantly the highest, reaching 0.869. In practical applications, when there was no external interference, the heading was a square wave signal, and when the time reached 90.11, the proposed method operated with a small difference between the planned heading and the actual heading. In addition, it was found that the control effect of the research method on ship operation remains highly stable when external interference is added during ship operation. The above results indicate that the designed method has significant advantages in ship trajectory control tasks and can effectively enhance the navigation safety and stability of ships.

Towards Model-Driven Explainable Artificial Intelligence: Function Identification with Grammatical Evolution

Machine learning is a well-matured discipline, and exploration of datasets can be performed in an efficient way, leading to accurate and operational prediction and decision models. On the other hand, most methods tend to produce black-box-type models, which can be considered a serious drawback. This is so, since in case of numerous practical applications, it is also required to justify, explain, and uncover the inner decision mechanism so that an in-depth understanding of the causal and functional dependencies becomes possible and some responsibility for the decision can be considered. This paper addresses the critical need for model-driven eXplainable Artificial Intelligence (XAI) by exploring the limitations inherent in existing explanatory mechanisms, such as LIME or SHAP, which rely solely on input data. This seems to be an intrinsic limitation and a conceptual error, as no expert domain knowledge can come into play, and no analytical models of the phenomena under investigation are created. In order to deal with this issue, this paper puts forward the idea of building open, white-box explanatory models. To do that, we propose employing grammatical evolution tools combined with expert domain knowledge. The results demonstrate that the developed models can effectively explain the structure and behavior of decision models in terms of components, connections, causality, and simple functional dependencies.

Expanding model transparency and learning potential through structural and behavioural debriefings

AbstractSimulation‐based learning environments have been recommended by researchers as educational tools to support learning in complex domains. However, studies have evidenced that subjects may nevertheless have great difficulty understanding and managing dynamic systems, with several assertions are being made regarding the impact of employing transparent model simulations on learning and performance outcomes. This research, therefore, examines the moderating impacts of structural and behavioural debriefings, which are concentrated respectively on critical variables and relations, and on the connection between model structure, action patterns and system behaviour. We propose a series of hypotheses regarding the impact of model transparency and prior debriefing conditions on participants' performance and their comprehension of the system dynamics. Three out of the six hypotheses were validated: prior structural debriefing alongside model transparency positively impacts performance; prior behavioural debriefing, when combined with model transparency and prior structural debriefing, positively impacts both the comprehension of model dynamics and performance.

LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification

BackgroundPrevious deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes.ResultsWe proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance.Scientific contributionWe present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.Graphical

Lightweight Low-Voltage AC Arc-Fault Detection Method Based on the Interpretability Method

Electrical fires are frequently caused by low-voltage AC series arc faults, which can result in significant injuries and property damage. The installation of arc-fault detection devices is mandated or recommended in many regions and countries across the world, yet the current devices’ detection accuracy is insufficient to completely eliminate the risk posed by arc faults. The method based on artificial intelligence is a solution with high detection accuracy, but the AI model is a ‘black box’. When a misjudgment occurs, the cause of the model error cannot be found fundamentally, and the modification and light weight of the model also presents significant difficulties when using the approach. Given the aforementioned issues, this research proposes a novel lightweight low-voltage AC arc-fault detection method based on the explainability approach. By applying the attention mechanism approach and performing a visual analysis, the contribution of arc features to model detection is determined. Model input data optimization and model structure simplification are achieved at the same time as increased model detection accuracy. Ultimately, an experimental prototype for arc-fault detection is designed and validated. Test results demonstrate the effectiveness of the method by demonstrating that the lightweight model maintains 99.69% detection accuracy, even after optimizing the input data by 80% and reducing the model parameters by 51.52%.

Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin

ABSTRACT Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables.

The importance of model structure and soil data detail on the simulations of crop growth and water use: A case study for sugarcane

Process-based crop models have faced rapid development over the last years, and many modelling platforms are now available and can be used in a wide range of conditions. Whilst the selection of a model should be suited to the purpose of its application, very few studies focused on the impact of choosing different model structures and data details on the simulation outputs. One important aspect is the soil water dynamics, which can be simulated at different levels of details in terms of data and approaches. In this study, we investigated the impact of model structure and data detail on simulations of sugarcane growth and irrigation scheduling. Three different soil water routines (Standalone, Tipping-Bucket, SWAP) were coupled with the SAMUCA model and calibrated with a comprehensive field experiment dataset. We also tested the influence of using simplified homogeneous (SL) and detailed (DL) soil profile information in model performance. The model framework was evaluated against independent field experiments across Brazil and used to simulate long-term sugarcane growth and irrigation scheduling. After calibration, the SWAP-DL showed the highest accuracy in soil moisture predictions, with a 6 % error (RRMSE), but the difference from TippingBucket-DL was small (8 %). While the performance of stalk dry mass, LAI and water-use efficiency simulations were within the range found in literature, comprehensive field experiments showing significant impacts of drought on sugarcane growth are still lacking for a more rigorous evaluation. Both SWAP and tipping-bucket approaches showed higher robustness to soil data detail as compared to the Standalone method, which should be avoided when soil water is critical for sugarcane growth. The use of tipping-bucket method may still be preferred when the research goal is focused on crop growth and soil parameters are limited. SWAP-SAMUCA may provide an extended ability to represent agrohydrological processes in sugarcane plantations and process understanding.

Psychometric properties of the Arabic version of PHEEM applied on a sample of medical residents in Syria

BackgroundThe clinical learning environment (CLE) plays a crucial role in shaping the learning experiences and professional development of medical professionals. Understanding and optimising this environment is essential for improving doctors’ knowledge acquisition, clinical skills, and overall well-being. The development of the Postgraduate Hospital Educational Environment Measure (PHEEM) and its translation to numerous languages has been a milestone in clinical education. Even though PHEEM was recently translated into Arabic, its psychometric properties in this form remain unevaluated. Therefore, this study aims to conduct a comprehensive psychometric analysis of the Arabic version of the PHEEM questionnaire.MethodsThis is a cross-sectional questionnaire survey validation study. The defined population were medical residents in Damascus, Syria. A paper-based survey as well as an online-based one were conducted using several non-probability sampling methods namely, convenience, river and, snowball sampling between June 15, 2023, and June 21, 2023. Both exploratory (EFA) and confirmatory (CFA) factor analyses were conducted. Several psychometric criteria were applied including scree plot, eigenvalue > 1.5 and the ‘proportion of variance accounted for’ criterion.ResultsA total of 543 participants completed the questionnaire (56.9% female). Kaiser-Meyer-Olkin measure for sample adequacy was high (0.937) and the P-value for Bartlett’s test was < 0.001. EFA revealed five meaningful factors which were labelled: perception of teachers, learner’s engagement and social participation, external regulation, work culture, and living conditions. These factors had the following eigenvalues: 12.6, 2.18, 2.03, 1.86, and 1.41 respectively, with a total explained variance of 43.45%. Cronbach’s Alpha was 0.938. CFA confirmed the model structure of EFA (SRMR = 0.067 and RMSEA = 0.066). The Average Variance Explained (AVE) value of any given factor was > 0.7.DiscussionThe Arabic PHEEM inventory demonstrated satisfactory psychometric properties. The extracted domains are of theoretical relevance to the psychosocial-material conceptual framework for learning environment. Nonetheless, this validation was performed in the Syrian context; therefore, future studies in other Arabic countries are recommended to support the applicability of Arabic PHEEM in the wide Arab World.

Pedagogical sentiment analysis based on the BERT-CNN-BiGRU-attention model in the context of intercultural communication barriers.

Amid the wave of globalization, the phenomenon of cultural amalgamation has surged in frequency, bringing to the fore the heightened prominence of challenges inherent in cross-cultural communication. To address these challenges, contemporary research has shifted its focus to human-computer dialogue. Especially in the educational paradigm of human-computer dialogue, analysing emotion recognition in user dialogues is particularly important. Accurately identify and understand users' emotional tendencies and the efficiency and experience of human-computer interaction and play. This study aims to improve the capability of language emotion recognition in human-computer dialogue. It proposes a hybrid model (BCBA) based on bidirectional encoder representations from transformers (BERT), convolutional neural networks (CNN), bidirectional gated recurrent units (BiGRU), and the attention mechanism. This model leverages the BERT model to extract semantic and syntactic features from the text. Simultaneously, it integrates CNN and BiGRU networks to delve deeper into textual features, enhancing the model's proficiency in nuanced sentiment recognition. Furthermore, by introducing the attention mechanism, the model can assign different weights to words based on their emotional tendencies. This enables it to prioritize words with discernible emotional inclinations for more precise sentiment analysis. The BCBA model has achieved remarkable results in emotion recognition and classification tasks through experimental validation on two datasets. The model has significantly improved both accuracy and F1 scores, with an average accuracy of 0.84 and an average F1 score of 0.8. The confusion matrix analysis reveals a minimal classification error rate for this model. Additionally, as the number of iterations increases, the model's recall rate stabilizes at approximately 0.7. This accomplishment demonstrates the model's robust capabilities in semantic understanding and sentiment analysis and showcases its advantages in handling emotional characteristics in language expressions within a cross-cultural context. The BCBA model proposed in this study provides effective technical support for emotion recognition in human-computer dialogue, which is of great significance for building more intelligent and user-friendly human-computer interaction systems. In the future, we will continue to optimize the model's structure, improve its capability in handling complex emotions and cross-lingual emotion recognition, and explore applying the model to more practical scenarios to further promote the development and application of human-computer dialogue technology.

A High-Precision Identification Method for Maize Leaf Diseases and Pests Based on LFMNet under Complex Backgrounds.

Maize, as one of the most important crops in the world, faces severe challenges from various diseases and pests. The timely and accurate identification of maize leaf diseases and pests is of great significance for ensuring agricultural production. Currently, the identification of maize leaf diseases and pests faces two key challenges: (1) In the actual process of identifying leaf diseases and pests, complex backgrounds can interfere with the identification effect. (2) The subtle features of diseases and pests are difficult to accurately extract. To address these challenges, this study proposes a maize leaf disease and pest identification model called LFMNet. Firstly, the localized multi-scale inverted residual convolutional block (LMSB) is proposed to perform preliminary down-sampling on the image, preserving important feature information for the subsequent extraction of fine disease and pest features in the model structure. Then, the feature localization bottleneck (FLB) is proposed to improve the model's ability to focus on and locate disease and pest characteristics and to reduce interference from complex backgrounds. Subsequently, the multi-hop local-feature fusion architecture (MLFFA) is proposed, which effectively addresses the problem of extracting subtle features by enhancing the extraction and fusion of global and local disease and pest features in images. After training and testing on a dataset containing 19,451 images of maize leaf diseases and pests, the LFMNet model demonstrated excellent performance, with an average identification accuracy of 95.68%, a precision of 95.91%, a recall of 95.78%, and an F1 score of 95.83%. Compared to existing models, it exhibits significant advantages, offering robust technical support for the precise identification of maize diseases and pests.

Uncertainty analysis on long-term runoff projection from the Budyko framework and a conceptual hydrological model

ABSTRACT Runoff projections are subject to uncertainties related to model structure and parameters. This study aims to analyze uncertainties in long-term runoff estimations from an empirical (Budyko framework) and a conceptual hydrological model (MHD-INPE). Results indicate that both MHD-INPE and Budyko estimations tend to overestimate long-term runoff during years of recurring droughts. Pareto front solutions in MHD-INPE exhibited small uncertainties in long-term runoff estimations regarding parameter calibration (bias between 5 and 7%); differences were observed in low (bellow 5% variation) and high (bellow 10% variation) daily runoff. Related to model structure uncertainties, both models follow similar patterns and performance for a qualitative analysis. Budyko's future projections tend to exceed MHD-INPE's during high precipitation estimates, where at 2000 mm yearly precipitation the estimated runoff from Budyko tends to be 100 mm greater than the hydrological model. Under arid conditions Budyko tends to estimate smaller runoff than MHD-INPE due to variations in soil moisture and water storage not properly represented in Budyko's parameter. Although uncertainties were identified related to model complexity and calibrated parameters, higher uncertainties were identified as related to the climate models. Therefore, the Budyko method is a viable alternative for first-order analysis of long-term impacts.

Pinball-Huber boosted extreme learning machine regression: a multiobjective approach to accurate power load forecasting

Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method’s effectiveness.

Exploring the effects of Saorilao-4 on the gut microbiota of pulmonary fibrosis model rats based on 16S rRNA sequencing.

Pulmonary fibrosis (PF) is a progressive and incurable lung disease for which treatment options are limited. Here, we aimed to conduct an exploratory study on the effects of the Mongolian medicine Saorilao-4 (SRL) on the gut microbiota structure, species abundance, and diversity of a rat PF model as well as the mechanisms underlying such effects. Rat fecal samples were analyzed using 16S rRNA sequencing technology. Bioinformatic and correlation analyses were performed on microbiota data to determine significant associations. SRL substantially attenuated the adverse effects exerted by PF on the structure and diversity of gut microbiota while regulating its alpha and beta diversities. Linear discriminant analysis effect size enabled the identification of 62 differentially abundant microbial taxa. Gut microbiota abundance analysis revealed that SRL significantly increased the relative abundance of bacterial phyla such as Firmicutes and Bacteroidetes. Moreover, SRL increased the proportion of beneficial bacteria, such as Lactobacillus and Bifidobacteriales, decreased the proportion of pathogenic bacteria, such as Rikenellaceae, and balanced the gut microbiota by regulating metabolic pathways. SRL may attenuate PF by regulating gut microbiota. This exploratory study establishes the groundwork for investigating the metagenomics of PF.

Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble

AbstractSelecting an appropriate model for a catchment is challenging, and choosing an inappropriate model can yield unreliable results. The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. In this study we benchmark AMSI's capabilities in two ways, using 12 hydro‐climatically diverse Model Parameter Estimation Experiment catchments. First, we calibrate parameter values for 7,488 different model structures and test AMSI's ability to find the best‐performing models in this set. Second, we compare the performance of these 7,488 models and AMSI's selection to the performance of 45 commonly used, structurally more diverse, conceptual models. In both cases, we quantify model accuracy (through the Kling‐Gupta Efficiency) and model adequacy (through various hydrologic signatures). AMSI effectively identifies high‐accuracy models among the 7,488 options, with Kling‐Gupta‐Efficiency scores comparable to the best among the 45 models. However, model adequacy remains poor for the accurate models, regardless of the selection method. In nine of the tested catchments, none of the most accurate models replicate observed signatures with less than 50% errors; in the remaining three catchments, only a handful of models do so. This paper thus provides strong empirical evidence that relying on aggregated efficiency metrics is unlikely to result in hydrologically adequate models, no matter how the models themselves are selected. Nevertheless, AMSI has been shown to effectively search the model hypothesis space it was given. Combined with an improved calibration approach it can therefore offer new ways to address the challenges of model structure selection.

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.