Level Of Accuracy Research Articles

Classified dataset, regression and machine learning modeling for prediction of phase transformation temperatures in steels

The prediction of the characteristic Martensite Start (Ms) temperature and Austenitic Nose Tip Temperature (ANTT) in steels is of scientific and technological importance; however, it faces significant challenges due to multiphysical complexity.In this study, we introduced a structured framework for data classification and hierarchical iterations aimed at predicting Ms (Martensite start temperature) and ANTT (Austenite non-transforming temperature). This framework was incorporated into two optimization models, leading to enhancements in accuracy, extrapolation capabilities, and generalization performance. First, we classified the collected Ms datasets hierarchically based on the alloying elements presented in steels, including carbon, austenite stabilizers, non-austenitization elements, and data credibility. Regression analyses of Ms temperatures concerning chemical compositions were then carried out using phenomenological variables from binary systems to multi-component systems in alignment with the spirit of CALPHAD modeling, which is renowned for its robust extrapolation abilities. By iteratively fitting the hierarchically classified datasets and implementing hierarchical iterations, we developed the CALPHAD-guided phenomenological variable (CGPV) Ms regression model. This model achieved improved accuracy levels, with R2 values of 0.9 for training and 0.87 for testing, surpassing most conventional regression models that do not account for compositional interactions. Furthermore, the CALPHAD-guided machine learning (CGML) model, constructed based on the classified datasets and hierarchical iterations but without utilizing phenomenological variables, demonstrated strong performance with R2 values of 0.98 and 0.86 for training and testing, respectively. The CGML model was demonstrated not only to reliably filter out problematic data in a dataset but also to unveil the unnoticed coupling between carbon and other alloying elements on Ms. Finally, the CGML method has been readily transferred to predict ANTT with high accuracy as well.

The Perceptions of Nuclear War: The Imbalanced Co-Orientation Between Russian and American Youth

On February 24, 2022, Russia attacked its neighboring country of Ukraine. From the early days of what Russian President Putin called a special military operation, fears mounted that the conflict may grow into World War III, effectively ending the world in nuclear Armageddon. This study compares the current views of American and Russian youths on the issues of global nuclear war and nuclear weapons, as well as explores their inclinations toward supporting a first nuclear strike, nuclear retaliation, and their willingness to make personal sacrifices in the event of a nuclear conflict. Drawing upon the theoretical framework of co-orientation theory, the study methodically assesses the levels of agreement, congruency, and accuracy for the populations of both countries on the issues related to the nuclear conflict. The results show that American and Russian youths are currently in a state of imbalanced co-orientation—a newly defined state in the co-orientation theory—when it comes to their opinions on nuclear war and nuclear weapons. In conclusion, the study underscores the urgent need to address this lack of alignment between the two countries, given the potentially catastrophic consequences of nuclear warfare. Therefore, the study draws upon the co-orientation theory’s insights and formulates strategic public diplomacy recommendations, designed to cultivate mutual understanding, and promote constructive dialogue between the people of the United States of America and the Russian Federation.

Predicting Heat Transfer Enhancement with Twisted Tape Inserts Using Fuzzy Logic Techniques in Heat Exchangers

Fuzzy logic, introduced by Lotfi Zadeh in 1965, is a powerful method for modelling complex experiments. This study utilizes fuzzy logic to simulate and predict heat transfer in a double-pipe heat exchanger equipped with wavy inserts. The inserts, in the form of twisted tapes, have varying twist ratios (TR=9, 7, 6). The study investigates a range of Reynolds numbers (Re) from 6000 to 18000, with friction factors ranging from 0.03620 to 0.08231, and Nusselt numbers (Nu) between 66.13 and 253.28. The results for different twist ratios are compared to the ideal case. The experimental results indicate that the highest heat transfer occurs with a twist ratio of 6, leading to a significant increase of 162% in the Nusselt number and a 36.21% rise in the friction factor compared to the ideal scenario. In the fuzzy logic framework, the input variables are the twist ratio (Tr), temperature, and Reynolds number (Re), while the output variables are the friction factor (f) and Nusselt number (Nu). The study demonstrates that the Mamdani fuzzy inference system is an exceptionally effective tool for predicting experimental outcomes, given its low error rate. Upon analysing the data, it is observed that the graphs plotting the Nusselt number versus Reynolds number and friction factor versus Reynolds number, derived from both experimental data and the fuzzy logic model, exhibit nearly identical trends with a margin of error of just 3%. This high level of accuracy underscores the reliability of the fuzzy logic model in replicating the experimental results.

Assessing the Tropospheric Impacts on Positioning Accuracy Using IGS02 Real-Time Service Data Versus Long-Convergence Static PPP in Gwagwalada, Abuja, Nigeria

Abstract: The International Association of Geodesy (IAG) has established the International GNSS Service-Real Time Service (IGS-RTS) as a service provider, offering real-time access to precise products like orbits, clock corrections, and code biases regarding satellite navigation and positioning system. These products serve as an alternative to ultra-rapid products in real-time applications. The performance of these products is assessed through daily statistics from Analysis Centres, which compare them to IGS rapid products. However, the accuracy of GPS real-time corrections for satellites during eclipsing periods was slightly reduced, attributed to the impact of environmental factors on the services. The speed of GNSS signals can be impacted by various atmospheric factors, including troposphere, temperature, pressure, and humidity, resulting in positioning inaccuracies and even giving rooms for signal jamming and hijacking. However, the unique weather conditions prevalent in the African continent are often overlooked during the development of error mitigation parameters and algorithms, which can lead to reduced accuracy in GNSS positioning in a region like Nigeria. The purpose of this study is to estimate the tropospheric impact on positioning with IGS02 Real Time Service data compared to long convergence Static-PPP in Gwagwalada Area Council, Abuja, Nigeria. The study adopts the determination of the GNSS Static observations (minimum of two hours per session) on the chosen stations as standard, determination of the IGS-RTS data observations using RTKLIB software; observations were done with IGS-RTS data stream of IGS02 and statistical tests were performed. The GNSS Static coordinates and IGS-RTS coordinates were validated from error due to troposphere, temperature, pressure, etc., with the computation of their mean horizontal and vertical uncertainties which have a similar level of accuracy but slightly differ at centimeter levels. The result shows the Root Mean Square (RMS) Error discrepancy of IGS02 at the Wet and Dry season, as compared with the Static-PPP was within 0.065(m) and 0.046(m) respectively.

Advancing student outcome predictions through generative adversarial networks

Predicting student outcomes is essential in educational analytics for creating personalised learning experiences. The effectiveness of these predictive models relies on having access to sufficient and accurate data. However, privacy concerns and the lack of student consent often restrict data collection, limiting the applicability of predictive models. To tackle this obstacle, we employ Generative Adversarial Networks, a type of Generative AI, to generate tabular data replicating and enlarging the dimensions of two distinct publicly available student datasets. The ‘Math dataset’ has 395 observations and 33 features, whereas the ‘Exam dataset’ has 1000 observations and 8 features. Using advanced Python libraries, Conditional Tabular Generative Adversarial Networks and Copula Generative Adversarial Networks, our methodology consists of two phases. First, a mirroring approach where we produce synthetic data matching the volume of the real datasets, focusing on privacy and evaluating predictive accuracy. Second, augmenting the real datasets with newly created synthetic observations to fill gaps in datasets that lack student data. We validate the synthetic data before employing these approaches using Correlation Analysis, Density Analysis, Correlation Heatmaps, and Principal Component Analysis. We then compare the predictive accuracy of whether students will pass or fail their exams across original, synthetic, and augmented datasets. Employing Feedforward Neural Networks, Convolutional Neural Networks, and Gradient-boosted Neural Networks, and using Bayesian optimisation for hyperparameter tuning, this research methodically examines the impact of synthetic data on prediction accuracy. We implement and optimize these models using Python. Our mirroring approach aims to achieve accuracy rates that closely align with the original data. Meanwhile, our augmenting approach seeks to reach a slightly higher accuracy level than when solely learning from the original data. Our findings provide actionable insights into leveraging advanced Generative AI techniques to enhance educational outcomes and meet our objectives successfully.

Surface Reconstruction from SLAM-Based Point Clouds: Results from the Datasets of the 2023 SIFET Benchmark

The rapid technological development that geomatics has been experiencing in recent years is leading to increasing ease, productivity and reliability of three-dimensional surveys, with portable laser scanner systems based on Simultaneous Localization and Mapping (SLAM) technology, gradually replacing traditional techniques in certain applications. Although the performance of such systems in terms of point cloud accuracy and noise level has been deeply investigated in the literature, there are fewer works about the evaluation of their use for surface reconstruction, cartographic production, and as-built Building Information Model (BIM) creation. The objective of this study is to assess the suitability of SLAM devices for surface modeling in an urban/architectural environment. To this end, analyses are carried out on the datasets acquired by three commercial portable laser scanners in the context of a benchmark organized in 2023 by the Italian Society of Photogrammetry and Topography (SIFET). In addition to the conventional point cloud assessment, we propose a comparison between the reconstructed mesh and a ground-truth model, employing a model-to-model methodology. The outcomes are promising, with the average distance between models ranging from 0.2 to 1.4 cm. However, the surfaces modeled from the terrestrial laser scanning point cloud show a level of detail that is still unmatched by SLAM systems.

Analisis Karakteristik Material Baja dengan Metode Digital Image Correlation (DIC)

The use of steel materials in building construction opens new opportunities for sustainable development, as steel exhibits corrosion resistance, durability, and reliability in terms of strength and ductility. Digital Image Correlation (DIC) is non-contact technique in which digital images of the surface of a test object are captured using high-resolution cameras. This study conducted measurements of strain distribution on the specimen's surface using the DIC method throughout the entire tensile testing process. The study particularly focuses on examining changes in strain distribution during the melting phase and the local deformation phase leading to fracture. In this research, a comparison will be made between the load-displacement curves obtained from experimental laboratory testing and the results analyzed using the DIC method for SS400-grade steel material. Based on the results of the tensile test and DIC analysis that have been conducted, conclusions have been drawn in the research. The tensile test results of SS400 steel material with a thickness of 6 mm, 8 mm, 10 mm, and 12 mm meet the quality requirements in the tested specification standards, and the results of the force-displacement curve between the experimental test results and the DIC method obtained a minimum deviation with a value below 10%,. Therefore, it can be concluded that the DIC method exhibits a reasonably good level of accuracy, making it suitable for validating the results of experimental tests.

Implementation of the Smart Indonesia Card Scholarship (KIP) Acceptance Using the K-NN Method (Case Study: Politeknik Siber Cerdika Internasional)

This study discusses the implementation of the K-Nearest Neighbor (K-NN) method in the process of receiving the Smart Indonesia Card (KIP) scholarship at the Cerdika International Cyber Polytechnic. The main purpose of this study is to improve accuracy and efficiency in the selection of KIP scholarship recipients. The K-NN method was chosen because of its ability to classify data based on the proximity of features between samples. This research involves analyzing data on prospective scholarship recipients which includes variables such as academic achievement, economic conditions, and extracurricular activities. The results of the implementation of the K-NN method show that this method can be used as an effective tool in the selection process of KIP scholarship recipients, with a fairly high level of accuracy compared to traditional methods. This finding is expected to help the Polytechnic in increasing transparency and fairness in providing scholarships.

Implementation of Extreme Learning Machine Method with Particle Swarm Optimization to Classify of Chronic Kidney Disease

Kidney Disease (CKD) appears as a pathological condition due to infection of the kidneys and blockages due to the formation of kidney stones. In the Indonesian context, kidney disease is the second most common disease after heart disease based on BPJS Health data. Notably, in this scenario, medical practitioners and individuals with specialized knowledge in the field are still faced with challenges in effectively classifying CKD cases, thereby making them vulnerable to erroneous diagnostic conclusions. The main objective underlying this particular research effort revolves around increasing the level of accuracy that characterizes the CKD classification process by orchestrating the incorporation of Particle Swarm Optimization (PSO) techniques into the operational framework of Extreme Learning Machines (ELM) with the aim of ensuring optimal results. Configuration of input weights and critical biases to achieve superior diagnostic results. The results obtained from the investigation process include many numerical parameters including but not limited to determining the ideal number of hidden nodes set at 11, population size 80, identification of the most preferred number of iterations denoted by the Best value of 20, aggregate inertia weight assessed at 0.5, along with the constants 1 (c1) and 2 (c2) each registering a value of 1, culminating in the achievement of an accuracy metric pegged at an impressive level of 98.50%. Consequently, the implications obtained from this empirical investigation strengthen the assertion that the use of PSO optimization strategies within the operational framework of ELM has the potential to yield major advances in the classification evaluation domain related to CKD diagnosis.

Performance of ChatGPT-3.5 and GPT-4 in national licensing examinations for medicine, pharmacy, dentistry, and nursing: a systematic review and meta-analysis

BackgroundChatGPT, a recently developed artificial intelligence (AI) chatbot, has demonstrated improved performance in examinations in the medical field. However, thus far, an overall evaluation of the potential of ChatGPT models (ChatGPT-3.5 and GPT-4) in a variety of national health licensing examinations is lacking. This study aimed to provide a comprehensive assessment of the ChatGPT models’ performance in national licensing examinations for medical, pharmacy, dentistry, and nursing research through a meta-analysis.MethodsFollowing the PRISMA protocol, full-text articles from MEDLINE/PubMed, EMBASE, ERIC, Cochrane Library, Web of Science, and key journals were reviewed from the time of ChatGPT’s introduction to February 27, 2024. Studies were eligible if they evaluated the performance of a ChatGPT model (ChatGPT-3.5 or GPT-4); related to national licensing examinations in the fields of medicine, pharmacy, dentistry, or nursing; involved multiple-choice questions; and provided data that enabled the calculation of effect size. Two reviewers independently completed data extraction, coding, and quality assessment. The JBI Critical Appraisal Tools were used to assess the quality of the selected articles. Overall effect size and 95% confidence intervals [CIs] were calculated using a random-effects model.ResultsA total of 23 studies were considered for this review, which evaluated the accuracy of four types of national licensing examinations. The selected articles were in the fields of medicine (n = 17), pharmacy (n = 3), nursing (n = 2), and dentistry (n = 1). They reported varying accuracy levels, ranging from 36 to 77% for ChatGPT-3.5 and 64.4–100% for GPT-4. The overall effect size for the percentage of accuracy was 70.1% (95% CI, 65–74.8%), which was statistically significant (p < 0.001). Subgroup analyses revealed that GPT-4 demonstrated significantly higher accuracy in providing correct responses than its earlier version, ChatGPT-3.5. Additionally, in the context of health licensing examinations, the ChatGPT models exhibited greater proficiency in the following order: pharmacy, medicine, dentistry, and nursing. However, the lack of a broader set of questions, including open-ended and scenario-based questions, and significant heterogeneity were limitations of this meta-analysis.ConclusionsThis study sheds light on the accuracy of ChatGPT models in four national health licensing examinations across various countries and provides a practical basis and theoretical support for future research. Further studies are needed to explore their utilization in medical and health education by including a broader and more diverse range of questions, along with more advanced versions of AI chatbots.

Optimized clustering-based fusion for skin lesion image classification: Leveraging marine predators algorithm

This manuscript presents a comprehensive approach to enhance the accuracy of skin lesion image classification based on the HAM10000 and BCN20000 datasets. Building on prior feature fusion models, this research introduces an optimized cluster-based fusion approach to address limitations observed in our previous methods. The study proposes two novel feature fusion strategies, KFS-MPA (using K-means) and DFS-MPA (using DBSCAN), for skin lesion classification. These approaches leverage optimized clustering-based deep feature fusion and the marine predator algorithm (MPA). Ten fused feature sets are evaluated using three classifiers on both datasets, and their performance is compared in terms of dimensionality reduction and accuracy improvement. The results consistently demonstrate that the DFS-MPA approach outperforms KFS-MPA and other compared fusion methods, achieving notable dimensionality reduction and the highest accuracy levels. ROC-AUC curves further support the superiority of DFS-MPA, highlighting its exceptional discriminative capabilities. Five-fold cross-validation tests and a comparison with the previously proposed feature fusion method (FOWFS-AJS) are performed, confirming the effectiveness of DFS-MPA in enhancing classification performance. The statistical validation based on the Friedman test and Bonferroni-Dunn test also supports DFS-MPA as a promising approach for skin lesion classification among the evaluated feature fusion methods. These findings emphasize the significance of optimized cluster-based deep feature fusion in skin lesion classification and establish DFS-MPA as the preferred choice for feature fusion in this study.

Using the Single-Term Haar Wavelet to Solve the State Variable Models of nth-Order Circuits

This study is dedicated to investigating the utilization of the single-term Haar wavelet approach for solving a state variable model of nth-order circuits. We provide the matrix calculations in a simplified manner to facilitate comprehension. Furthermore, a numerical mathematical illustration is presented, demonstrating the application of this technique to solving two first-order differential equations with both constant and variable coefficients. The study’s findings unequivocally demonstrate that the suggested approach surpasses previous methods, providing greater levels of accuracy and precision. Moreover, the single-term Haar wavelet approach is exceptionally user-friendly and efficient, necessitating only a minimal amount of processing resources.

Classification of Unisba Students' Graduation Time using Support Vector Machine Optimized with Grid Search Algorithm

Support Vector Machine is a classification method that finds the optimal hyperplane to separate two data classes. SVM has much better generalization performance than other methods. However, SVM needs to improve in determining hyperparameter values. Therefore, parameter optimization is necessary to determine the optimal hyperparameter value. Grid search is one of the parameter optimization methods that can improve the quality of SVM models. This study aims to assess the level of accuracy in predicting student graduation times by using five features that affect it. This study shows that the resulting SVM model optimized with the Grid Search Algorithm is quite consistent and prevents overfitting. By utilizing the results of SVM modelling, UNISBA is expected to improve the quality of graduates. The risk of delays in graduation can be considered early by paying attention to the background and achievements of students

Small Area Estimation of Maluku and Papua Island Child Poverty Levels in 2023

Child welfare issues such as child poverty pose a challenge for Indonesia. The provinces in Maluku and Papua have the highest rates of child poverty. Data on child poverty at regencies/municipalities level is needed to address this issue through targeted policies. The direct estimations have a Relative Standard Error (RSE) value of more than 25 percent, necessitating the use of an indirect method, Small Area Estimation (SAE). This study aims to compare the results of indirect estimates of the percentage of children aged 0-17 living in poverty at the regencies/municipalities level in Maluku and Papua using SAE Empirical Best Linear Unbiased Prediction (EBLUP) and Hierarchical Bayes Beta (HB Beta) methods. Susenas KOR March 2023 data is used to produce direct estimates, while Podes 2021 data is used to form auxiliary variables. The results indicate that the SAE HB Beta method provides estimates with better RSE compared to SAE EBLUP. All regencies/municipalities in the Maluku and Papua have a fairly good level of accuracy.

Improved determination of the S-wave velocity of rocks in dry and saturated conditions: Application of machine-learning algorithms

The determination of S-wave velocity (Vs) is of significant importance in various engineering disciplines, including mining, civil, and geotechnical engineering. It is beneficial to indirectly determine Vs under both dry and saturated conditions and to understand its relationship with influencing input variables: coring depth (H), durability index (DI), water content (Wa), dry density (ρd), saturated density (ρs), and porosity (n). In this study, we evaluate these relationships using three multiple machine-learning algorithms (MLAs): artificial neural network (ANN), fuzzy inference system (FIS), and gene expression programming (GEP), alongside a linear regression method (LRM) and predict both dry S-wave velocity (Vs-dry) and saturated S-wave velocity (Vs-sat) of rocks. The research involves the analysis of 90 datasets derived from samples of schist, phyllite, and sandstone rocks collected from Azad and Bakhtiari dam sites in Iran. The diversity of these datasets is a key advantage of this study, providing a solid foundation for models training and testing while enhancing the models’ generalizability. Model optimization techniques are employed in the Python, MATLAB, GenXProTools, and SPSS environments to identify the most effective versions of ANN, FIS, GEP, and LRM models, respectively. The prediction performance analysis reveals that all applied models yield acceptable levels of accuracy for predicting Vs-dry and Vs-sat. However, GEP emerges as the best model for predicting both Vs-dry and Vs-sat. The ANN and FIS models also achieve high levels of accuracy, while LRM performs comparatively less well. Additionally, sensitivity analysis conducted using the cosine amplitude method (CAM) highlights the influence of different variables on Vs-dry and Vs-sat. The ρd is found to be the most influential parameter on Vs-dry, whereas DI exhibits the least impact. Conversely, the ρs significantly affects Vs-sat, while Wa shows the lowest impact. The exceptional performance of these proposed MLAs confirms their applicability in real-world rock engineering and geotechnics projects, offering precise determination of Vs. The diversity of studied rock types and datasets, along with the use of cost-effective and easy measurable inputs, the determination of Vs in both dry and saturated status, and the application of robust MLAs for Vs determination are the main novelties of this study. However, further researches involving additional datasets and more rock types are required to validate these findings.

High-Efficiency Forward Modeling of Gravitational Fields in Spherical Harmonic Domain with Application to Lunar Topography Correction

Gravity forward modeling as a basic tool has been widely used for topography correction and 3D density inversion. The source region is usually discretized into tesseroids (i.e., spherical prisms) to consider the influence of the curvature of planets in global or large-scale problems. Traditional gravity forward modeling methods in spherical coordinates, including the Taylor expansion and Gaussian–Legendre quadrature, are all based on spatial domains, which mostly have low computational efficiency. This study proposes a high-efficiency forward modeling method of gravitational fields in the spherical harmonic domain, in which the gravity anomalies and gradient tensors can be expressed as spherical harmonic synthesis forms of spherical harmonic coefficients of 3D density distribution. A homogeneous spherical shell model is used to test its effectiveness compared with traditional spatial domain methods. It demonstrates that the computational efficiency of the proposed spherical harmonic domain method is improved by four orders of magnitude with a similar level of computational accuracy compared with the optimized 3D GLQ method. The test also shows that the computational time of the proposed method is not affected by the observation height. Finally, the proposed forward method is applied to the topography correction of the Moon. The results show that the gravity response of the topography obtained with our method is close to that of the optimized 3D GLQ method and is also consistent with previous results.

The effect of sports expertise on the performance of orienteering athletes’ real scene image recognition and their visual search characteristics

As a sport conducted in dynamically changing natural environments, orienteering places high demands on athletes' cognitive processing abilities and visual search efficiency. However, previous studies on orienteering have been primarily limited by the use of fixed stimulus materials on computer screens, which are unable to fully simulate authentic sports scenarios. To better understand the sports expertise of orienteering athletes in terms of their real scene image recognition performance and visual search characteristics, this study recruited 40 orienteering athletes, both experts and novices, as participants. By utilizing eye-tracking technology and setting observation points in real-world scenarios to conduct image recognition task tests, the ecological validity of the experiment was further enhanced. The results showed that the experts demonstrated a high level of accuracy and a short response time, with visual search characteristics including few saccade counts, low fixation frequency, concentrated fixation points, simple and clear fixation paths, and higher visual search efficiency. This study further reveals that long-term specialized training will lead to the formation of a unique cognitive structure related to the specific knowledge and long-term memory required by expert orienteering athletes, thereby promoting the development of expert advantage.

Simplification and improvement of cable tension identification based on effective vibration length estimated from multiple sensors located near one support

Effective vibration length (EVL) has been proposed and found able to accurately identify tension force in short cables with unknown boundaries. However, EVL estimation accuracy is degraded when measurements are distributed only near the cable-deck end. This study investigates the EVL and modal characteristics of cables under different factors using a finite element model. The corresponding conclusions are then utilized to simplify and improve the original method. Method 1 is proposed by simplifying the original method using a fixed EVL for different modes. Method 2 is developed by enhancing Method 1 with additional tests where masses are attached on the cable near the sensors. In numerical experiments with 1 % random noise, the proposed methods achieve 70 %–80 % of results with errors less than 5 % while 1 % of results from the original method achieve this level of accuracy. Full-scale cable experiments have further validated the effectiveness of the proposed methods.

Load resistance of masonry infilled panels for steel frames to mitigate progressive collapse caused by middle column missing

The load resistance of masonry infilled panels in upgrading the steel frame to mitigate progressive collapse was quantified experimentally and analytically in the current study. Four 1/2-scale steel subframes were tested, including two bare subframes without infilled panels for reference and two infilled subframes to assess the effects of infilled panels. Two types of connection were investigated in current study: welded connection and top and seat angle connection. The test result showed that infilled panels provided a greater improvement in initial stiffness and peak load of the steel subframe with top and seat angle connections compared to the infilled steel subframe with welded connections. On the premise that the connections retained their integrity, the strut mechanism of infilled panels could change from a diagonal strut mechanism forming in the whole panel to an off-diagonal strut mechanism formed in the remaining intact corner region with increasing vertical deflection. A novel equivalent multi-strut modelling method was proposed for the prediction of the infilled panel behavior with an acceptable level of accuracy. This model incorporates the shifts in the position of plastic hinges, which were influenced by the specific types of connection and the load-resisting mechanisms of the infilled panel. The analysis result demonstrated that the model slightly overestimated the load resistance of infilled panels within the steel frame with simple connections and slightly underestimated the load resistance of infilled panels within steel frame with semi-rigid or rigid connections.

Short-term power load forecasting based on Seq2Seq model integrating Bayesian optimization, temporal convolutional network and attention

Power load forecasting is of great significance to the electricity management. However, extant research is insufficient in comprehensively combining data processing and further optimization of existing prediction models. Therefore, this paper propose an improved power load prediction methods from two aspects: data processing and optimization of Sequence to Sequence (Seq2Seq) model. Firstly, in the data processing, Extreme Gradient Boosting (XGBoost) is adopted to eliminate the redundant features for feature extraction. Meanwhile, Successive Variational Mode Decomposition (SVMD) is employed to solve the unsteadiness and nonlinearities present in electricity data during the decomposition process. Secondly, the Seq2Seq model is selected and improved with a variety of machine learning methods. Specifically, input data features are extracted using Convolutional Neural Networks (CNN), enhancing the decoder's focus on vital information with the Attention mechanism (AM). Temporal Convolutional Network (TCN) serves as both the encoder and decoder of Seq2Seq, with further optimization of the model parameters through the Bayesian Optimization (BO) algorithm. Finally, the cases of two real power market datasets in Switzerland and Singapore illustrate the efficiency and superiority of proposed hybrid forecasting method. Through a comprehensive comparison and analysis with the other six models and four commonly used evaluation metrics, it is evident that the proposed method excels in performance, attaining the highest level of prediction accuracy, with the highest accuracy rate of 95.83 %. Consequently, it exhibits significant practical utility in the realm of power load forecasting.