Average Absolute Error Values Research Articles

Performance of repair pre-damaged RC columns using steel straps tensioning techniques (SSTT) confinement: experimental data and modelling

PurposeA model that extends study parameters to predict repaired column behaviour is efficient. Three-dimensional nonlinear finite element models were created in ABAQUS to simulate steel strap confinement with inclusion of pre-damaged levels.Design/methodology/approachExperimental and analytical studies demonstrate that restored reinforced concrete (RC) columns usually crush at mid-height under axial compressions. Numerical models verified RC column load-deformation. Although some specimens have considerable column stiffness differences, a numerical model based on statistical analysis matches experimental test results.FindingsIt shows that, finite element model exhibited a tendency to overestimate the stiffness of the columns, with an average absolute error (AAE) of 23.1%. The validation results indicate that the AAE values for strength and ductility were 15.1% and 12.3%. It has been demonstrated that the combination of strength and ductility is capable of yielding predictions with an error rate of approximately 20%. A parametric study focused on finite element model-predicted load bearing capacity reduction.Originality/valueA numerical analysis employing finite element modelling has been formulated to investigate the behaviour of confined columns. The model underwent validation through comparison with the experimental results. The validated model is utilised to perform additional parametric investigations on the confined column.

A short video sentiment analysis model based on multimodal feature fusion

With the development of the internet, the number of short video platform users has increased quickly. People's social entertainment mode has gradually changed from text to short video, generating many multimodal data. Therefore, traditional single-modal sentiment analysis can no longer fully adapt to multimodal data. To address this issue, this study proposes a short video sentiment analysis model based on multimodal feature fusion. This model analyzes the text, speech, and visual content in the video. Meanwhile, the information of the three modalities is integrated through a multi-head attention mechanism to analyze and classify emotions. The experimental results showed that when the training set size was 500, the recognition accuracy of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.96. The F1 score was 98, and the average absolute error value was 0.21. When the validation set size was 400, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 2.1 s. When the iterations were 60, the recognition time of the multimodal sentiment analysis model based on modal contribution recognition and multi-task learning was 0.9 s. The experimental results show that the proposed multimodal sentiment analysis model based on modal contribution recognition and multi-task learning has good model performance and can accurately identify emotions in short videos.

Promoting low carbon construction using alkali-activated materials: A modeling study for strength prediction and feature interaction

Abstract In place of Portland cement concrete, alkali-activated materials (AAMs) are becoming more popular because of their widespread use and low environmental effects. Unfortunately, reliable property predictions have been impeded by the restrictions of conventional materials science methods and the large compositional variability of AAMs. A support vector machine (SVM), a bagging regressor (BR), and a random forest regressor (RFR) were among the machine learning models developed in this study to assess the compressive strength (CS) of AAMs in an effort to gain an answer to this topic. Improving predictions in this crucial area was the goal of this study, which used a large dataset with 381 points and eight input factors. Also, the relevance of contributing components was assessed using a shapley additive explanations (SHAP) approach. In terms of predicting AAMs CS, RFR outperformed BR and SVM. Compared to the RFR model’s 0.96 R 2, the SVM and BR models’ R 2-values were 0.89 and 0.93, respectively. In addition, the RFR model’s greater accuracy was indicated by an average absolute error value of 4.08 MPa compared to the SVM’s 6.80 MPa and the BR’s 5.83 MPa, which provided further proof of their validity. According to the outcomes of the SHAP research, the two factors that contributed the most beneficially to the strength were aggregate volumetric ratio and reactivity. The factors that contributed the most negatively were specific surface area, silicate modulus, and sodium hydroxide concentration. Using the produced models to find the CS of AAMs for various input parameter values can help cut down on costly and time-consuming laboratory testing. In order to find the best amounts of raw materials for AAMs, academics and industries could find this SHAP study useful.

Calculation and Comparison of Water Saturation in Carbonate Formation by Different Methods

Water saturation is an important governing quality and quantity of the hydrocarbon evaluation, as reserve estimation. The challenge of the reservoir engineer is to resolve and understand the differences distribution of water saturation content by using different techniques. However, though out this work illustrate variety models to calculate water saturation which is compared with core measured. Therefore, water saturation is calculated using different models; Archie, Simandoux, Indonesia which are compared with Leverett J-Function (capillary pressure measurements). Archie's parameters of the studied reservoir rock; Cementation Factor (m) and Saturation Exponent (n) are derived from the log data used. Error of the water saturation estimation may significantly impose bias in the estimation of both quantities. In spite of, there are various factors affecting potential error in water saturation estimation, such as, average permeability, average porosity and Archie's parameters are used. Consequently, the comparison between results indicates that, Indonesia and Simandoux models are show the smallest average absolute error values, where Archie's model common one used for water saturation in Carbonate rocks. Three wells are selected to present his comparison.

High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algorithm for optimizing the hybrid kernel least squares support vector regression machine used in lithium-ion battery SOH prediction. This research extracted four key health features from the raw data of each battery in the Cycle dataset, which is publicly accessible. Data preprocessing of health features involved Pearson correlation analysis and Hampel filtering techniques. The framework of least squares support vector regression constructs a hybrid kernel function of polynomial kernel function and radial basis function. The integration of differential evolution and the law of survival of the fittest into the grey wolf algorithm enhances its optimization ability. The improved grey wolf algorithm optimizes the parameters of the hybrid kernel least squares support vector regression machine, improving the accuracy and robustness of the model. After data validation, it is known that the optimal average absolute error value predicted by the model can reach 0.32 %. This indicates that the proposed method is effective and feasible.

VR interactive input system based on INS and binocular vision fusion

In virtual reality interactive input systems, real-time and accurate spatial position and pose measurement is key to achieving a natural user interface. This study explores the application of inertial measurement units and binocular vision fusion technology in virtual reality interactive input systems, with the aim of improving the tracking accuracy of the system through optimized pose models and visual algorithms. A virtual reality measurement technology that integrates inertial measurement units and binocular vision is proposed by using an improved Kalman filtering algorithm to process inertial measurement units data, and combining it with the SURF algorithm optimized binocular vision system. Experimental results showed that fusion technology could reduce sensor noise and bias, improve the accuracy of pose estimation, and promote stable and robust motion tracking in virtual reality systems. In the angle range of -90 ° to 90 °, the average absolute error of the fusion system compared to the simple inertial measurement units pose calculation decreased from 3.371 ° to 1.369 ° In the experiment of measuring distance with a binocular vision system, the average absolute error value decreased to 1.532 mm, and the error range of the marker within the distance range of 500 mm to 650 mm was controlled within -1.3 mm to 1.2 mm. This study provides an effective solution for achieving high-precision virtual reality interactive input systems and is meaningful for the advancement of virtual reality technology.

Effect of feedstock properties on the kinetics of hydrocracking of heavy oils

The ability to predict changes in selectivity and quality of products of catalytic hydrocracking reaction is essential for the performance and economics of the process due to the variation in properties of the fractions derived from heavy crude oils. The influence of different hydrocarbon feedstocks on hydrocracking reaction kinetics was analyzed using detailed experimental data reported in the literature. A six–lump kinetic model in the presence and absence of diverse catalysts was used. For the predictions of the pseudocomponent yields using the estimated kinetic parameters, global average absolute error values less than 4.3 % concerning the experimental data were obtained. Clear trends are observed between the values of some reaction rate coefficients and properties of the diverse feedstocks. The analysis of the effect of feedstock properties on the kinetic parameters indicated that streams with high API and low H/C ratio have higher global reaction rate coefficients of vacuum residue conversion, observing an increase of up to three–fold the coefficient values concerning the rise of asphaltene content. The contents of carbon residue and sulfur influence the performance of certain reaction pathways and the production of coke for the different feedstocks studied and types of catalysts.

Mathematical models for predicting the higher heating value and ultimate analysis of biomass

The higher heating value (HHV) and elemental composition are important properties of biomass and they are necessary to perform computer simulations of the biomass thermal conversion processes. Both properties require equipment to measure that can be expensive, while the proximate analysis required equipment that is typically found in a laboratory. This work presents a general correlation, based on proximate analysis of biomass, to calculate elemental composition and two new correlations, based on proximate and ultimate analyses of biomass and char used for prediction of the higher heating value. The correlations based on ultimate analysis and proximate analysis show an average absolute error (AAE) of 5.59% and 6.81%, respectively, an average bias error (ABE) of 0.17% and 1.52%, a mean bias error (MBE) of − 0.09 and − 0.06, and root mean square error (RMSE) of 1.52 and 1.98 concerning the value of HHV. To calculate the elemental composition from proximate analysis, the correlations were proposed of C, H and O. The AAE of these correlations were 3.86%, 7.49%, and 5.68%, the ABE were − 1.10%, − 0.22%, − 2.23%, the MBE were 0.58, 0.06, and 1.14 while the RMSE were 2.65, 0.6, 3.45 with respect to measured values of C, H, O, respectively. The AAE values of the correlation proposed in this study using ultimate analysis are lower than those found for the other studied correlations.

Application of Mamdani Fuzzy Inference System in Poultry Weight Estimation.

Traditional manual weighing systems for birds on poultry farms are time-consuming and may compromise animal welfare. Although automatic weighing systems have been introduced as an alternative, they face limitations in accurately estimating the weight of heavy birds. Therefore, exploring alternative methods that offer improved efficiency and precision is necessary. One promising solution lies in the application of AI, which has the potential to revolutionize various aspects of poultry production and management, making it an indispensable tool for the modern poultry industry. This study aimed to develop an AI approach based on the FL model as a viable solution for estimating poultry weight. By incorporating expert knowledge and considering key input variables such as indoor temperature, indoor humidity, and feed consumption, FL-based models were developed with different configurations using Mamdani inferences and evaluated across eight different rearing periods in Samsun, Türkiye. This study's results demonstrated the effectiveness of FL-based models in estimating poultry weight. The models achieved varying average absolute error values across different age groups of broilers, ranging from 0.02% to 5.81%. These findings suggest that FL-based methods hold promise for accurate and efficient poultry weight estimation. This study opens up avenues for further research in the field, encouraging the exploration of FL-based approaches for improved poultry weight estimation in poultry farming operations.

Promoting the suitability of rice husk ash concrete in the building sector via contemporary machine intelligence techniques

Eco-friendly concrete is in great demand, and as a consequence, the necessity to find sustainable alternatives to ordinary cement has become critical. In recent years, there has been considerable interest in the potential benefits of utilizing rice husk ash (RHA) as a cement substitute in concrete. Evaluating concrete properties in a laboratory setup like compressive strength (CS) is a time-consuming and expensive process. The development of trustworthy and precise models for CS estimation might be a better strategy. To determine the CS of RHA concrete, this research used boosting ensemble algorithms, namely gradient boosting (GB), AdaBoost regressor (ABR), and extreme gradient boosting (XGB), over a vast literature database. The estimation models were validated using statistical measures, error distribution by plotting violin graphs, and k-fold analysis. Furthermore, SHapley Additive exPlanations (SHAP) analysis was utilized to assess the importance of contributing elements. The results showed that XGB had the greatest performance in estimating the CS of RHA concrete out of the three methods tested. While the GB and ABR models both had R2 values of 0.90 and 0.94, respectively, the XGB model achieved a value of 0.96. The violin graphs indicated that the average absolute error values for the GB, ABR, and XGB were 5.82, 4.65, and 3.53 MPa, implying the higher precision of the XGB model in estimating the CS of RHA concrete. The SHAP study demonstrated that the three most influential factors in increasing the strength were cement, specimen age, and rice husk ash. The construction sector may benefit from the application of such technologies by facilitating the development of quick and low-cost methods for identifying material qualities and the influence of input parameters.

Modeling the Kinetics of Heavy Crude Oil Cu-Oleate Aquathermolysis

During steam injection processes, oil-soluble catalysts have demonstrated high efficiency by improving the flow properties of heavy crude oil into the reservoir. In order to gain a deeper understanding of the effect of this catalyst type and its reaction mechanism, further studies based on complex kinetic models are required. The catalytic effect of copper oleate on aquathermolysis of heavy crude oil was studied using experimental data obtained with a water/oil ratio of 3:7, a temperature range of 250 to 300 °C, and up to 72 h of reaction time. A kinetic model was developed to predict the yield of the SARA fractions and gases. The calculated kinetic parameters presented a proper fit concerning experimental data with average absolute error values lower than 4%. Results showed that copper oleate reduces the polyaddition reactions of heavy fractions, favoring the production of lighter compounds. A set of in-series and parallel reactions indicate that the reaction gas and aromatic compounds are mainly produced from the asphaltene fraction, where aromatic fractions subsequently generate saturates hydrocarbons.

A personalised recommendation of mobile learning model based on content awareness

In order to overcome the problems of traditional recommendation methods such as large error in recommendation results and long time-consuming process of recommendation results generation, the paper proposes a personalised recommendation method based on content-aware mobile learning mode. First, the recommendation process architecture is designed, which mainly includes a user demand analysis module, a user preference analysis module, and a mobile learning model resource library decision module. Then, the energy function is used, and the dataset is inserted to design the content perception process. Finally, according to the perceptual results, a user emotional topic model with a supervision mechanism is used to complete personalised recommendation. The experimental results show that the average absolute error value of the recommendation results obtained by the method in this paper is between 0.06-0.15, the maximum recommendation result generation process takes only 4.5 s, and the clustering effect of different mobile learning modes is better.

A novel feature for monitoring the enzymatic harvesting process of adherent cell cultures based on lens-free imaging

Adherent cell cultures are often dissociated from their culture vessel (and each other) through enzymatic harvesting, where the detachment response is monitored by an operator. However, this approach is lacking standardisation and reproducibility, and prolonged exposure or too high concentrations can affect the cell’s viability and differentiation potential. Quantitative monitoring systems are required to characterise the cell detachment response and objectively determine the optimal time-point to inhibit the enzymatic reaction. State-of-the-art methodologies rely on bulky imaging systems and/or features (e.g. circularity) that lack robustness. In this study, lens-free imaging (LFI) technology was used to develop a novel cell detachment feature. Seven different donors were cultured and subsequently harvested with a (diluted) enzymatic harvesting solution after 3, 5 and 7 days of culture. Cell detachment was captured with the LFI set-up over a period of 20 min (every 20 s) and by optimising the reconstruction of the LFI intensity images, a new feature could be identified. Bright regions in the intensity image were identified as detaching cells and using image analysis, a method was developed to automatically extract this feature, defined as the percentage of detached cell regions. Next, the method was quantitatively and qualitatively validated on a diverse set of images. Average absolute error values of 1.49%, 1.34% and 1.97% were obtained for medium to high density and overconfluent cultures, respectively. The detachment response was quantified for all conditions and the optimal time for enzyme inhibition was reached when approximately 92.5% of the cells were detached. On average, inhibition times of 9.6–11.1 and 16.2–17.2 min were obtained for medium to high density and overconfluent cultures, respectively. In general, overconfluent cultures detached much slower, while their detachment rate was also decreased by the diluted harvesting solution. Moreover, several donors exhibited similar trends in cell detachment behaviour, with two clear outliers. Using the novel feature, measurements can be performed with an increased robustness, while the compact LFI design could pave the way for in situ monitoring in a variety of culture vessels, including bioreactors.

Application of Improved K-Means Algorithm in Collaborative Recommendation System

With the explosive growth of information resources in the age of big data, mankind has gradually fallen into a serious “information overload” situation. In the face of massive data, collaborative filtering algorithm plays an important role in information filtering and information refinement. However, the recommendation quality and efficiency of collaborative filtering recommendation algorithms are low. The research combines the improved artificial bee colony algorithm with K-means algorithm and applies them to the recommendation system to form a collaborative filtering recommendation algorithm. The experimental results show that the MAE value of the new fitness function is 0.767 on average, which has good separation and compactness in clustering effect. It shows high search accuracy and speed. Compared with the original collaborative filtering algorithm, the average absolute error value of this algorithm is low, and the running time is only 50 s. It improves the recommendation quality and ensures the recommendation efficiency, providing a new research path for the improvement of collaborative filtering recommendation algorithm.

The development of a chemical kinetic mechanism for combustion in supercritical carbon dioxide

Direct fired supercritical CO2 (sCO2) power cycles allow for the combustion of gaseous fuels under oxyfuel conditions with inherent carbon capture. As the CO2 is captured intrinsically, the efficiency penalty of capture on the overall plant is small, meaning that power plants achieve a similar efficiency to traditional fossil fuel power plants without carbon capture and storage. However, at high pressures and in large dilutions of CO2, combustion mechanisms are poorly understood. Therefore, in this paper sensitivity and quantitative analysis of four established chemical kinetic mechanisms have been employed to determine the most important reactions and the best performing mechanisms over a range of different conditions. CH3O2 chemistry was identified as a pivotal mechanism component for modelling methane combustion above 200 atm. The University of Sheffield (UoS) sCO2 mechanism created in the present work better models the ignition delay time (IDT) of high-pressure combustion in a large dilution of CO2. Quantitative analysis showed that the UoS sCO2 mechanism was the best fit to the greatest number of IDT datasets and had the lowest average absolute error value, thus indicating a superior performance compared to the four existing chemical kinetic mechanisms, well-validated for lower pressure conditions.

Predicting the precise number of software defects: Are we there yet?

Defect Number Prediction (DNP) models can offer more benefits than classification-based defect prediction . Recently, many researchers proposed to employ regression algorithms for DNP, and found that the algorithms achieve low Average Absolute Error (AAE) and high Pred(0.3) values. However, since the defect datasets generally contain many non-defective modules, even if a DNP model predicts the number of defects in all modules as zero, the AAE value of the model will be low and Pred(0.3) value will be high. Therefore, the good performance of the regression algorithms in terms of AAE and Pred(0.3) may be questioned due to the imbalanced distribution of the number of defects. To revisit the impact of regression algorithms for predicting the precise number of defects. We examine the practical effects of 12 widely-used regression algorithms, two data resampling algorithm (SmoteR and ROS), and three ensemble learning algorithms (gradient boosting regression, AdaBoost.R2, and Bagging), one feature selection method (information gain) and one parameter optimization method (grid search) for predicting the precise number of defects on the 18 PROMISE datasets. We propose to evaluate the AAE and Pred(0.3) values for the modules with different numbers of defects separately. The AAE values for defective modules are very high and the Pred(0.3) values are very low, i.e., the regression algorithms are very inaccurate for predicting the precise number of defects in defective modules. The problem of predicting the precise number of defects via regression algorithms is far from being solved. We recommend that software testers use regression algorithms to rank modules for testing resource allocation, rather than predict the precise number of defects to evaluate the software reliability and maintenance effort. In addition, most existing DNP studies employing the whole AAE and Pred(0.3) values of all modules as the evaluation metrics for the proposed DNP algorithms should be revisited.

Freeway accident duration prediction based on social network information

Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.

Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.

Nature inspired computation and ensemble neural network to build a robust model for spectral data

UV spectrophotometry was introduced for simultaneous determination of Itraconazole (ITZ) and Secnidazole (SEZ) in their mixture without any prior separation.In this study, fourteen nature-inspired algorithms combined with partial least squares (PLS) regression were used as baseline algorithms. Then, an ensemble neural networks model was introduced. The performance of the models was evaluated using parameters like the root average squared error (RASE), coefficient of determination (R2), and The average absolute error (AAE). RASE, R2 and AAE values of (0.1131, 0.9995, and 0.0819) and (0.1798, 0.9954, and 0.1365) were obtained for calibration and test sets of ITZ, respectively. RASE, R2 and AAE values of (0.5812, 0.9962, and 0.4360) and (0.4903, 0.9957 and 0.3917) were obtained for calibration and test sets of SEZ, respectively. The models in this study can be useful for the researchers who are interested to work on the simultaneous determination of active ingredients in pharmaceutical dosage forms using UV spectroscopy. The proposed method was applied to the pharmaceutical dosage form.

Chaos Theory Modelling for Temperature Time Series at Malaysian High Population Area during Dry Season

The aim of this study is to model the temperature time series at Malaysian high population area during dry season through chaos theory. The selected high population area is Shah Alam located in Selangor state of Malaysia. Chaos theory modelling is categorized into two parts namely analysis and prediction. Analysis by the phase space plot showed that the nature of the observed temperature time series is chaos. Hence, the time series is predicted via the chaotic model. Results from the chaotic model showed that the temperature time series is well predicted with Pearson correlation coefficient near to 1. The result is compared with the traditional method of autoregressive linear model. Based on the computed values of average absolute error, root mean squared error and Pearson correlation coefficient, the chaotic model is found better in predicting temperature time series at Shah Alam area during dry season. This indicates that the chaos theory is applicable for temperature time series at Malaysian high population area. This finding is expected to facilitate stakeholders such as Malaysian Meteorological Department and Department of Environment Malaysia in managing temperature and climate change problem.