Real Values Research Articles

Voltage Frequency Differential Protection Algorithm

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid’s protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand.

State of Health Estimation and Remaining Useful Life Prediction of Lithium-Ion Batteries by Charging Feature Extraction and Ridge Regression

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOH and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOH. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles.

Development of a Parametric Model for Calculating Cutting Forces in External Cylindrical Turning of 16MNCR5 Steel

The paper presents a mathematical model for calculating cutting forces during the machining of 16MnCr5 steel using the Sandvik CNMG 120408 16P25T tool. The modeling process involved the use of a test rig constructed based on the 16Д25 machine, which enabled the measurement of real values of spindle speed, longitudinal feed, cutting depth, and cutting forces. The results transmitted to a computer through the LTR-EU-8 workstation, equipped with galvanic isolated LTR modules and a synchronization interface. Based on the experimental results, the theoretical model demonstrated a deviation from actual measurements of no more than 4.72%. The study provides evidence that the cutting force calculations commonly presented by leading tool manufacturers are inherently overestimated. he difference in cutting forces can be 9%.

Optimal Power Flow in Hybrid AC/DC Microgrid using ANN for Cost Minimization

Currently, this work lays the ground work for sophisticated control methods and decision support systems in hybrid microgrid operations by providing insightful information about integrating artificial intelligence for improved microgrid control. In this work, a neural network (NN) method is proposed for power flow analysis in an IEEE 12-bus-based Hybrid AC/DC Microgrid. The study optimizes power dispatch, minimizes expenses, and minimizes losses in both AC and DC components. Simulation is carried using MATLAB software and the results are presented and analysed. The accuracy of the NN’s predictions of active power flows is demonstrated by training it on historical data and validating it on real-time observations. Regression plots comparing anticipated and real values demonstrate the effectiveness of NN-based analysis in reaching the ideal power distribution.

Importance of the innovation term for GARCH modelling of direct real estate returns

ABSTRACT The autoregressive heteroscedastic effects of the conditional volatility processes of direct real estate (capital value) returns are subject to a broad range of econometrics. However, while many specifications have been utilised in the empirical design, the literature has commonly modelled the innovation term within the GARCH volatility processes of real estate capital values through a Gaussian normal distribution. This a priori assumption falls short of the data characteristics exhibited by capital value returns, implying that capital value returns cannot be adequately modelled without adapting to the innovation term distribution. Misspecification can underestimate risk and lead to the over-allocation of riskier assets. This study investigates the impact of the a priori assumption about the innovation terms applied to capital value returns, as well as their robustness, through a time-varying framework. The main findings are that misspecification and parameterisation occur when assuming the normality of the innovation term, and that the application of a priori assumptions of the innovation term beyond those of a normal, Student’s t, or generalised error distribution currently employed in the literature can increase the validity of volatility models when applied to capital value returns. The application of the Johnson – SU distribution yields the best overall performance.

Novel Adaptive Control for Flexible-Joint Robots With Unknown Measurement Sensitivity

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dynamics. To this end, this paper proposes a novel tracking control scheme based on measurement information for flexible-joint robots with unknown measurement sensitivity, where the dependence on model information is eliminated with the elaborately constructed Lyapunov function candidates, and the real tracking error is still adjusted to an acceptable range even if there are measurement errors. Preliminary experiments on a flexible-joint robot developed by Quanser company demonstrate the feasibility and effectiveness of the proposed method. In future studies, designing an effective scheme to achieve direct preset tracking control is the focus of the work.

Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, FH3, and FH19 from the Yamama reservoir in the Faihaa Oil Field, southern Iraq. The framework includes: calculating permeability for uncored wells using the classical method and FZI method. Topological mapping of input space into clusters is achieved using the self-organizing map (SOM), as an unsupervised machine-learning technique. By leveraging data obtained from the four wells, the SOM is effectively employed to forecast the count of electrofacies present within the reservoir. According to the findings, the permeability calculated using the classical method that relies exclusively on porosity is not close enough to the actual values because of the heterogeneity of carbonate reservoirs. Using the FZI method, in contrast, displays more real values and offers the best correlation coefficient. Then, the SOM model and cluster analysis reveal the existence of five distinct groups.

TTSAD: TCN-Transformer-SVDD Model for Anomaly Detection in air traffic ADS-B data

ADS-B (Automatic Dependent Surveillance-Broadcast) is a key technology in the new generation air traffic surveillance system. However, it is vulnerable to various cyber attacks because it broadcasts data in plaintext format and lacks authentication mechanism. Previous research has rarely considered the application scenarios of ATM (Air Traffic Management) in commercial air transport, and there are the problems of low anomaly detection rate and the non-lightweight model. This paper focuses on ADS-B anomaly detection under the background of ATM. We propose the TTSAD (TCN-Transformer-SVDD Model for Anomaly Detection) model, which aims to address the problems of existing ADS-B anomaly detection methods including inadequate considerations of long-term dependencies and distribution characteristic, the non-lightweight model and the poor adaptive threshold. First, ADS-B time series is input into TCN (Temporal Convolutional Network) prediction module which predicts data in an accurate and quick way using causal convolution and dilated convolution. Then, the predicted ADS-B time series is input into Transformer reconstruction module which reconstructs data accurately and quickly based on Self-Attention and Multi-Head Attention mechanism. Finally, the difference values between the reconstructed values and the real values are input into SVDD (Support Vector Data Description) threshold determination module for an optimal threshold. Experimental results show that the TTSAD model can detect ADS-B anomaly data generated from attacks such as altitude slow offset and DOS (Denial of Service). The TTSAD model is superior to other machine learning methods in terms of recall rate, detection rate, accuracy rate, missing detection rate and false alarm rate. Furthermore, compared with other deep learning methods including LSTM, GRU and LSTM-AE, the TTSAD model has a shorter training time and a lightweight characteristic. This approach guarantees the information security of ADS-B, thereby improving the operational security of ATM.

Prediction of Economic Growth by Using Machine-Learning Algorithms through Sentiment Index Analysis in Economy of Pakistan

The neural network method's ability to mimic nonlinear communications without prior information is a major benefit for accurate function approximation. It differs from ARIMA and linear regression, which need linearity and may not work for all data sorts. Compared to (seasonal) ARIMA models, neural networks yield similar results. Neural networks also model and predict multivariate time series using causal and co-integration research. In contrast to the traditional economic forecasting approach, machine-learning techniques have prioritized precise forecasting without predetermined assumptions or conclusions. Owing to their enhanced predictive abilities and technological advancements, they provide more adaptability and have gained widespread acceptance in several fields. In terms of estimating US real estate values, machine-learning technology fared better than traditional econometric models, as shown. Total there were 131601 economic and financial news, which may include the phrases along with the names specified by the bank, and it may also include local media and mainstream. Machine learning uses the strength of data-driven algorithms and state-of-the-art computational techniques to improve decision-making in a range of industries. The findings of this research contribute to better forecasting practices and show how machine learning may provide more accurate forecasts in real-world scenarios.

Corporate Governance and Collateral Effect of Real Estate

The collateral channel literature predicts that soaring real estate prices increase the debt capacity of firms with more real estate asset holdings. This paper discusses the different relationships between collateral and investment. We find that firms with strong corporate governance were more likely to increase debt capacity via the collateral channel during the real estate boom from 1993 to 2006. Entrenched managers used less debt to finance their investments when their real estate collateral values increased. Our results hold even after controlling for non-collateralized debts and credit rating of the sample firms.

Wpływ pandemii COVID-19 na rynek wtórny nieruchomości. Studium przypadku

The topic of the paper is the analysis of the situation in the secondary real estate market in Krakow from 2017 to 2020. The research was conducted based on data obtained from the register of real estate prices and values. The results of the research helped detect changes occurring in the indicated years; the number of transactions in the housing market in the city oscillated around the value of 4,000 for each year. The high demand for real estate was significantly disrupted by 2020 and the outbreak of the COVID-19 pandemic. Despite an almost 50% drop in the number of transactions and reduced interest from potential buyers, the price of 1m2 of residential space rose steadily. Thus, the pandemic only affected the number of transactions, while no deceleration of the rising trend was observed in terms of the average transaction price or the average price per square meter.

Torques in the human upper ankle joint level and their importance in conservative and surgical treatment

The paper deals with the torques of external muscles acting on the upper ankle joint under weight-bearing conditions and their importance in diagnosing and treating the human foot. Experimental data were collected and calculations were performed. Based on the experiments with the biomechanical model of the foot and upper ankle joint, it was shown how the changes in the force arms of the external muscles of the foot under weight-bearing conditions, change the torque. The real values of muscle forces and torques of the external muscles of the foot were calculated. Taking into account the distance of the lines of muscle action from the axis of rotation of the upper ankle joint the rotational force of the muscles was calculated. The influence of changing the force arm on the rotational efficiency of the muscle balancing the moment of gravity was shown. Knowledge of muscle torque under weight-bearing conditions is crucial for correctly assessing foot biomechanics. It has been shown that torque (gravitational and muscular), not pure force, is crucial when assessing the rotational capacity of the analyzed joint. A change in the approach to diagnostics and treating paresis or weakness of extrinsic foot muscles was proposed through the manipulation of the distance of their action line from the axis of joint rotation.

Predicting the Value of Agricultural GDP in Iraq for the Period 2019—2030 by Applying the Markov Transition Matrix

This research aims to predict the values of agricultural GDP in Iraq for the period 2019–2030 using the Markov Transition Matrix, whereby the fifth state was chosen due to the convergence of the predicted values with the most recent real values in the original time series. In addition, the predictive error or predictive accuracy of the selected state was better compared to other states. The reason for choosing this methodology in forecasting is that it is based on probabilities derived from old historical data, but this methodology does not need old historical data for the purpose of extracting predictive values, even if the time series includes long-time series data. The predicted values follow the same path as the original time series and are not affected by the general trend of the original data, which gives us an indication that there is a problem of stagnation of agricultural GDP. Therefore, a recommendation that can be given to economic policymakers, in particular the agricultural ones, is the existent need to address the problems that the agricultural sector has always suffered from in order to break this stagnation.

Brain-inspired semantic data augmentation for multi-style images.

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new brain-inspired computer vision image data augmentation method which consists of two key components, namely, using Robust statistics and controlling the Coefficient of variance for DSU (RCDSU) and Feature Data Augmentation (FeatureDA). RCDSU calculates feature statistics (mean and standard deviation) with robust statistics to weaken the influence of outliers, making the statistics close to the real values and improving the robustness of deep learning models. By controlling the coefficient of variance, RCDSU makes the feature statistics shift with semantic preservation and increases shift range. FeatureDA controls the coefficient of variance similarly to generate the augmented features with semantics unchanged and increase the coverage of augmented features. RCDSU and FeatureDA are proposed to perform style transfer and content transfer in the feature space, and improve the generalization ability of the model at the style and content level respectively. On Photo, Art Painting, Cartoon, and Sketch (PACS) multi-style classification task, RCDSU plus FeatureDA achieves competitive accuracy. After adding Gaussian noise to PACS dataset, RCDSU plus FeatureDA shows strong robustness against outliers. FeatureDA achieves excellent results on CIFAR-100 image classification task. RCDSU plus FeatureDA can be applied as a novel brain-inspired semantic data augmentation method with implicit robot automation which is suitable for datasets with large style differences between training and testing data.

On the analytical modeling of fractal memelement and inverse memelement

SummaryIn this work, an improved analytical model of fractal memelement in which the pinched point shifting has been considered and the original analytical model of fractal inverse memelement have been proposed. These fractal circuit elements are the memelement, and inverse memelement operates based on the principle of electromagnetic in fractal time/space, which must be applied whenever the current flows through fractal media. These models are important because these memory elements can be realized based on the porous material, which is a fractal media. In addition, they can employ self‐similarity, which is hard to be simulated by using the traditional models. This is because such self‐similarity can be well explained by the fractal set‐based model, yet those traditional models are based on the set of real values. Therefore, for deriving the proposed models, the fractal calculus, which is oriented to the fractal set, has been adopted as the mathematical basis. From the analytical and numerical analyses based on the derived models, it has been found that both memelement and inverse memelement can retain their unique frequency characteristics despite being operated based on the abovementioned principle. In addition, their input–output relationships are mathematically differentiable albeit the inputs and outputs themselves are not.

Combining machine learning and econometrics: Application to commercial real estate prices

AbstractIn this article, we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex nonlinear fashion. The model is applied to a small sample of 2652 transactions in Phoenix (AZ) between 2001 and 2021. We only observe a limited number of property characteristics. The average out‐of‐sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out‐of‐sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.

FedAGAT: Real-time traffic flow prediction based on federated community and adaptive graph attention network

Predicting traffic flow is vital for optimizing intelligent transportation systems (ITS) and reducing congestion by forecasting traffic patterns accurately. However, current centralized TFP systems have limitations, e.g., slow training, high communication costs, and privacy concerns. To address these challenges, this study proposes FedAGAT, a system for short-term traffic flow prediction (TFP) based on federated community and adaptive spatial-temporal graph attention networks (AGAT). The FedAGAT system allows for local data processing and sharing only model updates with the server. This enables real-time, scalable, and secure TFP - essential capabilities for efficient ITS. AGAT is employed to capture intricate spatial-temporal dependencies in traffic flow, while federated learning facilitates decentralized learning, enhancing privacy. The FedAGAT prediction process involves four steps: dividing the local subnetwork using spectral community detection, locally training based on global parameters, uploading updated parameters, and creating a global model prediction based on the aggregated parameters. To evaluate the performance of FedAGAT, two real-world traffic datasets were utilized for benchmarking against seven statistical and deep learning models. Results demonstrate that FedAGAT provides relatively higher accuracy for short- and mid-term forecasting horizons. Moreover, FedAGAT predictions closely match real traffic flow values, and the overall performance is comparable to a global model, while requiring less time.

Theoretical Model that Explain the Knowledge of the CSE Program in Special Education Teachers: A Bayesian Approach

This study introduces new evidence of the theoretical model that connects 21 theoretical hypotheses of personal experience with sexual education knowledge. Participants were 82 Special Education students with a comparison group of 55 female students from Psychology and Natural Sciences Education studying special education. This correlational and exploratory study proposes an analysis path analyzed with a frequentist approach. It presents new empirical evidence from a Bayesian approach’s theoretical and methodological assumptions that offer estimates similar to the real values. The information in the model can be used to guide the education of Latinas who are Special Education teachers to identify other aspects that condition their teaching.

Plague, Religion and Urban Space in Sixteenth-Century Antwerp.

Antwerp's response to the outbreak of plague in the 1570s offers new insights into the effects of epidemics on urban communities in relation to their religious, economic, and spatial fabric. Antwerp's transition from a Catholic to Calvinist government in 1577, and back to Catholicism in 1585, allows us to study its reaction to and the effects of plague across religious boundaries within a short time span. Using GIS, we have compared various rich datasets concerning plague: the register of houses locked in quarantine; the health certificates issued by authorities; plague fatalities recorded in St. Jacob's parish; a wide range of urban regulations; and information about the size of households, their composition, rents and real estate values in Antwerp. Combined analysis shows that Catholics and Protestants, whose houses were concentrated in different city districts and who had distinct professional and economic profiles, experienced plague quite differently, both physically and spiritually.

Synthetic cap rate indices (1991-Covid era)

We introduce a method that combines Euclidean distancing and OLS techniques to project synthetic capitalization rate indices (‘SCXs’) for metropolitan statistical areas in the US. SCXs are projected independently of market prices, asset specific characteristics and geographic location (ex-ante). In contrast to market cap rates, driven by geographic proximity and market comparables, our new method is driven by economic proximity. We find SCXs provide better forward guidance than market cap rates for commercial real estate (‘CRE’) defaults and CRE values before and during the Covid pandemic. Our method establishes CRE benchmark cap rate indices across property types that explicitly connect CRE valuation at the MSA level to macroeconomic indicators through economic proximity.