Final Prediction Error Research Articles

Simulation and experimental thermal analysis of ultrasonic vibration-assisted high-efficiency deep grinding of γ-TiAl blade tenon

Gamma titanium-aluminum intermetallic compounds (γ-TiAl) have attracted considerable attention in the aerospace industry because of their outstanding thermal stability and broad range of properties. To analyze the grinding temperature during high-efficiency deep grinding (HEDG) and ultrasonic vibration-assisted high-efficiency deep grinding (UVHEDG) of γ-TiAl materials, both an analytical thermal model and a finite element simulation model were developed. Subsequent comparison trials between HEDG and UVHEDG were conducted to validate the accuracy of the simulation. The results show that the final prediction error can be effectively maintained within 15 %, and the temperature measurement curve fits well when an ultrasonic heat influence factor is incorporated into the 3D finite element model. The introduction of ultrasonic vibrations into the HEDG reduced the maximum grinding temperature by 39.1 %, effectively preventing grinding burns. The highest temperature consistently occurred in the top zone of the blade tenon tooth due to different heat conduction paths caused by the complex profile. This led to a 32.4 % and 31.9 % reduction in the grinding temperature in the bottom zone under HEDG and UVHEDG, respectively. The surface roughness of the machined blade tenon tooth reached 0.664 μm at the top and 0.721 μm at the bottom under UVHEDG, whereas it was 0.735 μm and 0.843 μm under HEDG.

Robust model averaging approach by Mallows-type criterion.

Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.

THE NEXUS BETWEEN FOREIGN DIRECT INVESTMENT AND NOMINAL EXCHANGE RATE, REAL GDP, AND CAPITAL STOCK IN TANZANIA

Aim: The paper aims to examine the relationship between FDI and the nominal exchange rate, real GDP, and capital stock in Tanzania using quantitative research methods and an econometric analysis. The analysis aims to provide insights into the factors that affect FDI and contribute to the existing literature on the relationship between FDI and economic growth. Methods: This study examines the relationship between FDI inflow, real GDP, capital stock, and the normal exchange rate in Tanzania using a robust research methodology. The study employs STATA 15 software and Akaike’s Information Criteria (AIC), Schwarz Information Criteria (SC), Final Prediction Error (FPE), and the Hannan Quinn (HQ) Information Criteria. In addition, the autoregressive model, the Johansen co-integration test, and the Toda-Yamamoto Granger causality (modified WALD) test were employed to determine the optimal lag. Results: The results indicate a bidirectional relationship between the nominal exchange rate and FDI in Tanzania, with FDI inflows influencing the nominal exchange rate volatility and vice versa. Furthermore, the results indicate that real GDP, capital stock, and the nominal exchange rate exert a unidirectional influence on FDI influx in Tanzania. Conclusions: The nominal exchange rate and capital stock have positive and negative correlations with foreign direct investment. Like many other African economies, Tanzania remains vulnerable to external forces despite making significant strides in stabilizing the exchange rate. It is recommended that the Central Bank of Tanzania – along with those of other African nations with similar economic structures – maintain a stable nominal exchange rate level as an incentive for foreign investors in order to increase the inflow of foreign direct investment.

Modeling and identification of the hygro-thermal parameters of mechanically-ventilated broiler house using prediction error: Assessment in cold conditions

Indoor air temperature and humidity moisture are of the foremost significance in climate control of broilers houses, and their impacts on poultry health and production depend on accurate control. The main objective of this work is to identify and assess a novel state-space model, to rapidly predict the hygro-thermal behavior of the livestock building. To achieve this analysis, various experimental measurements (e.g., ventilation rate, thermal heating, and air temperature and humidity) of two commercial poultry houses placed in the Mediterranean zone were monitored over cold conditions production cycle. The developed model was estimated and validated against a dataset of 25 days acquired under three different operation ventilation modes (min-ven, power and tunnel modes). Through simulation, the results showed that the predicted model and measured data were achieved a satisfactory accuracy with an averaged coefficients of determination R2 were 0.93 and 0.95, respectively, for the indoor air temperature and humidity models, and a root mean squared error (RMSE) of 0.3213 °C and 0.957 %. Additionally, the predictive model shows satisfying performances for the long horizon prediction with a final prediction error (FPE) equal to 0.084, which will prevent the intensely time-consuming process of getting precise physical parameters in regards the poultry house system.

WITHDRAWN: Efficient fragmentation and allocation on clustering in distributed environment (FACE)

Distributed data processing systems require a reliable storage structure that ensures consistent, high-scale, and scalable data access for all users. Data fragmentation and subsequent allocation are important aspects of a flexible distributed database system. Existing techniques may result in significant data loss while performing data allocation and fragmentation. To overcome these issues a novel fragmentation and Allocation on Clustering in Distributed Environment (FACE) is proposed for reliable storage with scalable data access for all users. Initially, the user query is given to distributed database system (DDBS), then the query is fed to the query parser. The query parser parses and converts a textual query into a query object. After parsing the query data, it is passed to the query optimizer, which optimizes the query to access the data more efficiently. In the next step, the query is fragmented using dual (horizontal and vertical) fuzzy C means clustering. In Dual fuzzy C means clustering, the vertical fragmentation focuses on clustering attribute values that are frequently accessed together, and horizontal fragmentation focuses on grouping records that meet data selection criteria. After fragmentation, the query is allocated using sequential rule mining, in which queries occur in sequential order. The allocated query is optimized using the Efficient Fragment Security (EFS) algorithm to normalize the parameters to achieve better results. The performance metrics of the proposed techniques are calculated based on Final Prediction Error (FPE), and Execution time. The proposed FACE achieves a lower execution time of the 1.56 s. The result shows that the proposed FACE decreases the overall execution time lower than the 4.70 s, 4.46 s, 3.3 Sec, and 3.14 s in MAPA, DMA, BBE, and RATS-HM respectively.

Assessing automatic data processing algorithms for RGB-D cameras to predict fruit size and weight in apples

Data acquired using an RGB-D Azure Kinect DK camera were used to assess different automatic algorithms to estimate the size, and predict the weight of non-occluded and occluded apples. The programming of the algorithms included: (i) the extraction of images of regions of interest (ROI) using manual delimitation of bounding boxes or binary masks; (ii) estimating the lengths of the major and minor geometric axes for the purpose of apple sizing; and (iii) predicting the final weight by allometric modelling. In addition to the use of bounding boxes, the algorithms also allowed other post-mask settings (circles, ellipses and rotated rectangles) to be implemented, and different depth options (distance between the RGB-D camera and the fruits detected) for subsequent sizing through the application of the thin lens theory. Both linear and nonlinear allometric models demonstrated the ability to predict apple weight with a high degree of accuracy (R2 greater than 0.942 and RMSE < 16 g). With respect to non-occluded apples, the best weight predictions were achieved using a linear allometric model including both the major and minor axes of the apples as predictors. The mean absolute percentage error (MAPE) ranged from 5.1% to 5.7% with respective RMSE of 11.09 g and 13.02 g, depending to whether circles, ellipses, or bounding boxes were used to adjust fruit shape. The results were therefore promising and open up the possibility of implementing reliable in-field apple measurements in real time. Importantly, final weight prediction error and intermediate size estimation errors (from sizing algorithms) interact but in a way that is not easily quantifiable when weight allometric models with implicit prediction error are used. In addition, allometric models should be reviewed when applied to other apple cultivars, fruit development stages or even for different fruit growth conditions depending on canopy management.

What Influenced Hanoi’s Apartment Price Bubble between 2010 and 2021?

This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series data. Specifically, we used the ADF unit test to test the stationarity of the variables based on the following criteria: AIC (Akaike information criterion); LR (likelihood ratio); FPE (final prediction error); HQ (Hanan–Quinn information criterion); and Schwarz (SC) to find the optimal lag (Lag) for the model. We also applied the Granger causality test to determine the correlation between the economic variables that appeared in the model with the PR index. We present the results of the research model through the push–response function and the variance decomposition to consider and evaluate the impact of the PR index shock on itself and the other variables. The literature in this field includes many studies that are similar to this one; however, no research has been conducted that has focused on analysing whether variables, such as per capita income and the urbanisation rate, influence the formation of real estate bubbles. This focus is especially relevant in Hanoi, which is an important part of the Vietnamese real estate market. Through this study, we aimed to fill this gap and to contribute to the references on the Hanoi real estate market and its influencing factors.

Analysis of the Influence of Temperature and Humidity on Rainfall in Sindh Province by Vector Autoregression (VAR)

The effects of climate change are relatively local, although it is a worldwide problem. Since climate change has already occurred, it has had a wide variety of effects in almost all regions of the country and has also had an impact on many economic sectors. Rainfall, temperature, cloud cover, wind speed, humidity, and heavy sunlight are the main climatic variables. In order to understand the subsequent changes of these climatic variables, the behaviour of these variables should be studied. which also helps to implement significant policies. Investigating the behaviour of the climatic factors in the past, present, and future is a major problem. Primary goal of this research project was to create an adequate vector autoregression (VAR) model that could forecast monthly temperature, humidity, and rainfall at three meteorological stations in Sindh Province, Pakistan. The Kwiatkowski–Phillips-Schmidt–Shin, Phillips–Perron, and Augmented Dickey–Fuller tests have all verified the stationarity of time series variables. Order of the VAR model was determined by applying Schwarz information criteria, Hannan-Quinn information criteria, Akaike information criteria, final prediction error, and likelihood ratio test. Ordinary least squares approach was the method utilized to estimate the parameters of the model. It was determined that the optimal models for this study were VAR (8) and VAR (7). The structural analyses were performed using the forecast error variance decomposition and impulse response function. These structural studies show that, in the future, humidity, temperature, and rainfall will all be endogenous. The research indicates that humidity and temperature both favour rainfall. When temperature and humidity are both high, rainfall is greatest, and when they are both low, it is minimum. This study suggests that the correlation between temperature and relative humidity holds negligible influence over changes in rainfall patterns.

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model’s accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires.

Data-Driven Modelling of Soil Moisture Dynamics for Smart Irrigation Scheduling

In the face of increasing water scarcity and uncertainties of climate change, improving crop water use efficiency and productivity, while minimizing negative environmental impacts, is becoming crucial to meet the surging global food demand. Smart irrigation has a potential of improving water use efficiency in precision agriculture especially when efficient irrigation control strategies are adopted. Conventionally, irrigation systems rely on heuristic methods to schedule irrigation which either leads to over-irrigation or under-irrigation. This influences the crop physiological characteristics as well as the water use efficiency. To tackle this menace, model-based irrigation management has been overemphasized. A closed-loop irrigation control strategy relies on a mathematical model of the system for irrigation scheduling decisions. In this study, a data-driven approach was used to learn soil moisture dynamics from a drip irrigated tomato in an open field agricultural system. A total number of 9674 data samples were collected using an ATMOS41 weather station, TERROS 12 soil moisture sensor and a YFS-201 flow sensor for crop evapotranspiration and precipitation, soil moisture and irrigation volumes respectively. Data driven modelling was then performed using the system identification toolbox in a MATLAB environment. The model formulation was a multi-input single-output (MISO) system with reference evapotranspiration, irrigation and rainfall as inputs and soil moisture as the output. Different model structures including transfer functions, state space models, polynomial models and ARX models were evaluated. Model performance was evaluated using the mean square error (MSE), final prediction error (FPE) and estimated fit of the model approaches. Simulation results indicate that the soil moisture dynamics model provides a satisfactory approximation of the process dynamics with a state space model giving an estimated fit of 97.04 %, MSE and FPE of 1.74×10−7 and 1.75×10−7 respectively. This model will be used to design a model predictive controller for smart irrigation scheduling in open field environmental conditions.

Impacts of monetary policies on the real estate bubble in Hanoi, Vietnam

The development of the real estate market always goes hand in hand with the fluctuation of the economy. In recent years, this market has experienced many recessions and «freezes» associated with the appearance of a real estate bubble. To approach this issue, this paper studies and gives an overview of the real estate bubble and the impact of monetary policies on the real estate bubble in Vietnam. This paper’s purpose is to identify and measure the influence of monetary policies, including interest rates, credit and money supply, on the real estate bubble in Ha Noi. The vector autoregression model (VAR) is used to test the interaction of the variables in the model. Dickey-Fuller test (DF) is applied to determine the stationarity of the variables, while the Akaike information criterion (AIC), Likelihood Ratio (LR), Final prediction error (FPE), Hannan-Quinn information criterion (HQ) and Schwarz criterion (SC) are used to find optimal lag of the model; then Granger causality test is utilized to determine the two-way correlation between variables. The results showed that the real estate bubble reacted quickly to shocks from macroeconomic factors representing the monetary policy, consisting of interbank interest rates, credit growth, and money supply growth. Thus, it is concluded that monetary policy is not only the cause of formation, but also one of the effective solutions to deflate the real estate bubble. AcknowledgmentThis research is funded by Vietnam Ministry of Education and Training (MOET) under grant number [B2022-NTH-03].

Forecasting temperature of the Saudi Arabian Province of Makkah using a discrete state–space modeling approach

The maximum and minimum air temperature components (Tmax and Tmin) play a crucial role in science. This study proposes a discrete-time identified state-space modeling approach in which the temperature fluctuation was modelled as a state-space system with the temperature time series as inputs. We aim to provide a tool for projecting future scenarios of Tmax and Tmin. The current research employs a prediction-focused methodology to system identification, with the overarching goal of developing a realistic and dynamic system model. Data on the Tmin and Tmax recorded in the Saudi Arabian province of Makkah are used to test the accuracy and robustness of the proposed methodology. The proposed model was developed utilizing 120 years' (1901–2020) worth of historical monthly time series data on Tmax and Tmin. It was applied to anticipate future temperatures over the ensuing 60 years (up to 2080). For maximum temperature projections, the fit to the data or prediction focus was 87.04% and 85.14%, respectively for the identification (training) and validation phases of the model development. Akaike’s Final Prediction Error (FPE) and Mean Squared Error (MSE) values were observed to be 0.37 °C and 0.34 °C, respectively. The prediction focus during the identification and validation phases were 86.25% and 84.78%, respectively for the minimum temperature projection. The FPE and MSE values were 0.41 °C and 0.37 °C, respectively for this instance. The findings demonstrate that the recommended discrete state-space modeling approach may be utilized to predict temperature variations in the future.

Flight Dynamics Modeling with Multi-Model Estimation Techniques: A Consolidated Framework

In modern era of aviation technology evolution, unmanned aerial vehicles have proved to be crucial in all fields including military and research. The development of robust control system and successful mission accomplishment requires an meticulous UAV model. The aim of this paper is to lay out an elaborate model estimation scheme using various model structure techniques including Auto-regressive Exogenous, Auto-regressive Moving Average exogenous, Box Jenkin’s, Output Error, and state space and non-linear Auto-regressive Exogenous. The obtained models are then analyzed and compared for best model quality parameters like residual analysis, final prediction error and fit percentages. The efficacy of developed model through proposed model is further validated using simulations data for UAV. Results demonstrate the model’s feasibility as it properly predicts system performance over a broad variety of operating situations. To the best of our knowledge, this is the first time in the literature that a model estimation study for UAV platform with such a wide variety of model structures has been presented.

Variable Screening and Model Averaging for Expectile Regressions

Expectile regression is a useful tool in modelling data with heterogeneous conditional distributions. This paper introduces two new concepts, i.e. the expectile correlation and expectile partial correlation, which can measure the contribution from each regressor to the response in expectile regression. In ultra‐high dimensional setting, the expectile partial correlation, which provides an importance ranking of the predictors, is found useful for variable screening. Theoretical results indicate that the proposed screening procedure can achieve the sure screening set. Additionally, a model selection method via extended Bayesian information criterion (EBIC) and a jackknife model averaging (JMA) method are suggested after the screening step to address model uncertainty. The screening consistency of EBIC, the asymptotic optimality of JMA in the sense of minimizing out‐of‐sample expectile final prediction error, and the sparsity of JMA weight are then established. Finally, numerical results demonstrate the nice performance of our proposed methods.

Resonance Detection Method and Realization of Bearing Fault Signal Based on Kalman Filter and Spectrum Analysis

The rolling bearing is an important part of mechanical equipment, and its performance significantly affects the quality and life of the mechanical equipment. This article uses the integrated fiber Bragg grating resonant structure sensor excited by periodic micro-shocks caused by micro faults to realize the extraction of information relating to potential faults. Because the fault signal is weak and can easily be interfered with by ambient noise, in order to extract the effective signal, this article determines the autoregressive model of bearing vibration by the final prediction error criterion and the recursive least squares estimation algorithm. The augmented state space model is established based on the autoregressive model. A Kalman filter is used to reduce the noise interference, and then the reduction noisy signal is analyzed by power spectrum and improved autocorrelation envelope spectrum to realize the detection of bearing faults. Through data analysis and method comparison, the proposed improved autocorrelation envelope spectrum analysis can directly extract the bearing fault frequency, which is superior to other methods such as cepstral analysis.

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

Revisiting Akaike’s Final Prediction Error and the Generalized Cross Validation Criteria in Regression from the Same Perspective: From Least Squares to Ridge Regression and Smoothing Splines

Mathematical model to predict COVID-19 mortality rate.

Covid-19 is a highly contagious viral infection that has recently become a pandemic. Since the beginning of the pandemic, the disease has affected millions of people and taken many people's lives. The purpose of this paper is to predict and compare the number of cases and mortality rate due to Covid-19 every quarter in 2020 and 2021 in three countries: Iran, the United States, and South Korea. The data of this study include the mortality rate of different countries of the world due to Covid-19, which has been approved by the World Health Organization (WHO). In this paper, to develop the mathematical model for mortality rate prediction, the data of the countries of Iran, the United States, and South Korea during the last two years from March 1, 2020, to March 1, 2022, have been used. In addition, the mortality trend was modeled using the MATLAB software toolbox version 2022b. During modeling, six methods including Fourier, Interpolant, Gaussian, Polynomial, Sum of Sine, and Smoothing Spline were implemented. Root Mean square error (RMSE) and final prediction error were used to evaluate the performance of these proposed methods. As a result of the analysis, it was shown that the Smoothing Spline model with the lowest error rate was capable of accurately evaluating and predicting Covid-19 incidence and mortality rate. Using RMSE, a prediction of the Covid-19 mortality rate for three countries is 3.76498×10-5. The values of R-Square and Adj R-sq were 1 in all the experiments, which indicates the full compliance of the prediction model. Using the proposed method, the incidence rate and mortality rate can be properly assessed and compared with each other in three countries. This provides a better view of the progression of the coronavirus outbreak in spring, summer, autumn, and winter. By using the proposed method, governments will be able to prevent disease and alert people to follow health guidelines more closely, thereby reducing infection numbers and mortality rates.

Land Subsidence Time Series Prediction Method Based on LSTM-AMSGD

Accurate prediction of geological subsidence is of great importance for geological hazard risk assessment. Various existing prediction models do not take into account the time correlation between geological subsidence, and the prediction effect lacks practical significance. In this paper, an LSTM-AMSGD-based land subsidence prediction method is proposed. Firstly, the high-precision time series inversion results of large-area land surface deformation are obtained by the small baseline interference technique with multiple principal image coherent targets. Secondly, a recurrent neural network (LSTM-AMSGD) is used as the network architecture. The final cumulative subsidence prediction error is within 0.3 mm, and the single-step prediction of more than 400,000 observation points can be completed in 126s. Therefore, the LSTM-AMSGD model in this paper is effective for the prediction of geological subsidence.

Surface similarity parameter: A new machine learning loss metric for oscillatory spatio-temporal data

Supervised machine learning approaches require the formulation of a loss functional to be minimized in the training phase. Sequential data are ubiquitous across many fields of research, and are often treated with Euclidean distance-based loss functions that were designed for tabular data. For smooth oscillatory data, those conventional approaches lack the ability to penalize amplitude, frequency and phase prediction errors at the same time, and tend to be biased towards amplitude errors. We introduce the surface similarity parameter (SSP) as a novel loss function that is especially useful for training machine learning models on smooth oscillatory sequences. Our extensive experiments on chaotic spatio-temporal dynamical systems indicate that the SSP is beneficial for shaping gradients, thereby accelerating the training process, reducing the final prediction error, increasing weight initialization robustness, and implementing a stronger regularization effect compared to using classical loss functions. The results indicate the potential of the novel loss metric particularly for highly complex and chaotic data, such as data stemming from the nonlinear two-dimensional Kuramoto–Sivashinsky equation and the linear propagation of dispersive surface gravity waves in fluids.

Hybrid model in machine learning–robust regression applied for sustainability agriculture and food security

&lt;span lang="EN-US"&gt;A dataset containing 1924 observations used in this study to evaluate the effect of 435 different independent variables on one dependent variable. Big data has some issues such as irrelevant variables and outliers. Therefore, this study focused on analysing and comparing the impact of three different variable selection based on machine learning techniques, including random forest (RF), support vector machines (SVM), and Boosting. Further, the M robust regression was applied to address the outliers using M–bi square, M–Hampel, and M–Huber. Random forest and M-Hampel results revealed the significant comparing from the other methods such as mean absolute error (MAE) 175.33995, mean square error (MSE) 31.8608, mean average percentage error (MAPE) 9.16091, sum of square error (SSE) 89270.45, R–square 0.829511, and R–square adjusted 0.82670. Also, these techniques indicated that the 8 selection criteria were lower than the other techniques including Akaike information criterion (AIC) 47.25915, generalized cross validation (GCV) 47.27169, hannan-quinn (HQ) 47.60351, RICE (47.2845), SCHWARZ 51.7099, sigma square (SGMASQ) 46.50605, SHIBATA 47.23489, and final prediction error (FPE) 47.25929. Therefore, the study recommended that the best random forest and M-Hampel models are helpful to show the minimum issues and efficient validation for analysing and comparing big data.&lt;/span&gt;