Machine acceleration time series prediction for dimensional accuracy of 3D printed parts

This study explores the influence of infill patterns on machine acceleration prediction in the realm of three-dimensional(3D)printing,particularly focusing on extrusion technology.Our primary objective was to develop a long short-term memory(LSTM)network capable of assessing this impact.We conducted an extensive analysis involving 12 distinct infill patterns,collecting time-series data to examine their effects on the acceleration of the printer’s bed.The LSTM network was trained using acceleration data from the adaptive cubic infill pattern,while the Archimedean chords infill pattern provided data for evaluating the network’s prediction accuracy.This involved utilizing offline time-series acceleration data as the training and testing datasets for the LSTM model.Specifically,the LSTM model was devised to predict the acceleration of a fused deposition modeling(FDM)printer using data from the adaptive cubic infill pattern.Rigorous testing yielded a root mean square error(RMSE)of 0.007144,reflecting the model’s precision.Further refinement and testing of the LSTM model were conducted using acceleration data from the Archimedean chords infill pattern,resulting in an RMSE of 0.007328.Notably,the developed LSTM model demonstrated superior performance compared to an optimized recurrent neural network(RNN)in predicting machine acceleration data.The empirical findings highlight that the adaptive cubic infill pattern considerably influences the dimensional accuracy of parts printed using FDM technology.

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence

In order to research brain problems using MRI,PET,and CT neuroimaging,a correct understanding of brain function is required.This has been considered in earlier times with the support of traditional algorithms.Deep learning process has also been widely considered in these genomics data processing system.In this research,brain disorder illness incliding Alzheimer’s disease,Schizophrenia and Parkinson’s diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods.Moeover,deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks(DBN).Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm(DBNJZZ)approach.The suggested approach is executed and tested by using the performance metric measure such as accuracy,root mean square error,Mean absolute error and mean absolute percentage error.Proposed DBNJZZ gives better performance than previously available methods.

Energy Efficient and Intelligent Mosquito Repellent Fuzzy Control System

Mosquitoes are of great concern for occasionally carrying noxious diseases(dengue,malaria,zika,and yellow fever).To control mosquitoes,it is very crucial to effectively monitor their behavioral trends and presence.Traditional mosquito repellent works by heating small pads soaked in repellant,which then diffuses a protected area around you,a great alternative to spraying yourself with insecticide.But they have limitations,including the range,turning them on manually,and then waiting for the protection to kick in when the mosquitoes may find you.This research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active time.The speed and active time depend on the repellent cartridge and the number of mosquitoes.The Mamdani model is used in the proposed fuzzy system(FS).The FS consists of identifying unambiguous inputs,a fuzzification process,rule evaluation,and a defuzzification process to produce unambiguous outputs.The input variables used are the repellent cartridge and the number of mosquitoes,and the speed of mosquito repellent is used as the output variable.The whole FS is designed and simulated using MATLAB Simulink R2016b.The proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation capability.Different simulations of the proposed model are performed in many nonlinear processes.Then,a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS,yielding a maximum relative error of 10%.The experimental outcomes show that the root mean square error is reduced by 67.68%,and the mean absolute percentage error is reduced by 52.46%.Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent.

COST 231-Hata Propagation Model Optimization in 1800 MHz Band Based on Magnetic Optimization Algorithm: Application to the City of Limbé

Network planning is essential for the construction and the development of wireless networks. The network planning cannot be possible without an appropriate propagation model which in fact is its foundation. Initially used mainly for mobile radio networks, the optimization of propagation model is becoming essential for efficient deployment of the network in different types of environment, namely rural, suburban and urban especially with the emergence of concepts such as digital terrestrial television, smart cities, Internet of Things (IoT) with wide deployment for different use cases such as smart grid, smart metering of electricity, gas and water. In this paper we use an optimization algorithm that is inspired by the principles of magnetic field theory namely Magnetic Optimization Algorithm (MOA) to tune COST231-Hata propagation model. The dataset used is the result of drive tests carry out on field in the town of Limbe in Cameroon. We take into account the standard K-factor model and then use the MOA algorithm in order to set up a propagation model adapted to the physical environment of a town. The town of Limbe is used as an implementation case, but the proposed method can be used everywhere. The calculation of the root mean square error (RMSE) between the real data from the radio measurements and the prediction data obtained after the implementation of MOA allows the validation of the results. A comparative study between the value of the RMSE obtained by the new model and those obtained by the optimization using linear regression, by the standard COST231-Hata models, and the free space model is also done, this allows us to conclude that the new model obtained using MOA for the city of Limbe is better and more representative of this local environment than the standard COST231-Hata model. The new model obtained can be used for radio planning in the city of Limbé in Cameroon.

Okumura Hata Propagation Model Optimization in 400 MHz Band Based on Differential Evolution Algorithm: Application to the City of Bertoua

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard k factors model and then uses the differential evolution algorithm to set up a propagation model adapted to the physical environment of the Cameroonian cities of Bertoua. Drive tests were made on the LTE TDD network in the city of Bertoua. Differential evolution algorithm is used as the optimization algorithm to deduct a propagation model which fits the environment of the considered town. The calculation of the root mean square error between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura Hata and free space models, allowed us to conclude that the new model obtained is better and more representative of our local environment than the Okumura Hata currently used. The implementation shows that Differential evolution can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the city of Bertoua in Cameroon.

Precision estimation and geomorphological analysis based on the DEM generated by InSAR: Taking Damxung-Yangbajain area as an example

Digital elevation model （DEM） can be generated by interferometric synthetic aperture radar （InSAR）. In this paper, the interferometric processing and analyses are carried out for Damxung-Yangbajain area in Tibet, using a pair of Europe remote-sensing satellite （ERS）-1/2 tandem SAR images acquired on 6 and 7 April 1996. A portion of the In- SAR-derived DEM is selected and compared with the 1：50 000 DEM to determine the precision of the InSAR-derived DEM. The comparison indicates that the root mean squared errors （RMSE）, which are used to evaluate error, are about 35, 60, 10, and 15 m in the studied area, mountainous area, basin area and near-fault area, respectively, suggesting that obvious errors are mainly in mountainous area. Besides, the limitation of InSAR technology to generate DEM is analyzed. Our investigation shows that InSAR is an effective tool in geodesy and an important complement to field surveying in some dangerous areas.

A plotless density estimator with a Norton-Rice distribution for ordered distances

A Norton-Rice distribution(NRD)is a versatile,flexible distribution for k ordered distances from a random location to the k nearest objects.In a context of plotless density estimation(PDE)with n randomly chosen sample locations,and distances measured to the k=6 nearest objects,the NRD provided a good fit to distance data from seven populations with a census of forest tree stem locations.More importantly,the three parameters of a NRD followed a simple trend with the order(1,…,6)of observed distances.The trend is quantified and exploited in a proposed new PDE through a joint maximum likelihood estimation of the NRD parameters expressed as a functions of distance order.In simulated probability sampling from the seven populations,the proposed PDE had the lowest overall bias with a good performance potential when compared to three alternative PDEs.However,absolute bias increased by 0.8 percentage points when sample size decreased from 20 to 10.In terms of root mean squared error(RMSE),the new proposed estimator was at par with an estimator published in Ecology when this study was wrapping up,but otherwise superior to the remaining two investigated PDEs.Coverage of nominal 95%confidence intervals averaged 0.94 for the new proposed estimators and 0.90,0.96,and 0.90 for the comparison PDEs.Despite tangible improvements in PDEs over the last decades,a globally least biased PDE remains elusive.

Estimating van Genuchten Model Parameters of Undisturbed Soils Using an Integral Method

The van Genuchten model is the most widely used soil water retention curve （SWRC） model. Two undisturbed soils （clay and loam） were used to evaluate the accuracy of the integral method to estimate van Genuchten model parameters and to determine SWRCs of undisturbed soils. SWRCs calculated by the integral method were compared with those measured by a high speed centrifuge technique. The accuracy of the calculated results was evaluated graphically, as well as by root mean square error （RMSE）, normalized root mean square error （NRMSE） and Willmott＇s index of agreement （1）. The results obtained from the integral method were quite similar to those by the centrifuge technique. The RMSEs （4.61 ×10^-5 for Eum-Orthic Anthrosol and 2.74 × 10^-4 for Los-Orthic Entisol） and NRMSEs （1.56 × 10^-4 for Eum- Orthic Anthrosol and 1.45 ×10^-3 for Los-Orthic Entisol） were relatively small. The 1 values were 0.973 and 0.943 for Eum-Orthic Anthrosol and Los-Orthic Entisol, respectively, indicating a good agreement between the integral method values and the centrifuge values. Therefore, the integral method could be used to estimate SWRCs of undisturbed clay and loam soils.

Application of SALTMED and HYDRUS-1D models for simulations of soil water content and soil salinity in controlled groundwater depth

Salinization is a gradual process that should be monitored.Modelling is a suitable alternative technique that saves time and cost for the field monitoring.But the performance of the models should be evaluated using the measured data.Therefore,the aim of this study was to evaluate and compare the SALTMED and HYDRUS-1D models using the measured soil water content,soil salinity and wheat yield data under different levels of saline irrigation water and groundwater depth.The field experiment was conducted in 2013 and in this research three controlled groundwater depths,i.e.,60(CD60),80(CD80)and 100(CD100)cm and two salinity levels of irrigation water,i.e.,4(EC4)and 8(EC8)dS/m were used in a complete randomized design with three replications.Soil water content and soil salinity were measured in soil profile and compared with the predicted values by the SALTMED and HYDRUS-1D models.Calibrations of the SALTMED and HYDRUS-1D models were carried out using the measured data under EC4-CD100 treatment and the data of the other treatments were used for validation.The statistical parameters including normalized root mean square error(NRMSE)and degree of agreement(d)showed that the values for predicting soil water content and soil salinity were more accurate in the HYDRUS-1D model than in the SALTMED model.The NRMSE and d values of the HYDRUS-1D model were 9.6%and 0.64 for the predicted soil water content and 6.2%and 0.98 for the predicted soil salinity,respectively.These indices of the SALTMED model were 10.6%and 0.81 for the predicted soil water content and 11.0%and 0.97 for the predicted soil salinity,respectively.According to the NRMSE and d values for the predicted wheat yield(9.8%and 0.91,respectively)and dry matter(2.9%and 0.99,respectively),we concluded that the SALTMED model predicted the wheat yield and dry matter accurately.

Cokriging of Soil Cation Exchange Capacity Using the First Principal Component Derived from Soil Physico-Chemical Properties

As soil cation exchange capacity （CEC） is a vital indicator of soil quality and pollutant sequestration capacity,a study was conducted to evaluate cokriging of CEC with the principal components derived from soil physico-chemical properties.In Qingdao,China,107 soil samples were collected.Soil CEC was estimated by using 86 soil samples for prediction and 21 soil samples for test.The first two principal components （PC1 and PC2） together explained 60.2% of the total variance of soil physico-chemical properties.The PC1 was highly correlated with CEC （r=0.76,P0.01）,whereas there was no significant correlation between CEC and PC2 （r=0.03）.The PC1 was then used as an auxiliary variable for the prediction of soil CEC.Mean error （ME） and root mean square error （RMSE） of kriging for the test dataset were-1.76 and 3.67 cmolc kg-1,and ME and RMSE of cokriging for the test dataset were-1.47 and 2.95 cmolc kg-1,respectively.The cross-validation R2 for the prediction dataset was 0.24 for kriging and 0.39 for cokriging.The results show that cokriging with PC1 is more reliable than kriging for spatial interpolation.In addition,principal components have the highest potential for cokriging predictions when the principal components have good correlations with the primary variables.

AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories

The COVID-19 disease has already spread to more than 213 countries and territories with infected(confirmed)cases of more than 27 million people throughout the world so far,while the numbers keep increasing.In India,this deadly disease was first detected on January 30,2020,in a student of Kerala who returned from Wuhan.Because of India’s high population density,different cultures,and diversity,it is a good idea to have a separate analysis of each state.Hence,this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of a regression model to predict the number of discharge patients and deaths in each state.The performance of the proposed prediction framework is determined by using three machine learning regression algorithms,namely Polynomial Regression(PR),Decision Tree Regression,and Random Forest(RF)Regression.The results show a comparative analysis of the states and union territories having more than 1000 cases,and the trained model is validated by testing it on further dates.The performance is evaluated using the RMSE metrics.The results show that the Polynomial Regression with an RMSE value of 0.08,shows the best performance in the prediction of the discharged patients.In contrast,in the case of prediction of deaths,Random Forest with a value of 0.14,shows a better performance than other techniques.

CARRIER FREQUENCY OFFSET ESTIMATION FOR INTERLEAVED OFDMA UPLINK BASED ON SUBSPACE PROCESSING

This paper investigates Carrier Frequency Offset (CFO) estimation in the uplink of the Orthogonal Frequency-Division Multiple Access (OFDMA) systems with the interleaved subcarrier assignment. CFOs between the transmitters and the uplink receiver will destroy orthogonality among different subcarriers, hence resulting in inter-carrier interference and multiuser interference. A two-stage frequency offset estimation algorithm based on subspace processing is proposed. The main advantage of the proposed method is that it can obtain the CFOs of all users simultaneously using only one OFDMA block. Compared with the previously known methods, it not only has a relatively low implementation complexity but is also suitable for random subchannel assignment.

Optimal sensor placement for structural response estimation

A methodology, termed estimation error minimization(EEM) method, was proposed to determine the optimal number and locations of sensors so as to better estimate the vibration response of the entire structure. Utilizing the limited sensor measurements, the entire structure response can be estimated based on the system equivalent reduction-expansion process(SEREP) method. In order to compare the capability of capturing the structural vibration response with other optimal sensor placement(OSP) methods, the effective independence(EI) method, modal kinetic energy(MKE) method and modal assurance criterion(MAC) method, were also investigated. A statistical criterion, root mean square error(RMSE), was employed to assess the magnitude of the estimation error between the real response and the estimated response. For investigating the effectiveness and accuracy of the above OSP methods, a 31-bar truss structure is introduced as a simulation example. The analysis results show that both the maximum and mean of the RMSE value obtained from the EEM method are smaller than those from other OSP methods, which indicates that the optimal sensor configuration obtained from the EEM method can provide a more accurate estimation of the entire structure response compared with the EI, MKE and MAC methods.

Application of ANFIS Model for Thailand’s Electric Vehicle Consumption

Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption.It is difficult to analyze the influence of fuel consumption with multiple and complex factors.The Adaptive Neuro-Fuzzy Inference System(ANFIS)approach was employed to develop a vehicle fuel consumption model based on multivariate input.The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting.The performance of the ANFIS network was validated using Root Mean Square Error(RMSE)and Mean Average Error(MAE)which related to the setting of ANFIS parameters.The experimental results indicated that the training data sam-ple,number,and type of membership functions are the most important factor affecting the performance of the ANFIS network.However,the number of epochs does not necessarily significantly improve the system performance,too many the number of epochs setting may not provide the best results and lead to excessive responding time.The results also demonstrate that three factors,consisted of the engine size,driving speed,and the number of passengers,are important factors that influence the change of vehicle fuel consumption.The selected ANFIS mod-els with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.

Evaluation of forecasting methods from selected stock market returns

Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making.Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices.However,there have been very few studies of groups of stock markets or indices.The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets.In this context,this study aimed to examine the predictive performance of linear,nonlinear,artificial intelligence,frequency domain,and hybrid models to find an appropriate model to forecast the stock returns of developed,emerging,and frontier markets.We considered the daily stock market returns of selected indices from developed,emerging,and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models.The results showed that no single model out of the five models could be applied uniformly to all markets.However,traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

Inter-comparison of Raingauges on Rainfall Amount and Intensity Measurements in a Tropical Environment

The HKO （Hong Kong Observatory） has been carrying out an inter-comparison of automatic raingauges since 2011 for identifying raingauges that can meet the ~ 5% accuracy requirement of the WMO （World Meteorological Organization） in measuring rainfall amount. The inter-comparison was conducted at HKO＇s meteorological stations at King＇s Park and Hong Kong International Airport in Hong Kong. Two 0.1-mm resolution Pluvio-OTT weighing gauges were introduced in 2013. This type of raingauges has outperformed others in the WMO＇s field inter-comparison held between October 2007 and April 2009. The performances of 14 raingauges, comprising five different measurement methods, viz. drop-counting, weighing, tipping bucket with software correction, tipping bucket with extra pulse correction and tipping bucket without correction, were evaluated. The focus was to study their performances in rainfall intensity measurement, especially during heavy rain situations. Different high rainfall intensity episodes were selected for analysis. Among these episodes, the maximum 1-minute rainfall intensity as high as around 130 mm/hr was recorded by the Pluvio-OTT raingauges. This paper serves to conclude the 3-year （2011-2013） inter-comparison exercise for rainfall amount measurements and to provide preliminary 1-year （2013） comparison results on rainfall intensity measurements.

Propagation Model Optimization Based on Ion Motion Optimization Algorithm for Efficient Deployment of eLTE Network

Propagation models are the foundation for radio planning in mobile networks. They are widely used during feasibility studies and initial network deployment, or during network extensions, particularly in new cities. They can be used to calculate the power of the signal received by a mobile terminal, evaluate the coverage radius, and calculate the number of cells required to cover a given area. This paper takes into account the standard K factors model and then uses the Ion motion optimization (IMO) algorithm to set up a propagation model adapted to the physical environment of each of the Cameroonian cities of Yaoundé and Bertoua for different frequencies and technologies. Drive tests were made on the CDMA network in the city of Yaoundé on one hand and on an LTE TDD network in the city of Bertoua on the other hand. IMO is used as the optimization algorithm to deduct a propagation model which fits the environment of the two considered towns. The calculation of the root-mean-square error (RMSE) between the actual data from the drive tests and the prediction data from the implemented model allows the validation of the obtained results. A comparative study made between the RMSE value obtained by the new model and those obtained by the Okumura-Hata and K factors standard models, allowed us to conclude that the new model obtained in each of these two cities is better and more representative of our local environment than the Okumura-Hata currently implemented. The implementation shows that IMO can perform well and solve this kind of optimization problem;the newly obtained models can be used for radio planning in the cities of Yaounde and Bertoua in Cameroon.

Comparative Skill of Numerical Weather Forecasts in Eastern Amazonia

The present study evaluates the performance of three numerical weather forecasting models: Global Forecast System (GFS), Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA), by means of the Mean Error (ME) and the Root Mean Square Error (RMSE), during the most rainy four months period (January to April 2012) on Eastern Amazonia. The models displayed errors of superestimation and underestimation with respect to the observed precipitation, mainly over center-north of Pará and all of Amapá, where the precipitation is higher. Among the analyzed models, GFS shows the best performance, except during January and March, when the model to underestimated precipitation, possibly due to the anomalously high values recorded.

Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary simulations.The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)algorithm.The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items.Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics.The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)vs.not dropping it.Moreover,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient.Most importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens dataset.Nearly 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF algorithm.Further validation was conducted using the 1M MovieLens and the Yahoo!Music Rating datasets.The models were constant and significant across all 3 datasets.