Search results for: prediction error

Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

Procedia PDF Downloads 88

Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Geeta Kasana, Kulbir Singh, Satvinder Singh

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In this paper, a steganographic scheme for digital images using Integer Wavelet Transform (IWT) is proposed. The cover image is decomposed into wavelet sub bands using IWT. Each of the subband is divided into blocks of equal size and secret data is embedded into the largest and smallest pixel values of each block of the subband. Visual quality of stego images is acceptable as PSNR between cover image and stego is above 40 dB, imperceptibility is maintained. Experimental results show better tradeoff between capacity and visual perceptivity compared to the existing algorithms. Maximum possible error analysis is evaluated for each of the wavelet subbands of an image.

Keywords: DWT, IWT, MSE, PSNR

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Authors: Hasan Keshavarzian, Tayebeh Nesari

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Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

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Authors: Arash Golzari Koloor

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Feedback seems to be an inseparable part of teaching a second/foreign language. One type of feedback is corrective feedback which is one type of error treatment in second language classrooms. This study is a report on the types of corrective feedback employed in an IELTS preparation course. The types of feedback, their frequencies, and their effectiveness are enlisted, enumerated, and interpreted. The results showed that explicit correction and recast were the most frequent types of feedback while repetition and elicitation were the least. The results also revealed that metalinguistic feedback, elicitation, and explicit correction were the most effective types of feedback and affected learners performance greatly.

Keywords: classroom interaction, corrective feedback, error treatment, oral performance

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Authors: Nirmal Yadav, Tanuja Srivastava

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Shannon theory is an exact method to recover a band limited signals from its sampled values in discrete implementation, using sinc interpolators. But sinc based results are not much satisfactory for band-limited calculations so that convolution with window function, having compact support, has been introduced. Convolution Backprojection algorithm with window function is an approximation algorithm. In this paper, the error has been calculated, arises due to this approximation nature of reconstruction algorithm. This result will be defined for fan beam projection data which is more faster than parallel beam projection.

Keywords: computed tomography, convolution backprojection, radon transform, fan beam

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Authors: Umit Duru

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The Soil and Water Assessment Tool (SWAT) was tested for prediction of water balance and sediment yield in the Ankara gauged basin, Turkey. The overall objective of this study was to evaluate the performance and applicability of the SWAT in this region of Turkey. Thirteen years of monthly stream flow, and suspended sediment, data were used for calibration and validation. This research assessed model performance based on differences between observed and predicted suspended sediment yield during calibration (1987-1996) and validation (1982-1984) periods. Statistical comparisons of suspended sediment produced values for NSE (Nash Sutcliffe efficiency), RE (relative error), and R² (coefficient of determination), of 0.81, -1.55, and 0.93, respectively, during the calibration period, and NSE, RE (%), and R² of 0.77, -2.61, and 0.87, respectively, during the validation period. Based on the analyses, SWAT satisfactorily simulated observed hydrology and sediment yields and can be used as a tool in decision making for water resources planning and management in the basin.

Keywords: calibration, GIS, sediment yield, SWAT, validation

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Authors: Md. Rashidul Hasan, Atikur Rahman Baizid

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The Bayesian estimation approach is a non-classical estimation technique in statistical inference and is very useful in real world situation. The aim of this paper is to study the Bayes estimators of the parameter of exponential distribution under different loss functions and then compared among them as well as with the classical estimator named maximum likelihood estimator (MLE). In our real life, we always try to minimize the loss and we also want to gather some prior information (distribution) about the problem to solve it accurately. Here the gamma prior is used as the prior distribution of exponential distribution for finding the Bayes estimator. In our study, we also used different symmetric and asymmetric loss functions such as squared error loss function, quadratic loss function, modified linear exponential (MLINEX) loss function and non-linear exponential (NLINEX) loss function. Finally, mean square error (MSE) of the estimators are obtained and then presented graphically.

Keywords: Bayes estimator, maximum likelihood estimator (MLE), modified linear exponential (MLINEX) loss function, Squared Error (SE) loss function, non-linear exponential (NLINEX) loss function

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Authors: M. Arifuzzaman, H. S. Kim

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Multiple sets of variables associated with expanded perlite particle consolidation in foam manufacturing were analyzed to develop a model for predicting perlite foam density. The consolidation of perlite particles based on the flotation method and compaction involves numerous variables leading to the final perlite foam density. The variables include binder content, compaction ratio, perlite particle size, various perlite particle densities and porosities, and various volumes of perlite at different stages of process. The developed model was found to be useful not only for prediction of foam density but also for optimization between compaction ratio and binder content to achieve a desired density. Experimental verification was conducted using a range of foam densities (0.15–0.5 g/cm3) produced with a range of compaction ratios (1.5-3.5), a range of sodium silicate contents (0.05–0.35 g/ml) in dilution, a range of expanded perlite particle sizes (1-4 mm), and various perlite densities (such as skeletal, material, bulk, and envelope densities). A close agreement between predictions and experimental results was found.

Keywords: expanded perlite, flotation method, foam density, model, prediction, sodium silicate

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Authors: Hary Aprianto Wijaya Siahaan, Bayu Edo Pratama

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Sea fishery's potential to become one of the nation's assets which very contributed to Indonesia's economy. This fishery potential not in spite of the availability of the chlorophyll in the territorial waters of Indonesia. The research was conducted using three methods, namely: statistics, comparative and analytical. The data used include MODIS sea temperature data imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, MODIS data of chlorophyll-a imaging results in Aqua satellite with a resolution of 4 km in 2002-2015, and Imaging results data ASCAT on MetOp and NOAA satellites with 27 km resolution in 2002-2015. The results of the processing of the data show that the incidence of upwelling in the south of East Java Sea began to happen in June identified with sea surface temperature anomaly below normal, the mass of the air that moves from the East to the West, and chlorophyll-a concentrations are high. In July the region upwelling events are increasingly expanding towards the West and reached its peak in August. Chlorophyll-a concentration prediction using multiple linear regression equations demonstrate excellent results to chlorophyll-a concentrations prediction in 2002 until 2015 with the correlation of predicted chlorophyll-a concentration indicate a value of 0.8 and 0.3 with RMSE value. On the chlorophyll-a concentration prediction in 2016 indicate good results despite a decline in the value of the correlation, where the correlation of predicted chlorophyll-a concentration in the year 2016 indicate a value 0.6, but showed improvement in RMSE values with 0.2.

Keywords: satellite, sea surface temperature, upwelling, wind stress

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Authors: Hernani Brinati, Mardel de Conti, Moyses Szajnbok, Valentina Domiciano

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This study brings a mathematical model and examples for the numerical prediction of submersible maneuvers in the horizontal and in the vertical planes. The geometry of the submarine is here taken as a body of revolution plus a sail, two horizontal and two vertical rudders. The model includes the representation of the hull resistance and of the propeller thrust and torque, what enables to consider the variation of the longitudinal component of the velocity of the ship when maneuvering. The hydrodynamic forces are represented through power series expansions of the acceleration and velocity components. The hydrodynamic derivatives for the body of revolution are mostly estimated based on fundamental principles applicable to the flow around airplane fuselages in the subsonic regime. The hydrodynamic forces for the sail and rudders are estimated based on a finite aspect ratio wing theory. The objective of this study is to build an expedite model for submarine maneuvers prediction, based on fundamental principles, which may be convenient in the early stages of the ship design. This model is tested against available numerical and experimental data.

Keywords: submarine maneuvers, submarine, maneuvering, dynamics

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Authors: I. M. Abd Rahim, H. Abdul Rahim, R. Ghazali

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There is numerous non-invasive blood glucose measurement technique developed by researchers, and near infrared (NIR) is the potential technique nowadays. However, there are some disagreements on the optimal wavelength range that is suitable to be used as the reference of the glucose substance in the blood. This paper focuses on the experimental data collection technique and also the analysis method used to analyze the data gained from the experiment. The selection of suitable linear and non-linear model structure is essential in prediction system, as the system developed need to be conceivably accurate.

Keywords: linear, near-infrared (NIR), non-invasive, non-linear, prediction system

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Authors: Raquel M. De sousa, Sofiane Labidi, Allan Kardec D. Barros, Alex O. Barradas Filho, Aldalea L. B. Marques

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Several parameters are established in order to measure biodiesel quality. One of them is the iodine value, which is an important parameter that measures the total unsaturation within a mixture of fatty acids. Limitation of unsaturated fatty acids is necessary since warming of a higher quantity of these ones ends in either formation of deposits inside the motor or damage of lubricant. Determination of iodine value by official procedure tends to be very laborious, with high costs and toxicity of the reagents, this study uses an artificial neural network (ANN) in order to predict the iodine value property as an alternative to these problems. The methodology of development of networks used 13 esters of fatty acids in the input with convergence algorithms of backpropagation type were optimized in order to get an architecture of prediction of iodine value. This study allowed us to demonstrate the neural networks’ ability to learn the correlation between biodiesel quality properties, in this case iodine value, and the molecular structures that make it up. The model developed in the study reached a correlation coefficient (R) of 0.99 for both network validation and network simulation, with Levenberg-Maquardt algorithm.

Keywords: artificial neural networks, biodiesel, iodine value, prediction

Procedia PDF Downloads 584

Authors: Hong Li, Haiyang Yu, Shiqing Cheng, Nai Cao, Zhiliang Shi

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Unconventional resources have gradually become the main direction for oil and gas exploration and development. However, the productivity of gas wells, the level of water production, and the seepage law in tight fractured gas reservoirs are very different. These are the reasons why production prediction is so difficult. Firstly, a three-dimensional multi-scale fracture and multiphase mathematical model based on an embedded discrete fracture model (EDFM) is established. And the material balance method is used to calculate the water body multiple according to the production performance characteristics of water-producing gas well. This will help construct a 'virtual water body'. Based on these, this paper presents a numerical simulation process that can adapt to different production modes of gas wells. The research results show that fractures have a double-sided effect. The positive side is that it can increase the initial production capacity, but the negative side is that it can connect to the water body, which will lead to the gas production drop and the water production rise both rapidly, showing a 'scissor-like' characteristic. It is worth noting that fractures with different angles have different abilities to connect with the water body. The higher the angle of gas well development, the earlier the water maybe break through. When the reservoir is a single layer, there may be a stable production period without water before the fractures connect with the water body. Once connected, a 'scissors shape' will appear. If the reservoir has multiple layers, the gas and water will produce at the same time. The above gas-water relationship can be matched with the gas well production date of the Xujiahe gas reservoir in the Sichuan Basin. This method is used to predict the productivity of a well with hydraulic fractures in this gas reservoir, and the prediction results are in agreement with on-site production data by more than 90%. It shows that this research idea has great potential in the productivity prediction of water-producing gas wells. Early prediction results are of great significance to guide the design of development plans.

Keywords: EDFM, multiphase, multilayer, water body

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Jie Xu, Zengshan Tian, Ze Li

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Previous indoor ranging or localization systems achieving high accuracy time of flight (ToF) estimation relied on two key points. One is to do strict time and frequency synchronization between the transmitter and receiver to eliminate equipment asynchronous errors such as carrier frequency offset (CFO), but this is difficult to achieve in a practical communication system. The other one is to extend the total bandwidth of the communication because the accuracy of ToF estimation is proportional to the bandwidth, and the larger the total bandwidth, the higher the accuracy of ToF estimation obtained. For example, ultra-wideband (UWB) technology is implemented based on this theory, but high precision ToF estimation is difficult to achieve in common WiFi or Bluetooth systems with lower bandwidth compared to UWB. Therefore, it is meaningful to study how to achieve high-precision ranging with lower bandwidth when the transmitter and receiver are asynchronous. To tackle the above problems, we propose a two-way channel error elimination theory and a frequency hopping-based carrier phase ranging algorithm to achieve high accuracy ranging under asynchronous conditions. The two-way channel error elimination theory uses the symmetry property of the two-way channel to solve the asynchronous phase error caused by the asynchronous transmitter and receiver, and we also study the effect of the two-way channel generation time difference on the phase according to the characteristics of different hardware devices. The frequency hopping-based carrier phase ranging algorithm uses frequency hopping to extend the equivalent bandwidth and incorporates a carrier phase ranging algorithm with multipath resolution to achieve a ranging accuracy comparable to that of UWB at 400 MHz bandwidth in the typical 80 MHz bandwidth of commercial WiFi. Finally, to verify the validity of the algorithm, we implement this theory using a software radio platform, and the actual experimental results show that the method proposed in this paper has a median ranging error of 5.4 cm in the 5 m range, 7 cm in the 10 m range, and 10.8 cm in the 20 m range for a total bandwidth of 80 MHz.

Keywords: frequency hopping, phase error elimination, carrier phase, ranging

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Authors: A. Brahma

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The drying of hydraulics materials is unavoidable and conducted to important spontaneous deformations. In this study, we show that it is possible to describe the drying shrinkage of the high-performance concrete by a simple expression. A multiple regression model was developed for the prediction of the drying shrinkage of the high-performance concrete. The assessment of the proposed model has been done by a set of statistical tests. The model developed takes in consideration the main parameters of confection and conservation. There was a very good agreement between drying shrinkage predicted by the multiple regression model and experimental results. The developed model adjusts easily to all hydraulic concrete types.

Keywords: hydraulic concretes, drying, shrinkage, prediction, modeling

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Authors: S. Mousavian, A. Abedianpour, A. Khanmohammadi, S. Hematian, Gh. Eidi Veisi

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Ionic liquids are finding a wide range of applications from reaction media to separations and materials processing. In these applications, Vapor–Liquid equilibrium (VLE) is the most important one. VLE for six systems at 353 K and activity coefficients at infinite dilution 〖(γ〗_i^∞) for various solutes (alkanes, alkenes, cycloalkanes, cycloalkenes, aromatics, alcohols, ketones, esters, ethers, and water) in the ionic liquids (1-ethyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide [EMIM][BTI], 1-hexyl-3-methyl imidazolium bis (trifluoromethylsulfonyl) imide [HMIM][BTI], 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide [OMIM][BTI], and 1-butyl-1-methylpyrrolidinium bis (trifluoromethylsulfonyl) imide [BMPYR][BTI]) have been used to train neural networks in the temperature range from (303 to 333) K. Densities of the ionic liquids, Hildebrant constant of substances, and temperature were selected as input of neural networks. The networks with different hidden layers were examined. Networks with seven neurons in one hidden layer have minimum error and good agreement with experimental data.

Keywords: ionic liquid, neural networks, VLE, dilute solution

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Authors: Chaluntorn Vichasilp, Sutee Wangtueai

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This study aims to develop a new method to detect active compounds in Thai herbs (1-deoxynojirimycin (DNJ) in mulberry leave, anthocyanin in Mao and curcumin in turmeric) using near infrared spectroscopy (NIRs). NIRs is non-destructive technique that rapid, non-chemical involved and low-cost determination. By NIRs and chemometrics technique, it was found that the DNJ prediction equation conducted with partial least square regression with cross-validation had low accuracy R2 (0.42) and SEP (31.87 mg/100g). On the other hand, the anthocyanin prediction equation showed moderate good results (R2 and SEP of 0.78 and 0.51 mg/g) with Multiplication scattering correction at wavelength of 2000-2200 nm. The high absorption could be observed at wavelength of 2047 nm and this model could be used as screening level. For curcumin prediction, the good result was obtained when applied original spectra with smoothing technique. The wavelength of 1400-2500 nm was created regression model with R2 (0.68) and SEP (0.17 mg/g). This model had high NIRs absorption at a wavelength of 1476, 1665, 1986 and 2395 nm, respectively. NIRs showed prospective technique for detection of some active compounds in Thai herbs.

Keywords: anthocyanin, curcumin, 1-deoxynojirimycin (DNJ), near infrared spectroscopy (NIRs)

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Authors: Eze R. Onukwugha

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The presence of cyanide in wastewater is known to inhibit the normal functioning of bio-reactors since it has the tendency to poison reactor micro-organisms. Bench scale models of activated sludge reactors with varying aspect ratios were operated for the treatment of cassava wastewater at several values of hydraulic retention time (HRT). The different values of HRT were achieved by the use of a peristaltic pump to vary the rate of introduction of the wastewater into the reactor. The main parameters monitored are the cyanide concentration and respective COD values of the influent and effluent. These observed values were then transformed into a mathematical model for the prediction of treatment efficiency.

Keywords: wastewater, aspect ratio, cyanide-inhibited wastewater, modeling

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Authors: Lela Mirtskhulava, Mariam Khunjgurua, Nino Lomineishvili, Koba Bakuria

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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability

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Authors: Dmitry Dikarev, Ajit Nimbalker, Alexei Davydov

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Polar coding is a novel example of error correcting codes, which can achieve Shannon limit at block length N→∞ with log-linear complexity. Active research is being carried to adopt this theoretical concept for using in practical applications such as 5th generation wireless communication systems. Cyclic redundancy check (CRC) error detection code is broadly used in conjunction with successive cancellation list (SCL) decoding algorithm to improve finite-length polar code performance. However, there are two issues: increase of code block payload overhead by CRC bits and decrease of CRC error-detection capability. This paper proposes a method to control CRC overhead and false alarm rate of polar decoding. As shown in the computer simulations results, the proposed method provides the ability to use any set of CRC polynomials with any list size while maintaining the desired level of false alarm rate. This level of flexibility allows using polar codes in 5G New Radio standard.

Keywords: 5G New Radio, channel coding, cyclic redundancy check, list decoding, polar codes

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Authors: Mehdi Rostamzadeh

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Genetic Algorithms (GAs) are an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this study, we apply GAs for fundamental and technical models of exchange rate determination in exchange rate market. In this framework, we estimated absolute and relative purchasing power parity, Mundell-Fleming, sticky and flexible prices (monetary models), equilibrium exchange rate and portfolio balance model as fundamental models and Auto Regressive (AR), Moving Average (MA), Auto-Regressive with Moving Average (ARMA) and Mean Reversion (MR) as technical models for Iranian Rial against European Union’s Euro using monthly data from January 1992 to December 2014. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria i.e. R-Squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE).Based on obtained Results, it seems that for explaining of Iranian Rial against EU Euro exchange rate behavior, fundamental models are better than technical models.

Keywords: exchange rate, genetic algorithm, fundamental models, technical models

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Authors: Melissa Langevin, Natalie Ward, Colleen Fitzgibbons, Christa Ramsey, Melanie Hogue, Anna Theresa Lobos

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Description: Root Cause Analysis (RCA) is used by health care teams to examine adverse events (AEs) to identify causes which then leads to recommendations for prevention Despite widespread use, RCA has limitations. Best practices have not been established for implementing recommendations or tracking the impact of interventions after AEs. During phase 1 of this study, we used simulation to analyze two fictionalized AEs that occurred in hospitalized paediatric patients to identify and understand how the errors occurred and generated recommendations to mitigate and prevent recurrences. Scenario A involved an error of commission (inpatient drug error), and Scenario B involved detecting an error that already occurred (critical care drug infusion error). Recommendations generated were: improved drug labeling, specialized drug kids, alert signs and clinical checklists. Aim: Use simulation to optimize interventions recommended post critical event analysis prior to implementation in the clinical environment. Methods: Suggested interventions from Phase 1 were designed and tested through scenario simulation in the clinical environment (medicine ward or pediatric intensive care unit). Each scenario was simulated 8 times. Recommendations were tested using different, voluntary teams and each scenario was debriefed to understand why the error was repeated despite interventions and how interventions could be improved. Interventions were modified with subsequent simulations until recommendations were felt to have an optimal effect and data saturation was achieved. Along with concrete suggestions for design and process change, qualitative data pertaining to employee communication and hospital standard work was collected and analyzed. Results: Each scenario had a total of three interventions to test. In, scenario 1, the error was reproduced in the initial two iterations and mitigated following key intervention changes. In scenario 2, the error was identified immediately in all cases where the intervention checklist was utilized properly. Independently of intervention changes and improvements, the simulation was beneficial to identify which of these should be prioritized for implementation and highlighted that even the potential solutions most frequently suggested by participants did not always translate into error prevention in the clinical environment. Conclusion: We conclude that interventions that help to change process (epinephrine kit or mandatory checklist) were more successful at preventing errors than passive interventions (signage, change in memory aids). Given that even the most successful interventions needed modifications and subsequent re-testing, simulation is key to optimizing suggested changes. Simulation is a safe, practice changing modality for institutions to use prior to implementing recommendations from RCA following AE reviews.

Keywords: adverse events, patient safety, pediatrics, root cause analysis, simulation

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Authors: Mingyang Sun, Guozheng Yan, Dasheng Liu, Lei Yang

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Driven by the demand of intelligent monitoring in rehabilitation centers or hospitals, a high accuracy real-time location system based on UWB (ultra-wideband) technology was proposed. The system measures precise location of a specific person, traces his movement and visualizes his trajectory on the screen for doctors or administrators. Therefore, doctors could view the position of the patient at any time and find them immediately and exactly when something emergent happens. In our design process, different algorithms were discussed, and their errors were analyzed. In addition, we discussed about a , simple but effective way of correcting the antenna delay error, which turned out to be effective. By choosing the best algorithm and correcting errors with corresponding methods, the system attained a good accuracy. Experiments indicated that the ranging error of the system is lower than 7 cm, the locating error is lower than 20 cm, and the refresh rate exceeds 5 times per second. In future works, by embedding the system in wearable IoT (Internet of Things) devices, it could provide not only physical parameters, but also the activity status of the patient, which would help doctors a lot in performing healthcare.

Keywords: intelligent monitoring, ultra-wideband technology, real-time location, IoT devices, smart healthcare

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Authors: Muhammad A. Alsubaie, Mubarak K. H. Alhajri, Tarek S. Altowaim

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A repetitive controller designed to accommodate periodic disturbances via state feedback is discussed. Periodic disturbances can be represented by a time delay model in a positive feedback loop acting on system output. A direct use of the small gain theorem solves the periodic disturbances problem via 1) isolating the delay model, 2) finding the overall system representation around the delay model and 3) designing a feedback controller that assures overall system stability and tracking error convergence. This paper addresses uncertainty conditions for the repetitive controller designed in state feedback in either past error feedforward or current error feedback using singular values. The uncertainty investigation is based on the overall system found and the stability condition associated with it; depending on the scheme used, to set an upper/lower limit weighting parameter. This creates a region that should not be exceeded in selecting the weighting parameter which in turns assures performance improvement against system uncertainty. Repetitive control problem can be described in lifted form. This allows the usage of singular values principle in setting the range for the weighting parameter selection. The Simulation results obtained show a tracking error convergence against dynamic system perturbation if the weighting parameter chosen is within the range obtained. Simulation results also show the advantage of weighting parameter usage compared to the case where it is omitted.

Keywords: model mismatch, repetitive control, singular values, state feedback

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Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

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Authors: Mojtaba Esmaeilzad Limoudehi

Abstract:

To evaluate the model ORYZA2000, under the management of irrigation and nitrogen fertilization experiment, a split plot with a randomized complete block design with three replications on hybrid cultivars (spring) in the 1388-1387 crop year was conducted at the Rice Research Institute. Permanent flood irrigation as the main plot in the fourth level, around 5 days, from 11 days to 8 days away, and the four levels of nitrogen fertilizer as the subplots 0, 90, 120, and 150 kg N Ha were considered. Simulated and measured values of leaf area index, grain yield, and biological parameters using the regression coefficient, t-test, the root mean square error (RMSE), and normalized root mean square error (RMSEn) were performed. Results, the normalized root mean square error of 10% in grain yield, the biological yield of 9%, and 23% of maximum LAI was determined. The simulation results show that grain yield and biological ORYZA2000 model accuracy are good but do not simulate maximum LAI well. The results show that the model can support ORYZA2000 test results and can be used under conditions of nitrogen fertilizer and irrigation management.

Keywords: evaluation, rice, nitrogen fertilizer, model ORYZA2000

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Authors: A. Daftari, W. Kudla

Abstract:

Liquefaction is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid cyclic loading. Liquefaction and related phenomena have been responsible for huge amounts of damage in historical earthquakes around the world. Modelling of soil behaviour is the main step in soil liquefaction prediction process. Nowadays, several constitutive models for sand have been presented. Nevertheless, only some of them can satisfy this mechanism. One of the most useful models in this term is UBCSAND model. In this research, the capability of this model is considered by using PLAXIS software. The real data of superstition hills earthquake 1987 in the Imperial Valley was used. The results of the simulation have shown resembling trend of the UBC3D-PLM model.

Keywords: liquefaction, plaxis, pore-water pressure, UBC3D-PLM

Procedia PDF Downloads 292

Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

Abstract:

Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

Procedia PDF Downloads 50