Search results for: posterior.

Authors: N. Feroze, M. Aslam

Abstract:

The article is concerned with analysis of failure rate (shape parameter) under the Topp Leone distribution using a Bayesian framework. Different loss functions and a couple of noninformative priors have been assumed for posterior estimation. The posterior predictive distributions have also been derived. A simulation study has been carried to compare the performance of different estimators. A real life example has been used to illustrate the applicability of the results obtained. The findings of the study suggest  that the precautionary loss function based on Jeffreys prior and singly type II censored samples can effectively be employed to obtain the Bayes estimate of the failure rate under Topp Leone distribution.

Keywords: loss functions, type II censoring, posterior distribution, Bayes estimators.

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Authors: T. N. Sindhu, M. Aslam

Abstract:

A comprehensive Bayesian analysis has been carried out in the context of informative and non-informative priors for the shape parameter of the Burr type X distribution under different symmetric and asymmetric loss functions. Elicitation of hyperparameter through prior predictive approach is also discussed. Also we derive the expression for posterior predictive distributions, predictive intervals and the credible Intervals. As an illustration, comparisons of these estimators are made through simulation study.

Keywords: Credible Intervals, Loss Functions, Posterior Predictive Distributions, Predictive Intervals.

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Authors: H. Bevrani, N. Najafi

Abstract:

This paper uses p-tolerance with the lowest posterior loss, quadratic loss function, average length criteria, average coverage criteria, and worst outcome criterion for computing of sample size to estimate proportion in Binomial probability function with Beta prior distribution. The proposed methodology is examined, and its effectiveness is shown.

Keywords: Bayesian inference, Beta-binomial Distribution, LPLcriteria, quadratic loss function.

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Authors: Yohei Saika, Yuji Haraguchi

Abstract:

We constructed a method of noise reduction for JPEG-compressed image based on Bayesian inference using the maximizer of the posterior marginal (MPM) estimate. In this method, we tried the MPM estimate using two kinds of likelihood, both of which enhance grayscale images converted into the JPEG-compressed image through the lossy JPEG image compression. One is the deterministic model of the likelihood and the other is the probabilistic one expressed by the Gaussian distribution. Then, using the Monte Carlo simulation for grayscale images, such as the 256-grayscale standard image “Lena" with 256 × 256 pixels, we examined the performance of the MPM estimate based on the performance measure using the mean square error. We clarified that the MPM estimate via the Gaussian probabilistic model of the likelihood is effective for reducing noises, such as the blocking artifacts and the mosquito noise, if we set parameters appropriately. On the other hand, we found that the MPM estimate via the deterministic model of the likelihood is not effective for noise reduction due to the low acceptance ratio of the Metropolis algorithm.

Keywords: Noise reduction, JPEG-compressed image, Bayesian inference, the maximizer of the posterior marginal estimate

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Authors: Yi-Fan Lin, Peter P. Howley, Frank A. Tuyl

Abstract:

Quality measurement and reporting systems are used in healthcare internationally. In Australia, the Australian Council on Healthcare Standards records and reports hundreds of clinical indicators (CIs) nationally across the healthcare system. These CIs are measures of performance in the clinical setting, and are used as a screening tool to help assess whether a standard of care is being met. Existing analysis and reporting of these CIs incorporate Bayesian methods to address sampling variation; however, such assessments are retrospective in nature, reporting upon the previous six or twelve months of data. The use of Bayesian methods within statistical process control for monitoring systems is an important pursuit to support more timely decision-making. Our research has developed and assessed a new graphical monitoring tool, similar to a control chart, based on the beta-binomial posterior predictive (BBPP) distribution to facilitate the real-time assessment of health care organizational performance via CIs. The BBPP charts have been compared with the traditional Bernoulli CUSUM (BC) chart by simulation. The more traditional “central” and “highest posterior density” (HPD) interval approaches were each considered to define the limits, and the multiple charts were compared via in-control and out-of-control average run lengths (ARLs), assuming that the parameter representing the underlying CI rate (proportion of cases with an event of interest) required estimation. Preliminary results have identified that the BBPP chart with HPD-based control limits provides better out-of-control run length performance than the central interval-based and BC charts. Further, the BC chart’s performance may be improved by using Bayesian parameter estimation of the underlying CI rate.

Keywords: Average run length, Bernoulli CUSUM chart, beta binomial posterior predictive distribution, clinical indicator, health care organization, highest posterior density interval.

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Authors: L. Korpinen, P. Taskinen, P. Rautio

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This study aimed to describe procedures that we developed to use in the quantitative, bone-specific SPECT/CT at our hospital. Our procedures included the following questions for choosing imaging protocols, which were based on a clinical doctor's referral: (1) Is she/he a cancer patient or not? (2) Are there any indications of inflammatory rheumatoid arthritis? We performed about 1,106 skeletal scintigraphies over two years. About 394 patients were studied with quantitative bone-specific single-photon emission computed tomography/computerized tomography (SPECT/CT) (i.e., about 36% of all bone scintigraphies). Approximately 64% of the patients were studied using the conventional Anterior-Posterior/Posterior-Anterior imaging. Our procedure has improved efficiency and decreased cycle times.

Keywords: Skeletal scintigraphy, SPECT/CT, imaging.

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Authors: N. Deetae, S. Sukparungsee, Y. Areepong, K. Jampachaisri

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This research is aimed to compare the percentages of correct classification of Empirical Bayes method (EB) to Classical method when data are constructed as near normal, short-tailed and long-tailed symmetric, short-tailed and long-tailed asymmetric. The study is performed using conjugate prior, normal distribution with known mean and unknown variance. The estimated hyper-parameters obtained from EB method are replaced in the posterior predictive probability and used to predict new observations. Data are generated, consisting of training set and test set with the sample sizes 100, 200 and 500 for the binary classification. The results showed that EB method exhibited an improved performance over Classical method in all situations under study.

Keywords: Classification, Empirical Bayes, Posterior predictive probability.

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Authors: Samuel A. Phillips, Emmanuel A. Ayanlowo, Rasaki O. Olanrewaju, Olayode Fatoki

Abstract:

This paper employs the Jeffrey's prior technique in the process of estimating the periodograms and frequency of sinusoidal model for unknown noisy time variants or oscillating events (data) in a Bayesian setting. The non-informative Jeffrey's prior was adopted for the posterior trigonometric function of the sinusoidal model such that Cramer-Rao Lower Bound (CRLB) inference was used in carving-out the minimum variance needed to curb the invariance structure effect for unknown noisy time observational and repeated circular patterns. An average monthly oscillating temperature series measured in degree Celsius (0C) from 1901 to 2014 was subjected to the posterior solution of the unknown noisy events of the sinusoidal model via Markov Chain Monte Carlo (MCMC). It was not only deduced that two minutes period is required before completing a cycle of changing temperature from one particular degree Celsius to another but also that the sinusoidal model via the CRLB-Jeffrey's prior for unknown noisy events produced a miniature posterior Maximum A Posteriori (MAP) compare to a known noisy events.

Keywords: Cramer-Rao Lower Bound (CRLB), Jeffrey's prior, Sinusoidal, Maximum A Posteriori (MAP), Markov Chain Monte Carlo (MCMC), Periodograms.

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Authors: Yohei Saika, Tatsuya Uezu

Abstract:

We constructed a method of phase unwrapping for a typical wave-front by utilizing the maximizer of the posterior marginal (MPM) estimate corresponding to equilibrium statistical mechanics of the three-state Ising model on a square lattice on the basis of an analogy between statistical mechanics and Bayesian inference. We investigated the static properties of an MPM estimate from a phase diagram using Monte Carlo simulation for a typical wave-front with synthetic aperture radar (SAR) interferometry. The simulations clarified that the surface-consistency conditions were useful for extending the phase where the MPM estimate was successful in phase unwrapping with a high degree of accuracy and that introducing prior information into the MPM estimate also made it possible to extend the phase under the constraint of the surface-consistency conditions with a high degree of accuracy. We also found that the MPM estimate could be used to reconstruct the original wave-fronts more smoothly, if we appropriately tuned hyper-parameters corresponding to temperature to utilize fluctuations around the MAP solution. Also, from the viewpoint of statistical mechanics of the Q-Ising model, we found that the MPM estimate was regarded as a method for searching the ground state by utilizing thermal fluctuations under the constraint of the surface-consistency condition.

Keywords: Bayesian inference, maximizer of the posterior marginal estimate, phase unwrapping, Monte Carlo simulation, statistical mechanics

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Authors: K. Jayakumar, S. Thangavel

Abstract:

In this paper proposed the effective fault detection of industrial drives by using Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN) system. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate, and execution time.

Keywords: Biorthogonal Wavelet Transform, Posterior Probabilistic Neural Network, Induction Motor.

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we propose the variational EM inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multiclass. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: Bayesian rule, Gaussian process classification model with multiclass, Gaussian process prior, human action classification, laplace approximation, variational EM algorithm.

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Authors: M. Mottaghi, F. Ghalichi, H. Badri Ghavifekr, H. Niroomand Oskui

Abstract:

This paper presents design and characterization of a microaccelerometer designated for integration into cataract surgical probe to detect hardness of different eye tissues during cataract surgery. Soft posterior lens capsule of eye can be easily damaged in comparison with hard opaque lens since the surgeon can not see directly behind cutting needle during the surgery. Presence of microsensor helps the surgeon to avoid rupturing posterior lens capsule which if occurs leads to severe complications such as glaucoma, infection, or even blindness. The microsensor having overall dimensions of 480 μm x 395 μm is able to deliver significant capacitance variations during encountered vibration situations which makes it capable to distinguish between different types of tissue. Integration of electronic components on chip ensures high level of reliability and noise immunity while minimizes space and power requirements. Physical characteristics and results on performance testing, proves integration of microsensor as an effective tool to aid the surgeon during this procedure.

Keywords: Cataract surgery, MEMS, Microsensor, Phacoemulsification.

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Authors: Azhar Hussain, Kashif Shahzad, Chunming Tang

Abstract:

this paper gives a novel approach towards real-time speed estimation of multiple traffic vehicles using fuzzy logic and image processing techniques with proper arrangement of camera parameters. The described algorithm consists of several important steps. First, the background is estimated by computing median over time window of specific frames. Second, the foreground is extracted using fuzzy similarity approach (FSA) between estimated background pixels and the current frame pixels containing foreground and background. Third, the traffic lanes are divided into two parts for both direction vehicles for parallel processing. Finally, the speeds of vehicles are estimated by Maximum a Posterior Probability (MAP) estimator. True ground speed is determined by utilizing infrared sensors for three different vehicles and the results are compared to the proposed algorithm with an accuracy of ± 0.74 kmph.

Keywords: Defuzzification, Fuzzy similarity approach, lane cropping, Maximum a Posterior Probability (MAP) estimator, Speed estimation

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Authors: Debora B. Moretti, Wiolene M. Nordi, Thaline Maira P. Cruz, José Eurico P. Cyrino, Raul Machado-Neto

Abstract:

Enzyme activity was evaluated in the intestine of juvenile dourado (Salminus brasiliensis) fed with diets containing 0, 10 or 20% of lyophilized bovine colostrum (LBC) inclusion for either 30 or 60 days. The intestinal enzymes acid and alkaline phosphatase (ACP and ALP, respectively), non-specific esterase (NSE), lipase (LIP), dipeptidyl aminopeptidase IV (DAP IV) and leucine aminopeptidase (LAP) were studied using histochemistry in four intestinal segments (S1, S2, S3 and posterior intestine). Weak proteolitic activity was observed in all intestinal segments for DAP IV and LAP. The activity of NSE and LIP was also weak in all intestines, except for the moderate activity of NSE in the S2 of 20% LBC group after 30 days and in the S1 of 0% LBC group after 60 days. The ACP was detected only in the S2 and S3 of the 10% LBC group after 30 days. Moderate and strong staining was observed in the first three intestinal segments for ALP and weak activity in the posterior intestine. The activity of DAP IV, LAP and ALP were also present in the cytoplasm of the enterocytes. In the present results, bovine colostrum feeding did not cause alterations in activity of intestinal enzymes.

Keywords: Carnivorous fish, enterocyte, intestinal epithelium, teleost.

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Authors: Miika Toivanen, Jouko Lampinen

Abstract:

This paper presents an online method that learns the corresponding points of an object from un-annotated grayscale images containing instances of the object. In the first image being processed, an ensemble of node points is automatically selected which is matched in the subsequent images. A Bayesian posterior distribution for the locations of the nodes in the images is formed. The likelihood is formed from Gabor responses and the prior assumes the mean shape of the node ensemble to be similar in a translation and scale free space. An association model is applied for separating the object nodes and background nodes. The posterior distribution is sampled with Sequential Monte Carlo method. The matched object nodes are inferred to be the corresponding points of the object instances. The results show that our system matches the object nodes as accurately as other methods that train the model with annotated training images.

Keywords: Bayesian modeling, Gabor filters, Online learning, Sequential Monte Carlo.

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x, and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts a motivating starting point. In this work, we extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zP , zN]. The zP component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zN component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach, coined Augmented Posterior CDE (AP-CDE), only requires a simple modification on the common normalizing flow framework, while significantly improving the interpretation of the latent component, since zP represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of x-related variations due to factors such as lighting condition and subject id, from the other random variations. Further, the experiments show that an unconditional NF neural network, based on an unsupervised model of z, such as Gaussian mixture, fails to generate interpretable results.

Keywords: Conditional density estimation, image generation, normalizing flow, supervised dimension reduction.

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: Multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, Importance sampling, approximate posterior distribution, Marginal likelihood evidence.

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Authors: Abdelouahab Moussaoui, Nabila Ferahta, Victor Chen

Abstract:

The development of aid's systems for the medical diagnosis is not easy thing because of presence of inhomogeneities in the MRI, the variability of the data from a sequence to the other as well as of other different source distortions that accentuate this difficulty. A new automatic, contextual, adaptive and robust segmentation procedure by MRI brain tissue classification is described in this article. A first phase consists in estimating the density of probability of the data by the Parzen-Rozenblatt method. The classification procedure is completely automatic and doesn't make any assumptions nor on the clusters number nor on the prototypes of these clusters since these last are detected in an automatic manner by an operator of mathematical morphology called skeleton by influence zones detection (SKIZ). The problem of initialization of the prototypes as well as their number is transformed in an optimization problem; in more the procedure is adaptive since it takes in consideration the contextual information presents in every voxel by an adaptive and robust non parametric model by the Markov fields (MF). The number of bad classifications is reduced by the use of the criteria of MPM minimization (Maximum Posterior Marginal).

Keywords: Clustering, Automatic Classification, SKIZ, MarkovFields, Image segmentation, Maximum Posterior Marginal (MPM).

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Authors: L. Melzerová, T. Janda, M. Šejnoha, J. Šejnoha

Abstract:

Two finite element (FEM) models are presented in this paper to address the random nature of the response of glued timber structures made of wood segments with variable elastic moduli evaluated from 3600 indentation measurements. This total database served to create the same number of ensembles as was the number of segments in the tested beam. Statistics of these ensembles were then assigned to given segments of beams and the Latin Hypercube Sampling (LHS) method was called to perform 100 simulations resulting into the ensemble of 100 deflections subjected to statistical evaluation. Here, a detailed geometrical arrangement of individual segments in the laminated beam was considered in the construction of two-dimensional FEM model subjected to in fourpoint bending to comply with the laboratory tests. Since laboratory measurements of local elastic moduli may in general suffer from a significant experimental error, it appears advantageous to exploit the full scale measurements of timber beams, i.e. deflections, to improve their prior distributions with the help of the Bayesian statistical method. This, however, requires an efficient computational model when simulating the laboratory tests numerically. To this end, a simplified model based on Mindlin’s beam theory was established. The improved posterior distributions show that the most significant change of the Young’s modulus distribution takes place in laminae in the most strained zones, i.e. in the top and bottom layers within the beam center region. Posterior distributions of moduli of elasticity were subsequently utilized in the 2D FEM model and compared with the original simulations.

Keywords: Bayesian inference, FEM, four point bending test, laminated timber, parameter estimation, prior and posterior distribution, Young’s modulus.

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Authors: N. Haghjou, M. J. Abdekhodaie, Y. L. Cheng, M. Saadatmand

Abstract:

Intravitreal injection (IVI) is the most common treatment for eye posterior segment diseases such as endopthalmitis, retinitis, age-related macular degeneration, diabetic retinopathy, uveitis, and retinal detachment. Most of the drugs used to treat vitreoretinal diseases, have a narrow concentration range in which they are effective, and may be toxic at higher concentrations. Therefore, it is critical to know the drug distribution within the eye following intravitreal injection. Having knowledge of drug distribution, ophthalmologists can decide on drug injection frequency while minimizing damage to tissues. The goal of this study was to develop a computer model to predict intraocular concentrations and pharmacokinetics of intravitreally injected drugs. A finite volume model was created to predict distribution of two drugs with different physiochemical properties in the rabbit eye. The model parameters were obtained from literature review. To validate this numeric model, the in vivo data of spatial concentration profile from the lens to the retina were compared with the numeric data. The difference was less than 5% between the numerical and experimental data. This validation provides strong support for the numerical methodology and associated assumptions of the current study.

Keywords: Posterior segment, Intravitreal injection (IVI), Pharmacokinetic, Modelling, Finite volume method.

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods.

Keywords: Human action recognition, Bayesian HMM, Dirichlet process mixture model, Gaussian-Wishart emission model, Variational Bayesian inference, Prior distribution and approximate posterior distribution, KTH dataset.

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Authors: A. Chowdhury, P. Egodawatta, J. M. McGree, A. Goonetilleke

Abstract:

Hydrologic models are increasingly used as tools to predict stormwater quantity and quality from urban catchments. However, due to a range of practical issues, most models produce gross errors in simulating complex hydraulic and hydrologic systems. Difficulty in finding a robust approach for model calibration is one of the main issues. Though automatic calibration techniques are available, they are rarely used in common commercial hydraulic and hydrologic modelling software e.g. MIKE URBAN. This is partly due to the need for a large number of parameters and large datasets in the calibration process. To overcome this practical issue, a framework for automatic calibration of a hydrologic model was developed in R platform and presented in this paper. The model was developed based on the time-area conceptualization. Four calibration parameters, including initial loss, reduction factor, time of concentration and time-lag were considered as the primary set of parameters. Using these parameters, automatic calibration was performed using Approximate Bayesian Computation (ABC). ABC is a simulation-based technique for performing Bayesian inference when the likelihood is intractable or computationally expensive to compute. To test the performance and usefulness, the technique was used to simulate three small catchments in Gold Coast. For comparison, simulation outcomes from the same three catchments using commercial modelling software, MIKE URBAN were used. The graphical comparison shows strong agreement of MIKE URBAN result within the upper and lower 95% credible intervals of posterior predictions as obtained via ABC. Statistical validation for posterior predictions of runoff result using coefficient of determination (CD), root mean square error (RMSE) and maximum error (ME) was found reasonable for three study catchments. The main benefit of using ABC over MIKE URBAN is that ABC provides a posterior distribution for runoff flow prediction, and therefore associated uncertainty in predictions can be obtained. In contrast, MIKE URBAN just provides a point estimate. Based on the results of the analysis, it appears as though ABC the developed framework performs well for automatic calibration.

Keywords: Automatic calibration framework, approximate Bayesian computation, hydrologic and hydraulic modelling, MIKE URBAN software, R platform.

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Authors: Zviad Ghadua, Biswa Bhattacharya

Abstract:

The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.

Keywords: Flash flood, Bayesian, flash flood guidance, FFG, forecasting, Posina.

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Authors: Joo-Hack Lee, Jin-Seung Choi, Dong-Won Kang, Jeong-Woo Seo, Ju-Young Kim, Dae-Hyeok Kim, Seung-Tae Yang, Gye-Rae Tack

Abstract:

For cycling, the analysis of the pedal force is one of the important factors in the study of exercise ability assessment and overuse injuries. In past studies, a two-axis measurement sensor was used at the sagittal plane to measure the force only in the anterior, posterior, and vertical directions and to analyze the loss of force and the injury on the frontal plane due to the forces in the right and left directions. In this study, which is a basic study on diverse analyses of the pedal force that consider the forces on the sagittal plane and the frontal plane, a three-axis pedal force measurement sensor was developed to measure the anterior-posterior (Fx), medio-lateral (Fz), and vertical (Fy) forces. The sensor was fabricated with a size and shape similar to those of the general flat pedal, and had a 550g weight that allowed smooth pedaling. Its measurement range was ±1000 N for Fx and Fz and ±2000 N for Fy, and its non-linearity, hysteresis, and repeatability were approximately 0.5%. The data were sampled at 1000 Hz using a signal collector. To use the developed sensor, the pedaling efficiency (index of efficiency, IE) and the range of left and right (medio-lateral, ML) forces were measured with two seat heights (low and high). The results of the measurement showed that the IE was higher and the force range in the ML direction was lower with the high position than with the low position. The developed measurement sensor and its application results will be useful in understanding and explaining the complicated pedaling technique, and will enable diverse kinematic analyses of the pedal force on the sagittal plane and the frontal plane.

Keywords: Cycling, Index of effectiveness, Pedal force.

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Authors: Ahmed R. Z. Baghdadi, Amira A. A. Abdallah

Abstract:

Background: With the perceived pain and poor function experienced following knee arthroplasty, patients usually feel un-satisfied. Yet, a controversy still persists on the appropriate operative technique that doesn’t affect proprioception much.

Purpose: This study compared the effects of Cruciate Retaining (CR) and Posterior Stabilized (PS) total knee arthroplasty (TKA) and uni-compartmental knee arthroplasty (UKA) on dynamic balance, pain and functional performance following rehabilitation.

Methods: Fifteen patients with CRTKA (group I), fifteen with PSTKA (group II), fifteen with UKA (group III) and fifteen indicated for arthroplasty but weren’t operated on yet (group IV) participated in the study. The mean age was 54.53±3.44, 55.13±3.48, 52.8±1.93 and 55.33±2.32 years and BMI 35.7±3.03, 35.7±1.99, 35.6±1.88 and 35.73±1.03 kg/m² for group I, II, III and IV respectively. The Berg Balance Scale (BBS), WOMAC pain subscale and Timed Up-and-Go (TUG) and Stair-Climbing (SC) tests were used for assessment. Assessments were conducted four and eight weeks pre- and post-operatively with the control group being assessed at the same time intervals. The post-operative rehabilitation involved hospitalization (1^st week), home-based (2^nd-4^th weeks), and outpatient clinic (5^th-8^th weeks) programs.

Results: The Mixed design MANOVA revealed that group III had significantly higher BBS scores, and lower pain scores and TUG and SC time than groups I and II four and eight weeks post-operatively. In addition, group I had significantly lower pain scores and SC time compared with group II eight weeks post-operatively. Moreover, the BBS scores increased significantly and the pain scores and TUG and SC time decreased significantly eight weeks post-operatively compared with the three other assessments in group I, II and III with the opposite being true four weeks post-operatively.

Interpretation/Conclusion: CRTKA is preferable to PSTKA with UKA being generally superior to TKA, possibly due to the preserved human proprioceptors in the un-excised compartmental articular surface.

Keywords: Dynamic Balance, Functional Performance, Knee Arthroplasty, Pain.

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Authors: Ahmed R. Z. Baghdadi, Mona H. Gamal Eldein

Abstract:

Background: With the perceived pain and poor function experienced following knee arthroplasty, patients usually feel un-satisfied. Yet, a controversy still persists on the appropriate operative technique that doesn’t affect proprioception much. Purpose: This study compared the effects of Cruciate Retaining (CR) and Posterior Stabilized (PS) total knee arthroplasty (TKA on dynamic balance, pain and functional performance following rehabilitation. Methods: Thirty patients with CRTKA (group I), thirty with PSTKA (group II) and fifteen indicated for arthroplasty but weren’t operated on yet (group III) participated in the study. The mean age was 54.53±3.44, 55.13±3.48 and 55.33±2.32 years and BMI 35.7±3.03, 35.7±1.99 and 35.73±1.03 kg/m2 for groups I, II and III respectively. The Berg Balance Scale (BBS), WOMAC pain subscale and Timed Up-and-Go (TUG) and Stair-Climbing (SC) tests were used for assessment. Assessments were conducted four weeks preand post-operatively, three, six and twelve months post-operatively with the control group being assessed at the same time intervals. The post-operative rehabilitation involved hospitalization (1st week), home-based (2nd-4th weeks), and outpatient clinic (5th-12th weeks) programs, follow-up to all groups for twelve months. Results: The Mixed design MANOVA revealed that group I had significantly lower pain scores and SC time compared with group II three, six and twelve months post-operatively. Moreover, the BBS scores increased significantly and the pain scores and TUG and SC time decreased significantly six months post-operatively compared with four weeks pre- and post-operatively and three months postoperatively in groups I and II with the opposite being true four weeks post-operatively. But no significant differences in BBS scores, pain scores and TUG and SC time between six and twelve months postoperatively in groups I and II. Interpretation/Conclusion: CRTKA is preferable to PSTKA, possibly due to the preserved human proprioceptors in the un-excised PCL.

Keywords: Dynamic Balance, Functional Performance, Knee Arthroplasty, Long-Term.

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Authors: I. Oloyede

Abstract:

The COVID-19 dataset is obtained by extracting weather, longitude, latitude, ISO3666, cases and death of coronavirus patients across the globe. The data were extracted for a period of eight day choosing uniform time within the specified period. Then mapping of cases and deaths with reverence to continents were obtained. Bayesian Geostastical modelling was carried out on the dataset. The study found out that countries in the tropical region suffered less deaths/attacks compared to countries in the temperate region, this is due to high temperature in the tropical region.

Keywords: COVID-19, Bayesian, geostastical modelling, prior, posterior.

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

Abstract:

Performance of different filtering approaches depends on modeling of dynamical system and algorithm structure. For modeling and smoothing the data the evaluation of posterior distribution in different filtering approach should be chosen carefully. In this paper different filtering approaches like filter KALMAN, EKF, UKF, EKS and smoother RTS is simulated in some trajectory tracking of path and accuracy and limitation of these approaches are explained. Then probability of model with different filters is compered and finally the effect of the noise variance to estimation is described with simulations results.

Keywords: Gaussian approximation, KALMAN smoother, Parameter estimation.

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Authors: Yohei Saika

Abstract:

On the basis of Bayesian inference using the maximizer of the posterior marginal estimate, we carry out phase unwrapping using multiple interferograms via generalized mean-field theory. Numerical calculations for a typical wave-front in remote sensing using the synthetic aperture radar interferometry, phase diagram in hyper-parameter space clarifies that the present method succeeds in phase unwrapping perfectly under the constraint of surface- consistency condition, if the interferograms are not corrupted by any noises. Also, we find that prior is useful for extending a phase in which phase unwrapping under the constraint of the surface-consistency condition. These results are quantitatively confirmed by the Monte Carlo simulation.

Keywords: Bayesian inference, generalized mean-field theory, phase unwrapping, statistical mechanics.

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Authors: Pedro M. A. Vitoriano, Tito. G. Amaral

Abstract:

Automated Optical Inspection (AOI) Systems are commonly used on Printed Circuit Boards (PCB) manufacturing. The use of this technology has been proven as highly efficient for process improvements and quality achievements. The correct extraction of the component for posterior analysis is a critical step of the AOI process. Nowadays, the Pattern Matching Algorithm is commonly used, although this algorithm requires extensive calculations and is time consuming. This paper will present an improved algorithm for the component localization process, with the capability of implementation in a parallel execution system.

Keywords: AOI, automated optical inspection, SMD, surface mounting devices, pattern matching, parallel execution.

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