Search results for: inverse Gaussian distribution

Authors: Jianhua Zhou, Yuwen Zhang

Abstract:

A two-step multigrid approach is proposed to solve the inverse heat conduction problem in a 3-D object under laser irradiation. In the first step, the location of the laser center is estimated using a coarse and uniform grid system. In the second step, the front-surface temperature is recovered in good accuracy using a multiple grid system in which fine mesh is used at laser spot center to capture the drastic temperature rise in this region but coarse mesh is employed in the peripheral region to reduce the total number of sensors required. The effectiveness of the two-step approach and the multiple grid system are demonstrated by the illustrative inverse solutions. If the measurement data for the temperature and heat flux on the back surface do not contain random error, the proposed multigrid approach can yield more accurate inverse solutions. When the back-surface measurement data contain random noise, accurate inverse solutions cannot be obtained if both temperature and heat flux are measured on the back surface.

Keywords: conduction, inverse problems, conjugated gradient method, laser

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Septimia Sarbu

Abstract:

The entropy rate of a stochastic process is a fundamental concept in information theory. It provides a limit to the amount of information that can be transmitted reliably over a communication channel, as stated by Shannon's coding theorems. Recently, researchers have focused on developing new measures of information that generalize Shannon's classical theory. The aim is to design more efficient information encoding and transmission schemes. This paper continues the study of generalized entropy rates, by deriving a closed-form solution to the Sharma-Mittal entropy rate for Gaussian processes. Using the squeeze theorem, we solve the limit in the definition of the entropy rate, for different values of alpha and beta, which are the parameters of the Sharma-Mittal entropy. In the end, we compare it with Shannon and Rényi's entropy rates for Gaussian processes.

Keywords: generalized entropies, Sharma-Mittal entropy rate, Gaussian processes, eigenvalues of the covariance matrix, squeeze theorem

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Authors: Mohamed Hafid, Marcel Lacroix

Abstract:

This study presents an inverse analysis for predicting the thermal conductivities and the heat flux of a high-temperature metallurgical reactor simultaneously. Once these thermal parameters are predicted, the time-varying thickness of the protective phase-change bank that covers the inside surface of the brick walls of a metallurgical reactor can be calculated. The enthalpy method is used to solve the melting/solidification process of the protective bank. The inverse model rests on the Levenberg-Marquardt Method (LMM) combined with the Broyden method (BM). A statistical analysis for the thermal parameter estimation is carried out. The effect of the position of the temperature sensors, total number of measurements and measurement noise on the accuracy of inverse predictions is investigated. Recommendations are made concerning the location of temperature sensors.

Keywords: inverse heat transfer, phase change, metallurgical reactor, Levenberg–Marquardt method, Broyden method, bank thickness

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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Authors: S. Gómez, G. Sánchez, J. Zarama, M. Castañeda Ramos, J. Escoto Alcántar, J. Torres, A. Núñez, S. Santana, F. Nájera, J. A. Lopez

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This paper shows in detail the mathematical model of direct and inverse kinematics for a robot manipulator (welding type) with four degrees of freedom. Using the D-H parameters, screw theory, numerical, geometric and interpolation methods, the theoretical and practical values of the position of robot were determined using an optimized algorithm for inverse kinematics obtaining the values of the particular joints in order to determine the virtual paths in a relatively short time.

Keywords: kinematics, degree of freedom, optimization, robot manipulator

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Authors: Jéssica Ferreira Rodrigues, Júlio Silvio De Sousa Bueno Filho, Vanessa Rios De Souza, Ana Carla Marques Pinheiro

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This study aimed to evaluate the implications of the block size and testing order on efficiency and precision of preference estimation for Dulce de leche samples. Efficiency was defined as the inverse of the average variance of pairwise comparisons among treatments. Precision was defined as the inverse of the variance of treatment means (or effects) estimates. The experiment was originally designed to test 16 treatments as a series of 8 Sudoku 16x16 designs being 4 randomized independently and 4 others in the reverse order, to yield balance in testing order. Linear mixed models were assigned to the whole experiment with 112 testers and all their grades, as well as their partially balanced subgroups, namely: a) experiment with the four initial EU; b) experiment with EU 5 to 8; c) experiment with EU 9 to 12; and b) experiment with EU 13 to 16. To record responses we used a nine-point hedonic scale, it was assumed a mixed linear model analysis with random tester and treatments effects and with fixed test order effect. Analysis of a cumulative random effects probit link model was very similar, with essentially no different conclusions and for simplicity, we present the results using Gaussian assumption. R-CRAN library lme4 and its function lmer (Fit Linear Mixed-Effects Models) was used for the mixed models and libraries Bayesthresh (default Gaussian threshold function) and ordinal with the function clmm (Cumulative Link Mixed Model) was used to check Bayesian analysis of threshold models and cumulative link probit models. It was noted that the number of samples tested in the same session can influence the acceptance level, underestimating the acceptance. However, proving a large number of samples can help to improve the samples discrimination.

Keywords: acceptance, block size, mixed linear model, testing order, testing order

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Jyoti Wadhwa, Arvinder Singh

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This work presents the theoretical investigation of an enhanced second-harmonic generation of higher-order Gaussian laser beam in plasma having a density ramp. The mechanism responsible for the self-focusing of a laser beam in plasma is considered to be the relativistic mass variation of plasma electrons under the effect of a highly intense laser beam. Using the moment theory approach and considering the Wentzel-Kramers-Brillouin approximation for the non-linear Schrodinger wave equation, the differential equation is derived, which governs the spot size of the higher-order Gaussian laser beam in plasma. The nonlinearity induced by the laser beam creates the density gradient in the background plasma electrons, which is responsible for the excitation of the electron plasma wave. The large amplitude electron plasma wave interacts with the fundamental beam, which further produces the coherent radiations with double the frequency of the incident beam. The analysis shows the important role of the different modes of higher-order Gaussian laser beam and density ramp on the efficiency of generated harmonics.

Keywords: density rippled plasma, higher order Gaussian laser beam, moment theory approach, second harmonic generation.

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Authors: Prerana Sharma

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Effect of relativistic nonlinearity on stimulated Raman scattering of the propagating laser beam carrying null intensity in center (hollow Gaussian beam) by excited plasma wave are studied in a collisionless plasma. The construction of the equations is done employing the fluid theory which is developed with partial differential equation and Maxwell’s equations. The analysis is done using eikonal method. The phenonmenon of Stimulated Raman scattering is shown along with the excitation of seed plasma wave. The power of plasma wave and back reflectivity is observed for higher order of hollow Gaussian beam. Back reflectivity is studied numerically for various orders of HGLB with different value of plasma density, laser power and beam radius. Numerical analysis shows that these parameters play vital role on reflectivity characteristics.

Keywords: Hollow Gaussian beam, relativistic nonlinearity, plasma physics, Raman scattering

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Authors: Y. T. Tsai, Jin H. Huang

Abstract:

The sound pressure level (SPL) of the moving-coil loudspeaker (MCL) is often simulated and analyzed using the lumped parameter model. However, the SPL of a MCL cannot be simulated precisely in the high frequency region, because the value of cone effective area is changed due to the geometry variation in different mode shapes, it is also related to affect the acoustic radiation mass and resistance. Herein, the paper presents the inverse method which has a high ability to measure the value of cone effective area in various frequency points, also can estimate the MCL electroacoustic parameters simultaneously. The proposed inverse method comprises the direct problem, adjoint problem, and sensitivity problem in collaboration with nonlinear conjugate gradient method. Estimated values from the inverse method are validated experimentally which compared with the measured SPL curve result. Results presented in this paper not only improve the accuracy of lumped parameter model but also provide the valuable information on loudspeaker cone design.

Keywords: inverse problem, cone effective area, loudspeaker, nonlinear conjugate gradient method

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Authors: Abbas Hani, Maryam Jassasizadeh

Abstract:

The concentrations of Cd, Pb, and Ni were determined from 40 soil samples collected in surface soils of Khark Island. Geostatistic methods and GIS were used to identify heavy metal sources and their spatial pattern. Principal component analysis coupled with correlation between heavy metals showed that level of mentioned heavy metal was lower than the standard level. Then the data obtained from the soil analyzing were studied for the purposes of normal distribution. The best way of interior finding for cadmium and nickel was ordinary kriging and the best way of interpolation of lead was inverse distance weighted. The result of this study help us to understand heavy metals distribution and make decision for remediation of soil pollution.

Keywords: geostatistics, ordinary kriging, heavy metals, GIS, Khark

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Authors: Hacene Benkhoula, Mohamed Badreddine Benabdella, Hamid Bouzeboudja, Abderrahmane Asraoui

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The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given.

Keywords: wind power, Gaussien process, modelling, forecasting

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Authors: Wanatchapong Kongkaew

Abstract:

This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Phuoc Si Nguyen

Abstract:

Infinite impulse response (IIR) filters can be designed from an analogue low pass prototype by using frequency transformation in the s-domain and bilinear z-transformation with pre-warping frequency; this method is known as frequency transformation from the s-domain to the z-domain. This paper will introduce a new method to transform an IIR digital filter to another type of IIR digital filter (low pass, high pass, band pass, band stop or narrow band) using a technique based on inverse bilinear z-transformation and inverse matrices. First, a matrix equation is derived from inverse bilinear z-transformation and Pascal’s triangle. This Low Pass Digital to Digital Filter Pascal Matrix Equation is used to transform a low pass digital filter to other digital filter types. From this equation and the inverse matrix, a Digital to Digital Filter Pascal Matrix Equation can be derived that is able to transform any IIR digital filter. This paper will also introduce some specific matrices to replace the inverse matrix, which is difficult to determine due to the larger size of the matrix in the current method. This will make computing and hand calculation easier when transforming from one IIR digital filter to another in the digital domain.

Keywords: bilinear z-transformation, frequency transformation, inverse bilinear z-transformation, IIR digital filters

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Authors: Mukesh Kumar Shah, Tushar Gupta

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An Upgraded Cuckoo Search Algorithm is proposed here to solve optimization problems based on the improvements made in the earlier versions of Cuckoo Search Algorithm. Short comings of the earlier versions like slow convergence, trap in local optima improved in the proposed version by random initialization of solution by suggesting an Improved Lambda Iteration Relaxation method, Random Gaussian Distribution Walk to improve local search and further proposing Greedy Selection to accelerate to optimized solution quickly and by “Study Nearby Strategy” to improve global search performance by avoiding trapping to local optima. It is further proposed to generate better solution by Crossover Operation. The proposed strategy used in algorithm shows superiority in terms of high convergence speed over several classical algorithms. Three standard algorithms were tested on a 6-generator standard test system and the results are presented which clearly demonstrate its superiority over other established algorithms. The algorithm is also capable of handling higher unit systems.

Keywords: economic dispatch, gaussian selection operator, prohibited operating zones, ramp rate limits

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Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases, design of experiments technique to postulate multiple linear regression equations have been used. Nowadays, Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: smaw, genetic algorithm, bead geometry, optimization/inverse mapping

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Authors: D. S. Nagesh, G. L. Datta

Abstract:

In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases design of experiments technique to postulate multiple linear regression equations have been used. Nowadays Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: SMAW, genetic algorithm, bead geometry, optimization/inverse mapping

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Authors: Mohammadamin Abbasnejad

Abstract:

The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works have been done during the past few years which has their own advantages and disadvantages. In this work, we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+, and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition.

Keywords: Gaussian Conditional Random Field, ADMM, convergence, gradient descent

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Authors: Ajay R, Rammohan B, Sridhar K S S, Gurusharan N

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The objective of the work is to develop a numerical method to solve the inverse mode shape problem by determining the cross-sectional area of a structure for the desired mode shape via the vibration response study of the humerus bone, which is in the form of a cantilever beam with anisotropic material properties. The humerus bone is the long bone in the arm that connects the shoulder to the elbow. The mode shape is assumed to be a higher-order polynomial satisfying a prescribed set of boundary conditions to converge the numerical algorithm. The natural frequency and the mode shapes are calculated for different boundary conditions to find the cross-sectional area of humerus bone from Eigenmode shape with the aid of the inverse mode shape algorithm. The cross-sectional area of humerus bone validates the mode shapes of specific boundary conditions. The numerical method to solve the inverse mode shape problem is validated in the biomedical application by finding the cross-sectional area of a humerus bone in the human arm.

Keywords: Cross-sectional area, Humerus bone, Inverse mode shape problem, Mode shape

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Authors: Alyaa M. Younes, Nermine Harraz, Mohammad H. Elwany

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Lithium ion batteries are currently used for many applications including satellites, electric vehicles and mobile electronics. Their ability to store relatively large amount of energy in a limited space make them most appropriate for critical applications. Evaluation of the life of these batteries and their reliability becomes crucial to the systems they support. Reliability of Li-Ion batteries has been mainly considered based on its lifetime. However, another important factor that can be considered critical in many applications such as in electric vehicles is the cycle duration. The present work presents the results of an experimental investigation on the degradation behavior of a Laptop Li-ion battery (type TKV2V) and the effect of applied load on the battery cycle time. The reliability was evaluated using an accelerated life test. Least squares linear regression with median rank estimation was used to estimate the Weibull distribution parameters needed for the reliability functions estimation. The probability density function, failure rate and reliability function under each of the applied loads were evaluated and compared. An inverse power model is introduced that can predict cycle time at any stress level given.

Keywords: accelerated life test, inverse power law, lithium-ion battery, reliability evaluation, Weibull distribution

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Jiazhen Chen, Yue Sun, Joanne Yip, Kit-Lun Yick

Abstract:

The design of sports bras poses a considerable challenge due to the difficulty in accurately predicting the wearing result after computer-aided design (CAD). It needs repeated physical try-on or virtual try-on to obtain a comfortable pressure range during motion. Specifically, in the context of running, the exact support area and force exerted on the breasts remain unclear. Consequently, obtaining an effective method to design the sports bra pads shape becomes particularly challenging. This predicament hinders the successful creation and production of sports bras that cater to women's health needs. The purpose of this study is to propose an effective method to obtain the 3D shape of sports bra pads and to understand the relationship between the supporting force and the 3D shape parameters of the pads. Firstly, the static 3D shape of the sports bra pad and human motion data (Running) are obtained by using the 3D scanner and advanced 4D scanning technology. The 3D shape of the sports bra pad is parameterised and simplified by Free-form Deformation (FFD). Then the sub-models of sports bra and human body are constructed by segmenting and meshing them with MSC Apex software. The material coefficient of sports bras is obtained by material testing. The Marc software is then utilised to establish a dynamic contact model between the human breast and the sports bra pad. To realise the reverse design of the sports bra pad, this contact model serves as a forward model for calculating the inverse problem. Based on the forward contact model, the inverse problem of the 3D shape parameters of the sports bra pad with the target bra-wearing pressure range as the boundary condition is solved. Finally, the credibility and accuracy of the simulation are validated by comparing the experimental results with the simulations by the FE model on the pressure distribution. On the one hand, this research allows for a more accurate understanding of the support area and force distribution on the breasts during running. On the other hand, this study can contribute to the customization of sports bra pads for different individuals. It can help to obtain sports bra pads with comfortable dynamic pressure.

Keywords: sports bra design, breast motion, running, inverse problem, finite element dynamic model

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Authors: Mohamed Barbary, Mohamed H. Abd El-Azeem

Abstract:

Maneuvering extended object tracking (M-EOT) using high-resolution inverse synthetic aperture radar (ISAR) observations has been gaining momentum recently. This work presents a new robust implementation of the multiple models (MM) multi-Bernoulli (MB) filter for M-EOT, where the M-EOT’s ISAR observations are characterized using a skewed (SK) non-symmetrically normal distribution. To cope with the possible abrupt change of kinematic state, extension, and observation distribution over an extended object when a target maneuvers, a multiple model technique is represented based on MB-track-before-detect (TBD) filter supported by SK-sub-random matrix model (RMM) or sub-ellipses framework. Simulation results demonstrate this remarkable impact.

Keywords: maneuvering extended objects, ISAR, skewed normal distribution, sub-RMM, MM-MB-TBD filter

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Authors: Djamel Remache, Serge Dos Santos, Michael Cliez, Michel Gratton, Patrick Chabrand, Jean-Marie Rossi, Jean-Louis Milan

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Skin tissue is an inhomogeneous and anisotropic material. Uniaxial tensile testing is one of the primary testing techniques for the mechanical characterization of skin at large scales. In order to predict the mechanical behavior of materials, the direct or inverse analytical approaches are often used. However, in case of an inhomogeneous and anisotropic material as skin tissue, analytical approaches are not able to provide solutions. The numerical simulation is thus necessary. In this work, the uniaxial tensile test and the FEM (finite element method) based inverse method were used to identify the anisotropic mechanical properties of porcine skin tissue. The uniaxial tensile experiments were performed using Instron 8800 tensile machine®. The uniaxial tensile test was simulated with FEM, and then the inverse optimization approach (or the inverse calibration) was used for the identification of mechanical properties of the samples. Experimentally results were compared to finite element solutions. The results showed that the finite element model predictions of the mechanical behavior of the tested skin samples were well correlated with experimental results.

Keywords: mechanical skin tissue behavior, uniaxial tensile test, finite element analysis, inverse optimization approach

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Authors: Jin-Wei Liang, Hung-Yi Chen, Lung Lin

Abstract:

In this paper feedforward controller is designed to eliminate nonlinear hysteresis behaviors of a piezoelectric stack actuator (PSA) driven system. The control design is based on inverse Prandtl-Ishlinskii (P-I) hysteresis model identified using particle swarm optimization (PSO) technique. Based on the identified P-I model, both the inverse P-I hysteresis model and feedforward controller can be determined. Experimental results obtained using the inverse P-I feedforward control are compared with their counterparts using hysteresis estimates obtained from the identified Bouc-Wen model. Effectiveness of the proposed feedforward control scheme is demonstrated. To improve control performance feedback compensation using traditional PID scheme is adopted to integrate with the feedforward controller.

Keywords: the Bouc-Wen hysteresis model, particle swarm optimization, Prandtl-Ishlinskii model, automation engineering

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Authors: Sunghoon Park, Hank Kim, Jinwon An, Sungzoon Cho

Abstract:

Internet of Things allows one to collect data from facilities which are then used to monitor them and even predict malfunctions in advance. Conventional quality control methods focus on setting a normal range on a sensor value defined between a lower control limit and an upper control limit, and declaring as an anomaly anything falling outside it. However, interactions among sensor values are ignored, thus leading to suboptimal performance. We propose a multivariate approach which takes into account many sensor values at the same time. In particular Gaussian Mixture Model is used which is trained to maximize likelihood value using Expectation-Maximization algorithm. The number of Gaussian component distributions is determined by Bayesian Information Criterion. The negative Log likelihood value is used as an anomaly score. The actual usage scenario goes like a following. For each instance of sensor values from a facility, an anomaly score is computed. If it is larger than a threshold, an alarm will go off and a human expert intervenes and checks the system. A real world data from Building energy system was used to test the model.

Keywords: facility anomaly detection, gaussian mixture model, anomaly score, expectation maximization algorithm

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Authors: Sergio Curilef, Boris Atenas

Abstract:

Systems with long-range interactions are very common in nature. They are observed from the atomic scale to the astronomical scale and exhibit anomalies, such as inequivalence of ensembles, negative heat capacity, ergodicity breaking, nonequilibrium phase transitions, quasistationary states, and anomalous diffusion. These anomalies are exacerbated when special initial conditions are imposed; in particular, we use the so-called water bag initial conditions that stand for a uniform distribution. Several theoretical and practical implications are discussed here. A potential energy inspired by dipole-dipole interactions is proposed to build the dipole-type Hamiltonian mean-field model. As expected, the dynamics is novel and general to the behavior of systems with long-range interactions, which is obtained through molecular dynamics technique. Two plateaus sequentially emerge before arriving at equilibrium, which are corresponding to two different quasistationary states. The first plateau is a type of quasistationary state the lifetime of which depends on a power law of N and the second plateau seems to be a true quasistationary state as reported in the literature. The general behavior of the model according to its dynamics and thermodynamics is described. Using numerical simulation we characterize the mean kinetic energy, caloric curve, and the diffusion law through the mean square of displacement. The present challenge is to characterize the distributions in phase space. Certainly, the equilibrium state is well characterized by the Gaussian distribution, but quasistationary states in general depart from any Gaussian function.

Keywords: dipole-type interactions, dynamics and thermodynamics, mean field model, quasistationary states

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Authors: Malika Yaici, Kamel Hariche

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In control engineering, systems described by matrix fractions are studied through properties of block roots, also called solvents. These solvents are usually dealt with in a block Vandermonde matrix form. Inverses and determinants of Vandermonde matrices and block Vandermonde matrices are used in solving problems of numerical analysis in many domains but require costly computations. Even though Vandermonde matrices are well known and method to compute inverse and determinants are many and, generally, based on interpolation techniques, methods to compute the inverse and determinant of a block Vandermonde matrix have not been well studied. In this paper, some properties of these matrices and iterative algorithms to compute the determinant and the inverse of a block Vandermonde matrix are given. These methods are deducted from the partitioned matrix inversion and determinant computing methods. Due to their great size, parallelization may be a solution to reduce the computations cost, so a parallelization of these algorithms is proposed and validated by a comparison using algorithmic complexity.

Keywords: block vandermonde matrix, solvents, matrix polynomial, matrix inverse, matrix determinant, parallelization

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Authors: Hamsasew Hankebo Lemago, Dóra Hessz, Tamás Igricz, Zoltán Erdélyi, , Imre Miklós Szilágyi

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Metal oxide inverse opals are a promising class of photocatalysts with a unique hierarchical structure. Atomic layer deposition (ALD) is a versatile technique for the synthesis of high-precision metal oxide thin films, including inverse opals. In this study, we report the synthesis of TiO₂, ZnO, and Al₂O₃ inverse opal and their composites photocatalysts using thermal or plasma-enhanced ALD. The synthesized photocatalysts were characterized using a variety of techniques, including scanning electron microscopy (SEM)-energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Raman spectroscopy, photoluminescence (PL), ellipsometry, and UV-visible spectroscopy. The results showed that the ALD-synthesized metal oxide inverse opals had a highly ordered structure and a tunable pore size. The PL spectroscopy results showed low recombination rates of photogenerated electron-hole pairs, while the ellipsometry and UV-visible spectroscopy results showed tunable optical properties and band gap energies. The photocatalytic activity of the samples was evaluated by the degradation of methylene blue under visible light irradiation. The results showed that the ALD-synthesized metal oxide inverse opals exhibited high photocatalytic activity, even under visible light irradiation. The composites photocatalysts showed even higher activity than the individual metal oxide inverse opals. The enhanced photocatalytic activity of the composites can be attributed to the synergistic effect between the different metal oxides. For example, Al₂O₃ can act as a charge carrier scavenger, which can reduce the recombination of photogenerated electron-hole pairs. The ALD-synthesized metal oxide inverse opals and their composites are promising photocatalysts for a variety of applications, such as wastewater treatment, air purification, and energy production. The ALD-synthesized metal oxide inverse opals and their composites are promising photocatalysts for a variety of applications, such as wastewater treatment, air purification, and energy production.

Keywords: ALD, metal oxide inverse opals, photocatalysis, composites

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