Abstract: The current trends in affect recognition research are
to consider continuous observations from spontaneous natural
interactions in people using multiple feature modalities, and to
represent affect in terms of continuous dimensions, incorporate
spatio-temporal correlation among affect dimensions, and provide
fast affect predictions. These research efforts have been propelled
by a growing effort to develop affect recognition system that
can be implemented to enable seamless real-time human-computer
interaction in a wide variety of applications. Motivated by these
desired attributes of an affect recognition system, in this work
a multi-dimensional affect prediction approach is proposed by
integrating multivariate Relevance Vector Machine (MVRVM) with
a recently developed Output-associative Relevance Vector Machine
(OARVM) approach. The resulting approach can provide fast
continuous affect predictions by jointly modeling the multiple affect
dimensions and their correlations. Experiments on the RECOLA
database show that the proposed approach performs competitively
with the OARVM while providing faster predictions during testing.
Abstract: Bisphenol A (BPA) is an organic synthetic compound that has many applications in various industries and is known as persistent pollutant. The aim of this research was to evaluate the efficiency of bone ash and banana peel as adsorbents for BPA adsorption from aqueous solution by using Response Surface Methodology. The effects of some variables such as sorbent dose, detention time, solution pH, and BPA concentration on the sorption efficiency was examined. All analyses were carried out according to Standard Methods. The sample size was performed using Box-Benken design and also optimization of BPA removal was done using response surface methodology (RSM). The results showed that the BPA adsorption increases with increasing of contact time and BPA concentration. However, it decreases with higher pH. More adsorption efficiency of a banana peel is very smaller than a bone ash so that BPA removal for bone ash and banana peel is 62 and 28 percent, respectively. It is concluded that a bone ash has a good ability for the BPA adsorption.
Abstract: This study presents a conformational model of the helical structures of globular protein particularly ferritin in the framework of white noise path integral formulation by using Associated Legendre functions, Bessel and convolution of Bessel and trigonometric functions as modulating functions. The model incorporates chirality features of proteins and their helix-turn-helix sequence structural motif.
Abstract: Different knitted structures and knitted parameters play a vital role in the stretch and recovery management of compression sportswear in addition to the materials use to generate this stretch and recovery behavior of the fabric. The present work was planned to predict the different performance indicators of a compression sportswear fabric with some ground parameters i.e. base yarn stitch length (polyester as base yarn and spandex as plating yarn involve to make a compression fabric) and linear density of the spandex which is a key material of any sportswear fabric. The prediction models were generated by response surface method for performance indicators such as stretch & recovery percentage, compression generated by the garment on body, total elongation on application of high power force and load generated on certain percentage extension in fabric. Certain physical properties of the fabric were also modeled using these two parameters.
Abstract: Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.
Abstract: In the present study, an ecofriendly biocomposite namely calcium alginate immobilized Ammi Visnaga (Khella) extraction waste (SWAV/CA) was prepared by electrostatic extrusion method and used on the cadmium biosorption from aqueous phase with and without the assistance of ultrasound in batch conditions. The influence of low frequency ultrasound (37 and 80 KHz) on the cadmium biosorption kinetics was studied. The obtained results show that the ultrasonic irradiation significantly enhances and improves the efficiency of the cadmium removal. The Pseudo first order, Pseudo-second-order, Intraparticle diffusion, and Elovich models were evaluated using the non-linear curve fitting analysis method. Modeling of kinetic results shows that biosorption process is best described by the pseudo-second order and Elovich, in both the absence and presence of ultrasound.
Abstract: Since the heart of the hybrid system is the fuel cell and it has vital impact on efficiency and performance of cycle, in this study, the major modeling of electrochemical reaction within the fuel cell is analyzed. Also, solid oxide fuel cell is integrated with the gas turbine and thermodynamic analysis on different elements of hybrid system is applied. Next, in predefined operational points of hybrid cycle, the simulation results are obtained. Then, different source of irreversibility in fuel cell is modeled and influence of different major parameters on different irreversibility is computed and applied. Then, the effect of important parameters such as thickness and surface of electrolyte fuel cell are simulated in fuel cell and its dependency to these parameters is explained. At the end of the paper, different impact of parameters on fuel cell with a gas turbine and current density and voltage of fuel cell are simulated.
Abstract: This paper presents the findings of successful implementation of Business Process Reengineering (BPR) of cement dispatch activities in a cement manufacturing plant located in India. Simulation model was developed for the purpose of identifying and analyzing the areas for improvement. The company was facing a problem of low throughput rate and subsequent forced stoppages of the plant leading to a high production loss of 15000MT per month. It was found from the study that the present systems and procedures related to the in-plant logistics plant required significant changes. The major recommendations included process improvement at the entry gate, reducing the cycle time at the security gate and installation of an additional weigh bridge. This paper demonstrates how BPR can be implemented for improving the in-plant logistics process. Various recommendations helped the plant to increase its throughput by 14%.
Abstract: This paper reviews a number of theoretical aspects
for implementing an explicit spatial perspective in econometrics
for modelling non-continuous data, in general, and count data, in
particular. It provides an overview of the several spatial econometric
approaches that are available to model data that are collected with
reference to location in space, from the classical spatial econometrics
approaches to the recent developments on spatial econometrics to
model count data, in a Bayesian hierarchical setting. Considerable
attention is paid to the inferential framework, necessary for
structural consistent spatial econometric count models, incorporating
spatial lag autocorrelation, to the corresponding estimation and
testing procedures for different assumptions, to the constrains and
implications embedded in the various specifications in the literature. This review combines insights from the classical spatial
econometrics literature as well as from hierarchical modeling and
analysis of spatial data, in order to look for new possible directions
on the processing of count data, in a spatial hierarchical Bayesian
econometric context.
Abstract: This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.
Abstract: As DNA microarray data contain relatively small
sample size compared to the number of genes, high dimensional
models are often employed. In high dimensional models, the selection
of tuning parameter (or, penalty parameter) is often one of the crucial
parts of the modeling. Cross-validation is one of the most common
methods for the tuning parameter selection, which selects a parameter
value with the smallest cross-validated score. However, selecting a
single value as an ‘optimal’ value for the parameter can be very
unstable due to the sampling variation since the sample sizes of
microarray data are often small. Our approach is to choose multiple candidates of tuning parameter
first, then average the candidates with different weights depending
on their performance. The additional step of estimating the weights
and averaging the candidates rarely increase the computational cost,
while it can considerably improve the traditional cross-validation. We
show that the selected value from the suggested methods often lead to
stable parameter selection as well as improved detection of significant
genetic variables compared to the tradition cross-validation via real
data and simulated data sets.
Abstract: Recent advances of Building Information Modeling (BIM) especially in the Middle East have increased remarkably. Dubai has been taking a lead on this by making it mandatory for BIM to be adopted for all projects that involve complex architecture designs. This is because BIM is a dynamic process that assists all stakeholders in monitoring the project status throughout different project phases with great transparency. It focuses on utilizing information technology to improve collaboration among project participants during the entire life cycle of the project from the initial design, to the supply chain, resource allocation, construction and all productivity requirements. In view of this trend, the paper examines the extent of applying BIM in the State of Kuwait, by exploring practitioners’ perspectives on BIM, especially their perspectives on main barriers and main advantages. To this end structured interviews were carried out based on questionnaires and with a range of different construction professionals. The results revealed that practitioners perceive improved communication and mitigated project risks by encouraged collaboration between project participants. However, it was also observed that the full implementation of BIM in the State of Kuwait requires concerted efforts to make clients demanding BIM, counteract resistance to change among construction professionals and offer more training for design team members. This paper forms part of an on-going research effort on BIM and its application in the State of Kuwait and it is on this basis that further research on the topic is proposed.
Abstract: In this paper, a genetic-neural-network (GNN) based large-signal model for GaN HEMTs is presented along with its parameters extraction procedure. The model is easy to construct and implement in CAD software and requires only DC and S-parameter measurements. An improved decomposition technique is used to model self-heating effect. Two GNN models are constructed to simulate isothermal drain current and power dissipation, respectively. The two model are then composed to simulate the drain current. The modeling procedure was applied to a packaged GaN-on-Si HEMT and the developed model is validated by comparing its large-signal simulation with measured data. A very good agreement between the simulation and measurement is obtained.
Abstract: During manned exploration of space, missions will require astronaut crewmembers to perform Extra Vehicular Activities (EVAs) for a variety of tasks. These EVAs take place after long periods of operations in space, and in and around unique vehicles, space structures and systems. Considering the remoteness and time spans in which these vehicles will operate, EVA system operations should utilize common worksites, tools and procedures as much as possible to increase the efficiency of training and proficiency in operations. All of the preparations need to be carried out based on studies of astronaut motions. Until now, development and training activities associated with the planned EVAs in Russian and U.S. space programs have relied almost exclusively on physical simulators. These experimental tests are expensive and time consuming. During the past few years a strong increase has been observed in the use of computer simulations due to the fast developments in computer hardware and simulation software. Based on this idea, an effort to develop a computational simulation system to model human dynamic motion for EVA is initiated. This study focuses on the simulation of an astronaut moving the orbital replaceable units into the worksites or removing them from the worksites. Our physics-based methodology helps fill the gap in quantitative analysis of astronaut EVA by providing a multisegment human arm model. Simulation work described in the study improves on the realism of previous efforts, incorporating joint stops to account for the physiological limits of range of motion. To demonstrate the utility of this approach human arm model is simulated virtually using ADAMS/LifeMOD® software. Kinematic mechanism for the astronaut’s task is studied from joint angles and torques. Simulation results obtained is validated with numerical simulation based on the principles of Newton-Euler method. Torques determined using mathematical model are compared among the subjects to know the grace and consistency of the task performed. We conclude that due to uncertain nature of exploration-class EVA, a virtual model developed using multibody dynamics approach offers significant advantages over traditional human modeling approaches.
Abstract: By GEO5 FEM program with four rockfill slope
modeling and stability analysis was performed for S1, S2, S3 and
S4 slopes where landslides of the shalefills were limited. Effective
angle of internal friction (φ'°) 17°-22.5°, the effective cohesion (c')
from 0.5 to 1.8 kPa, saturated unit weight 1.78-2.43 g/cm3, natural
unit weight 1.9-2.35 g/cm3, dry unit weight 1.97-2.40 g/cm3, the
permeability coefficient of 1x10-4 - 6.5x10-4 cm/s. In cross-sections
of the slope, GEO 5 FEM program possible critical surface tension
was examined. Rockfill dump design was made to prevent sliding
slopes. Bulk material designated geotechnical properties using also
GEO5 programs FEM and stability program via a safety factor
determined and calculated according to the values S3 and S4 No.
slopes are stable S1 and S2 No. slopes were close to stable state
that has been found to be risk. GEO5 programs with limestone rock
fill dump through FEM program was found to exhibit stability.
Abstract: Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems.
Abstract: An accuracy nonlinear analysis of a deep beam resting on elastic perfectly plastic soil is carried out in this study. In fact, a nonlinear finite element modeling for large deflection and moderate rotation of Euler-Bernoulli beam resting on linear and nonlinear random soil is investigated. The geometric nonlinear analysis of the beam is based on the theory of von Kàrmàn, where the Newton-Raphson incremental iteration method is implemented in a Matlab code to solve the nonlinear equation of the soil-beam interaction system. However, two analyses (deterministic and probabilistic) are proposed to verify the accuracy and the efficiency of the proposed model where the theory of the local average based on the Monte Carlo approach is used to analyze the effect of the spatial variability of the soil properties on the nonlinear beam response. The effect of six main parameters are investigated: the external load, the length of a beam, the coefficient of subgrade reaction of the soil, the Young’s modulus of the beam, the coefficient of variation and the correlation length of the soil’s coefficient of subgrade reaction. A comparison between the beam resting on linear and nonlinear soil models is presented for different beam’s length and external load. Numerical results have been obtained for the combination of the geometric nonlinearity of beam and material nonlinearity of random soil. This comparison highlighted the need of including the material nonlinearity and spatial variability of the soil in the geometric nonlinear analysis, when the beam undergoes large deflections.
Abstract: In this work we make a bifurcation analysis for a
single compartment representation of Traub model, one of the most
important conductance-based models. The analysis focus in two
principal parameters: current and leakage conductance. Study of
stable and unstable solutions are explored; also Hop-bifurcation and
frequency interpretation when current varies is examined. This study
allows having control of neuron dynamics and neuron response when
these parameters change. Analysis like this is particularly important
for several applications such as: tuning parameters in learning
process, neuron excitability tests, measure bursting properties of the
neuron, etc. Finally, a hardware implementation results were
developed to corroborate these results.
Abstract: In recent years, the use of renewable energy resources
instead of pollutant fossil fuels and other forms has increased.
Photovoltaic generation is becoming increasingly important as a
renewable resource since it does not cause in fuel costs, pollution,
maintenance, and emitting noise compared with other alternatives
used in power applications. In this paper, Perturb and Observe and
Incremental Conductance methods are used to improve energy
conversion efficiency under different environmental conditions. PI
controllers are used to control easily DC-link voltage, active and
reactive currents. The whole system is simulated under standard
climatic conditions (1000 W/m2, 250C) in MATLAB and the
irradiance is varied from 1000 W/m2 to 300 W/m2. The use of PI
controller makes it easy to directly control the power of the grid
connected PV system. Finally the validity of the system will be
verified through the simulations in MATLAB/Simulink environment.
Abstract: In this work, we propose an algorithm developed under Python language for the modeling of ordinary scalar Bessel beams and their discrete superpositions and subsequent calculation of optical forces exerted over dielectric spherical particles. The mathematical formalism, based on the generalized Lorenz-Mie theory, is implemented in Python for its large number of free mathematical (as SciPy and NumPy), data visualization (Matplotlib and PyJamas) and multiprocessing libraries. We also propose an approach, provided by a synchronized Software as Service (SaaS) in cloud computing, to develop a user interface embedded on a mobile application, thus providing users with the necessary means to easily introduce desired unknowns and parameters and see the graphical outcomes of the simulations right at their mobile devices. Initially proposed as a free Android-based application, such an App enables data post-processing in cloud-based architectures and visualization of results, figures and numerical tables.