Abstract: This research paper designs a unique motion planner
of multiple platoons of nonholonomic car-like robots as a feasible
solution to the lane changing/merging maneuvers. The decentralized
planner with a leaderless approach and a path-guidance principle
derived from the Lyapunov-based control scheme generates collision
free avoidance and safe merging maneuvers from multiple lanes to a
single lane by deploying a split/merge strategy. The fixed obstacles
are the markings and boundaries of the road lanes, while the moving
obstacles are the robots themselves. Real and virtual road lane
markings and the boundaries of road lanes are incorporated into a
workspace to achieve the desired formation and configuration of the
robots. Convergence of the robots to goal configurations and the
repulsion of the robots from specified obstacles are achieved by
suitable attractive and repulsive potential field functions,
respectively. The results can be viewed as a significant contribution
to the avoidance algorithm of the intelligent vehicle systems (IVS).
Computer simulations highlight the effectiveness of the split/merge
strategy and the acceleration-based controllers.
Abstract: The compatibility of optical resonators with microfluidic systems may be relevant for chemical and biological applications. Here, a fluorescent-core microcavity (FCM) is investigated as a refractometric sensor for heavy oils. A high-index film of silicon quantum dots (QDs) was formed inside the capillary, supporting cylindrical fluorescence whispering gallery modes (WGMs). A set of standard refractive index oils was injected into a capillary, causing a shift of the WGM resonances toward longer wavelengths. A maximum sensitivity of 240 nm/RIU (refractive index unit) was found for a nominal oil index of 1.74. As well, a sensitivity of 22 nm/RIU was obtained for a lower index of 1.48, more typical of fuel hydrocarbons. Furthermore, the observed spectra and sensitivities were compared to theoretical predictions and reproduced via FDTD simulations, showing in general an excellent agreement. This work demonstrates the potential use of FCMs for oil sensing applications and the more generally for detecting liquid solutions with a high refractive index or high viscosity.
Abstract: The flow field over a flat roof model building has been numerically investigated in order to determine threedimensional CFD guidelines for the calculation of the turbulent flow over a structure immersed in an atmospheric boundary layer. To this purpose, a complete validation campaign has been performed through a systematic comparison of numerical simulations with wind tunnel experimental data. Wind tunnel measurements and numerical predictions have been compared for five different vertical positions, respectively from the upstream leading edge to the downstream bottom edge of the analyzed model. Flow field characteristics in the neighborhood of the building model have been numerically investigated, allowing a quantification of the capabilities of the CFD code to predict the flow separation and the extension of the recirculation regions. The proposed calculations have allowed the development of a preliminary procedure to be used as guidance in selecting the appropriate grid configuration and corresponding turbulence model for the prediction of the flow field over a three-dimensional roof architecture dominated by flow separation.
Abstract: In this paper, we study the formation control problem
for car-like mobile robots. A team of nonholonomic mobile robots navigate in a terrain with obstacles, while maintaining a desired
formation, using a leader-following strategy. A set of artificial potential field functions is proposed using the direct Lyapunov
method for the avoidance of obstacles and attraction to their designated targets. The effectiveness of the proposed control laws to verify the feasibility of the model is demonstrated through computer simulations
Abstract: Adaptive echo cancellers with two-path algorithm are
applied to avoid the false adaptation during the double-talk situation.
In the two-path algorithm, several transfer logic solutions have been
proposed to control the filter update. This paper presents an improved
transfer logic solution. It improves the convergence speed of the
two-path algorithm, and allows the reduction of the memory elements
and computational complexity. Results of simulations show the
improved performance of the proposed solution.
Abstract: Scaffolds play a key role in tissue engineering and can be produced in many different ways depending on the applications and the materials used. Most researchers used an experimental trialand- error approach into new biomaterials but computer simulation applied to tissue engineering can offer a more exhaustive approach to test and screen out biomaterials. This paper develops the model of scaffolds and Computational Fluid Dynamics that show the value of computer simulations in determining the influence of the geometrical scaffold parameter porosity, pore size and shape on the permeability of scaffolds, magnitude of velocity, drop pressure, shear stress distribution and level and the proper design of the geometry of the scaffold. This creates a need for more advanced studies that include aspects of dynamic conditions of a micro fluid passing through the scaffold were characterized for tissue engineering applications and differentiation of tissues within scaffolds.
Abstract: Complex assemblies of interacting proteins carry out
most of the interesting jobs in a cell, such as metabolism, DNA
synthesis, mitosis and cell division. These physiological properties
play out as a subtle molecular dance, choreographed by underlying
regulatory networks that control the activities of cyclin-dependent
kinases (CDK). The network can be modeled by a set of nonlinear
differential equations and its behavior predicted by numerical
simulation. In this paper, an innovative approach has been proposed
that uses genetic algorithms to mine a set of behavior data output by
a biological system in order to determine the kinetic parameters of
the system. In our approach, the machine learning method is
integrated with the framework of existent biological information in a
wiring diagram so that its findings are expressed in a form of system
dynamic behavior. By numerical simulations it has been illustrated
that the model is consistent with experiments and successfully shown
that such application of genetic algorithms will highly improve the
performance of mathematical model of the cell division cycle to
simulate such a complicated bio-system.
Abstract: In this paper we propose a method which improves the efficiency of video coding. Our method combines an adaptive GOP (group of pictures) structure and the shot cut detection. We have analyzed different approaches for shot cut detection with aim to choose the most appropriate one. The next step is to situate N frames to the positions of detected cuts during the process of video encoding. Finally the efficiency of the proposed method is confirmed by simulations and the obtained results are compared with fixed GOP structures of sizes 4, 8, 12, 16, 32, 64, 128 and GOP structure with length of entire video. Proposed method achieved the gain in bit rate from 0.37% to 50.59%, while providing PSNR (Peak Signal-to-Noise Ratio) gain from 1.33% to 0.26% in comparison to simulated fixed GOP structures.
Abstract: The performance of an image filtering system depends
on its ability to detect the presence of noisy pixels in the image. Most
of the impulse detection schemes assume the presence of salt and
pepper noise in the images and do not work satisfactorily in case of
uniformly distributed impulse noise. In this paper, a new algorithm is
presented to improve the performance of switching median filter in
detection of uniformly distributed impulse noise. The performance of
the proposed scheme is demonstrated by the results obtained from
computer simulations on various images.
Abstract: Understanding how airborne pathogens are
transported through hospital wards is essential for determining the
infection risk to patients and healthcare workers. This study utilizes
Computational Fluid Dynamics (CFD) simulations to explore
possible pathogen transport within a six-bed partitioned Nightingalestyle
hospital ward.
Grid independence of a ward model was addressed using the Grid
Convergence Index (GCI) from solutions obtained using three fullystructured
grids. Pathogens were simulated using source terms in
conjunction with a scalar transport equation and a RANS turbulence
model. Errors were found to be less than 4% in the calculation of air
velocities but an average of 13% was seen in the scalar field.
A parametric study of variations in the pathogen release point
illustrated that its distribution is strongly influenced by the local
velocity field and the degree of air mixing present.
Abstract: In many applications, it is a priori known that the
target function should satisfy certain constraints imposed by, for
example, economic theory or a human-decision maker. Here we
consider partially monotone problems, where the target variable
depends monotonically on some of the predictor variables but not all.
We propose an approach to build partially monotone models based
on the convolution of monotone neural networks and kernel
functions. The results from simulations and a real case study on
house pricing show that our approach has significantly better
performance than partially monotone linear models. Furthermore, the
incorporation of partial monotonicity constraints not only leads to
models that are in accordance with the decision maker's expertise,
but also reduces considerably the model variance in comparison to
standard neural networks with weight decay.
Abstract: We present design, fabrication, and characterization of
a small (12 mm × 12 mm × 8 mm) movable railway vehicle for sensor
carrying. The miniature railway vehicle (MRV) was mainly composed
of a vibrational structure and three legs. A railway was designed and
fabricated to power and guide the MRV. It also transmits the sensed
data from the MRV to the signal processing unit. The MRV with legs
on the railway was moving due to its high-frequency vibration. A
model was derived to describe the motion. Besides, FEM simulations
were performed to design the legs. Then, the MRV and the railway
were fabricated by precision machining. Finally, an infrared sensor
was carried and tested. The result shows that the MRV without loading
was moving along the railway and its maximum speed was 12.2 mm/s.
Moreover, the testing signal was sensed by the MRV.
Abstract: In this paper we describe the design and implementation of a parallel algorithm for data assimilation with ensemble Kalman filter (EnKF) for oil reservoir history matching problem. The use of large number of observations from time-lapse seismic leads to a large turnaround time for the analysis step, in addition to the time consuming simulations of the realizations. For efficient parallelization it is important to consider parallel computation at the analysis step. Our experiments show that parallelization of the analysis step in addition to the forecast step has good scalability, exploiting the same set of resources with some additional efforts.
Abstract: The intelligent fuzzy input estimator is used to estimate
the input force of the rigid bar structural system in this study. The
fuzzy Kalman filter without the input term and the fuzzy weighting
recursive least square estimator are two main portions of this method.
The practicability and accuracy of the proposed method were verified
with numerical simulations from which the input forces of a rigid bar
structural system were estimated from the output responses. In order to
examine the accuracy of the proposed method, a rigid bar structural
system is subjected to periodic sinusoidal dynamic loading. The
excellent performance of this estimator is demonstrated by comparing
it with the use of difference weighting function and improper the
initial process noise covariance. The estimated results have a good
agreement with the true values in all cases tested.
Abstract: This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
Abstract: The most common type of controller being used in
the industry is PI(D) controller which has been used since 1945 and
is still being widely used due to its efficiency and simplicity. In
most cases, the PI(D) controller was tuned without taking into
consideration of the effect of actuator saturation. In real processes,
the most common actuator which is valve will act as constraint and
restrict the controller output. Since the controller is not designed to
encounter saturation, the process may windup and consequently
resulted in large oscillation or may become unstable. Usually, an
antiwindup compensator is added to the feedback control loop to
reduce the deterioration effect of integral windup. This research
aims to specifically control processes with constraints. The
proposed method was applied to two different types of food
processes, which are blending and spray drying. Simulations were
done using MATLAB and the performances of the proposed
method were compared with other conventional methods. The
proposed technique was able to control the processes and avoid
saturation such that no anti windup compensator is needed.
Abstract: A highly optimized implementation of binary mixture
diffusion with no initial bulk velocity on graphics processors is
presented. The lattice Boltzmann model is employed for simulating
the binary diffusion of oxygen and nitrogen into each other with
different initial concentration distributions. Simulations have been
performed using the latest proposed lattice Boltzmann model that
satisfies both the indifferentiability principle and the H-theorem for
multi-component gas mixtures. Contemporary numerical
optimization techniques such as memory alignment and increasing
the multiprocessor occupancy are exploited along with some novel
optimization strategies to enhance the computational performance on
graphics processors using the C for CUDA programming language.
Speedup of more than two orders of magnitude over single-core
processors is achieved on a variety of Graphical Processing Unit
(GPU) devices ranging from conventional graphics cards to
advanced, high-end GPUs, while the numerical results are in
excellent agreement with the available analytical and numerical data
in the literature.
Abstract: This paper presents a new method which applies an
artificial bee colony algorithm (ABC) for capacitor placement in
distribution systems with an objective of improving the voltage profile
and reduction of power loss. The ABC algorithm is a new population
based meta heuristic approach inspired by intelligent foraging behavior
of honeybee swarm. The advantage of ABC algorithm is that
it does not require external parameters such as cross over rate and
mutation rate as in case of genetic algorithm and differential evolution
and it is hard to determine these parameters in prior. The other
advantage is that the global search ability in the algorithm is implemented
by introducing neighborhood source production mechanism
which is a similar to mutation process. To demonstrate the validity
of the proposed algorithm, computer simulations are carried out on
69-bus system and compared the results with the other approach
available in the literature. The proposed method has outperformed the
other methods in terms of the quality of solution and computational
efficiency.
Abstract: Organ motion, especially respiratory motion, is a technical challenge to radiation therapy planning and dosimetry. This motion induces displacements and deformation of the organ tissues within the irradiated region which need to be taken into account when simulating dose distribution during treatment. Finite element modeling (FEM) can provide a great insight into the mechanical behavior of the organs, since they are based on the biomechanical material properties, complex geometry of organs, and anatomical boundary conditions. In this paper we present an original approach that offers the possibility to combine image-based biomechanical models with particle transport simulations. We propose a new method to map material density information issued from CT images to deformable tetrahedral meshes. Based on the principle of mass conservation our method can correlate density variation of organ tissues with geometrical deformations during the different phases of the respiratory cycle. The first results are particularly encouraging, as local error quantification of density mapping on organ geometry and density variation with organ motion are performed to evaluate and validate our approach.
Abstract: The problem of robust fuzzy control for a class of
nonlinear fuzzy impulsive singular perturbed systems with
time-varying delay is investigated by employing Lyapunov functions.
The nonlinear delay system is built based on the well-known T–S
fuzzy model. The so-called parallel distributed compensation idea is
employed to design the state feedback controller. Sufficient conditions
for global exponential stability of the closed-loop system are derived
in terms of linear matrix inequalities (LMIs), which can be easily
solved by LMI technique. Some simulations illustrate the effectiveness
of the proposed method.