Abstract: In the design stage of a new building, the energy model of this building is often required for the analysis of the performance on energy efficiency. In practice, a certain degree of geometric simplification should be done in the establishment of building energy models, since the detailed geometric features of a real building are hard to be described perfectly in most energy simulation engine, such as ESP-r, eQuest or EnergyPlus. Actually, the detailed description is not necessary when the result with extremely high accuracy is not demanded. Therefore, this paper analyzed the relationship between the error of the simulation result from building energy models and the geometric simplification of the models. Finally, the following two parameters are selected as the indices to characterize the geometric feature of in building energy simulation: the southward projected area and total side surface area of the building. Based on the parameterization method, the simplification from an arbitrary column building to a typical shape (a cuboid) building can be made for energy modeling. The result in this study indicates that no more than 7% prediction error of annual cooling/heating load will be caused by the geometric simplification for those buildings with the ratio of southward projection length to total perimeter of the bottom of 0.25~0.35, which means this method is applicable for building performance simulation.
Abstract: Over-parameterized neural networks have attracted a
great deal of attention in recent deep learning theory research,
as they challenge the classic perspective of over-fitting when
the model has excessive parameters and have gained empirical
success in various settings. While a number of theoretical works
have been presented to demystify properties of such models, the
convergence properties of such models are still far from being
thoroughly understood. In this work, we study the convergence
properties of training two-hidden-layer partially over-parameterized
fully connected networks with the Rectified Linear Unit activation via
gradient descent. To our knowledge, this is the first theoretical work
to understand convergence properties of deep over-parameterized
networks without the equally-wide-hidden-layer assumption and
other unrealistic assumptions. We provide a probabilistic lower bound
of the widths of hidden layers and proved linear convergence rate of
gradient descent. We also conducted experiments on synthetic and
real-world datasets to validate our theory.
Abstract: Architecture plane form is an important consideration in the design of green buildings due to its significant impact on energy performance. The most effective method to consider energy performance in the early design stages is parametric modelling. This paper presents a methodology to program plane forms using MATLAB language, generating 16 kinds of plane forms by changing four designed parameters. DesignBuilder (an energy consumption simulation software) was proposed to simulate the energy consumption of the generated planes. A regression mathematical model was established to study the relationship between the plane forms and their energy consumption. The main finding of the study suggested that there was a cubic function relationship between the depth-ratio of U-shaped buildings and energy consumption, and there is also a cubic function relationship between the width-ratio and energy consumption. In the design, the depth-ratio of U-shaped buildings should not be less than 2.5, and the width-ratio should not be less than 2.
Abstract: This paper presents a prediction performance of
feedforward Multilayer Perceptron (MLP) and Echo State Networks
(ESN) trained with extended Kalman filter. Feedforward neural
networks and ESN are powerful neural networks which can track and
predict nonlinear signals. However, their tracking performance
depends on the specific signals or data sets, having the risk of
instability accompanied by large error. In this study we explore this
process by applying different network size and leaking rate for
prediction of nonlinear or chaotic signals in MLP neural networks.
Major problems of ESN training such as the problem of initialization
of the network and improvement in the prediction performance are
tackled. The influence of coefficient of activation function in the
hidden layer and other key parameters are investigated by simulation
results. Extended Kalman filter is employed in order to improve the
sequential and regulation learning rate of the feedforward neural
networks. This training approach has vital features in the training of
the network when signals have chaotic or non-stationary sequential
pattern. Minimization of the variance in each step of the computation
and hence smoothing of tracking were obtained by examining the
results, indicating satisfactory tracking characteristics for certain
conditions. In addition, simulation results confirmed satisfactory
performance of both of the two neural networks with modified
parameterization in tracking of the nonlinear signals.
Abstract: In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.
Abstract: This paper is a continuation of the work carried out by various turbulence modelers in Oceanography on the topic of oceanic turbulent mixing. It evaluates the evolution of ocean water temperature and salinity by the appropriate modeling of turbulent mixing utilizing proper prescription of eddy viscosity. Many modelers in past have suggested including terms like shear, buoyancy and vorticity to be the parameters that decide the slow pressure strain correlation. We add to it the fact that dissipation anisotropy also modifies the correlation through eddy viscosity parameterization. This recalibrates the established correlation constants slightly and gives improved results. This anisotropization of dissipation implies that the critical Richardson’s number increases much beyond unity (to 1.66) to accommodate enhanced mixing, as is seen in reality. The model is run for a couple of test cases in the General Ocean Turbulence Model (GOTM) and the results are presented here.
Abstract: The Numerical weather prediction (NWP) models are
considered powerful tools for guiding quantitative rainfall prediction.
A couple of NWP models exist and are used at many operational
weather prediction centers. This study considers two models namely
the Consortium for Small–scale Modeling (COSMO) model and the
Weather Research and Forecasting (WRF) model. It compares the
models’ ability to predict rainfall over Uganda for the period 21st
April 2013 to 10th May 2013 using the root mean square (RMSE)
and the mean error (ME). In comparing the performance of the
models, this study assesses their ability to predict light rainfall events
and extreme rainfall events. All the experiments used the default
parameterization configurations and with same horizontal resolution
(7 Km). The results show that COSMO model had a tendency of
largely predicting no rain which explained its under–prediction. The
COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly
(p = 0.014) higher magnitude of error compared to the WRF
model (RMSE: 11.86; ME: -1.09). However the COSMO model
(RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better
than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light
rainfall events. All the models under–predicted extreme rainfall events
with the COSMO model (RMSE: 43.63; ME: -39.58) presenting
significantly higher error magnitudes than the WRF model (RMSE:
35.14; ME: -26.95). This study recommends additional diagnosis of
the models’ treatment of deep convection over the tropics.
Abstract: Rainfall is a major climatic parameter affecting
many sectors such as health, agriculture and water resources. Its
quantitative prediction remains a challenge to weather forecasters
although numerical weather prediction models are increasingly being
used for rainfall prediction. The performance of six convective
parameterization schemes, namely the Kain-Fritsch scheme, the
Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D
scheme, the Grell-Fretas scheme, the New Tiedke scheme of the
weather research and forecast (WRF) model regarding quantitative
rainfall prediction over Uganda is investigated using the root mean
square error for the March-May (MAM) 2013 season. The MAM
2013 seasonal rainfall amount ranged from 200 mm to 900 mm over
Uganda with northern region receiving comparatively lower rainfall
amount (200–500 mm); western Uganda (270–550 mm); eastern
Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A
spatial variation in simulated rainfall amount by different convective
parameterization schemes was noted with the Kain-Fritsch scheme
over estimating the rainfall amount over northern Uganda (300–750
mm) but also presented comparable rainfall amounts over the eastern
Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny,
and the Grell-3D underestimated the rainfall amount over most
parts of the country especially the eastern region (300–600 mm).
The Grell-Fretas captured rainfall amount over the northern region
(250–450 mm) but also underestimated rainfall over the lake Victoria
Basin (150–300 mm) while the New Tiedke generally underestimated
rainfall amount over many areas of Uganda. For deterministic rainfall
prediction, the Grell-Fretas is recommended for rainfall prediction
over northern Uganda while the Kain-Fritsch scheme is recommended
over eastern region.
Abstract: Finding the optimal 3D path of an aerial vehicle under
flight mechanics constraints is a major challenge, especially when
the algorithm has to produce real time results in flight. Kinematics
models and Pythagorian Hodograph curves have been widely used
in mobile robotics to solve this problematic. The level of difficulty
is mainly driven by the number of constraints to be saturated at the
same time while minimizing the total length of the path. In this paper,
we suggest a pragmatic algorithm capable of saturating at the same
time most of dimensioning helicopter 3D trajectories’ constraints
like: curvature, curvature derivative, torsion, torsion derivative, climb
angle, climb angle derivative, positions. The trajectories generation
algorithm is able to generate versatile complex 3D motion primitives
feasible by a helicopter with parameterization of the curvature and the
climb angle. An upper ”motion primitives’ concatenation” algorithm
is presented based. In this article we introduce a new way of designing
three-dimensional trajectories based on what we call the ”Dubins
gliding symmetry conjecture”. This extremely performing algorithm
will be soon integrated to a real-time decisional system dealing with
inflight safety issues.
Abstract: Large rotating systems, especially gear drives and gearboxes, occur as parts of many mechanical devices transmitting the torque with relatively small loss of power. With the increased demand for high speed machinery, mathematical modeling and
dynamic analysis of gear drives gained importance. Mathematical description of such mechanical systems is a complex task evolving for several decades. In gear drive dynamic models, which include flexible shafts, bearings and gearing and use the finite elements, nonlinear effects due to gear mesh and bearings are usually ignored, for such models have large number of degrees of freedom (DOF) and it is computationally expensive to analyze nonlinear systems with large number of DOF. Therefore, these models are not suitable for simulation of nonlinear behavior with amplitude jumps in frequency response. The contribution uses a methodology of nonlinear large rotating system modeling which is based on degrees of freedom (DOF) number reduction using modal synthesis method (MSM).
The MSM enables significant DOF number reduction while keeping
the nonlinear behavior of the system in a specific frequency range.
Further, the MSM with DOF number reduction is suitable for
including detail models of nonlinear couplings (mainly gear and
bearing couplings) into the complete gear drive models. Since each
subsystem is modeled separately using different FEM systems, it
is advantageous to parameterize models of subsystems and to use
the parameterization for optimization of chosen design parameters.
Final complex model of gear drive is assembled in MATLAB and
MATLAB tools are used for dynamical analysis of the nonlinear
system. The contribution is further focused on developing of a
methodology for investigation of behavior of the system by Nonlinear
Normal Modes with combination of the MSM using numerical
continuation method. The proposed methodology will be tested using
a two-stage gearbox including its housing.
Abstract: The mixing of two or more liquids is very common in many industrial applications from automotive to food processing. CFD simulations of these processes require comparison with test results. In many cases it is practically impossible. Therefore, comparison provides with scalable tests. So, parameterization of the problem is sufficient to capture the performance of the mixer.
However, the influence of geometrical and thermo-physical parameters on the mixing is not well understood.
In this work influence of geometrical and thermal parameters was studied. It was shown that for full developed turbulent flows (Re > 104), Pet»const and concentration of secondary fluid ~ F(r/l).
In other words, the mixing is practically independent of total flow rate and scale for a given geometry and ratio of flow rates of mixing flows. This statement was proved in present work for different geometries and mixtures such as EGR and water-urea mixture.
Present study has been shown that the best way to improve the mixing is to establish geometry with the lowest Pet number possible by intensifying the turbulence in the domain. This is achievable by using step geometry, impinging flow EGR on a wall, or EGR jets, with a strong change in the flow direction, or using swirler like flow in the domain or combination all of these factors. All of these results are applicable to any mixtures of no compressible fluids.
Abstract: The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.
Abstract: The purpose of this paper is to investigate the
influence of a number of variables on the conditional mean and
conditional variance of credit spread changes. The empirical analysis
in this paper is conducted within the context of bivariate GARCH-in-
Mean models, using the so-called BEKK parameterization. We show
that credit spread changes are determined by interest-rate and equityreturn
variables, which is in line with theory as provided by the
structural models of default. We also identify the credit spread
change volatility as an important determinant of credit spread
changes, and provide evidence on the transmission of volatility
between the variables under study.
Abstract: The incorporation of computational fluid dynamics in the design of modern hydraulic turbines appears to be necessary in order to improve their efficiency and cost-effectiveness beyond the traditional design practices. A numerical optimization methodology is developed and applied in the present work to a Turgo water turbine. The fluid is simulated by a Lagrangian mesh-free approach that can provide detailed information on the energy transfer and enhance the understanding of the complex, unsteady flow field, at very small computing cost. The runner blades are initially shaped according to hydrodynamics theory, and parameterized using Bezier polynomials and interpolation techniques. The use of a limited number of free design variables allows for various modifications of the standard blade shape, while stochastic optimization using evolutionary algorithms is implemented to find the best blade that maximizes the attainable hydraulic efficiency of the runner. The obtained optimal runner design achieves considerably higher efficiency than the standard one, and its numerically predicted performance is comparable to a real Turgo turbine, verifying the reliability and the prospects of the new methodology.
Abstract: In this paper, we studied some properties of geodesic on some open surfaces: Hyperboloid, Paraboloid and Funnel Surface. Geodesic equation in the v-Clairaut parameterization was calculated and reduced to definite integral. Some geodesics on some open surfaces as mention above were classified by Clairaut's relation.
Abstract: In this paper, we present an experimental testing for
a new algorithm that determines an optimal controller-s coefficients
for output variance reduction related to Linear Time Invariant (LTI)
Systems. The algorithm features simplicity in calculation, generalization
to minimal and non-minimal phase systems, and could be
configured to achieve reference tracking as well as variance reduction
after compromising with the output variance. An experiment of DCmotor
velocity control demonstrates the application of this new
algorithm in designing the controller. The results show that the
controller achieves minimum variance and reference tracking for a
preset velocity reference relying on an identified model of the motor.
Abstract: This paper proposes evaluation of sound parameterization methods in recognizing some spoken Arabic words, namely digits from zero to nine. Each isolated spoken word is represented by a single template based on a specific recognition feature, and the recognition is based on the Euclidean distance from those templates. The performance analysis of recognition is based on four parameterization features: the Burg Spectrum Analysis, the Walsh Spectrum Analysis, the Thomson Multitaper Spectrum Analysis and the Mel Frequency Cepstral Coefficients (MFCC) features. The main aim of this paper was to compare, analyze, and discuss the outcomes of spoken Arabic digits recognition systems based on the selected recognition features. The results acqired confirm that the use of MFCC features is a very promising method in recognizing Spoken Arabic digits.
Abstract: A new stochastic algorithm called Probabilistic Global Search Johor (PGSJ) has recently been established for global optimization of nonconvex real valued problems on finite dimensional Euclidean space. In this paper we present convergence guarantee for this algorithm in probabilistic sense without imposing any more condition. Then, we jointly utilize this algorithm along with control
parameterization technique for the solution of constrained optimal control problem. The numerical simulations are also included to illustrate the efficiency and effectiveness of the PGSJ algorithm in the solution of control problems.
Abstract: Among all geo-hydrological relationships, rainfallrunoff
relationship is of utmost importance in any hydrological
investigation and water resource planning. Spatial variation, lag time
involved in obtaining areal estimates for the basin as a whole can
affect the parameterization in design stage as well as in planning
stage. In conventional hydrological processing of data, spatial aspect
is either ignored or interpolated at sub-basin level. Temporal
variation when analysed for different stages can provide clues for its
spatial effectiveness. The interplay of space-time variation at pixel
level can provide better understanding of basin parameters.
Sustenance of design structures for different return periods and their
spatial auto-correlations should be studied at different geographical
scales for better management and planning of water resources.
In order to understand the relative effect of spatio-temporal
variation in hydrological data network, a detailed geo-hydrological
analysis of Betwa river catchment falling in Lower Yamuna Basin is
presented in this paper. Moreover, the exact estimates about the
availability of water in the Betwa river catchment, especially in the
wake of recent Betwa-Ken linkage project, need thorough scientific
investigation for better planning. Therefore, an attempt in this
direction is made here to analyse the existing hydrological and
meteorological data with the help of SPSS, GIS and MS-EXCEL
software. A comparison of spatial and temporal correlations at subcatchment
level in case of upper Betwa reaches has been made to
demonstrate the representativeness of rain gauges. First, flows at
different locations are used to derive correlation and regression
coefficients. Then, long-term normal water yield estimates based on
pixel-wise regression coefficients of rainfall-runoff relationship have
been mapped. The areal values obtained from these maps can
definitely improve upon estimates based on point-based
extrapolations or areal interpolations.
Abstract: In this paper we study a system composed by carbon
nanotube (CNT) and bundle of carbon nanotube (BuCNT) interacting
with a specific fatty acid as molecular probe. Full system is
represented by open nanotube (or nanotubes) and the linoleic acid
(LA) relaxing due the interaction with CNT and BuCNT. The LA has
in his form an asymmetric shape with COOH termination provoking
a close BuCNT interaction mainly by van der Waals force field. The
simulations were performed by classical molecular dynamics with
standard parameterizations.
Our results show that these BuCNT and CNT are dynamically
stable and it shows a preferential interaction position with LA
resulting in three features: (i) when the LA is interacting with CNT
and BuCNT (including both termination, CH2 or COOH), the LA is
repelled; (ii) when the LA terminated with CH2 is closer to open
extremity of BuCNT, the LA is also repelled by the interaction
between them; and (iii) when the LA terminated with COOH is
closer to open extremity of BuCNT, the LA is encapsulated by the
BuCNT. These simulations are part of a more extensive work on
searching efficient selective molecular devices and could be useful to
reach this goal.