Abstract: Nowadays, Heritage Building Information Modeling (HBIM) is considered an efficient tool to represent and manage information of Cultural Heritage (CH). The basis of this tool relies on a 3D model generally obtained from a Cloud-to-BIM procedure. There are different methods to create an HBIM model that goes from manual modeling based on the point cloud to the automatic detection of shapes and the creation of objects. The selection of these methods depends on the desired Level of Development (LOD), Level of Information (LOI), Grade of Generation (GOG) as well as on the availability of commercial software. This paper presents the 3D modeling of a stone masonry chapel using Recap Pro, Revit and Dynamo interface following a three-step methodology. The first step consists of the manual modeling of simple structural (e.g., regular walls, columns, floors, wall openings, etc.) and architectural (e.g., cornices, moldings and other minor details) elements using the point cloud as reference. Then, Dynamo is used for generative modeling of complex structural elements such as vaults, infills and domes. Finally, semantic information (e.g., materials, typology, state of conservation, etc.) and pathologies are added within the HBIM model as text parameters and generic models’ families respectively. The application of this methodology allows the documentation of CH following a relatively simple to apply process that ensures adequate LOD, LOI and GOG levels. In addition, the easy implementation of the method as well as the fact of using only one BIM software with its respective plugin for the scan-to-BIM modeling process means that this methodology can be adopted by a larger number of users with intermediate knowledge and limited resources, since the BIM software used has a free student license.
Abstract: t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.
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: The pancreas is an elongated organ that extends across the abdomen, below the stomach. In addition, it secretes certain enzymes that aid in food digestion. The pancreas also manufactures hormones responsible for regulating blood glucose levels. In the present paper, we propose a mathematical model to study the homeostasis of glucose and insulin in healthy human, and a simulation of this model, which depicts the physiological events after a meal, will be represented in ordinary humans. The aim of this paper is to design an algorithm which regulates the level of glucose in the blood. The algorithm applied the concept of expert system for performing an algorithm control in the form of an "active" used to prescribe the rate of insulin infusion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. The results showed a performance of the control system.
Abstract: The article shows results of the project which aims at evaluating possibilities of effective development and exploitation of natural gas from argillaceous series of the Autochthonous Miocene in the Carpathian Foredeep. To achieve the objective, the research team develop a world-trend based but unique methodology of processing and interpretation, adjusted to data, local variations and petroleum characteristics of the area. In order to determine the zones in which maximum volumes of hydrocarbons might have been generated and preserved as shale gas reservoirs, as well as to identify the most preferable well sites where largest gas accumulations are anticipated a number of task were accomplished. Evaluation of petrophysical properties and hydrocarbon saturation of the Miocene complex is based on laboratory measurements as well as interpretation of well-logs and archival data. The studies apply mercury porosimetry (MICP), micro CT and nuclear magnetic resonance imaging (using the Rock Core Analyzer). For prospective location (e.g. central part of Carpathian Foredeep – Brzesko-Wojnicz area) reprocessing and reinterpretation of detailed seismic survey data with the use of integrated geophysical investigations has been made. Construction of quantitative, structural and parametric models for selected areas of the Carpathian Foredeep is performed on the basis of integrated, detailed 3D computer models. Modeling are carried on with the Schlumberger’s Petrel software. Finally, prospective zones are spatially contoured in a form of regional 3D grid, which will be framework for generation modelling and comprehensive parametric mapping, allowing for spatial identification of the most prospective zones of unconventional gas accumulation in the Carpathian Foredeep. Preliminary results of research works indicate a potentially prospective area for occurrence of unconventional gas accumulations in the Polish part of Carpathian Foredeep.
Abstract: Goals and Objectives: A typical analysis of survival data involves the modeling of time-to-event data, such as the time till death. A frailty model is a random effect model for time-to-event data, where the random effect has a multiplicative influence on the baseline hazard function. This article aims to investigate the use of gamma frailty model with concomitant variable in order to individualize the prognostic factors that influence the liver cirrhosis patients’ survival times. Methods: During the one-year study period (May 2008-May 2009), data have been used from the recorded information of patients with liver cirrhosis who were scheduled for liver transplantation and were followed up for at least seven years in Imam Khomeini Hospital in Iran. In order to determine the effective factors for cirrhotic patients’ survival in the presence of latent variables, the gamma frailty distribution has been applied. In this article, it was considering the parametric model, such as Exponential and Weibull distributions for survival time. Data analysis is performed using R software, and the error level of 0.05 was considered for all tests. Results: 305 patients with liver cirrhosis including 180 (59%) men and 125 (41%) women were studied. The age average of patients was 39.8 years. At the end of the study, 82 (26%) patients died, among them 48 (58%) were men and 34 (42%) women. The main cause of liver cirrhosis was found hepatitis 'B' with 23%, followed by cryptogenic with 22.6% were identified as the second factor. Generally, 7-year’s survival was 28.44 months, for dead patients and for censoring was 19.33 and 31.79 months, respectively. Using multi-parametric survival models of progressive and regressive, Exponential and Weibull models with regard to the gamma frailty distribution were fitted to the cirrhosis data. In both models, factors including, age, bilirubin serum, albumin serum, and encephalopathy had a significant effect on survival time of cirrhotic patients. Conclusion: To investigate the effective factors for the time of patients’ death with liver cirrhosis in the presence of latent variables, gamma frailty model with parametric distributions seems desirable.
Abstract: One of the biggest challenges in nonparametric
regression is the curse of dimensionality. Additive models are known
to overcome this problem by estimating only the individual additive
effects of each covariate. However, if the model is misspecified, the
accuracy of the estimator compared to the fully nonparametric one
is unknown. In this work the efficiency of completely nonparametric
regression estimators such as the Loess is compared to the estimators
that assume additivity in several situations, including additive and
non-additive regression scenarios. The comparison is done by
computing the oracle mean square error of the estimators with regards
to the true nonparametric regression function. Then, a backward
elimination selection procedure based on the Akaike Information
Criteria is proposed, which is computed from either the additive or
the nonparametric model. Simulations show that if the additive model
is misspecified, the percentage of time it fails to select important
variables can be higher than that of the fully nonparametric approach.
A dimension reduction step is included when nonparametric estimator
cannot be computed due to the curse of dimensionality. Finally, the
Boston housing dataset is analyzed using the proposed backward
elimination procedure and the selected variables are identified.
Abstract: Integrated systems for product design, manufacturing, and lifecycle management are difficult to implement and customize. Commercial software vendors, including CAD/CAM and third party PDM/PLM developers, create user interfaces and functionality that allow their products to be applied across many industries. The result is that systems become overloaded with functionality, difficult to navigate, and use terminology that is unfamiliar to engineers and production personnel. For example, manufacturers of automotive, aeronautical, electronics, and household products use similar but distinct methods and processes. Furthermore, each company tends to have their own preferred tools and programs for controlling work and information flow and that connect design, planning, and manufacturing processes to business applications. This paper presents a methodology and a case study that addresses these issues and suggests that in the future more companies will develop personalized applications that fit to the natural way that their business operates. A functioning system has been implemented at a highly competitive U.S. aerospace tooling and component supplier that works with many prominent airline manufacturers around the world including The Boeing Company, Airbus, Embraer, and Bombardier Aerospace. During the last three years, the program has produced significant benefits such as the automatic creation and management of component and assembly designs (parametric models and drawings), the extensive use of lightweight 3D data, and changes to the way projects are executed from beginning to end. CATIA (CAD/CAE/CAM) and a variety of programs developed in C#, VB.Net, HTML, and SQL make up the current system. The web-based platform is facilitating collaborative work across multiple sites around the world and improving communications with customers and suppliers. This work demonstrates that the creative use of Application Programming Interface (API) utilities, libraries, and methods is a key to automating many time-consuming tasks and linking applications together.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: This paper presents a nonparametric method to obtain the hazard rate “Bathtub curve” for power system components. The model is a mixture of the three known phases of a component life, the decreasing failure rate (DFR), the constant failure rate (CFR) and the increasing failure rate (IFR) represented by three parametric Weibull models. The parameters are obtained from a simultaneous fitting process of the model to the Kernel nonparametric hazard rate curve. From the Weibull parameters and failure rate curves the useful lifetime and the characteristic lifetime were defined. To demonstrate the model the historic time-to-failure of distribution transformers were used as an example. The resulted “Bathtub curve” shows the failure rate for the equipment lifetime which can be applied in economic and replacement decision models.
Abstract: This paper presents a Gaussian process model-based
short-term electric load forecasting. The Gaussian process model is
a nonparametric model and the output of the model has Gaussian
distribution with mean and variance. The multiple Gaussian process
models as every hour ahead predictors are used to forecast future
electric load demands up to 24 hours ahead in accordance with the
direct forecasting approach. The separable least-squares approach that
combines the linear least-squares method and genetic algorithm is
applied to train these Gaussian process models. Simulation results
are shown to demonstrate the effectiveness of the proposed electric
load forecasting.
Abstract: This study is aimed towards the automisation of basic patternmaking for traditional clothes for the purpose of mass production using AutoCAD to apply AutoLISP feature under software Hazi Attire. A standard dress form (industrial form) with the size of small (S), medium (M) and large (L) size is measured using full body scanning machine. Later, the pattern for the clothes is designed parametrically based on the measured dress form. Hazi Attire program is used within the framework of AutoCAD to generate the basic pattern of front bodice, back bodice, front skirt, back skirt and sleeve block (sloper). The generation of pattern is based on the parameters inputted by user, whereby in this study, the parameters were determined based on the measured size of dress form. The finalized pattern parameter shows that the pattern fit perfectly on the dress form. Since the pattern is generated almost instantly, these proved that using the AutoLISP programming, the manufacturing lead time for the mass production of the traditional clothes can be decreased.
Abstract: Parametric models have been quite popular for
studying human growth, particularly in relation to biological
parameters such as peak size velocity and age at peak size velocity.
Longitudinal data are generally considered to be vital for fittinga
parametric model to individual-specific data, and for studying the
distribution of these biological parameters in a human population.
However, cross-sectional data are easier to obtain than longitudinal
data. In this paper, we present a method of combining longitudinal
and cross-sectional data for the purpose of estimating the distribution
of the biological parameters. We demonstrate, through simulations in
the special case ofthePreece Baines model, how estimates based on
longitudinal data can be improved upon by harnessing the
information contained in cross-sectional data.We study the extent of
improvement for different mixes of the two types of data, and finally
illustrate the use of the method through data collected by the Indian
Statistical Institute.
Abstract: This paper studies the optimum design for reducing
optical loss of an 8x8 mechanical type optical switch due to the
temperature change. The 8x8 optical switch is composed of a base, 8
input fibers, 8 output fibers, 3 fixed mirrors and 17 movable mirrors.
First, an innovative switch configuration is proposed with
thermal-compensated design. Most mechanical type optical switches
have a disadvantage that their precision and accuracy are influenced
by the ambient temperature. Therefore, the thermal-compensated
design is to deal with this situation by using materials with different
thermal expansion coefficients (α). Second, a parametric modeling
program is developed to generate solid models for finite element
analysis, and the thermal and structural behaviors of the switch are
analyzed. Finally, an integrated optimum design program, combining
Autodesk Inventor Professional software, finite element analysis
software, and genetic algorithms, is developed for improving the
thermal behaviors that the optical loss of the switch is reduced. By
changing design parameters of the switch in the integrated design
program, the final optimum design that satisfies the design constraints
and specifications can be found.
Abstract: A dead leg is a typical subsea production system
component. CFD is required to model heat transfer within the dead
leg. Unfortunately its solution is time demanding and thus not
suitable for fast prediction or repeated simulations. Therefore there is
a need to create a thermal FEA model, mimicking the heat flows and
temperatures seen in CFD cool down simulations.
This paper describes the conventional way of tuning and a new
automated way using parametric model order reduction (PMOR)
together with an optimization algorithm. The tuned FE analyses
replicate the steady state CFD parameters within a maximum error in
heat flow of 6 % and 3 % using manual and PMOR method
respectively. During cool down, the relative error of the tuned FEA
models with respect to temperature is below 5% comparing to the
CFD. In addition, the PMOR method obtained the correct FEA setup
five times faster than the manually tuned FEA.
Abstract: In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.
Abstract: Magneto-rheological (MR) fluid damper is a semiactive
control device that has recently received more attention by the
vibration control community. But inherent hysteretic and highly
nonlinear dynamics of MR fluid damper is one of the challenging
aspects to employ its unique characteristics. The combination of
artificial neural network (ANN) and fuzzy logic system (FLS) have
been used to imitate more precisely the behavior of this device.
However, the derivative-based nature of adaptive networks causes
some deficiencies. Therefore, in this paper, a novel approach that
employ genetic algorithm, as a free-derivative algorithm, to enhance
the capability of fuzzy systems, is proposed. The proposed method
used to model MR damper. The results will be compared with
adaptive neuro-fuzzy inference system (ANFIS) model, which is one
of the well-known approaches in soft computing framework, and two
best parametric models of MR damper. Data are generated based on
benchmark program by applying a number of famous earthquake
records.
Abstract: An alternative approach to the use of Discrete Fourier
Transform (DFT) for Magnetic Resonance Imaging (MRI) reconstruction
is the use of parametric modeling technique. This method
is suitable for problems in which the image can be modeled by
explicit known source functions with a few adjustable parameters.
Despite the success reported in the use of modeling technique as an
alternative MRI reconstruction technique, two important problems
constitutes challenges to the applicability of this method, these are
estimation of Model order and model coefficient determination. In
this paper, five of the suggested method of evaluating the model
order have been evaluated, these are: The Final Prediction Error
(FPE), Akaike Information Criterion (AIC), Residual Variance (RV),
Minimum Description Length (MDL) and Hannan and Quinn (HNQ)
criterion. These criteria were evaluated on MRI data sets based on the
method of Transient Error Reconstruction Algorithm (TERA). The
result for each criterion is compared to result obtained by the use of a
fixed order technique and three measures of similarity were evaluated.
Result obtained shows that the use of MDL gives the highest measure
of similarity to that use by a fixed order technique.
Abstract: This paper presents an optimization of the hull
separation, i.e. transverse clearance. The main objective is to identify
the feasible speed ranges and find the optimum transverse clearance
considering the minimum wave-making resistance. The dimensions
and the weight of hardware systems installed in the catamaran
structured fuel cell powered USV (Unmanned Surface Vehicle) were
considered as constraints. As the CAE (Computer Aided Engineering)
platform FRIENDSHIP-Framework was used. The hull surface
modeling, DoE (Design of Experiment), Tangent search optimization,
tool integration and the process automation were performed by
FRIENDSHIP-Framework. The hydrodynamic result was evaluated
by XPAN the potential solver of SHIPFLOW.
Abstract: The development of aid's systems for the medical
diagnosis is not easy thing because of presence of inhomogeneities in
the MRI, the variability of the data from a sequence to the other as
well as of other different source distortions that accentuate this
difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is
described in this article. A first phase consists in estimating the
density of probability of the data by the Parzen-Rozenblatt method.
The classification procedure is completely automatic and doesn't
make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an
automatic manner by an operator of mathematical morphology called
skeleton by influence zones detection (SKIZ). The problem of
initialization of the prototypes as well as their number is transformed
in an optimization problem; in more the procedure is adaptive since it
takes in consideration the contextual information presents in every
voxel by an adaptive and robust non parametric model by the
Markov fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior
Marginal).