Abstract: In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.
Abstract: Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.
Abstract: In this paper, we present a neural-network (NN) based
approach to represent a nonlinear Tagagi-Sugeno (T-S) system. A
linear differential inclusion (LDI) state-space representation is utilized
to deal with the NN models. Taking advantage of the LDI
representation, the stability conditions and controller design are
derived for a class of nonlinear structural systems. Moreover, the
concept of utilizing the Parallel Particle Swarm Optimization (PPSO)
algorithm to solve the common P matrix under the stability criteria is
given in this paper.
Abstract: The copyrights system is a combination of different elements. The number, content and the correlation of these elements are different for different legal orders. The models of copyrights systems display this system in terms of the interaction of economic and author's moral rights. Monistic and dualistic models are the most popular ones. The article deals with different points of view on the monism and dualism in copyright system. A specific model of the copyright in Switzerland in the XXth century is analyzed. The evolution of a French dualistic model of copyright is shown. The author believes that one should talk not about one, but rather about a number of dualism forms of copyright system.
Abstract: A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Abstract: This paper presents breast cancer detection by
observing the specific absorption rate (SAR) intensity for
identification tumor location, the tumor is identified in coordinates
(x,y,z) system. We examined the frequency between 4-8 GHz to look
for the most appropriate frequency. Results are simulated in
frequency 4-8 GHz, the model overview include normal breast with
50 mm radian, 5 mm diameter of tumor, and ultra wideband (UWB)
bowtie antenna. The models are created and simulated in CST
Microwave Studio. For this simulation, we changed antenna to 5
location around the breast, the tumor can be detected when an
antenna is close to the tumor location, which the coordinate of
maximum SAR is approximated the tumor location. For reliable, we
experiment by random tumor location to 3 position in the same size
of tumor and simulation the result again by varying the antenna
position in 5 position again, and it also detectable the tumor position
from the antenna that nearby tumor position by maximum value of
SAR, which it can be detected the tumor with precision in all
frequency between 4-8 GHz.
Abstract: This paper presents a comparative study between two
neural network models namely General Regression Neural Network
(GRNN) and Back Propagation Neural Network (BPNN) are used
to estimate radial overcut produced during Electrical Discharge
Machining (EDM). Four input parameters have been employed:
discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and
discharge voltage (V). Recently, artificial intelligence techniques, as
it is emerged as an effective tool that could be used to replace
time consuming procedures in various scientific or engineering
applications, explicitly in prediction and estimation of the complex
and nonlinear process. The both networks are trained, and the
prediction results are tested with the unseen validation set of the
experiment and analysed. It is found that the performance of both the
networks are found to be in good agreement with average percentage
error less than 11% and the correlation coefficient obtained for the
validation data set for GRNN and BPNN is more than 91%. However,
it is much faster to train GRNN network than a BPNN and GRNN is
often more accurate than BPNN. GRNN requires more memory space
to store the model, GRNN features fast learning that does not require
an iterative procedure, and highly parallel structure. GRNN networks
are slower than multilayer perceptron networks at classifying new
cases.
Abstract: One of the main biomedical problem lies in detecting dependencies in semi structured data. Solution includes biomedical portal and algorithms (integral rating health criteria, multidimensional data visualization methods). Biomedical portal allows to process diagnostic and research data in parallel mode using Microsoft System Center 2012, Windows HPC Server cloud technologies. Service does not allow user to see internal calculations instead it provides practical interface. When data is sent for processing user may track status of task and will achieve results as soon as computation is completed. Service includes own algorithms and allows diagnosing and predicating medical cases. Approved methods are based on complex system entropy methods, algorithms for determining the energy patterns of development and trajectory models of biological systems and logical–probabilistic approach with the blurring of images.
Abstract: A 3D-conjugate numerical investigation was conducted to predict heat transfer characteristics in a rectangular cross-sectional micro-channel employing simultaneously developing Tow-phase flows. The sole purpose for analyzing two phase flow heat transfer in rectangular micro channel is to pin point what are the different factors affecting this phenomenon. Different methods and techniques have been undertaken to analyze the equations arising constituting the flow of heat from gas phase to liquid phase and vice versa.Different models of micro channels have been identified and analyzed. How the geometry of micro channels affects their activity i.e. of circular and non-circular geometry has also been reviewed. To the study the results average Nusselt no plotted against the Reynolds no has been taken into consideration to study average heat exchange in micro channels against applied heat flux. High heat fluxes up to 140 W/cm2 were applied to investigate micro-channel thermal characteristics.
Abstract: The optimization of biological systems, which is a branch of metabolic engineering, has generated a lot of industrial and academic interest for a long time. In the last decade, metabolic engineering approaches based on mathematical optimizations have been used extensively for the analysis and manipulation of metabolic networks. In practical optimization of metabolic reaction networks, designers have to manage the nature of uncertainty resulting from qualitative characters of metabolic reactions, e.g., the possibility of enzyme effects. A deterministic approach does not give an adequate representation for metabolic reaction networks with uncertain characters. Fuzzy optimization formulations can be applied to cope with this problem. A fuzzy multi-objective optimization problem can be introduced for finding the optimal engineering interventions on metabolic network systems considering the resilience phenomenon and cell viability constraints. The accuracy of optimization results depends heavily on the development of essential kinetic models of metabolic networks. Kinetic models can quantitatively capture the experimentally observed regulation data of metabolic systems and are often used to find the optimal manipulation of external inputs. To address the issues of optimizing the regulatory structure of metabolic networks, it is necessary to consider qualitative effects, e.g., the resilience phenomena and cell viability constraints. Combining the qualitative and quantitative descriptions for metabolic networks makes it possible to design a viable strain and accurately predict the maximum possible flux rates of desired products. Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. Two case studies will present in the conference to illustrate the phenomena.
Abstract: The aim of this research is to evaluate the effectiveness of software quality assurance approaches of Sri Lankan offshore software development organizations, and to propose a framework which could be used across all offshore software development organizations.
An empirical study was conducted using derived framework from popular software quality evaluation models. The research instrument employed was a questionnaire survey among thirty seven Sri Lankan registered offshore software development organizations.
The findings demonstrate a positive view of Effectiveness of Software Quality Assurance – the stronger predictors of Stability, Installability, Correctness, Testability and Changeability. The present study’s recommendations indicate a need for much emphasis on software quality assurance for the Sri Lankan offshore software development organizations.
Abstract: In the process of information transmission (concept verbalization) we deal mostly with the substance (contents), and then pay attention to the form. Recalling events from the remote past, often we cannot exactly reproduce specific heard or pronounced words, as well as the syntactic structures. We remember events, feelings, images; we recall the general contents of the discourse. The thought gets a specific language form only during the concept verbalization phase. With minimum time for pondering, depending on the language competence level, the grammar and syntactic shaping often occurs automatically with the use of famous models and stereotypes. This means that the language form adapts itself to the consciousness, and not vice versa.
Abstract: A review of the literature on supply chain delivery models which use delivery windows to measure delivery performance is presented. The review herein serves to meet the following objectives: (i) provide a synthesis of previously published literature on supply chain delivery performance models, (ii) provide in one paper a consolidation of research that can serve as a single source to keep researchers up to date with the research developments in supply chain delivery models, and (iii) identify gaps in the modeling of supply chain delivery performance which could stimulate new research agendas.
Abstract: Portugal is an important wine and olive oil producer, activities which generate a high quantity of residues commonly called grape stalks and olive cake, respectively. In this work grape stalks and olive cake were used as lignocellulosic adsorbents for wastewater containing lead treatment. To attain a better knowledge of the factors that could influence the quality of the treated wastewater, a chemical characterization of the materials used in the treatment was done. To access the ecotoxicological safety of the treated wastewater, several tests were performed.
The results of the toxicity test show that the samples leachate has a mild effect on the living models tested. The tests performed in lemna and bacteria were the most sensible to toxicity effects of the samples. The results obtained in this work evidenced the importance of use of simple and fast toxicity tests to predict impacts in the environment.
Abstract: A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.
Abstract: This paper discusses the performance of critical
trajectory method (CTrj) for power system transient stability analysis
under various loading settings and heavy fault condition. The method
obtains Controlling Unstable Equilibrium Point (CUEP) which is
essential for estimation of power system stability margins. The CUEP
is computed by applying the CTrjto the boundary controlling unstable
equilibrium point (BCU) method. The Proposed method computes a
trajectory on the stability boundary that starts from the exit point and
reaches CUEP under certain assumptions. The robustness and
effectiveness of the method are demonstrated via six power system
models and five loading conditions. As benchmark is used
conventional simulation method whereas the performance is compared
with and BCU Shadowing method.
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: Anaerobic modeling is a useful tool to describe and
simulate the condition and behaviour of anaerobic treatment units for
better effluent quality and biogas generation. The present
investigation deals with the anaerobic treatment of brewery
wastewater with varying organic loads. The chemical oxygen demand
(COD) and total suspended solids (TSS) of the influent and effluent
of the bioreactor were determined at various retention times to
generate data for kinetic coefficients. The bio-kinetic coefficients in
the modified Stover–Kincannon kinetic and methane generation
models were determined to study the performance of anaerobic
digestion process. At steady-state, the determination of the kinetic
coefficient (K), the endogenous decay coefficient (Kd), the maximum
growth rate of microorganisms (μmax), the growth yield coefficient
(Y), ultimate methane yield (Bo), maximum utilization rate constant
Umax and the saturation constant (KB) in the model were calculated to
be 0.046 g/g COD, 0.083 (d¯¹), 0.117 (d-¹), 0.357 g/g, 0.516 (L
CH4/gCODadded), 18.51 (g/L/day) and 13.64 (g/L/day) respectively.
The outcome of this study will help in simulation of anaerobic model
to predict usable methane and good effluent quality during the
treatment of industrial wastewater. Thus, this will protect the
environment, conserve natural resources, saves time and reduce cost
incur by the industries for the discharge of untreated or partially
treated wastewater. It will also contribute to a sustainable long-term
clean development mechanism for the optimization of the methane
produced from anaerobic degradation of waste in a close system.
Abstract: This paper presents a complete dynamic modeling
of a membrane distillation process. The model contains two
consistent dynamic models. A 2D advection-diffusion equation
for modeling the whole process and a modified heat equation
for modeling the membrane itself. The complete model describes
the temperature diffusion phenomenon across the feed, membrane,
permeate containers and boundary layers of the membrane. It gives
an online and complete temperature profile for each point in the
domain. It explains heat conduction and convection mechanisms that
take place inside the process in terms of mathematical parameters, and
justify process behavior during transient and steady state phases. The
process is monitored for any sudden change in the performance at any
instance of time. In addition, it assists maintaining production rates
as desired, and gives recommendations during membrane fabrication
stages. System performance and parameters can be optimized
and controlled using this complete dynamic model. Evolution of
membrane boundary temperature with time, vapor mass transfer along
the process, and temperature difference between membrane boundary
layers are depicted and included. Simulations were performed over
the complete model with real membrane specifications. The plots
show consistency between 2D advection-diffusion model and the
expected behavior of the systems as well as literature. Evolution
of heat inside the membrane starting from transient response till
reaching steady state response for fixed and varying times is
illustrated.
Abstract: Information technology has made a pivotal progress across disparate disciplines, one of which is AEC (Architecture, Engineering and Construction) industry. CAD is a form of computer-aided building modulation that architects, engineers and contractors use to create and view two- and three-dimensional models. The AEC industry also uses building information modeling (BIM), a newer computerized modeling system that can create four-dimensional models; this software can greatly increase productivity in the AEC industry. BIM models generate open source IFC (Industry Foundation Classes) files which aim for interoperability for exchanging information throughout the project lifecycle among various disciplines. The methods developed in previous studies require either an IFC schema or MVD and software applications, such as an IFC model server or a Building Information Modeling (BIM) authoring tool, to extract a partial or complete IFC instance model. This paper proposes an efficient algorithm for extracting a partial and total model from an Industry Foundation Classes (IFC) instance model without an IFC schema or a complete IFC model view definition (MVD).