Abstract: In this paper, we proposed a method to classify each
type of natural rock texture. Our goal is to classify 26 classes of rock
textures. First, we extract five features of each class by using
principle component analysis combining with the use of applied
spatial frequency measurement. Next, the effective node number of
neural network was tested. We used the most effective neural
network in classification process. The results from this system yield
quite high in recognition rate. It is shown that high recognition rate
can be achieved in separation of 26 stone classes.
Abstract: Nowadays, desalination of salt water is considered an important industrial process. In many parts of the world, particularly in the gulf countries, the multi-stage flash (MSF) water desalination has an essential contribution in the production of fresh water. In this study, a simple mathematical model is defined to design a MSF desalination system and the feasibility of using the MSF desalination process in proximity of a 42 MW power plant is investigated. This power plant can just provide 10 ton/h superheated steam from low pressure (LP) section of heat recovery steam generator (HRSG) for thermal desalting system. The designed MSF system with gained output ratio (GOR) of 10.3 has 24 flashing stages and can produce 2480 ton/d of fresh water. The expected performance characteristics of the designed MSF desalination plant are determined. In addition, the effect of motive water pressure on the amount of non-condensable gases removed by water jet vacuum pumps is investigated.
Abstract: Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback.
Abstract: In this paper, we propose a robust disease detection
method, called adaptive orientation code matching (Adaptive OCM),
which is developed from a robust image registration algorithm:
orientation code matching (OCM), to achieve continuous and
site-specific detection of changes in plant disease. We use two-stage
framework for realizing our research purpose; in the first stage,
adaptive OCM was employed which could not only realize the
continuous and site-specific observation of disease development, but
also shows its excellent robustness for non-rigid plant object searching
in scene illumination, translation, small rotation and occlusion changes
and then in the second stage, a machine learning method of support
vector machine (SVM) based on a feature of two dimensional (2D)
xy-color histogram is further utilized for pixel-wise disease
classification and quantification. The indoor experiment results
demonstrate the feasibility and potential of our proposed algorithm,
which could be implemented in real field situation for better
observation of plant disease development.
Abstract: Continuous innovation is becoming a necessity if
firms want to stay competitive. Different factors influence the rate of
innovation in a firm, among which corporate culture has often been
recognized among the most important factors. In this paper we argue
that the development of corporate culture that will support and foster
innovation must be accompanied with an appropriate reward system.
A research conducted among Croatian firms showed that a
statistically significant relationship exists among corporate culture
that supports innovations and reward system features.
Abstract: In recent methodological articles related to structural equation modeling (SEM), the question of how to measure endogenous formative variables has been raised as an urgent, unresolved issue. This research presents an empirical application from the CRM system development context to test a recently developed technique, which makes it possible to measure endogenous formative constructs in structural models. PLS path modeling is used to demonstrate the feasibility of measuring antecedent relationships at the formative indicator level, not the formative construct level. Empirical results show that this technique is a promising approach to measure antecedent relationships of formative constructs in SEM.
Abstract: This paper presents a hybrid electric scooter model developed and simulated using Matlab/Simulink. This hybrid scooter modeled has a parallel hybrid structure. The main propulsion units consist of a two stroke internal combustion engine and a hub motor attached to the front wheel of the scooter. The methodology used to optimize the energy and fuel consumption of the hybrid electric scooter is the multi-mode approach. Various case studies were presented to check the model and were compared to the literatures. Results shown that the model developed was feasible and valuable.
Abstract: This study aims to segment objects using the K-means
algorithm for texture features. Firstly, the algorithm transforms color
images into gray images. This paper describes a novel technique for
the extraction of texture features in an image. Then, in a group of
similar features, objects and backgrounds are differentiated by using
the K-means algorithm. Finally, this paper proposes a new object
segmentation algorithm using the morphological technique. The
experiments described include the segmentation of single and multiple
objects featured in this paper. The region of an object can be
accurately segmented out. The results can help to perform image
retrieval and analyze features of an object, as are shown in this paper.
Abstract: Using Dynamic Bayesian Networks (DBN) to model genetic regulatory networks from gene expression data is one of the major paradigms for inferring the interactions among genes. Averaging a collection of models for predicting network is desired, rather than relying on a single high scoring model. In this paper, two kinds of model searching approaches are compared, which are Greedy hill-climbing Search with Restarts (GSR) and Markov Chain Monte Carlo (MCMC) methods. The GSR is preferred in many papers, but there is no such comparison study about which one is better for DBN models. Different types of experiments have been carried out to try to give a benchmark test to these approaches. Our experimental results demonstrated that on average the MCMC methods outperform the GSR in accuracy of predicted network, and having the comparable performance in time efficiency. By proposing the different variations of MCMC and employing simulated annealing strategy, the MCMC methods become more efficient and stable. Apart from comparisons between these approaches, another objective of this study is to investigate the feasibility of using DBN modeling approaches for inferring gene networks from few snapshots of high dimensional gene profiles. Through synthetic data experiments as well as systematic data experiments, the experimental results revealed how the performances of these approaches can be influenced as the target gene network varies in the network size, data size, as well as system complexity.
Abstract: The compression-absorption heat pump (C-A HP), one
of the promising heat recovery equipments that make process hot
water using low temperature heat of wastewater, was evaluated by
computer simulation. A simulation program was developed based on
the continuity and the first and second laws of thermodynamics. Both
the absorber and desorber were modeled using UA-LMTD method. In
order to prevent an unfeasible temperature profile and to reduce
calculation errors from the curved temperature profile of a mixture,
heat loads were divided into lots of segments. A single-stage
compressor was considered. A compressor cooling load was also
taken into account. An isentropic efficiency was computed from the
map data. Simulation conditions were given based on the system
consisting of ordinarily designed components. The simulation results
show that most of the total entropy generation occurs during the
compression and cooling process, thus suggesting the possibility that
system performance can be enhanced if a rectifier is introduced.
Abstract: This article presents the developments of efficient
algorithms for tablet copies comparison. Image recognition has
specialized use in digital systems such as medical imaging,
computer vision, defense, communication etc. Comparison between
two images that look indistinguishable is a formidable task. Two
images taken from different sources might look identical but due to
different digitizing properties they are not. Whereas small variation
in image information such as cropping, rotation, and slight
photometric alteration are unsuitable for based matching
techniques. In this paper we introduce different matching
algorithms designed to facilitate, for art centers, identifying real
painting images from fake ones. Different vision algorithms for
local image features are implemented using MATLAB. In this
framework a Table Comparison Computer Tool “TCCT" is
designed to facilitate our research. The TCCT is a Graphical Unit
Interface (GUI) tool used to identify images by its shapes and
objects. Parameter of vision system is fully accessible to user
through this graphical unit interface. And then for matching, it
applies different description technique that can identify exact
figures of objects.
Abstract: The increasing demand for sufficient and clean
energy forces industrial and service companies to align their strategies towards efficient consumption. This trend refers also to the
residential building sector. There, large amounts of energy consumption are caused by house and facility heating. Many of the
operated hot water heating systems lack hydraulic balanced working
conditions for heat distribution and –transmission and lead to
inefficient heating. Through hydraulic balancing of heating systems,
significant energy savings for primary and secondary energy can be
achieved. This paper addresses the use of KNX-technology (Smart
Buildings) in residential buildings to ensure a dynamic adaption of
hydraulic system's performance, in order to increase the heating
system's efficiency. In this paper, the procedure of heating system
segmentation into hydraulically independent units (meshes) is
presented. Within these meshes, the heating valve are addressed and
controlled by a central facility server. Feasibility criteria towards
such drivers will be named. The dynamic hydraulic balance is
achieved by positioning these valves according to heating loads, that
are generated from the temperature settings in the corresponding
rooms. The energetic advantages of single room heating control
procedures, based on the application FacilityManager, is presented.
Abstract: Stream Control Transmission Protocol (SCTP) has been
proposed to provide reliable transport of real-time communications.
Due to its attractive features, such as multi-streaming and multihoming,
the SCTP is often expected to be an alternative protocol
for TCP and UDP. In the original SCTP standard, the secondary path
is mainly regarded as a redundancy. Recently, most of researches
have focused on extending the SCTP to enable a host to send its
packets to a destination over multiple paths simultaneously. In order
to transfer packets concurrently over the multiple paths, the SCTP
should be well designed to avoid unnecessary fast retransmission
and the mis-estimation of congestion window size through the paths.
Therefore, we propose an Enhanced Cooperative ACK SCTP (ECASCTP)
to improve the path recovery efficiency of multi-homed host
which is under concurrent multiple transfer mode. We evaluated the
performance of our proposed scheme using ns-2 simulation in terms
of cwnd variation, path recovery time, and goodput. Our scheme
provides better performance in lossy and path asymmetric networks.
Abstract: In the proposed method for Web page-ranking, a
novel theoretic model is introduced and tested by examples of order
relationships among IP addresses. Ranking is induced using a
convexity feature, which is learned according to these examples
using a self-organizing procedure. We consider the problem of selforganizing
learning from IP data to be represented by a semi-random
convex polygon procedure, in which the vertices correspond to IP
addresses. Based on recent developments in our regularization
theory for convex polygons and corresponding Euclidean distance
based methods for classification, we develop an algorithmic
framework for learning ranking functions based on a Computational
Geometric Theory. We show that our algorithm is generic, and
present experimental results explaining the potential of our approach.
In addition, we explain the generality of our approach by showing its
possible use as a visualization tool for data obtained from diverse
domains, such as Public Administration and Education.
Abstract: Microarray data profiles gene expression on a whole
genome scale, therefore, it provides a good way to study associations
between gene expression and occurrence or progression of cancer.
More and more researchers realized that microarray data is helpful
to predict cancer sample. However, the high dimension of gene
expressions is much larger than the sample size, which makes this
task very difficult. Therefore, how to identify the significant genes
causing cancer becomes emergency and also a hot and hard research
topic. Many feature selection algorithms have been proposed in
the past focusing on improving cancer predictive accuracy at the
expense of ignoring the correlations between the features. In this
work, a novel framework (named by SGS) is presented for stable gene
selection and efficient cancer prediction . The proposed framework
first performs clustering algorithm to find the gene groups where
genes in each group have higher correlation coefficient, and then
selects the significant genes in each group with Bayesian Lasso and
important gene groups with group Lasso, and finally builds prediction
model based on the shrinkage gene space with efficient classification
algorithm (such as, SVM, 1NN, Regression and etc.). Experiment
results on real world data show that the proposed framework often
outperforms the existing feature selection and prediction methods,
say SAM, IG and Lasso-type prediction model.
Abstract: Traditionally, VLSI implementations of spiking
neural nets have featured large neuron counts for fixed computations
or small exploratory, configurable nets. This paper presents the
system architecture of a large configurable neural net system
employing a dedicated mapping algorithm for projecting the targeted
biology-analog nets and dynamics onto the hardware with its
attendant constraints.
Abstract: Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.
Abstract: Ireland developed a National Strategy 2030 that
argued for the creation of a new form of higher education institution,
a Technological University. The research reported here reviews the
first stage of this partnership development. The study found that
national policy can create system capacity and change, but that
individual partners may have more to gain or lose in collaborating.
When presented as a zero-sum activity, fear among partners is high.
The level of knowledge and networking within the higher education
system possessed by each partner contributed to decisions to
participate or not in a joint proposal for collaboration. Greater
success resulted when there were gains for all partners. This research
concludes that policy mandates can provide motivation to
collaborate, but that the partnership needs to be built more on shared
values versus coercion by mandates.
Abstract: The feature of HIV genome is in a wide range because
of it is highly heterogeneous. Hence, the infection ability of the virus changes related with different chemokine receptors. From this point,
R5 and X4 HIV viruses use CCR5 and CXCR5 coreceptors respectively while R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have been studied to
classify by using the coreceptors of HIV genome.
The aim of this study is to develop the optimal Multilayer
Perceptron (MLP) for high classification accuracy of HIV sub-type viruses. To accomplish this purpose, the unit number in hidden layer
was incremented one by one, from one to a particular number. The statistical data of R5X4, R5 and X4 viruses was preprocessed by the
signal processing methods. Accessible residues of these virus sequences were extracted and modeled by Auto-Regressive Model
(AR) due to the dimension of residues is large and different from each other. Finally the pre-processed dataset was used to evolve MLP with various number of hidden units to determine R5X4
viruses. Furthermore, ROC analysis was used to figure out the optimal MLP structure.
Abstract: In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.