Abstract: The Virtual Reality (VR) is becoming increasingly
important for business, education, and entertainment, therefore VR
technology have been applied for training purposes in the areas of
military, safety training and flying simulators. In particular, the
superior and high reliability VR training system is very important in
immersion. Manipulation training in immersive virtual environments
is difficult partly because users must do without the hap contact with
real objects they rely on in the real world to orient themselves and
their manipulated.
In this paper, we create a convincing questionnaire of immersion
and an experiment to assess the influence of immersion on
performance in VR training system. The Immersion Questionnaire
(IQ) included spatial immersion, Psychological immersion, and
Sensory immersion. We show that users with a training system
complete visual attention and detection of signals. Twenty subjects
were allocated to a factorial design consisting of two different VR
systems (Desktop VR and Projector VR). The results indicated that
different VR representation methods significantly affected the
participants- Immersion dimensions.
Abstract: Reverse engineering of full-genomic interaction networks based on compendia of expression data has been successfully applied for a number of model organisms. This study adapts these approaches for an important non-model organism: The major human fungal pathogen Candida albicans. During the infection process, the pathogen can adapt to a wide range of environmental niches and reversibly changes its growth form. Given the importance of these processes, it is important to know how they are regulated. This study presents a reverse engineering strategy able to infer fullgenomic interaction networks for C. albicans based on a linear regression, utilizing the sparseness criterion (LASSO). To overcome the limited amount of expression data and small number of known interactions, we utilize different prior-knowledge sources guiding the network inference to a knowledge driven solution. Since, no database of known interactions for C. albicans exists, we use a textmining system which utilizes full-text research papers to identify known regulatory interactions. By comparing with these known regulatory interactions, we find an optimal value for global modelling parameters weighting the influence of the sparseness criterion and the prior-knowledge. Furthermore, we show that soft integration of prior-knowledge additionally improves the performance. Finally, we compare the performance of our approach to state of the art network inference approaches.
Abstract: One of the determinants of a firm-s prosperity is the
customers- perceived service quality and satisfaction. While service
quality is wide in scope, and consists of various dimensions, there
may be differences in the relative importance of these dimensions in
affecting customers- overall satisfaction of service quality.
Identifying the relative rank of different dimensions of service quality
is very important in that it can help managers to find out which
service dimensions have a greater effect on customers- overall
satisfaction. Such an insight will consequently lead to more effective
resource allocation which will finally end in higher levels of
customer satisfaction. This issue – despite its criticality- has not
received enough attention so far. Therefore, using a sample of 240
bank customers in Iran, an artificial neural network is developed to
address this gap in the literature. As customers- evaluation of service
quality is a subjective process, artificial neural networks –as a brain
metaphor- may appear to have a potentiality to model such a
complicated process. Proposing a neural network which is able to
predict the customers- overall satisfaction of service quality with a
promising level of accuracy is the first contribution of this study. In
addition, prioritizing the service quality dimensions in affecting
customers- overall satisfaction –by using sensitivity analysis of
neural network- is the second important finding of this paper.
Abstract: Manganese steel (Hadfield) is one of the important
alloys in industry due to its special properties. High work hardening
ability with appropriate toughness and ductility are the properties that
caused this alloy to be used in wear resistance parts and in high
strength condition. Heat treatment is the main process through which
the desired mechanical properties and microstructures are obtained in
Hadfield steel. In this study various heat treatment cycles, differing in
austenising temperature, time and quenching solution are applied. For
this purpose, the same samples of manganese steel was heat treated in
9 different cycles, and then the mechanical properties and
microstructures were investigated. Based on the results of the study,
the optimum heat treatment cycle was obtained.
Abstract: The distinction among urban, periurban and rural areas represents a classical example of uncertainty in land classification. Satellite images, geostatistical analysis and all kinds of spatial data are very useful in urban sprawl studies, but it is important to define precise rules in combining great amounts of data to build complex knowledge about territory. Rough Set theory may be a useful method to employ in this field. It represents a different mathematical approach to uncertainty by capturing the indiscernibility. Two different phenomena can be indiscernible in some contexts and classified in the same way when combining available information about them. This approach has been applied in a case of study, comparing the results achieved with both Map Algebra technique and Spatial Rough Set. The study case area, Potenza Province, is particularly suitable for the application of this theory, because it includes 100 municipalities with different number of inhabitants and morphologic features.
Abstract: There is lot of work done in prediction of the fault proneness of the software systems. But, it is the severity of the faults that is more important than number of faults existing in the developed system as the major faults matters most for a developer and those major faults needs immediate attention. In this paper, we tried to predict the level of impact of the existing faults in software systems. Neuro-Fuzzy based predictor models is applied NASA-s public domain defect dataset coded in C programming language. As Correlation-based Feature Selection (CFS) evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. So, CFS is used for the selecting the best metrics that have highly correlated with level of severity of faults. The results are compared with the prediction results of Logistic Models (LMT) that was earlier quoted as the best technique in [17]. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provide a relatively better prediction accuracy as compared to other models and hence, can be used for the modeling of the level of impact of faults in function based systems.
Abstract: Monitoring lightning electromagnetic pulses (sferics)
and other terrestrial as well as extraterrestrial transient radiation signals
is of considerable interest for practical and theoretical purposes
in astro- and geophysics as well as meteorology. Managing a continuous
flow of data, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.
Abstract: The nature of consumer products causes the difficulty
in forecasting the future demands and the accuracy of the forecasts
significantly affects the overall performance of the supply chain
system. In this study, two data mining methods, artificial neural
network (ANN) and support vector machine (SVM), were utilized to
predict the demand of consumer products. The training data used was
the actual demand of six different products from a consumer product
company in Thailand. The results indicated that SVM had a better
forecast quality (in term of MAPE) than ANN in every category of
products. Moreover, another important finding was the margin
difference of MAPE from these two methods was significantly high
when the data was highly correlated.
Abstract: Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Abstract: Meeting users- requirements is one of predictors of project success. There should be a match between the expectations of the users and the perception of key project personnel with respect to usability and functionality. The aim of this study is to make a comparison of key project personnel-s and potential users- (customer representatives) evaluations of the relative importance of usability and functionality factors in a software design project. Analytical Network Process (ANP) was used to analyze the relative importance of the factors. The results show that navigation and interaction are the most significant factors,andsatisfaction and efficiency are the least important factors for both groups. Further, it can be concluded that having similar orders and scores of usability and functionality factors for both groups shows that key project personnel have captured the expectations and requirements of potential users accurately.
Abstract: This study, focusing on the importance of encouraging
outdoor activities for children, aims to propose and implement a Web-GIS based outdoor education program for junior high schools,
which will then be evaluated by users. Specifically, for the purpose of improved outdoor activities in the junior high school education, the
outdoor education program, with chiefly using the Web-GIS that provides a good information provision and sharing tool, is proposed
and implemented before being evaluated by users. The conclusion of
this study can be summarized in the following two points.
(1) A five -step outdoor education program based on Web-GIS was
proposed for a “second school" at junior high schools that was then implemented before being evaluated by teachers as users.
(2) Based on the results of evaluation by teachers, it was clear that the general operation of Web-GIS based outdoor education program with them only is difficult due to their lack of knowledge regarding Web-GIS and that support staff who can effectively utilize Web-GIS are essential.
Abstract: Swarm principles are increasingly being used to design controllers for the coordination of multi-robot systems or, in general, multi-agent systems. This paper proposes a two-dimensional Lagrangian swarm model that enables the planar agents, modeled as point masses, to swarm whilst effectively avoiding each other and obstacles in the environment. A novel method, based on an extended Lyapunov approach, is used to construct the model. Importantly, the Lyapunov method ensures a form of practical stability that guarantees an emergent behavior, namely, a cohesive and wellspaced swarm with a constant arrangement of individuals about the swarm centroid. Computer simulations illustrate this basic feature of collective behavior. As an application, we show how multiple planar mobile unicycle-like robots swarm to eventually form patterns in which their velocities and orientations stabilize.
Abstract: Female breast cancer is the second in frequency after cervical cancer. Surgery is the most common treatment for breast cancer, followed by chemotherapy as a treatment of choice. Although effective, it causes serious side effects. Controlled-release drug delivery is an alternative method to improve the efficacy and safety of the treatment. It can release the dosage of drug between the minimum effect concentration (MEC) and minimum toxic concentration (MTC) within tumor tissue and reduce the damage of normal tissue and the side effect. Because an in vivo experiment of this system can be time-consuming and labor-intensive, a mathematical model is desired to study the effects of important parameters before the experiments are performed. Here, we describe a 3D mathematical model to predict the release of doxorubicin from pluronic gel to treat human breast cancer. This model can, ultimately, be used to effectively design the in vivo experiments.
Abstract: We derive simple sets of equations to describe the microwave response of a thin film of magnetized hydrogen plasma in the presence of carbon nanotubes, which were grown by ironcatalyzed high-pressure disproportionation (HiPco). By considering the interference effects due to multiple reflections between thin plasma film interfaces, we present the effects of the continuously changing external magnetic field and plasma parameters on the reflected power, absorbed power, and transmitted power in the system. The simulation results show that the interference effects play an important role in the reflectance, transmittance and absorptance of microwave radiation at the magnetized plasma slab. As a consequence, the interference effects lead to a sinusoidal variation of the reflected intensity and can greatly reduce the amount of reflection power, but the absorption power increases.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: Information Technology (IT) projects are always
accompanied by various risks and because of high rate of failure in
such projects, managing risks in order to neutralize or at least
decrease their effects on the success of the project is strongly
essential. In this paper, fuzzy analytical hierarchy process (FAHP) is
exploited as a means of risk evaluation methodology to prioritize and
organize risk factors faced in IT projects. A real case of IT projects, a
project of design and implementation of an integrated information
system in a vehicle producing company in Iran is studied. Related
risk factors are identified and then expert qualitative judgments about
these factors are acquired. Translating these judgments to fuzzy
numbers and using them as an input to FAHP, risk factors are then
ranked and prioritized by FAHP in order to make project managers
aware of more important risks and enable them to adopt suitable
measures to deal with these highly devastative risks.
Abstract: This paper describes a new measuring algorithm for
three-dimensional (3-D) braided composite material .Braided angle is
an important parameter of braided composites. The objective of this
paper is to present an automatic measuring system. In the paper, the
algorithm is performed by using vcµ6.0 language on PC. An
advanced filtered algorithm for image of 3-D braided composites
material performs has been developed. The procedure is completely
automatic and relies on the gray scale information content of the
images and their local wavelet transform modulus maxims.
Experimental results show that the proposed method is feasible.
The algorithm was tested on both carbon-fiber and glass-fiber
performs.
Abstract: Covering approximation spaces is a class of important
generalization of approximation spaces. For a subset X of a covering
approximation space (U, C), is X definable or rough? The
answer of this question is uncertain, which depends on covering
approximation operators endowed on (U, C). Note that there are many
various covering approximation operators, which can be endowed
on covering approximation spaces. This paper investigates covering
approximation spaces endowed ten covering approximation operators
respectively, and establishes some relations among definable subsets,
inner definable subsets and outer definable subsets in covering approximation
spaces, which deepens some results on definable subsets
in approximation spaces.
Abstract: This paper investigates the optimization problem of
multi-product aggregate production planning (APP) with fuzzy data.
From a comprehensive viewpoint of conserving the fuzziness of input
information, this paper proposes a method that can completely
describe the membership function of the performance measure. The
idea is based on the well-known Zadeh-s extension principle which
plays an important role in fuzzy theory. In the proposed solution
procedure, a pair of mathematical programs parameterized by
possibility level a is formulated to calculate the bounds of the
optimal performance measure at a . Then the membership function of
the optimal performance measure is constructed by enumerating
different values of a . Solutions obtained from the proposed method
contain more information, and can offer more chance to achieve the
feasible disaggregate plan. This is helpful to the decision-maker in
practical applications.
Abstract: Lignocellulosic materials are new targeted source to
produce second generation biofuels like biobutanol. However, this
process is significantly resisted by the native structure of biomass.
Therefore, pretreatment process is always essential to remove
hemicelluloses and lignin prior to the enzymatic hydrolysis.
The goals of pretreatment are removing hemicelluloses and
lignin, increasing biomass porosity, and increasing the enzyme
accessibility. The main goal of this research is to study the important
variables such as pretreatment temperature and time, which can give
the highest total sugar yield in pretreatment step by using dilute
phosphoric acid. After pretreatment, the highest total sugar yield of
13.61 g/L was obtained under an optimal condition at 140°C for 10
min of pretreatment time by using 1.75% (w/w) H3PO4 and at 15:1
liquid to solid ratio. The total sugar yield of two-stage process
(pretreatment+enzymatic hydrolysis) of 27.38 g/L was obtained.