Abstract: Although electrical motors are still the main devices
used in vehicular exhaust comprises more than 95 percent of the air
pollution in Taiwan's largest city, Taipei. On average, all commuters in Taipei travel 13.6 km daily, while motorcycle commuters travel 12.2 km. The convenience and mobility of motorcycles makes them
irreplaceable in Taiwan city traffic but they add significantly to air pollution problems. In order to improve air pollution conditions, some
new types of vehicles have been proposed, such as fuel cell driven and
hybrid energy vehicles. In this study, we develop a model pneumatic hybrid motorcycle system and simulate its acceleration and mileage
(km/L) performance. The results show that the pneumatic hybrid
motorcycle can improve efficiency.
Abstract: Iris-based biometric system is gaining its importance in several applications. However, processing of iris biometric is a challenging and time consuming task. Detection of iris part in an eye image poses a number of challenges such as, inferior image quality, occlusion of eyelids and eyelashes etc. Due to these problems it is not possible to achieve 100% accuracy rate in any iris-based biometric authentication systems. Further, iris detection is a computationally intensive task in the overall iris biometric processing. In this paper, we address these two problems and propose a technique to localize iris part efficiently and accurately. We propose scaling and color level transform followed by thresholding, finding pupil boundary points for pupil boundary detection and dilation, thresholding, vertical edge detection and removal of unnecessary edges present in the eye images for iris boundary detection. Scaling reduces the search space significantly and intensity level transform is helpful for image thresholding. Experimental results show that our approach is comparable with the existing approaches. Following our approach it is possible to detect iris part with 95-99% accuracy as substantiated by our experiments on CASIA Ver-3.0, ICE 2005, UBIRIS, Bath and MMU iris image databases.
Abstract: Intelligent Transportation System integrates various modern advanced technologies into the ground transportation system, and it will be the goal of urban transport system in the future because of its comprehensive effects. However, it also brings some problems, such as project performance assessment, fairness of benefiting groups, fund management, which are directly related to its operation and implementation. Wuhan has difficulties in organizing transportation because of its nature feature (river and lake), therefore, calling Service of Taxi plays an important role in transportation. This paper researches on calling Service of Taxi in Wuhan, based on quantitative and qualitative analysis. It analyzes its operations management systematically, including business model, finance, usage analysis and users evaluation. As for business model, it is that the government leads the operation at the initial stage, and the third part dominates the operation at the mature stage, which not only eases the pressure of the third part and benefits the spread of the calling service at the initial stage, but also alleviates financial pressure of government and improve the efficiency of the operation at the mature stage. As for finance, it draws that this service will bring heavy financial burden of equipments, but it will be alleviated in the future because of its spread. As for usage analysis, through data comparison, this service can bring some benefits for taxi drivers, and time and spatial distribution of usage have certain features. As for user evaluation, it analyzes using group and the reason why choosing it. At last, according to the analysis above, the paper puts forward the potentials, limitations, and future development strategies for it.
Abstract: The future of business intelligence (BI) is to integrate
intelligence into operational systems that works in real-time
analyzing small chunks of data based on requirements on continuous
basis. This is moving away from traditional approach of doing
analysis on ad-hoc basis or sporadically in passive and off-line mode
analyzing huge amount data. Various AI techniques such as expert
systems, case-based reasoning, neural-networks play important role
in building business intelligent systems. Since BI involves various
tasks and models various types of problems, hybrid intelligent
techniques can be better choice. Intelligent systems accessible
through web services make it easier to integrate them into existing
operational systems to add intelligence in every business processes.
These can be built to be invoked in modular and distributed way to
work in real time. Functionality of such systems can be extended to
get external inputs compatible with formats like RSS. In this paper,
we describe a framework that use effective combinations of these
techniques, accessible through web services and work in real-time.
We have successfully developed various prototype systems and done
few commercial deployments in the area of personalization and
recommendation on mobile and websites.
Abstract: The last two decades witnessed some advances in the development of an Arabic character recognition (CR) system. Arabic CR faces technical problems not encountered in any other language that make Arabic CR systems achieve relatively low accuracy and retards establishing them as market products. We propose the basic stages towards a system that attacks the problem of recognizing online Arabic cursive handwriting. Rule-based methods are used to perform simultaneous segmentation and recognition of word portions in an unconstrained cursively handwritten document using dynamic programming. The output of these stages is in the form of a ranked list of the possible decisions. A new technique for text line separation is also used.
Abstract: We present a method for the selection of students
in interdisciplinary studies based on the hybrid averaging
operator. We assume that the available information given in
the problem is uncertain so it is necessary to use interval
numbers. Therefore, we suggest a new type of hybrid
aggregation called uncertain induced generalized hybrid
averaging (UIGHA) operator. It is an aggregation operator
that considers the weighted average (WA) and the ordered
weighted averaging (OWA) operator in the same formulation.
Therefore, we are able to consider the degree of optimism of
the decision maker and grades of importance in the same
approach. By using interval numbers, we are able to represent
the information considering the best and worst possible results
so the decision maker gets a more complete view of the
decision problem. We develop an illustrative example of the
proposed scheme in the selection of students in
interdisciplinary studies. We see that with the use of the
UIGHA operator we get a more complete representation of the
selection problem. Then, the decision maker is able to
consider a wide range of alternatives depending on his
interests. We also show other potential applications that could
be used by using the UIGHA operator in educational problems
about selection of different types of resources such as
students, professors, etc.
Abstract: According to the majority and to stereotypes in a simple everyman religious processes in the world in general, and Kazakhstan in particular, have only negative trends. The main reason for the author's opinion is seen in the fact that the media in the pursuit of ratings and sensation, more inclined to highlight the negative aspects of events in the country and the world of processes forgetting or casually mentioning the positive initiatives and achievements. That is why the article is mainly revealed positive trends in mind that the problems of fanaticism, terrorism and the confrontation of society on various issues, a lot has been written and detailed. This article describes the stages in the development of relations between religion and state, as well as institutionalization, networking and assistance in the correct orientation of religious activities in the country.
Abstract: In the context of spectrum surveillance, a method to
recover the code of spread spectrum signal is presented, whereas the
receiver has no knowledge of the transmitter-s spreading sequence.
The approach is based on a genetic algorithm (GA), which is forced to
model the received signal. Genetic algorithms (GAs) are well known
for their robustness in solving complex optimization problems.
Experimental results show that the method provides a good
estimation, even when the signal power is below the noise power.
Abstract: .Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. This paper discusses the issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA. Implementation method with resource/speed tradeoff is proposed to handle signed decimal numbers. The VHDL coding developed is tested using Xilinx XC V50hq240 Chip. To improve the speed of operation a lookup table method is used. The problems involved in using a lookup table (LUT) for a nonlinear function is discussed. The percentage saving in resource and the improvement in speed with an LUT for a neuron is reported. An attempt is also made to derive a generalized formula for a multi-input neuron that facilitates to estimate approximately the total resource requirement and speed achievable for a given multilayer neural network. This facilitates the designer to choose the FPGA capacity for a given application. Using the proposed method of implementation a neural network based application, namely, a Space vector modulator for a vector-controlled drive is presented
Abstract: Iron in groundwater is one of the problems that render the water unsuitable for drinking. The concentration above 0.3 mg/L is common in groundwater. The conventional method of removal is by precipitation under oxic condition. In this study, iron removal under anaerobic conditions was examined by batch experiment as a main purpose. The process involved by purging of groundwater samples with H2S to form iron sulfide. Removal up to 83% for 1 mg/L iron solution was achieved. The removal efficiency dropped to 82% and 75% for the higher initial iron concentrations 3.55 and 5.01 mg/L, respectively. The average residual sulfide concentration in water after the process was 25*g/L. The Eh level during the process was -272 mV. The removal process was found to follow the first order reaction with average rate constant of 4.52 x 10-3. The half-life for the concentrations to reduce from initial values was 157 minutes.
Abstract: For a given specific problem an efficient algorithm has been the matter of study. However, an alternative approach orthogonal to this approach comes out, which is called a reduction. In general for a given specific problem this reduction approach studies how to convert an original problem into subproblems. This paper proposes a formal modeling language to support this reduction approach in order to make a solver quickly. We show three examples from the wide area of learning problems. The benefit is a fast prototyping of algorithms for a given new problem. It is noted that our formal modeling language is not intend for providing an efficient notation for data mining application, but for facilitating a designer who develops solvers in machine learning.
Abstract: The classical temporal scan statistic is often used to
identify disease clusters. In recent years, this method has become as a
very popular technique and its field of application has been notably
increased. Many bioinformatic problems have been solved with this
technique. In this paper a new scan fuzzy method is proposed. The
behaviors of classic and fuzzy scan techniques are studied with
simulated data. ROC curves are calculated, being demonstrated the
superiority of the fuzzy scan technique.
Abstract: Workflow Management Systems (WfMS) alloworganizations to streamline and automate business processes and reengineer their structure. One important requirement for this type of system is the management and computation of the Quality of Service(QoS) of processes and workflows. Currently, a range of Web processes and workflow languages exist. Each language can be characterized by the set of patterns they support. Developing andimplementing a suitable and generic algorithm to compute the QoSof processes that have been designed using different languages is a difficult task. This is because some patterns are specific to particular process languages and new patterns may be introduced in future versions of a language. In this paper, we describe an adaptive algorithm implemented to cope with these two problems. The algorithm is called adaptive since it can be dynamically changed as the patterns of a process language also change.
Abstract: A novel idea presented in this paper is to combine
multihop routing with single-frequency networks (SFNs) for a
broadcasting scenario. An SFN is a set of multiple nodes that transmit
the same data simultaneously, resulting in transmitter macrodiversity.
Two of the most important performance factors of multihop
networks, node reachability and routing robustness, are analyzed.
Simulation results show that our proposed SFN-D routing algorithm
improves the node reachability by 37 percentage points as compared
to non-SFN multihop routing. It shows a diversity gain of 3.7 dB,
meaning that 3.7 dB lower transmission powers are required for the
same reachability. Even better results are possible for larger
networks. If an important node becomes inactive, this algorithm can
find new routes that a non-SFN scheme would not be able to find.
Thus, two of the major problems in multihopping are addressed;
achieving robust routing as well as improving node reachability or
reducing transmission power.
Abstract: Taking into account that many problems of natural
sciences and engineering are reduced to solving initial-value problem
for ordinary differential equations, beginning from Newton, the
scientists investigate approximate solution of ordinary differential
equations. There are papers of different authors devoted to the
solution of initial value problem for ODE. The Euler-s known
method that was developed under the guidance of the famous
scientists Adams, Runge and Kutta is the most popular one among
these methods.
Recently the scientists began to construct the methods preserving
some properties of Adams and Runge-Kutta methods and called them
hybrid methods. The constructions of such methods are investigated
from the middle of the XX century. Here we investigate one
generalization of multistep and hybrid methods and on their base we
construct specific methods of accuracy order p = 5 and p = 6 for
k = 1 ( k is the order of the difference method).
Abstract: Data mining has been used very frequently to extract
hidden information from large databases. This paper suggests the use
of decision trees for continuously extracting the clinical reasoning in
the form of medical expert-s actions that is inherent in large number
of EMRs (Electronic Medical records). In this way the extracted data
could be used to teach students of oral medicine a number of orderly
processes for dealing with patients who represent with different
problems within the practice context over time.
Abstract: The objective of this paper is to review and assess the
methodological issues and problems in marketing research, data and
knowledge mining in Turkey. As a summary, academic marketing
research publications in Turkey have significant problems. The most
vital problem seems to be related with modeling. Most of the
publications had major weaknesses in modeling. There were also,
serious problems regarding measurement and scaling, sampling and
analyses. Analyses myopia seems to be the most important problem
for young academia in Turkey. Another very important finding is the
lack of publications on data and knowledge mining in the academic
world.
Abstract: The purpose of the article is to illustrate the main
characteristics of the corporate governance challenge facing the
countries of South-Eastern Europe (SEE) and to subsequently
determine and assess the extensiveness and effectiveness of corporate
governance regulations in these countries. Therefore, we start with an
overview on the subject of the key problems of corporate governance
in transition. We then address the issue of corporate governance
measurement for SEE countries. To this end, we include a review of
the methodological framework for determining both the
extensiveness and the effectiveness of corporate governance
legislation. We then focus on the actual analysis of the quality of
corporate governance codes, as well as of legal institutions
effectiveness and provide a measure of corporate governance in
Romania and other SEE emerging markets. The paper concludes by
emphasizing the corporate governance enforcement gap and by
identifying research issues that require further study.
Abstract: In this paper a functional interpretation of quantum
theory (QT) with emphasis on quantum field theory (QFT) is proposed.
Besides the usual statements on relations between a functions
initial state and final state, a functional interpretation also contains
a description of the dynamic evolution of the function. That is, it
describes how things function. The proposed functional interpretation
of QT/QFT has been developed in the context of the author-s work
towards a computer model of QT with the goal of supporting
the largest possible scope of QT concepts. In the course of this
work, the author encountered a number of problems inherent in the
translation of quantum physics into a computer program. He came
to the conclusion that the goal of supporting the major QT concepts
can only be satisfied, if the present model of QT is supplemented
by a "functional interpretation" of QT/QFT. The paper describes a
proposal for that
Abstract: In this paper, a new learning algorithm based on a
hybrid metaheuristic integrating Differential Evolution (DE) and
Reduced Variable Neighborhood Search (RVNS) is introduced to train
the classification method PROAFTN. To apply PROAFTN, values of
several parameters need to be determined prior to classification. These
parameters include boundaries of intervals and relative weights for
each attribute. Based on these requirements, the hybrid approach,
named DEPRO-RVNS, is presented in this study. In some cases, the
major problem when applying DE to some classification problems
was the premature convergence of some individuals to local optima.
To eliminate this shortcoming and to improve the exploration and
exploitation capabilities of DE, such individuals were set to iteratively
re-explored using RVNS. Based on the generated results on
both training and testing data, it is shown that the performance of
PROAFTN is significantly improved. Furthermore, the experimental
study shows that DEPRO-RVNS outperforms well-known machine
learning classifiers in a variety of problems.