Abstract: In this paper a genetic algorithm (GA) with dual-fitness function is proposed and applied to solve the deterministic identical machine scheduling problem. The mating fitness function value was used to determine the mating for chromosomes, while the selection fitness function value was used to determine their survivals. The performance of this algorithm was tested on deterministic identical machine scheduling using simulated data. The results obtained from the proposed GA were compared with classical GA and integer programming (IP). Results showed that dual-fitness function GA outperformed the classical single-fitness function GA with statistical significance for large problems and was competitive to IP, particularly when large size problems were used.
Abstract: The purpose of the research is to investigate the energetic feature of the backpack load on soldier’s gait with variation of the trunk flexion angle. It is believed that the trunk flexion variation of the loaded gait may cause a significant difference in the energy cost which is often in practice in daily life. To this end, seven healthy Korea military personnel participated in the experiment and are tested under three different walking postures comprised of the small, natural and large trunk flexion. There are around 5 degree differences of waist angle between each trunk flexion. The ground reaction forces were collected from the force plates and motion kinematic data are measured by the motion capture system. Based on these data, the impulses, momentums and mechanical works done on the center of body mass (COM) during the double support phase were computed. The result shows that the push-off and heel strike impulse are not relevant to the trunk flexion change, however the mechanical work by the push-off and heel strike were changed by the trunk flexion variation. It is because the vertical velocity of the COM during the double support phase is increased significantly with an increase in the trunk flexion. Therefore, we can know that the gait efficiency of the loaded gait depends on the trunk flexion angle. Also, even though the gravitational impulse and pre-collision momentum are changed by the trunk flexion variation, the after-collision momentum is almost constant regardless of the trunk flexion variation.
Abstract: The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
Abstract: The aim of this paper is to examine the relationship among CO2 per capita emissions, energy consumption, economic growth and bilateral trade between Singapore and Malaysia for the 1970-2011 period. ARDL model and Granger causality tests are employed for the analysis. Results of bound F-statistics suggest that long-run relationship exists between CO2 per capita (PCO2) and its determinants. The EKC hypothesis is not supported in Malaysia. Carbon emissions are mainly determined by energy consumption in the short and long run. While, exports to Singapore is a significant variable in explaining PCO2 emissions in Malaysia in long-run. Furthermore, we find a unidirectional causal relationship running from economic growth to PCO2 emissions.
Abstract: The industrial automation is dependent upon pneumatic control systems. The industrial units are now controlled with digital control systems to tackle the process variables like Temperature, Pressure, Flow rates and Composition.
This research work produces an evaluation of the response time fluctuations for proportional mode, proportional integral and proportional integral derivative modes of automated chemical process control. The controller output is measured for different values of gain with respect to time in three modes (P, PI and PID). In case of P-mode for different values of gain the controller output has negligible change. When the controller output of PI-mode is checked for constant gain, it can be seen that by decreasing the integral time the controller output has showed more fluctuations. The PID mode results have found to be more interesting in a way that when rate minute has changed, the controller output has also showed fluctuations with respect to time. The controller output for integral mode and derivative mode are observed with lesser steady state error, minimum offset and larger response time to control the process variable. The tuning parameters in case of P-mode are only steady state gain with greater errors with respect to controller output. The integral mode showed controller outputs with intermediate responses during integral gain (ki). By increasing the rate minute the derivative gain (kd) also increased which showed the controlled oscillations in case of PID mode and lesser overshoot.
Abstract: Taiwan was the first country in Asia to announce
“Nuclear-Free Homeland" in 2002. In 2008, the new government
released the Sustainable Energy Policy Guidelines to lower the
nationwide CO2 emissions some time between 2016 and 2020 back to
the level of year 2008, further abatement of CO2 emissions is planed in
year 2025 when CO2 emissions will decrease to the level of year 2000.
Besides, under consideration of the issues of energy, environment and
economics (3E), the new government declared that the nuclear power
is a carbon-less energy option. This study analyses the effects of
nuclear power generation for CO2 abatement scenarios in Taiwan. The
MARKAL-MACRO energy model was adopted to evaluate economic
impacts and energy deployment due to life extension of existing
nuclear power plants and build new nuclear power units in CO2
abatement scenarios. The results show that CO2 abatement effort is
expensive. On the other hand, nuclear power is a cost-effective choice.
The GDP loss rate in the case of building new nuclear power plants is
around two thirds of the Nuclear-Free Homeland case. Nuclear power
generation has the capacity to provide large-scale CO2 free electricity.
Therefore, the results show that nuclear power is not only an option for
Taiwan, but also a requisite for Taiwan-s CO2 reduction strategy.
Abstract: In this paper, the existence, multiplicity and
noexistence of positive solutions for a class of semipositone
discrete boundary value problems with two parameters is
studied by applying nonsmooth critical point theory and
sub-super solutions method.
Abstract: This paper presents a customized deformable model
for the segmentation of abdominal and thoracic aortic aneurysms in
CTA datasets. An important challenge in reliably detecting aortic
aneurysm is the need to overcome problems associated with intensity
inhomogeneities and image noise. Level sets are part of an important
class of methods that utilize partial differential equations (PDEs) and
have been extensively applied in image segmentation. A Gaussian
kernel function in the level set formulation, which extracts the local
intensity information, aids the suppression of noise in the extracted
regions of interest and then guides the motion of the evolving contour
for the detection of weak boundaries. The speed of curve evolution
has been significantly improved with a resulting decrease in
segmentation time compared with previous implementations of level
sets. The results indicate the method is more effective than other
approaches in coping with intensity inhomogeneities.
Abstract: Thousands of masters athletes participate
quadrennially in the World Masters Games (WMG), yet this cohort
of athletes remains proportionately under-investigated. Due to a
growing global obesity pandemic in context of benefits of physical
activity across the lifespan, the prevalence of obesity in this unique
population was of particular interest. Data gathered on a sub-sample
of 535 football code athletes, aged 31-72 yrs ( =47.4, s =±7.1),
competing at the Sydney World Masters Games (2009) demonstrated
a significantly (p
Abstract: In this paper, an extreme learning machine with an automatic segmentation algorithm is applied to heart disorder classification by heart sound signals. From continuous heart sound signals, the starting points of the first (S1) and the second heart pulses (S2) are extracted and corrected by utilizing an inter-pulse histogram. From the corrected pulse positions, a single period of heart sound signals is extracted and converted to a feature vector including the mel-scaled filter bank energy coefficients and the envelope coefficients of uniform-sized sub-segments. An extreme learning machine is used to classify the feature vector. In our cardiac disorder classification and detection experiments with 9 cardiac disorder categories, the proposed method shows significantly better performance than multi-layer perceptron, support vector machine, and hidden Markov model; it achieves the classification accuracy of 81.6% and the detection accuracy of 96.9%.
Abstract: There are many classical algorithms for finding
routing in FPGA. But Using DNA computing we can solve the routes
efficiently and fast. The run time complexity of DNA algorithms is
much less than other classical algorithms which are used for solving
routing in FPGA. The research in DNA computing is in a primary
level. High information density of DNA molecules and massive
parallelism involved in the DNA reactions make DNA computing a
powerful tool. It has been proved by many research accomplishments
that any procedure that can be programmed in a silicon computer can
be realized as a DNA computing procedure. In this paper we have
proposed two tier approaches for the FPGA routing solution. First,
geometric FPGA detailed routing task is solved by transforming it
into a Boolean satisfiability equation with the property that any
assignment of input variables that satisfies the equation specifies a
valid routing. Satisfying assignment for particular route will result in
a valid routing and absence of a satisfying assignment implies that
the layout is un-routable. In second step, DNA search algorithm is
applied on this Boolean equation for solving routing alternatives
utilizing the properties of DNA computation. The simulated results
are satisfactory and give the indication of applicability of DNA
computing for solving the FPGA Routing problem.
Abstract: This paper analyses the structural changes in
education sector since the introduction of liberalization policy in
India. This paper explains how the so-called non-profit trusts and
societies appropriated the liberalization policy and enhanced
themselves as new capitalist class in higher education sector. Over
the decades, the policy witnessed the role of private sector in terms
of maintaining market equilibrium. The state also witnessed the
incompatibility of the private sector in inculcating the values of
social justice. The most important consequence of the policy is to
witness the rise of new capitalist class and academic capitalism.
When the state came to realize that it no longer cope up with
market demands, it opens the entry of private sector in higher
education. Concessions and tax exemptions were provided to the
trusts and societies to establish higher education institutions. There
is a basic difference between western countries and India in
providing higher education by the trusts and societies. In western
countries the big business houses contributed their surplus
revenues to promote higher education and research as a
complementary service to society and nation. In India, several
entrepreneurs came up with business motive using education
sector. Over the period, they accumulated wealth at the cost of
students and concessions from the government. Four major results
can now be identified: production of manpower in view of market
demands; reduction of standards in higher education; bypassing the
values of social justice; and the rise of new capitalist class from the
business of education. This paper tries to substantiate these issues
with the inputs from case studies.
Abstract: Code mobility technologies attract more and more developers and consumers. Numerous domains are concerned, many platforms are developed and interest applications are realized. However, developing good software products requires modeling, analyzing and proving steps. The choice of models and modeling languages is so critical on these steps. Formal tools are powerful in analyzing and proving steps. However, poorness of classical modeling language to model mobility requires proposition of new models. The objective of this paper is to provide a specific formalism “Coloured Reconfigurable Nets" and to show how this one seems to be adequate to model different kinds of code mobility.
Abstract: In this paper we propose a new classification method for automatic sleep scoring using an artificial neural network based decision tree. It attempts to treat sleep scoring progress as a series of two-class problems and solves them with a decision tree made up of a group of neural network classifiers, each of which uses a special feature set and is aimed at only one specific sleep stage in order to maximize the classification effect. A single electroencephalogram (EEG) signal is used for our analysis rather than depending on multiple biological signals, which makes greatly simplifies the data acquisition process. Experimental results demonstrate that the average epoch by epoch agreement between the visual and the proposed method in separating 30s wakefulness+S1, REM, S2 and SWS epochs was 88.83%. This study shows that the proposed method performed well in all the four stages, and can effectively limit error propagation at the same time. It could, therefore, be an efficient method for automatic sleep scoring. Additionally, since it requires only a small volume of data it could be suited to pervasive applications.
Abstract: Today advertising is actively penetrating into many spheres of our lives. We cannot imagine the existence of a lot of economic activities without advertising. That mostly concerns trade and services. Everyone of us should look better into the everyday communication and carefully consider the amount and the quality of the information we receive as well as its influence on our behaviour. Special attention should be paid to the young generation. Theoretical and practical research has proved the ever growing influence of information (especially the one contained in advertising) on a society; on its economics, culture, religion, politics and even people-s private lives and behaviour. Children have plenty of free time and, therefore, see a lot of different advertising. Though education of children is in the hands of parents and schools, advertising makers and customers should think with responsibility about the selection of time and transmission channels of child targeted advertising. The purpose of the present paper is to investigate the influence of advertising upon consumer views and behaviour of children in different age groups. The present investigation has clarified the influence of advertising as a means of information on a certain group of society, which in the modern information society is the most vulnerable – children. In this paper we assess children-s perception and their understanding of advertising.
Abstract: In this article, we are dealing with a model consisting of a classical Van der Pol oscillator coupled gyroscopically to a linear oscillator. The major problem is analyzed. The regular dynamics of the system is considered using analytical methods. In this case, we provide an approximate solution for this system using parameter-expansion method. Also, we find approximate values for frequencies of the system. In parameter-expansion method the solution and unknown frequency of oscillation are expanded in a series by a bookkeeping parameter. By imposing the non-secularity condition at each order in the expansion the method provides different approximations to both the solution and the frequency of oscillation. One iteration step provides an approximate solution which is valid for the whole solution domain.
Abstract: Biometrics, which refers to identifying an individual
based on his or her physiological or behavioral characteristics, has
the capability to reliably distinguish between an authorized person
and an imposter. Signature verification systems can be categorized as
offline (static) and online (dynamic). This paper presents a neural
network based recognition of offline handwritten signatures system
that is trained with low-resolution scanned signature images.
Abstract: Debates on residential satisfaction topic have been
vigorously discussed in family house setting. Nonetheless, less or
lack of attention was given to survey on student residential
satisfaction in the campus house setting. This study, however, tried to
fill in the gap by focusing more on the relationship between students-
socio-economic backgrounds and student residential satisfaction with
their on-campus student housing facilities. Two-stage cluster
sampling method was employed to classify the respondents. Then,
self-administered questionnaires were distributed face-to-face to the
students. In general, it was confirmed that the students- socioeconomic
backgrounds have significantly influence the students-
satisfaction with their on-campus student housing facilities. The main
influential factors were revealed as the economic status, sense of
sharing, and the ethnicity of roommates. Likewise, this study could
also provide some useful feedback for the universities administration
in order to improve their student housing facilities.
Abstract: In this paper, we investigated the characteristic of a
clinical dataseton the feature selection and classification
measurements which deal with missing values problem.And also
posed the appropriated techniques to achieve the aim of the activity;
in this research aims to find features that have high effect to mortality
and mortality time frame. We quantify the complexity of a clinical
dataset. According to the complexity of the dataset, we proposed the
data mining processto cope their complexity; missing values, high
dimensionality, and the prediction problem by using the methods of
missing value replacement, feature selection, and classification.The
experimental results will extend to develop the prediction model for
cardiology.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. After a preprocessing
step, the documents are typically represented as large sparse vectors.
When training classifiers on large collections of documents, both the
time and memory restrictions can be quite prohibitive. This justifies
the application of feature selection methods to reduce the
dimensionality of the document-representation vector. In this paper,
we present three feature selection methods: Information Gain,
Support Vector Machine feature selection called (SVM_FS) and
Genetic Algorithm with SVM (called GA_SVM). We show that the
best results were obtained with GA_SVM method for a relatively
small dimension of the feature vector.