Abstract: The Object of this paper is to design and analyze a
Hysteresis modulation based sliding mode control (HMSMC) for
positive output elementary super lift Luo converter (POESLLC),
which is the start-of-the-art DC-DC converter. The positive output
elementary super lift Luo converter performs the voltage
conversion from positive source voltage to positive load voltage.
This paper proposes a HMSMC capable of providing the good
steady state and dynamic performance compared to conventional
controllers. Dynamic equations describing the positive output
elementary super lift luo converter are derived by using state space
average method. The simulation model of the positive output
elementary super lift Luo converter with its control circuit is
implemented in Matlab/Simulink. The HMSMC for positive
output elementary super lift Luo converter is tested for line
changes, load changes and also for components variations.
Abstract: This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochastic in nature and have been successfully applied to solve many Non Polynomial hard problems. Results obtained show that Genetic algorithms out perform Ant Colony optimization technique when tested on the VLSI circuit bi-partitioning problem.
Abstract: In this paper, taking Chinese Nanjing Metro ALSTOM vehicle synthesis brake as an example, the subway with synthetic brake shoe formula components of final product performance, has done a lot of research and performance test, final is drawn with hybrid fiber as reinforcing material, modified phenolic resin as matrix material, and then filling friction modifier performance, by the hot pressing process made a new type of domestic subway brake shoe. The product of the test performance indicators that can replace the similar foreign products.
Abstract: Mobile adhoc network (MANET) is a collection of
mobile devices which form a communication network with no preexisting
wiring or infrastructure. Multiple routing protocols have
been developed for MANETs. As MANETs gain popularity, their
need to support real time applications is growing as well. Such
applications have stringent quality of service (QoS) requirements
such as throughput, end-to-end delay, and energy. Due to dynamic
topology and bandwidth constraint supporting QoS is a challenging
task. QoS aware routing is an important building block for QoS
support. The primary goal of the QoS aware protocol is to determine
the path from source to destination that satisfies the QoS
requirements. This paper proposes a new energy and delay aware
protocol called energy and delay aware TORA (EDTORA) based on
extension of Temporally Ordered Routing Protocol (TORA).Energy
and delay verifications of query packet have been done in each node.
Simulation results show that the proposed protocol has a higher
performance than TORA in terms of network lifetime, packet
delivery ratio and end-to-end delay.
Abstract: The psychological well-being of a family is a subjective matter for evaluation, all the more when it involves the element of religions, whether Islam, Christianity, Buddhism or Hinduism. Each of these religions emphasises similar values and morals on family psychological well-being. This comparative study is specifically to determine the role of religion on family psychological well-being in Pekan district, Pahang, Malaysia. The study adopts a quantitative and qualitative mixed method design and considers a total of 412 samples of parents and children for the quantitative study, and 21 samples for the qualitative study. The quantitative study uses simple random sampling, whereas the qualitative sampling is purposive. The instrument for quantitative study is Ryff’s Psychological Well-being Scale and the qualitative study involves the construction of a guidelines protocol for in-depth interviews of respondents. The quantitative study uses the SPSS version .19 with One Way Anova, and the qualitative analysis is manual based on transcripts with specific codes and themes. The results show nonsignificance, that is, no significant difference among religions in all family psychological well-being constructs in the comparison of Islam, Christianity, Buddhism and Hinduism, thereby accepting a null hypothesis and rejecting an alternative hypothesis. The qualitative study supports the quantitative study, that is, all 21 respondents explain that no difference exists in psychological wellbeing in the comparison of teachings in all the religious mentioned. These implications may be used as guidelines for government and non-government bodies in considering religion as an important element in family psychological well-being in the long run.
Abstract: This paper describes a new method for extracting the fetal heart rate (fHR) and the fetal heart rate variability (fHRV) signal non-invasively using abdominal maternal electrocardiogram (mECG) recordings. The extraction is based on the fundamental frequency (Fourier-s) theorem. The fundamental frequency of the mother-s electrocardiogram signal (fo-m) is calculated directly from the abdominal signal. The heart rate of the fetus is usually higher than that of the mother; as a result, the fundamental frequency of the fetal-s electrocardiogram signal (fo-f) is higher than that of the mother-s (fo-f > fo-m). Notch filters to suppress mother-s higher harmonics were designed; then a bandpass filter to target fo-f and reject fo-m is implemented. Although the bandpass filter will pass some other frequencies (harmonics), we have shown in this study that those harmonics are actually carried on fo-f, and thus have no impact on the evaluation of the beat-to-beat changes (RR intervals). The oscillations of the time-domain extracted signal represent the RR intervals. We have also shown in this study that zero-to-zero evaluation of the periods is more accurate than the peak-to-peak evaluation. This method is evaluated both on simulated signals and on different abdominal recordings obtained at different gestational ages.
Abstract: To understand life as biological system, evolutionary
understanding is indispensable. Protein interactions data are rapidly
accumulating and are suitable for system-level evolutionary analysis.
We have analyzed yeast protein interaction network by both
mathematical and biological approaches. In this poster presentation,
we inferred the evolutionary birth periods of yeast proteins by
reconstructing phylogenetic profile. It has been thought that hub
proteins that have high connection degree are evolutionary old. But
our analysis showed that hub proteins are entirely evolutionary new.
We also examined evolutionary processes of protein complexes. It
showed that member proteins of complexes were tend to have
appeared in the same evolutionary period. Our results suggested that
protein interaction network evolved by modules that form the
functional unit. We also reconstructed standardized phylogenetic trees
and calculated evolutionary rates of yeast proteins. It showed that
there is no obvious correlation between evolutionary rates and
connection degrees of yeast proteins.
Abstract: The effects of irrigation with dairy factory wastewater on soil properties were investigated at two sites that had received irrigation for > 60 years. Two adjoining paired sites that had never received DFE were also sampled as well as another seven fields from a wider area around the factory. In comparison with paired sites that had not received effluent, long-term wastewater irrigation resulted in an increase in pH, EC, extractable P, exchangeable Na and K and ESP. These changes were related to the use of phosphoric acid, NaOH and KOH as cleaning agents in the factory. Soil organic C content was unaffected by DFE irrigation but the size (microbial biomass C and N) and activity (basal respiration) of the soil microbial community were increased. These increases were attributed to regular inputs of soluble C (e.g. lactose) present as milk residues in the wastewater. Principal component analysis (PCA) of the soils data from all 11sites confirmed that the main effects of DFE irrigation were an increase in exchangeable Na, extractable P and microbial biomass C, an accumulation of soluble salts and a liming effect. PCA analysis of soil bacterial community structure, using PCR-DGGE of 16S rDNA fragments, generally separated individual sites from one another but did not group them according to irrigation history. Thus, whilst the size and activity of the soil microbial community were increased, the structure and diversity of the bacterial community remained unaffected.
Abstract: Honeycomb sandwich panels are increasingly used in the construction of space vehicles because of their outstanding strength, stiffness and light weight properties. However, the use of honeycomb sandwich plates comes with difficulties in the design process as a result of the large number of design variables involved, including composite material design, shape and geometry. Hence, this work deals with the presentation of an optimal design of hexagonal honeycomb sandwich structures subjected to space environment. The optimization process is performed using a set of algorithms including the gravitational search algorithm (GSA). Numerical results are obtained and presented for a set of algorithms. The results obtained by the GSA algorithm are much better compared to other algorithms used in this study.
Abstract: This paper addresses the design of predictive
networked controller with adaptation of a communication delay. The
networked control system contains random delays from sensor to
controller and from controller to actuator. The proposed predictive
controller includes an adaptation loop which decreases the influence
of communication delay on the control performance. Also, the
predictive controller contains a filter which improves the robustness
of the control system. The performance of the proposed adaptive
predictive controller is demonstrated by simulation results in
comparison with PI controller and predictive controller with constant
delay.
Abstract: This paper examines the problem of strategic
management in highly turbulent dynamic business environmental
conditions. As shown the high complexity of the problem can be
managed with the use of System Dynamics Models and Computer
Simulation in obtaining insights, and thorough understanding of the
interdependencies between the organizational structure and the
business environmental elements, so that effective product –market
strategies can be designed. Simulation reveals the underlying forces
that hold together the structure of an organizational system in relation
to its environment. Such knowledge will contribute to the avoidance
of fundamental planning errors and enable appropriate proactive well
focused action.
Abstract: Response Surface Methodology (RSM) is a powerful
and efficient mathematical approach widely applied in the
optimization of cultivation process. Cellulase enzyme production by
Trichoderma reesei RutC30 using agricultural waste rice straw and
banana fiber as carbon source were investigated. In this work,
sequential optimization strategy based statistical design was
employed to enhance the production of cellulase enzyme through
submerged cultivation. A fractional factorial design (26-2) was applied
to elucidate the process parameters that significantly affect cellulase
production. Temperature, Substrate concentration, Inducer
concentration, pH, inoculum age and agitation speed were identified
as important process parameters effecting cellulase enzyme synthesis.
The concentration of lignocelluloses and lactose (inducer) in the
cultivation medium were found to be most significant factors. The
steepest ascent method was used to locate the optimal domain and a
Central Composite Design (CCD) was used to estimate the quadratic
response surface from which the factor levels for maximum
production of cellulase were determined.
Abstract: For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.
Abstract: Diabetes Mellitus is a chronic metabolic disorder, where the improper management of the blood glucose level in the diabetic patients will lead to the risk of heart attack, kidney disease and renal failure. This paper attempts to enhance the diagnostic accuracy of the advancing blood glucose levels of the diabetic patients, by combining principal component analysis and wavelet neural network. The proposed system makes separate blood glucose prediction in the morning, afternoon, evening and night intervals, using dataset from one patient covering a period of 77 days. Comparisons of the diagnostic accuracy with other neural network models, which use the same dataset are made. The comparison results showed overall improved accuracy, which indicates the effectiveness of this proposed system.
Abstract: The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
Abstract: This paper focuses on the data-driven generation
of fuzzy IF...THEN rules. The resulted fuzzy rule base can be
applied to build a classifier, a model used for prediction, or
it can be applied to form a decision support system. Among
the wide range of possible approaches, the decision tree and
the association rule based algorithms are overviewed, and two
new approaches are presented based on the a priori fuzzy
clustering based partitioning of the continuous input variables.
An application study is also presented, where the developed
methods are tested on the well known Wisconsin Breast Cancer
classification problem.
Abstract: The method described in this paper deals with the problems of T-wave detection in an ECG. Determining the position of a T-wave is complicated due to the low amplitude, the ambiguous and changing form of the complex. A wavelet transform approach handles these complications therefore a method based on this concept was developed. In this way we developed a detection method that is able to detect T-waves with a sensitivity of 93% and a correct-detection ratio of 93% even with a serious amount of baseline drift and noise.
Abstract: This paper has examined the energy consumption characteristics in six different buildings including apartments, offices, commercial buildings, hospitals, hotels and educational facilities. Then 5-hectare (50000m2) development site for respective building-s type has been assumed as case study to evaluate the introduction effect of Combined Heat and Power (CHP). All kinds of CHP systems with different distributed generation technologies including Gas Turbine (GT), Gas Engine (GE), Diesel Engine (DE), Solid Oxide Fuel Cell (SOFC) and Polymer Electrolyte Fuel Cell (PEFC), have been simulated by using HEATMAP, CHP system analysis software. And their primary energy utilization efficiency, energy saving ratio and CO2 reduction ratio have evaluated and compared respectively. The results can be summarized as follows: Various buildings have their special heat to power ratio characteristics. Matching the heat to power ratio demanded from an individual building with that supplied from a CHP system is very important. It is necessary to select a reasonable distributed generation technologies according to the load characteristics of various buildings. Distributed generation technologies with high energy generating efficiency and low heat to power ratio, like SOFC and PEFC is more reasonable selection for Building Combined Heat and Power (BCHP). CHP system is an attractive option for hotels, hospitals and apartments in Japan. The users can achieve high energy saving and environmental benefit by introducing a CHP systems. In others buildings, especially like commercial buildings and offices, the introduction of CHP system is unreasonable.
Abstract: This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.
Abstract: Bologna process has influenced enhancing studentcentered
learning in Estonian higher education since 2009, but there
is no information about what helps or hinders students to achieve
learning outcomes and how quality of student-centered learning
might be improved. The purpose of this study is to analyze two
questions from outcome-based course evaluation questionnaire which
is used in Estonian Entrepreneurship University of Applied Sciences.
In this qualitative research, 384 students from 22 different courses
described what helped and hindered them to achieve learning
outcomes. The analysis showed that the aspects that hinder students
to achieve learning outcomes are mostly personal: time management,
family and personal matters, motivation and non-academic activities.
The results indicate that students- learning is commonly supported by
school, where teacher, teaching and characteristics of teaching
methods help mostly to achieve learning outcomes, also learning
material, practical assignments and independent study was brought
up as one of the key elements.