Abstract: In this paper, we have compared the performance of a Turbo and Trellis coded optical code division multiple access (OCDMA) system. The comparison of the two codes has been accomplished by employing optical orthogonal codes (OOCs). The Bit Error Rate (BER) performances have been compared by varying the code weights of address codes employed by the system. We have considered the effects of optical multiple access interference (OMAI), thermal noise and avalanche photodiode (APD) detector noise. Analysis has been carried out for the system with and without double optical hard limiter (DHL). From the simulation results it is observed that a better and distinct comparison can be drawn between the performance of Trellis and Turbo coded systems, at lower code weights of optical orthogonal codes for a fixed number of users. The BER performance of the Turbo coded system is found to be better than the Trellis coded system for all code weights that have been considered for the simulation. Nevertheless, the Trellis coded OCDMA system is found to be better than the uncoded OCDMA system. Trellis coded OCDMA can be used in systems where decoding time has to be kept low, bandwidth is limited and high reliability is not a crucial factor as in local area networks. Also the system hardware is less complex in comparison to the Turbo coded system. Trellis coded OCDMA system can be used without significant modification of the existing chipsets. Turbo-coded OCDMA can however be employed in systems where high reliability is needed and bandwidth is not a limiting factor.
Abstract: We present a non standard Euclidean vehicle
routing problem adding a level of clustering, and we revisit the use
of self-organizing maps as a tool which naturally handles such
problems. We present how they can be used as a main operator
into an evolutionary algorithm to address two conflicting
objectives of route length and distance from customers to bus stops
minimization and to deal with capacity constraints. We apply the
approach to a real-life case of combined clustering and vehicle
routing for the transportation of the 780 employees of an
enterprise. Basing upon a geographic information system we
discuss the influence of road infrastructures on the solutions
generated.
Abstract: This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines.
Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the
dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the
effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.
Abstract: The aim of this contribution is to present a new
approach in modeling the electrical activity of the human heart. A
recurrent artificial neural network is being used in order to exhibit a
subset of the dynamics of the electrical behavior of the human heart.
The proposed model can also be used, when integrated, as a
diagnostic tool of the human heart system.
What makes this approach unique is the fact that every model is
being developed from physiological measurements of an individual.
This kind of approach is very difficult to apply successfully in many
modeling problems, because of the complexity and entropy of the
free variables describing the complex system. Differences between
the modeled variables and the variables of an individual, measured at
specific moments, can be used for diagnostic purposes. The sensor
fusion used in order to optimize the utilization of biomedical sensors
is another point that this paper focuses on. Sensor fusion has been
known for its advantages in applications such as control and
diagnostics of mechanical and chemical processes.
Abstract: Saffron (Crocus sativus) is cultivated as spices,
medicinal and aromatic plant species. At autumn season, heavy
rainfall can cause flooding stress and inhibits growth of saffron. Thus
this research was conducted to study the effect of silver ion (as an
ethylene inhibitor) on growth of saffron under flooding conditions.
The corms of saffron were soaked with one concentration of nano
silver (0, 40, 80 or 120 ppm) and then planting under flooding stress
or non flooding stress conditions. Results showed that number of
roots, root length, root fresh and dry weight, leaves fresh and dry
weight were reduced by 10 day flooding stress. Soaking saffron
corms with 40 or 80 ppm concentration of nano silver rewarded the
effect of flooding stress on the root number, by increasing it.
Furthermore, 40 ppm of nano silver increased root length in stress.
Nano silver 80 ppm in flooding stress, increased leaves dry weight.
Abstract: Proteomics is one of the largest areas of research for
bioinformatics and medical science. An ambitious goal of proteomics
is to elucidate the structure, interactions and functions of all proteins
within cells and organisms. Predicting Protein-Protein Interaction
(PPI) is one of the crucial and decisive problems in current research.
Genomic data offer a great opportunity and at the same time a lot of
challenges for the identification of these interactions. Many methods
have already been proposed in this regard. In case of in-silico
identification, most of the methods require both positive and negative
examples of protein interaction and the perfection of these examples
are very much crucial for the final prediction accuracy. Positive
examples are relatively easy to obtain from well known databases. But
the generation of negative examples is not a trivial task. Current PPI
identification methods generate negative examples based on some
assumptions, which are likely to affect their prediction accuracy.
Hence, if more reliable negative examples are used, the PPI prediction
methods may achieve even more accuracy. Focusing on this issue, a
graph based negative example generation method is proposed, which
is simple and more accurate than the existing approaches. An
interaction graph of the protein sequences is created. The basic
assumption is that the longer the shortest path between two
protein-sequences in the interaction graph, the less is the possibility of
their interaction. A well established PPI detection algorithm is
employed with our negative examples and in most cases it increases
the accuracy more than 10% in comparison with the negative pair
selection method in that paper.
Abstract: The paper considers a novel modular and intrinsically safe redundant robotic system with biologically inspired actuators (pneumatic artificial muscles and rubber bellows actuators). Similarly to the biological systems, the stiffness of the internal parallel modules, representing 2 DOF joints in the serial robotic chains, is controlled by co-activation of opposing redundant actuator groups in the null-space of the module Jacobian, without influencing the actual robot position. The decoupled position/stiffness control allows the realization of variable joint stiffness according to different force-displacement relationships. The variable joint stiffness, as well as limited pneumatic muscle/bellows force ability, ensures internal system safety that is crucial for development of human-friendly robots intended for human-robot collaboration. The initial experiments with the system prototype demonstrate the capabilities of independently, simultaneously controlling both joint (Cartesian) motion and joint stiffness. The paper also presents the possible industrial applications of snake-like robots built using the new modules.
Abstract: In this paper, a periodic predator-prey system with harvesting terms and Holling II type functional response is considered. Sufficient criteria for the existence of at least sixteen periodic solutions are established by using the well known continuation theorem due to Mawhin. An example is given to illustrate the main result.
Abstract: This paper presents the results of a preventive maintenance application-based study and modeling of failure rates in breakers of electrical distribution systems. This is a critical issue in the reliability assessment of a system. In the analysis conducted in this paper, the impacts of failure rate variations caused by a preventive maintenance are examined. This is considered as a part of a Reliability Centered Maintenance (RCM) application program. A number of load point reliability indices is derived using the mathematical model of the failure rate, which is established using the observed data in a distribution system.
Abstract: Photoplethysmography is a simple measurement of the
variation in blood volume in tissue. It detects the pulse signal of heart
beat as well as the low frequency signal of vasoconstriction and
vasodilation. The transmission type measurement is limited to only a
few specific positions for example the index finger that have a short
path length for light. The reflectance type measurement can be
conveniently applied on most parts of the body surface. This study
analyzed the factors that determine the quality of reflectance
photoplethysmograph signal including the emitter-detector distance,
wavelength, light intensity, and optical properties of skin tissue.
Light emitting diodes (LEDs) with four different visible
wavelengths were used as the light emitters. A phototransistor was
used as the light detector. A micro translation stage adjusts the
emitter-detector distance from 2 mm to 15 mm.
The reflective photoplethysmograph signals were measured on
different sites. The optimal emitter-detector distance was chosen to
have a large dynamic range for low frequency drifting without signal
saturation and a high perfusion index. Among these four wavelengths,
a yellowish green (571nm) light with a proper emitter-detection
distance of 2mm is the most suitable for obtaining a steady and reliable
reflectance photoplethysmograph signal
Abstract: This paper introduces a new method called ARPDC (Advanced Robust Parallel Distributed Compensation) for automatic control of nonlinear systems. This method improves a quality of robust control by interpolating of robust and optimal controller. The weight of each controller is determined by an original criteria function for model validity and disturbance appreciation. ARPDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy systems and Parallel Distributed Compensation (PDC) control scheme. The relaxed stability conditions of ARPDC control of nominal system have been derived. The advantages of presented method are demonstrated on the inverse pendulum benchmark problem. From comparison between three different controllers (robust, optimal and ARPDC) follows, that ARPDC control is almost optimal with the robustness close to the robust controller. The results indicate that ARPDC algorithm can be a good alternative not only for a robust control, but in some cases also to an adaptive control of nonlinear systems.
Abstract: The influences of pulsed electric fields on early
physiological development in Arabidopsis thaliana were studied.
Inside a 4-mm electroporation cuvette, pre-germination seeds were
subjected to high-intensity, nanosecond electrical pulses generated
using laboratory-assembled pulsed electric field system. The field
strength was varied from 5 to 20 kV.cm-1 and the pulse width and the
pulse number were maintained at 10 ns and 100, respectively,
corresponding to the specific treatment energy from 300 J.kg-1 to 4.5
kJ.kg-1. Statistical analyses on the average leaf area 5 and 15 days
following pulsed electric field treatment showed that the effects
appear significant the second week after treatments with a maximum
increase of 80% compared to the control (P < 0.01).
Abstract: Among various HLM techniques, the Multivariate Hierarchical Linear Model (MHLM) is desirable to use, particularly when multivariate criterion variables are collected and the covariance structure has information valuable for data analysis. In order to reflect prior information or to obtain stable results when the sample size and the number of groups are not sufficiently large, the Bayes method has often been employed in hierarchical data analysis. In these cases, although the Markov Chain Monte Carlo (MCMC) method is a rather powerful tool for parameter estimation, Procedures regarding MCMC have not been formulated for MHLM. For this reason, this research presents concrete procedures for parameter estimation through the use of the Gibbs samplers. Lastly, several future topics for the use of MCMC approach for HLM is discussed.
Abstract: This paper studies the application of a variety of
sawdust materials in the production of lightweight insulating bricks.
First, the mineralogical and chemical composition of clays was determined. Next, ceramic bricks were fabricated with different
quantities of materials (3–6 and 9 wt. % for sawdust, 65 wt. % for grey clay, 24–27 and 30 wt. % for yellow clay and 2 wt% of tuff).
These bricks were fired at 800 and 950 °C. The effect of adding this sawdust on the technological behaviour of the brick was assessed by
drying and firing shrinkage, water absorption, porosity, bulk density
and compressive strength. The results have shown that the optimum
sintering temperature is 950 °C. Below this temperature, at 950 °C,
increased open porosity was observed, which decreased the compressive strength of the bricks. Based on the results obtained, the
optimum amounts of waste were 9 wt. % sawdust of eucalyptus, 24 wt. % shaping moisture and 1.6 particle size diameter. These percentages produced bricks whose mechanical properties were
suitable for use as secondary raw materials in ceramic brick
production.
Abstract: This paper describes the project and development of a
very low-cost and small electronic prototype, especially designed for
monitoring and controlling existing home automation alarm systems
(intruder, smoke, gas, flood, etc.), via TCP/IP, with a typical web
browser. Its use will allow home owners to be immediately alerted
and aware when an alarm event occurs, and being also able to
interact with their home automation alarm system, disarming, arming
and watching event alerts, with a personal wireless Wi-Fi PDA or
smartphone logged on to a dedicated predefined web page, and using
also a PC or Laptop.
Abstract: This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Abstract: Technology transfer of renewable energy technologies is very often unsuccessful in the developing world. Aside from challenges that have social, economic, financial, institutional and environmental dimensions, technology transfer has generally been misunderstood, and largely seen as mere delivery of high tech equipment from developed to developing countries or within the developing world from R&D institutions to society. Technology transfer entails much more, including, but not limited to: entire systems and their component parts, know-how, goods and services, equipment, and organisational and managerial procedures. Means to facilitate the successful transfer of energy technologies, including the sharing of lessons are subsequently extremely important for developing countries as they grapple with increasing energy needs to sustain adequate economic growth and development. Improving the success of technology transfer is an ongoing process as more projects are implemented, new problems are encountered and new lessons are learnt. Renewable energy is also critical to improve the quality of lives of the majority of people in developing countries. In rural areas energy is primarily traditional biomass. The consumption activities typically occur in an inefficient manner, thus working against the notion of sustainable development. This paper explores the implementation of technology transfer in the developing world (sub-Saharan Africa). The focus is necessarily on RETs since most rural energy initiatives are RETs-based. Additionally, it aims to highlight some lessons drawn from the cited RE projects and identifies notable differences where energy technology transfer was judged to be successful. This is done through a literature review based on a selection of documented case studies which are judged against the definition provided for technology transfer. This paper also puts forth research recommendations that might contribute to improved technology transfer in the developing world. Key findings of this paper include: Technology transfer cannot be complete without satisfying pre-conditions such as: affordability, maintenance (and associated plans), knowledge and skills transfer, appropriate know how, ownership and commitment, ability to adapt technology, sound business principles such as financial viability and sustainability, project management, relevance and many others. It is also shown that lessons are learnt in both successful and unsuccessful projects.
Abstract: Deep cold rolling (DCR) is a cold working process, which easily produces a smooth and work-hardened surface by plastic deformation of surface irregularities. In the present study, the influence of main deep cold rolling process parameters on the surface roughness and the hardness of AISI 4140 steel were studied by using fractional factorial design of experiments. The assessment of the surface integrity aspects on work material was done, in terms of identifying the predominant factor amongst the selected parameters, their order of significance and setting the levels of the factors for minimizing surface roughness and/or maximizing surface hardness. It was found that the ball diameter, rolling force, initial surface roughness and number of tool passes are the most pronounced parameters, which have great effects on the work piece-s surface during the deep cold rolling process. A simple, inexpensive and newly developed DCR tool, with interchangeable collet for using different ball diameters, was used throughout the experimental work presented in this paper.
Abstract: Global environmental changes lead to increased frequency and scale of natural disaster, Taiwan is under the influence of global warming and extreme weather. Therefore, the vulnerability was increased and variability and complexity of disasters is relatively enhanced. The purpose of this study is to consider the source and magnitude of hazard characteristics on the tourism industry. Using modern risk management concepts, integration of related domestic and international basic research, this goes beyond the Taiwan typhoon disaster risk assessment model and evaluation of loss. This loss evaluation index system considers the impact of extreme weather, in particular heavy rain on the tourism industry in Taiwan. Consider the extreme climate of the compound impact of disaster for the tourism industry; we try to make multi-hazard risk assessment model, strategies and suggestions. Related risk analysis results are expected to provide government department, the tourism industry asset owners, insurance companies and banking include tourist disaster risk necessary information to help its tourism industry for effective natural disaster risk management.
Abstract: Dried soy protein hydrolysate powder was added to
the burger in order to enhance the oxidative stability as well as
decreases the microbial spoilage. The soybean bioactive compounds
(soy protein hydrolysate) as antioxidant and antimicrobial were added
at level of 1, 2 and 3 %.Chemical analysis and physical properties
were affected by protein hydrolysate addition. The TBA values were
significantly affected (P < 0.05) by the storage period and the level of
soy protein hydrolysate. All the tested soybean protein hydrolysate
additives showed strong antioxidant properties. Samples of soybean
protein hydrolysate showed the lowest (P < 0.05) TBA values at each
time of storage.
The counts of all determined microbiological indicators were
significantly (P < 0.05) affected by the addition of the soybean
protein hydrolysate. Decreasing trends of different extent were also
observed in samples of the treatments for total viable counts,
Coliform, Staphylococcus aureus, yeast and molds. Storage period
was being significantly (P < 0.05) affected on microbial counts in all
samples Staphylococcus aureus were the most sensitive microbe
followed by Coliform group of the sample containing protein
hydrolysate, while molds and yeast count showed a decreasing trend
but not significant (P < 0.05) until the end of the storage period
compared with control sample. Sensory attributes were also
performed, added protein hydrolysate exhibits beany flavor which
was clear about samples of 3% protein hydrolysate.