Abstract: One of the main concerns about parallel mechanisms
is the presence of singular points within their workspaces. In singular
positions the mechanism gains or loses one or several degrees of
freedom. It is impossible to control the mechanism in singular
positions. Therefore, these positions have to be avoided. This is a
vital need especially in computer controlled machine tools designed
and manufactured on the basis of parallel mechanisms. This need has
to be taken into consideration when selecting design parameters. A
prerequisite to this is a thorough knowledge about the effect of
design parameters and constraints on singularity. In this paper,
quality condition index was introduced as a criterion for evaluating
singularities of different configurations of a hexapod mechanism
obtainable by different design parameters. It was illustrated that this
method can effectively be employed to obtain the optimum
configuration of hexapod mechanism with the aim of avoiding
singularity within the workspace. This method was then employed to
design the hexapod table of a CNC milling machine.
Abstract: Wireless sensor network is formed with the combination of sensor nodes and sink nodes. Recently Wireless sensor network has attracted attention of the research community. The main application of wireless sensor network is security from different attacks both for mass public and military. However securing these networks, by itself is a critical issue due to many constraints like limited energy, computational power and lower memory. Researchers working in this area have proposed a number of security techniques for this purpose. Still, more work needs to be done.In this paper we provide a detailed discussion on security in wireless sensor networks. This paper will help to identify different obstacles and requirements for security of wireless sensor networks as well as highlight weaknesses of existing techniques.
Abstract: Changes in stem diameter of orchid plants were
investigated in a control growing climate. Previous studies have
focused on stem diameter in relation to plant water on terrestrial
plants in order to schedule the irrigation. The objective of this work
was to evaluate the ability of the strain gauges to capture changes in
the epiphytes plant stem. Experiments were carried out by using the
sympodial orchid, Dendrobium Sonia in a stressed condition. From
the findings, the sensor can detect changes in the plant stem and the
result can easily be used as a reference for further studies for the
development of a proper watering system.
Abstract: The purpose of this study was to investigate the relationship between hope and resilience with work engagement. A total of 422 staff nurses working in three public hospitals in Peninsular Malaysia participated in this study. Statistical results using regression analysis revealed that hope and resilience were positively related to work engagement. Possible reasons for these findings, as well as their implications and future research directions are discussed.
Abstract: In this paper we present an autoregressive model with
neural networks modeling and standard error backpropagation
algorithm training optimization in order to predict the gross domestic
product (GDP) growth rate of four countries. Specifically we propose
a kind of weighted regression, which can be used for econometric
purposes, where the initial inputs are multiplied by the neural
networks final optimum weights from input-hidden layer after the
training process. The forecasts are compared with those of the
ordinary autoregressive model and we conclude that the proposed
regression-s forecasting results outperform significant those of
autoregressive model in the out-of-sample period. The idea behind
this approach is to propose a parametric regression with weighted
variables in order to test for the statistical significance and the
magnitude of the estimated autoregressive coefficients and
simultaneously to estimate the forecasts.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: This research was conducted in the Pua Watershed whereas located in the Upper Nan River Basin in Nan province, Thailand. Nan River basin originated in Nan province that comprises of many tributary streams to produce as inflow to the Sirikit dam provided huge reservoir with the storage capacity of 9510 million cubic meters. The common problems of most watersheds were found i.e. shortage water supply for consumption and agriculture utilizations, deteriorate of water quality, flood and landslide including debris flow, and unstable of riverbank. The Pua Watershed is one of several small river basins that flow through the Nan River Basin. The watershed includes 404 km2 representing the Pua District, the Upper Nan Basin, or the whole Nan River Basin, of 61.5%, 18.2% or 1.2% respectively. The Pua River is a main stream producing all year streamflow supplying the Pua District and an inflow to the Upper Nan Basin. Its length approximately 56.3 kilometers with an average slope of the channel by 1.9% measured. A diversion weir namely Pua weir bound the plain and mountainous areas with a very steep slope of the riverbed to 2.9% and drainage area of 149 km2 as upstream watershed while a mild slope of the riverbed to 0.2% found in a river reach of 20.3 km downstream of this weir, which considered as a gauged basin. However, the major branch streams of the Pua River are ungauged catchments namely: Nam Kwang and Nam Koon with the drainage area of 86 and 35 km2 respectively. These upstream watersheds produce runoff through the 3-streams downstream of Pua weir, Jao weir, and Kang weir, with an averaged annual runoff of 578 million cubic meters. They were analyzed using both statistical data at Pua weir and simulated data resulted from the hydrologic modeling system (HEC–HMS) which applied for the remaining ungauged basins. Since the Kwang and Koon catchments were limited with lack of hydrological data included streamflow and rainfall. Therefore, the mathematical modeling: HEC-HMS with the Snyder-s hydrograph synthesized and transposed methods were applied for those areas using calibrated hydrological parameters from the upstream of Pua weir with continuously daily recorded of streamflow and rainfall data during 2008-2011. The results showed that the simulated daily streamflow and sum up as annual runoff in 2008, 2010, and 2011 were fitted with observed annual runoff at Pua weir using the simple linear regression with the satisfied correlation R2 of 0.64, 062, and 0.59, respectively. The sensitivity of simulation results were come from difficulty using calibrated parameters i.e. lag-time, coefficient of peak flow, initial losses, uniform loss rates, and missing some daily observed data. These calibrated parameters were used to apply for the other 2-ungauged catchments and downstream catchments simulated.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: Explosive forming is one of the unconventional
techniques in which, most commonly, the water is used as the
pressure transmission medium. One of the newest methods in
explosive forming is gas detonation forming which uses a normal
shock wave derived of gas detonation, to form sheet metals. For this
purpose a detonation is developed from the reaction of H2+O2
mixture in a long cylindrical detonation tube. The detonation wave
goes through the detonation tube and acts as a blast load on the steel
blank and forms it. Experimental results are compared with a finite
element model; and the comparison of the experimental and
numerical results obtained from strain, thickness variation and
deformed geometry is carried out. Numerical and experimental
results showed approximately 75 – 90 % similarity in formability of
desired shape. Also optimum percent of gas mixture obtained when
we mix 68% H2 with 32% O2.
Abstract: In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result.
Abstract: To decompose organochlorides by bioremediation, co-culture biohydrogen producer and dehalogenation microorganisms is a useful method. In this study, we combined these two characteristics from a biohydrogen producer, Rhodopseudomonas palustris, and a dehalogenation microorganism, Pseudomonas putida, to enchance halorespiration in R. palustris. The genes encoding cytochrome P450cam system (camC, camA, and camB) from P. putida were expressed in R. palustris with designated expression plasmid. All tested strains were cultured to log phase then presented pentachloroethane (PCA) in media. The vector control strain could degrade PCA about 78% after 16 hours, however, the cytochrome P450cam system expressed strain, CGA-camCAB, could completely degrade PCA in 12 hours. While taking chlorinated aromatic, 3-chlorobenzoate, as sole carbon source or present benzoate as co-substrate, CGA-camCAB presented faster growth rate than vector control strain.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: The development of entrepreneurial competences of
farmers has been pointed out as a necessary condition for the
modernization of land in facing the phenomenon of globalization.
However, the educational processes involved in such a development
have been studied little, especially in emerging economies. This
research aims to enlighten some of the critical issues behind the early
stages of the transformation of farmers into entrepreneurs, through in
depth interviews with farmers, entrepreneurial promoters and public
officials participating in a public pilot project in Mexico. Although
major impacts were expected only in the long run, important positive
changes in the mind set of farmers and other participants were found
in early stages of the intervention. Apparently, the farmers started a
process of becoming more conscious about the importance of
preserving the aquiferous resources, as well as more market and
entrepreneurial oriented.
Abstract: We constructed a method of phase unwrapping for a typical wave-front by utilizing the maximizer of the posterior marginal (MPM) estimate corresponding to equilibrium statistical mechanics of the three-state Ising model on a square lattice on the basis of an analogy between statistical mechanics and Bayesian inference. We investigated the static properties of an MPM estimate from a phase diagram using Monte Carlo simulation for a typical wave-front with synthetic aperture radar (SAR) interferometry. The simulations clarified that the surface-consistency conditions were useful for extending the phase where the MPM estimate was successful in phase unwrapping with a high degree of accuracy and that introducing prior information into the MPM estimate also made it possible to extend the phase under the constraint of the surface-consistency conditions with a high degree of accuracy. We also found that the MPM estimate could be used to reconstruct the original wave-fronts more smoothly, if we appropriately tuned hyper-parameters corresponding to temperature to utilize fluctuations around the MAP solution. Also, from the viewpoint of statistical mechanics of the Q-Ising model, we found that the MPM estimate was regarded as a method for searching the ground state by utilizing thermal fluctuations under the constraint of the surface-consistency condition.
Abstract: Metallic micro parts are playing an important role in micro-fabrication industry. Recently, we have demonstrated a new deformation mechanism for micro-formability of polycrystalline materials. Different depressed micro-features smaller than the grain size have been successfully fabricated on 6061 aluminum alloy (AA6061) substrates with good fidelity. To further verify this proposed deformation mechanism that grain size is not a limiting factor, we demonstrate here that in addition of depressed features, protruded micro-features on a polycrystalline substrate can similarly be fabricated.
Abstract: The purpose of this research is: a) to investigate how
the HR practices influence psychological contracts, b) to examine the
influence of psychological contracts to individual behavior and to
contribute individually, c) to study the psychological contact through
leadership. This research using mixed methods, qualitative and
quantitative research methods were utilized to gather the data
collected using a qualitative method by the HR Manager who is in
charge of the trainings from the staffs and quantitative method
(survey) by using questionnaire was utilized to draw upon and to
elaborate on the recurring themes present during the interviews. The
survey was done to 400 staffs of the company. The study found that
leadership styles supporting the firm’s HR strategy is the key in
making psychological contracts that benefit both the firm and its
members.
Abstract: The aim of the study was to follow changes of powervelocity
relationship in female volleyball players during an annual
training cycle. The study was conducted on eleven female volleyball
players: age 21.6±1.7 years, body height 177.9±4.7 cm, body mass
71.3±6.6 kg and training experience 8.6±3.3 years. Power–velocity
relationship was determined from five maximal 10-second
cycloergometer efforts with external loads equal: 2.5, 5.0, 7.5, 10.0
and 12.5% of body weight (BW) before (I) and after (II) the
preparatory period, after the first (III) and second (IV) competitive
season. The maximal power output increased from 9.30±0.85 W•kg–1
(I) to 9.50±0.96 W•kg–1 (II), 9.77±0.96 W•kg–1 (III) and 9.95±1.13
W•kg–1 (IV, p
Abstract: In this paper we describe a hybrid technique of Minimax search and aggregate Mahalanobis distance function synthesis to evolve Awale game player. The hybrid technique helps to suggest a move in a short amount of time without looking into endgame database. However, the effectiveness of the technique is heavily dependent on the training dataset of the Awale strategies utilized. The evolved player was tested against Awale shareware program and the result is appealing.
Abstract: In this paper we investigated a number of the Internet
congestion control algorithms that has been developed in the last few
years. It was obviously found that many of these algorithms were
designed to deal with the Internet traffic merely as a train of
consequent packets. Other few algorithms were specifically tailored
to handle the Internet congestion caused by running media traffic that
represents audiovisual content. This later set of algorithms is
considered to be aware of the nature of this media content. In this
context we briefly explained a number of congestion control
algorithms and hence categorized them into the two following
categories: i) Media congestion control algorithms. ii) Common
congestion control algorithms. We hereby recommend the usage of
the media congestion control algorithms for the reason of being
media content-aware rather than the other common type of
algorithms that blindly manipulates such traffic. We showed that the
spread of such media content-aware algorithms over Internet will
lead to better congestion control status in the coming years. This is
due to the observed emergence of the era of digital convergence
where the media traffic type will form the majority of the Internet
traffic.
Abstract: Soils are normally dried in either a convection oven or stove. Laboratory moisture content testing indicated that the typical drying durations for a convection oven were, 24 hours. The purpose of this study was to determine the accuracy and soil drying duration of both, moisture content and liquid limit using microwave radiation. The soils were tested with both, convection and microwave ovens. The convection oven was considered to produce the true values for both, natural moisture content and liquid limit of soils; it was, therefore, used as a basis for comparison for the results of the microwave ovens. The samples used in this study were obtained from different projects of Consulting Engineering Bureau of College of Engineering of Sulaimani University. These samples were collected from different locations and at the different depths and consist mostly of brown and light brown clay and silty clay. A total of 102 samples were prepared. 26 of them were tested for natural moisture determination, while the other 76 were used for liquid limits determination