Abstract: It was determined that woody biomass and livestock excreta can be utilized as hydrogen resources and hydrogen produced from such sources can be used to fill fuel cell vehicles (FCVs) at hydrogen stations. It was shown that the biomass transport costs for hydrogen production may be reduced the costs for co-generation. In the Tokyo Metropolitan Area, there are only a few sites capable of producing hydrogen from woody biomass in amounts greater than 200 m3/h-the scale required for a hydrogen station to be operationally practical. However, in the case of livestock excreta, it was shown that 15% of the municipalities in this area are capable of securing sufficient biomass to be operationally practical for hydrogen production. The differences in feasibility of practical operation depend on the type of biomass.
Abstract: Under the limitation of investment budget, a utility
company is required to maximize the utilization of their existing
assets during their life cycle satisfying both engineering and financial
requirements. However, utility does not have knowledge about the
status of each asset in the portfolio neither in terms of technical nor
financial values. This paper presents a knowledge based model for
the utility companies in order to make an optimal decision on power
transformer with their utilization. CommonKADS methodology, a
structured development for knowledge and expertise representation,
is utilized for designing and developing knowledge based model. A
case study of One MVA power transformer of Nepal Electricity
Authority is presented. The results show that the reusable knowledge
can be categorized, modeled and utilized within the utility company
using the proposed methodologies. Moreover, the results depict that
utility company can achieve both engineering and financial benefits
from its utilization.
Abstract: This paper presents a generalized form of the
mechanistic deconvolution technique (GMD) to modeling image sensors applicable in various pan–tilt planes of view. The mechanistic deconvolution technique (UMD) is modified with the
given angles of a pan–tilt plane of view to formulate constraint parameters and characterize distortion effects, and thereby, determine
the corrected image data. This, as a result, does not require experimental setup or calibration. Due to the mechanistic nature of
the sensor model, the necessity for the sensor image plane to be
orthogonal to its z-axis is eliminated, and it reduces the dependency on image data. An experiment was constructed to evaluate the
accuracy of a model created by GMD and its insensitivity to changes in sensor properties and in pan and tilt angles. This was compared
with a pre-calibrated model and a model created by UMD using two sensors with different specifications. It achieved similar accuracy
with one-seventh the number of iterations and attained lower mean error by a factor of 2.4 when compared to the pre-calibrated and
UMD model respectively. The model has also shown itself to be robust and, in comparison to pre-calibrated and UMD model, improved the accuracy significantly.
Abstract: The Knowledge Management (KM) Criteria is an
essential foundation to evaluate KM outcomes. Different sets of
criteria were developed and tailored by many researchers to
determine the results of KM initiatives. However, literature review
has emphasized on incomplete set of criteria for evaluating KM
outcomes. Hence, this paper tried to address the problem of
determining the criteria for measuring knowledge management
outcomes among different types of Malaysian organizations.
Successively, this paper was assumed to develop widely accepted
criteria to measure success of knowledge management efforts for
Malaysian organizations. Our analysis approach was based on the
ANOVA procedure to compare a set of criteria among different types
of organizations. This set of criteria was exploited from literature
review. It is hoped that this study provides a better picture for
different types of Malaysian organizations to establish a
comprehensive set of criteria due to measure results of KM programs.
Abstract: The upgrading of low quality crude natural gas (NG) is attracting interest due to high demand of pipeline-grade gas in recent years. Membrane processes are commercially proven technology for the removal of impurities like carbon dioxide from NG. In this work, cross flow mathematical model has been suggested to be incorporated with ASPEN HYSYS as a user defined unit operation in order to design the membrane system for CO2/CH4 separation. The effect of operating conditions (such as feed composition and pressure) and membrane selectivity on the design parameters (methane recovery and total membrane area required for the separation) has been studied for different design configurations. These configurations include single stage (with and without recycle) and double stage membrane systems (with and without permeate or retentate recycle). It is shown that methane recovery can be improved by recycling permeate or retentate stream as well as by using double stage membrane systems. The ASPEN HYSYS user defined unit operation proposed in the study has potential to be applied for complex membrane system design and optimization.
Abstract: Education, as the most important resource in any country, has multiplying effects on all facets of development in a society. The new social realities, particularly the interplay between democratization of education; unprecedented developments in IT sector; emergence of knowledge society, liberalization of economy and globalization have greatly influenced the educational process of all nations. This turbulence entails upon education to undergo dramatic changes to keep up with the new expectations. Growth of entrepreneurship among Indian women is highly important for empowering them and this is highly essential for socio-economic development of a society. Unfortunately in India there is poor acceptance of entrepreneurship among women as unfounded myths and fears restrain them to be enterprising. To remove these inhibitions, education system needs to be re-engineered to make entrepreneurship more acceptable. This paper empirically analyses the results of a survey done on around 500 female graduates in North India to measure and evaluate various entrepreneurial traits present in them. A formative model has been devised in this context, which should improve the teaching-learning process in our education system, which can lead to sustainable growth of women entrepreneurship in India.
Abstract: Interaction effects of xanthan gum (XG), carboxymethyl
cellulose (CMC), and locust bean gum (LBG) on the flow properties
of oil-in-water emulsions were investigated by a mixture design
experiment. Blends of XG, CMC and LBG were prepared according
to an augmented simplex-centroid mixture design (10 points) and used
at 0.5% (wt/wt) in the emulsion formulations. An appropriate
mathematical model was fitted to express each response as a function
of the proportions of the blend components that are able to
empirically predict the response to any blend of combination of the
components. The synergistic interaction effect of the ternary
XG:CMC:LBG blends at approximately 33-67% XG levels was
shown to be much stronger than that of the binary XG:LBG blend at
50% XG level (p < 0.05). Nevertheless, an antagonistic interaction
effect became significant as CMC level in blends was more than 33%
(p < 0.05). Yield stress and apparent viscosity (at 10 s-1) responses
were successfully fitted with a special quartic model while flow
behaviour index and consistency coefficient were fitted with a full
quartic model (R2
adjusted ≥ 0.90). This study found that a mixture
design approach could serve as a valuable tool in better elucidating
and predicting the interaction effects beyond the conventional twocomponent
blends.
Abstract: Motion capturing technology has been used for quite a
while and several research has been done within this area. Nevertheless,
we discovered open issues within current motion capturing
environments. In this paper we provide a state-of-the-art overview of
the addressed research areas and show issues with current motion
capturing environments. Observations, interviews and questionnaires
have been used to reveal the challenges actors are currently facing in
a motion capturing environment. Furthermore, the idea to create a
more immersive motion capturing environment to improve the acting
performances and motion capturing outcomes as a potential solution
is introduced. It is hereby the goal to explain the found open issues
and the developed ideas which shall serve for further research as a
basis. Moreover, a methodology to address the interaction and
systems design issues is proposed. A future outcome could be that
motion capture actors are able to perform more naturally, especially
if using a non-body-worn solution.
Abstract: In this study, a Loop Back Algorithm for component
connected labeling for detecting objects in a digital image is
presented. The approach is using loop back connected component
labeling algorithm that helps the system to distinguish the object
detected according to their label. Deferent than whole window
scanning technique, this technique reduces the searching time for
locating the object by focusing on the suspected object based on
certain features defined. In this study, the approach was also
implemented for a face detection system. Face detection system is
becoming interesting research since there are many devices or
systems that require detecting the face for certain purposes. The input
can be from still image or videos, therefore the sub process of this
system has to be simple, efficient and accurate to give a good result.
Abstract: A low bit rate still image compression scheme by
compressing the indices of Vector Quantization (VQ) and generating
residual codebook is proposed. The indices of VQ are compressed by
exploiting correlation among image blocks, which reduces the bit per
index. A residual codebook similar to VQ codebook is generated that
represents the distortion produced in VQ. Using this residual
codebook the distortion in the reconstructed image is removed,
thereby increasing the image quality. Our scheme combines these two
methods. Experimental results on standard image Lena show that our
scheme can give a reconstructed image with a PSNR value of 31.6 db
at 0.396 bits per pixel. Our scheme is also faster than the existing VQ
variants.
Abstract: When the results of the total element concentrations using USEPA method 3051A are compared to the sequential extraction analyses (i.e. the sum of fractions BCR1, BCR2 and BRC3), it can be calculated that the recovery values of elements varied between 56.8-% and 69.4-% in the bottom ash, and between 11.3-% and 70.9-% in the fly ash. This indicates that most of the elements in the ashes do not occur as readily soluble forms.
Abstract: Cloud computing is becoming more and more matured over the last few years and consequently the demands for better cloud services is increasing rapidly. One of the research topics to improve cloud services is the desktop computing in virtualized environment. This paper aims at the development of an adaptive virtual desktop service in cloud computing platform based on our previous research on the virtualization technology. We implement cloud virtual desktop and application software streaming technology that make it possible for providing Virtual Desktop as a Service (VDaaS). Given the development of remote desktop virtualization, it allows shifting the user’s desktop from the traditional PC environment to the cloud-enabled environment, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. Users no longer need to burden the platform maintenances and drastically reduces the overall cost of hardware and software licenses. Moreover, this flexible remote desktop service represents the next significant step to the mobile workplace, and it lets users access their desktop environments from virtually anywhere.
Abstract: In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
Abstract: This paper describes a practical approach to design
and develop a hybrid learning with acceleration feedback control
(HLC) scheme for input tracking and end-point vibration suppression
of flexible manipulator systems. Initially, a collocated proportionalderivative
(PD) control scheme using hub-angle and hub-velocity
feedback is developed for control of rigid-body motion of the system.
This is then extended to incorporate a further hybrid control scheme
of the collocated PD control and iterative learning control with
acceleration feedback using genetic algorithms (GAs) to optimize the
learning parameters. Experimental results of the response of the
manipulator with the control schemes are presented in the time and
frequency domains. The performance of the HLC is assessed in terms
of input tracking, level of vibration reduction at resonance modes and
robustness with various payloads.
Abstract: The present paper deals with the analysis and development of noise-reduction transformer that has a filter function for conductive noise transmission. Two types of prototype noise-reduction transformers with two different output voltages are proposed. To determine an optimum design for the noise-reduction transformer, noise attenuation characteristics are discussed based on the experiments and the equivalent circuit analysis. The analysis gives a relation between the circuit parameters and the noise attenuation. High performance step-down noise-reduction transformer for direct power supply to electronics equipment is developed. The input voltage of the transformer is 100 V and the output voltage is 5 V. Frequency characteristics of noise attenuation are discussed, and prevention of pulse noise transmission is demonstrated. Normal mode noise attenuation of this transformer is –80 dB, and common mode exceeds –90 dB. The step-down noise-reduction transformer eliminates pulse noise efficiently.
Abstract: This paper presents the vibrations suppression of a thermoelastic beam subject to sudden heat input by a distributed piezoelectric actuators. An optimization problem is formulated as the minimization of a quadratic functional in terms of displacement and velocity at a given time and with the least control effort. The solution method is based on a combination of modal expansion and variational approaches. The modal expansion approach is used to convert the optimal control of distributed parameter system into the optimal control of lumped parameter system. By utilizing the variational approach, an explicit optimal control law is derived and the determination of the corresponding displacement and velocity is reduced to solving a set of ordinary differential equations.
Abstract: We developed an effective microfluidic device for photoreactions with low reflectance and good heat conductance. The performance of this microfluidic device was tested by carrying out a photoreactive synthesis of benzopinacol and acetone from benzophenone and 2-propanol. The yield reached 36% with an irradiation time of 469.2 s and was improved by more than 30% when compared to the values obtained by the batch method. Therefore, the microfluidic device was found to be effective for improving the yields of photoreactions.
Abstract: The innovation performance of nations has been
repeatedly measured in the literature. We argue that while the
literature offers many suggestions, their theoretical foundation is
often weak and the underlying assumptions are rarely discussed. In
this paper, we systematize various mechanisms by which spatial units
influence nation and firms' innovation activities. On the basis of this,
common innovation performance measures and analyses are
discussed and evaluated. It is concluded that there is no general best
way of measuring the innovation performance of spatial units. In
fact, the most interesting insights can be obtained using a multitude
of different approaches at the same time.
Abstract: The most planted cover crops in the Czech Republic
are mustard (Sinapis alba) and phacelia (Phacelia tanacetifolia
Benth.). A field trial was executed to evaluate root system size (RSS)
in eight varieties of mustard and five varieties of phacelia on two
locations, in three BBCH phases and in two years. The relationship
between RSS and aboveground biomass was inquired. The root
system was assessed by measuring its electric capacity. Aboveground
mass and root samples to be evaluated by means of a digital image
analysis were recovered in the BBCH phase 70. The yield of
aboveground biomass of mustard was always statistically
significantly higher than that of phacelia. Mustard showed a
statistically significant negative correlation between root length
density (RLD) within 10 cm and aboveground biomass weight (r = -
0.46*). Phacelia featured a statistically significant correlation
between aboveground biomass production and nitrate nitrogen
content in soil (r=0.782**).
Abstract: The aim of this paper is to rank the impact of Object
Oriented(OO) metrics in fault prediction modeling using Artificial
Neural Networks(ANNs). Past studies on empirical validation of
object oriented metrics as fault predictors using ANNs have focused
on the predictive quality of neural networks versus standard
statistical techniques. In this empirical study we turn our attention to
the capability of ANNs in ranking the impact of these explanatory
metrics on fault proneness. In ANNs data analysis approach, there is
no clear method of ranking the impact of individual metrics. Five
ANN based techniques are studied which rank object oriented
metrics in predicting fault proneness of classes. These techniques are
i) overall connection weights method ii) Garson-s method iii) The
partial derivatives methods iv) The Input Perturb method v) the
classical stepwise methods. We develop and evaluate different
prediction models based on the ranking of the metrics by the
individual techniques. The models based on overall connection
weights and partial derivatives methods have been found to be most
accurate.