Abstract: Near infrared (NIR) spectroscopy has always been of
great interest in the food and agriculture industries. The development
of prediction models has facilitated the estimation process in recent
years. In this study, 110 crude palm oil (CPO) samples were used to
build a free fatty acid (FFA) prediction model. 60% of the collected
data were used for training purposes and the remaining 40% used for
testing. The visible peaks on the NIR spectrum were at 1725 nm and
1760 nm, indicating the existence of the first overtone of C-H bands.
Principal component regression (PCR) was applied to the data in
order to build this mathematical prediction model. The optimal
number of principal components was 10. The results showed
R2=0.7147 for the training set and R2=0.6404 for the testing set.
Abstract: Self-compacting concrete (SCC) developed in Japan
in the late 80s has enabled the construction industry to reduce
demand on the resources, improve the work condition and also
reduce the impact of environment by elimination of the need for
compaction. Fuzzy logic (FL) approaches has recently been used to
model some of the human activities in many areas of civil
engineering applications. Especially from these systems in the model
experimental studies, very good results have been obtained. In the
present study, a model for predicting compressive strength of SCC
containing various proportions of fly ash, as partial replacement of
cement has been developed by using Fuzzy Inference System (FIS).
For the purpose of building this model, a database of experimental
data were gathered from the literature and used for training and
testing the model. The used data as the inputs of fuzzy logic models
are arranged in a format of five parameters that cover the total binder
content, fly ash replacement percentage, water content,
superplasticizer and age of specimens. The training and testing results
in the fuzzy logic model have shown a strong potential for predicting
the compressive strength of SCC containing fly ash in the considered
range.
Abstract: Many of the ever-growing elderly population require
exercise, such as running, for health management. One important
element of a runner’s training is the choice of shoes for exercise; shoes
are important because they provide the interface between the feet and
road. When we purchase shoes, we may instinctively choose a pair
after trying on many different pairs of shoes. Selecting the shoes
instinctively may work, but it does not guarantee a suitable fit for
running activities. Therefore, if we could select suitable shoes for each
runner from the viewpoint of brain activities, it would be helpful for
validating shoe selection. In this paper, we describe how brain
activities show different characteristics during particular task,
corresponding to different properties of shoes. Using five subjects, we
performed a verification experiment, applying weight, softness, and
flexibility as shoe properties. In order to affect the shoe property’s
differences to the brain, subjects run for 10 min. Before and after
running, subjects conducted a paced auditory serial addition task
(PASAT) as the particular task; and the subjects’ brain activities
during the PASAT are evaluated based on oxyhemoglobin and
deoxyhemoglobin relative concentration changes, measured by
near-infrared spectroscopy (NIRS). When the brain works actively,
oxihemoglobin and deoxyhemoglobin concentration drastically
changes; therefore, we calculate the maximum values of concentration
changes. In order to normalize relative concentration changes after
running, the maximum value are divided by before running maximum
value as evaluation parameters. The classification of the groups of
shoes is expressed on a self-organizing map (SOM). As a result,
deoxyhemoglobin can make clusters for two of the three types of
shoes.
Abstract: In this paper, Fuzzy C-Means clustering with
Expectation Maximization-Gaussian Mixture Model based hybrid
modeling algorithm is proposed for Continuous Tamil Speech
Recognition. The speech sentences from various speakers are used
for training and testing phase and objective measures are between the
proposed and existing Continuous Speech Recognition algorithms.
From the simulated results, it is observed that the proposed algorithm
improves the recognition accuracy and F-measure up to 3% as
compared to that of the existing algorithms for the speech signal from
various speakers. In addition, it reduces the Word Error Rate, Error
Rate and Error up to 4% as compared to that of the existing
algorithms. In all aspects, the proposed hybrid modeling for Tamil
speech recognition provides the significant improvements for speechto-
text conversion in various applications.
Abstract: The research conducted in early seventies apparently
assumed the existence of a universal decision model for union
negotiators and furthermore tended to regard financial information as
a ‘neutral’ input into a rational decision making process. However,
research in the eighties began to question the neutrality of financial
information as an input in collective bargaining rather viewing it as a
potentially effective means for controlling the labour force.
Furthermore, this later research also started challenging the simplistic
assumptions relating particularly to union objectives which have
underpinned the earlier search for universal union decision models.
Despite the above developments there seems to be a dearth of studies
in developing countries concerning the use of financial information in
collective bargaining. This paper seeks to begin to remedy this
deficiency. Utilising a case study approach based on two enterprises,
one in the public sector and the other a multinational, the universal
decision model is rejected and it is argued that the decision whether
or not to use financial information is a contingent one and such a
contingency is largely defined by the context and environment in
which both union and management negotiators work. An attempt is
also made to identify the factors constraining as well as promoting
the use of financial information in collective bargaining, these being
regarded as unique to the organisations within which the case studies
are conducted.
Abstract: Marine Protected Areas can benefit from nature based
tourism, monitoring environmental impacts and also become target
for human presence. From more than 3 million tourists visiting
Cozumel Island every year, an average of 2,8 million arrive by cruise
ship, and 41% are estimated to have motivation for water activities.
The destination is relying so much on the tourism activity, that scuba
diving and snorkeling in the National Park Reef of Cozumel sustain
the major economic activity. In order to achieve the sustainable
development indicator designed for regional environmental
development, the PNAC offers a training course to tourism providers
to access the protected area. This way, the update of the last 5 years
of such training is directed to diving staff, boat crew and
professionals, making them able to assist in managing the natural
resource. Moreover, the case study is an example to be used for
raising awareness among tourists visiting protected areas.
Abstract: Musculoskeletal injuries in school children could be
reduced improving trunk strength and hamstring flexibility. Low
levels of trunk muscle strength and hamstring flexibility may result in
acute and musculoskeletal chronic diseases. The Pilates Method can
be appropriate to improve these physical condition attributes and has
been rarely employed by this social group. On the other hand, it has
been shown that trunk strength and flexibility are different between
genders, but there is no evidence about the effect of exercise
programs designed to improve both items in school children.
Therefore the objective of this study was to measure the effect of a
six-week Pilates-based exercise program in 14 year old school
children trunk strength and hamstring flexibility, establishing
differences in gender. The sample was composed of 57 students
divided into experimental group (EG; n=30) and control group (CG;
n=27). Bench Trunk Curl test (BTC), Sörensen test and Toe-touch
test (TT) were used to measure dynamic muscular resistance in trunk
flexion, isometric strength in trunk extension and hamstring
flexibility, respectively. EG utilized the Pilates exercise program
during six-weeks (2 days/week, 55minutes/session). After this period
of training, EG improved trunk strength and hamstring flexibility
significantly but there were no significant differences within CG.
Although boys were better in BTC test and girls were better in TT
test, there were no significant differences between them.
Abstract: The effective development of a geoscience education
and training program takes account of the rapidly changing
environment in the geoscience market, includes information about
resource-rich countries which have international education demands.
In this paper, we introduce the geoscience program run by the
International School for Geoscience Resources at the Korea Institute
of Geoscience and Mineral Resources (IS-Geo of KIGAM), and show
its remarkable performance. To further effective geoscience program
planning and operation, we present recommendations for strategic
management for customer-oriented operation with a more favorable
program format and advanced training aids. Above all, the IS-Geo of
KIGAM should continue improve through ‘plan-do-see-feedback’
activities based on the recommendations.
Abstract: In this study, we aim to demonstrate a microgrid
system experimental simulation for an easy understanding of a
large-scale microgrid system. This model is required for industrial
training and learning environments. However, in order to create an
exact representation of a microgrid system, the laboratory-scale
system must fulfill the requirements of a grid-connected inverter, in
which power values are assigned to the system to cope with the
intermittent output from renewable energy sources. Aside from that,
during fluctuations in load capacity, the grid-connected system must
be able to supply power from the utility grid side and microgrid side in
a balanced manner. Therefore, droop control is installed in the
inverter’s control board to maintain a balanced power sharing in both
sides. This power control in a stand-alone condition and droop control
in a grid-connected condition must be implemented in order to
maintain a stabilized system. Based on the experimental results, power
control and droop control can both be applied in the system by
comparing the experimental and reference values.
Abstract: In this paper, the effect of admixtures on the tensional
strength of concrete in Urmia-lake water have been investigated. We
made different types of concretes with the ratio of w/c and replaced
different percentages of micro-silica, air-entraining, super plasticizer,
corrosion-inhibiting, and caulk with two types of cement I and II as
well as investigating in both ordinary water and Urmia-lake water.
The tensional strength was investigated on these samples.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: The paper presents combined automatic speech
recognition (ASR) of English and machine translation (MT) for
English and Croatian and Croatian-English language pairs in the
domain of business correspondence. The first part presents results of
training the ASR commercial system on English data sets, enriched
by error analysis. The second part presents results of machine
translation performed by free online tool for English and Croatian
and Croatian-English language pairs. Human evaluation in terms of
usability is conducted and internal consistency calculated by
Cronbach's alpha coefficient, enriched by error analysis. Automatic
evaluation is performed by WER (Word Error Rate) and PER
(Position-independent word Error Rate) metrics, followed by
investigation of Pearson’s correlation with human evaluation.
Abstract: Nowadays, the amounts of companies which tend to
have an Enterprise Resource Planning (ERP) application are
increasing. Although ERP projects are expensive, time consuming,
and complex, there are some successful experiences. These days,
developing countries are striving to implement ERP projects
successfully; however, there are many obstacles. Therefore, these
projects would be failed or partially failed. This paper concerns the
implementation of a successful ERP implementation, IFS, in Iran at
Dana Geophysics Company (DGC). After a short review of ERP and
ERP market in Iran, we propose a three phases deployment
methodology (phase 1: Preparation and Business Process
Management (BPM) phase 2: implementation and phase 3: testing,
golive-1 (pilot) and golive-2 (final)). Then, we present five guidelines
(Project Management, Change Management, Business Process
Management (BPM), Training& Knowledge Management, and
Technical Management), which were chose as work streams. In this
case study we present lessons learned in Project management and
Business process Management.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: Business interpreting talents are in badly need for local
economic development, but currently there are problems of traditional
business interpreting training mode in China. In view of the good
opportunity for college business interpreters provided by international
trading center development in Qingdao China and with the aim of
being in line with market demand and enhancing business interpreters'
employment competitive advantage, this paper aims to explore how to
cultivate interdisciplinary business interpreting talents based on
market demand.
Abstract: This paper aims at finding a suitable neural network
for monitoring congestion level in electrical power systems. In this
paper, the input data has been framed properly to meet the target
objective through supervised learning mechanism by defining normal
and abnormal operating conditions for the system under study. The
congestion level, expressed as line congestion index (LCI), is
evaluated for each operating condition and is presented to the NN
along with the bus voltages to represent the input and target data.
Once, the training goes successful, the NN learns how to deal with a
set of newly presented data through validation and testing
mechanism. The crux of the results presented in this paper rests on
performance comparison of a multi-layered feed forward neural
network with eleven types of back propagation techniques so as to
evolve the best training criteria. The proposed methodology has been
tested on the standard IEEE-14 bus test system with the support of
MATLAB based NN toolbox. The results presented in this paper
signify that the Levenberg-Marquardt backpropagation algorithm
gives best training performance of all the eleven cases considered in
this paper, thus validating the proposed methodology.
Abstract: Frequent, continuous speech training has proven to be
a necessary part of a successful speech therapy process, but
constraints of traveling time and employment dispensation become
key obstacles especially for individuals living in remote areas or for
dependent children who have working parents. In order to ameliorate
speech difficulties with ample guidance from speech therapists, a
website has been developed that supports speech therapy and training
for people with articulation disorders in the standard Thai language.
This web-based program has the ability to record speech training
exercises for each speech trainee. The records will be stored in a
database for the speech therapist to investigate, evaluate, compare
and keep track of all trainees’ progress in detail. Speech trainees can
request live discussions via video conference call when needed.
Communication through this web-based program facilitates and
reduces training time in comparison to walk-in training or
appointments. This type of training also allows people with
articulation disorders to practice speech lessons whenever or
wherever is convenient for them, which can lead to a more regular
training processes.
Abstract: The importance of the formal specification in the
software life cycle is barely concealing to anyone. Formal
specifications use mathematical notation to describe the properties of
information system precisely, without unduly constraining the way in
how these properties are achieved. Having a correct and quality
software specification is not easy task. This study concerns with how
a group of rectifiers can communicate with each other and work to
prepare and produce a correct formal software specification. WBCS
has been implemented based mainly in the proposed supported
cooperative work model and a survey conducted on the existing Webbased
collaborative writing tools. This paper aims to assess the
feasibility of executing the web-based collaboration process using
WBCS. The purpose of conducting this test is to test the system as a
whole for functionality and fitness for use based on the evaluation
test plan.
Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: This research analyzes factors affecting the success of Bitcoin Value within Thailand and develops a guideline for self-reliance for effective business implementation. Samples in this study included 121 people through surveys. The results revealed four main factors affecting the success as follows: 1) A great majority didn't know what Bitcoin was. 2) Didn't grasp the concept of a digital currency or see the benefit of a digital currency. 3) There is a great need to educate the next generation of learners on the benefits of Bitcoin within the community. 4) Future Career training should be pursued in applied Bitcoin development.
The guideline for self-reliance planning consisted of 4 aspects: 1) Local communities need to develop awareness of the usefulness of Bitcoin and share the value of Bitcoin among friends and family. 2) Computer Science and Business Management staff should develop skills to expand on the benefits of Bitcoin within their departments. 3) Further research should be pursued on how Bitcoin Value can improve business and tourism within Thailand. Local communities should focus on developing Bitcoin awareness by encouraging street vendors to accept Bitcoin as another form of payment for services rendered. 4) Development planning: by arranging meet up groups to conduct further education on Bitcoin and share solutions on adoption into every day usage.