Abstract: In the field of fashion design, 3D Mannequin is a kind
of assisting tool which could rapidly realize the design concepts.
While the concept of 3D Mannequin is applied to the computer added
fashion design, it will connect with the development and the
application of design platform and system. Thus, the situation
mentioned above revealed a truth that it is very critical to develop a
module of 3D Mannequin which would correspond with the necessity
of fashion design. This research proposes a concrete plan that
developing and constructing a system of 3D Mannequin with Kinect.
In the content, ergonomic measurements of objective human features
could be attained real-time through the implement with depth camera
of Kinect, and then the mesh morphing can be implemented through
transformed the locations of the control-points on the model by
inputting those ergonomic data to get an exclusive 3D mannequin
model. In the proposed methodology, after the scanned points from the
Kinect are revised for accuracy and smoothening, a complete human
feature would be reconstructed by the ICP algorithm with the method
of image processing. Also, the objective human feature could be
recognized to analyze and get real measurements. Furthermore, the
data of ergonomic measurements could be applied to shape morphing
for the division of 3D Mannequin reconstructed by feature curves. Due
to a standardized and customer-oriented 3D Mannequin would be
generated by the implement of subdivision, the research could be
applied to the fashion design or the presentation and display of 3D
virtual clothes. In order to examine the practicality of research
structure, a system of 3D Mannequin would be constructed with JAVA
program in this study. Through the revision of experiments the
practicability-contained research result would come out.
Abstract: This study, for its research subjects, uses patients who
had undergone total knee replacement surgery from the database of the
National Health Insurance Administration. Through the review of
literatures and the interviews with physicians, important factors are
selected after careful screening. Then using Cross Entropy Method,
Genetic Algorithm Logistic Regression, and Particle Swarm
Optimization, the weight of each factor is calculated and obtained. In
the meantime, Excel VBA and Case Based Reasoning are combined
and adopted to evaluate the system. Results show no significant
difference found through Genetic Algorithm Logistic Regression and
Particle Swarm Optimization with over 97% accuracy in both
methods. Both ROC areas are above 0.87. This study can provide
critical reference to medical personnel as clinical assessment to
effectively enhance medical care quality and efficiency, prevent
unnecessary waste, and provide practical advantages to resource
allocation to medical institutes.
Abstract: In this paper a new model for center of motion
creating is proposed. This new method uses cables. So, it is very
useful in robots because it is light and has easy assembling process.
In the robots which need to be in touch with some things this method
is so useful. It will be described in the following. The accuracy of the
idea is proved by two experiments. This system could be used in the
robots which need a fixed point in the contact with some things and
make a circular motion.
Abstract: In present study, it was aimed to determine potential
agricultural lands (PALs) in Gokceada (Imroz) Island of Canakkale
province, Turkey. Seven-band Landsat 8 OLI images acquired on
July 12 and August 13, 2013, and their 14-band combination image
were used to identify current Land Use Land Cover (LULC) status.
Principal Component Analysis (PCA) was applied to three Landsat
datasets in order to reduce the correlation between the bands. A total
of six Original and PCA images were classified using supervised
classification method to obtain the LULC maps including 6 main
classes (“Forest”, “Agriculture”, “Water Surface”, “Residential Area-
Bare Soil”, “Reforestation” and “Other”). Accuracy assessment was
performed by checking the accuracy of 120 randomized points for
each LULC maps. The best overall accuracy and Kappa statistic
values (90.83%, 0.8791% respectively) were found for PCA images
which were generated from 14-bands combined images called 3-
B/JA.
Digital Elevation Model (DEM) with 15 m spatial resolution
(ASTER) was used to consider topographical characteristics. Soil
properties were obtained by digitizing 1:25000 scaled soil maps of
Rural Services Directorate General. Potential Agricultural Lands
(PALs) were determined using Geographic information Systems
(GIS). Procedure was applied considering that “Other” class of
LULC map may be used for agricultural purposes in the future
properties. Overlaying analysis was conducted using Slope (S), Land
Use Capability Class (LUCC), Other Soil Properties (OSP) and Land
Use Capability Sub-Class (SUBC) properties.
A total of 901.62 ha areas within “Other” class (15798.2 ha) of
LULC map were determined as PALs. These lands were ranked as
“Very Suitable”, “Suitable”, “Moderate Suitable” and “Low
Suitable”. It was determined that the 8.03 ha were classified as “Very
Suitable” while 18.59 ha as suitable and 11.44 ha as “Moderate
Suitable” for PALs. In addition, 756.56 ha were found to be “Low
Suitable”. The results obtained from this preliminary study can serve
as basis for further studies.
Abstract: Under active stress conditions, a rigid cantilever
retaining wall tends to rotate about a pivot point located within the
embedded depth of the wall. For purely granular and cohesive soils, a
methodology was previously reported called minimization of moment
ratio to determine the location of the pivot point of rotation. The
usage of this new methodology is to estimate the rotational stability
safety factor. Moreover, the degree of improvement required in a
backfill to get a desired safety factor can be estimated by the concept
of the shear strength demand. In this article, the accuracy of this
method for another type of cantilever walls called Contiguous Bored
Pile (CBP) retaining wall is evaluated by using physical modeling
technique. Based on observations, the results of moment ratio
minimization method are in good agreement with the results of the
carried out physical modeling.
Abstract: By the evolvement in technology, the way of
expressing opinions switched direction to the digital world. The
domain of politics, as one of the hottest topics of opinion mining
research, merged together with the behavior analysis for affiliation
determination in texts, which constitutes the subject of this paper.
This study aims to classify the text in news/blogs either as
Republican or Democrat with the minimum number of features. As
an initial set, 68 features which 64 were constituted by Linguistic
Inquiry and Word Count (LIWC) features were tested against 14
benchmark classification algorithms. In the later experiments, the
dimensions of the feature vector reduced based on the 7 feature
selection algorithms. The results show that the “Decision Tree”,
“Rule Induction” and “M5 Rule” classifiers when used with “SVM”
and “IGR” feature selection algorithms performed the best up to
82.5% accuracy on a given dataset. Further tests on a single feature
and the linguistic based feature sets showed the similar results. The
feature “Function”, as an aggregate feature of the linguistic category,
was found as the most differentiating feature among the 68 features
with the accuracy of 81% in classifying articles either as Republican
or Democrat.
Abstract: Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).
Abstract: To explore how the brain may recognise objects in its
general,accurate and energy-efficient manner, this paper proposes the
use of a neuromorphic hardware system formed from a Dynamic
Video Sensor (DVS) silicon retina in concert with the SpiNNaker
real-time Spiking Neural Network (SNN) simulator. As a first step
in the exploration on this platform a recognition system for dynamic
hand postures is developed, enabling the study of the methods used
in the visual pathways of the brain. Inspired by the behaviours of
the primary visual cortex, Convolutional Neural Networks (CNNs)
are modelled using both linear perceptrons and spiking Leaky
Integrate-and-Fire (LIF) neurons.
In this study’s largest configuration using these approaches, a
network of 74,210 neurons and 15,216,512 synapses is created and
operated in real-time using 290 SpiNNaker processor cores in parallel
and with 93.0% accuracy. A smaller network using only 1/10th of the
resources is also created, again operating in real-time, and it is able
to recognise the postures with an accuracy of around 86.4% - only
6.6% lower than the much larger system. The recognition rate of the
smaller network developed on this neuromorphic system is sufficient
for a successful hand posture recognition system, and demonstrates
a much improved cost to performance trade-off in its approach.
Abstract: This paper presents an optimization method for
reducing the number of input channels and the complexity of the
feed-forward NARX neural network (NN) without compromising the
accuracy of the NN model. By utilizing the correlation analysis
method, the most significant regressors are selected to form the input
layer of the NN structure. An application of vehicle dynamic model
identification is also presented in this paper to demonstrate the
optimization technique and the optimal input layer structure and the
optimal number of neurons for the neural network is investigated.
Abstract: Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.
Abstract: The fatigue life of tubular joints commonly found in
offshore industry is not only dependent on the value of hot-spot stress
(HSS), but is also significantly influenced by the through-thethickness
stress distribution characterized by the degree of bending
(DoB). The determination of DoB values in a tubular joint is essential
for improving the accuracy of fatigue life estimation using the stresslife
(S–N) method and particularly for predicting the fatigue crack
growth based on the fracture mechanics (FM) approach. In the
present paper, data extracted from finite element (FE) analyses of
tubular KT-joints, verified against experimental data and parametric
equations, was used to investigate the effects of geometrical
parameters on DoB values at the crown 0°, saddle, and crown 180°
positions along the weld toe of central brace in tubular KT-joints
subjected to axial loading. Parametric study was followed by a set of
nonlinear regression analyses to derive DoB parametric formulas for
the fatigue analysis of KT-joints under axial loads. The tubular KTjoint
is a quite common joint type found in steel offshore structures.
However, despite the crucial role of the DoB in evaluating the fatigue
performance of tubular joints, this paper is the first attempt to study
and formulate the DoB values in KT-joints.
Abstract: There are many perceived advantages of microwave
ablation have driven researchers to develop innovative antennas to
effectively treat deep-seated, non-resectable hepatic tumors. In this
paper a coaxial antenna with a miniaturized sleeve choke has been
discussed for microwave interstitial ablation therapy, in order to
reduce backward heating effects irrespective of the insertion depth
into the tissue. Two dimensional Finite Element Method (FEM) is
used to simulate and measure the results of miniaturized sleeve choke
antenna. This paper emphasizes the importance of factors that can
affect simulation accuracy, which include mesh resolution, surface
heating and reflection coefficient. Quarter wavelength choke
effectiveness has been discussed by comparing it with the unchoked
antenna with same dimensions.
Abstract: In the present study, RBF neural networks were used
for predicting the performance and emission parameters of a
biodiesel engine. Engine experiments were carried out in a 4 stroke
diesel engine using blends of diesel and Honge methyl ester as the
fuel. Performance parameters like BTE, BSEC, Tex and emissions
from the engine were measured. These experimental results were
used for ANN modeling.
RBF center initialization was done by random selection and by
using Clustered techniques. Network was trained by using fixed and
varying widths for the RBF units. It was observed that RBF results
were having a good agreement with the experimental results.
Networks trained by using clustering technique gave better results
than using random selection of centers in terms of reduced MRE and
increased prediction accuracy. The average MRE for the performance
parameters was 3.25% with the prediction accuracy of 98% and for
emissions it was 10.4% with a prediction accuracy of 80%.
Abstract: This paper contains the description of argumentation
approach for the problem of inductive concept formation. It is
proposed to use argumentation, based on defeasible reasoning with
justification degrees, to improve the quality of classification models,
obtained by generalization algorithms. The experiment’s results on
both clear and noisy data are also presented.
Abstract: The grain quality of chickpea in Iran is low and
instable, which may be attributed to the evolution of cultivars with a
narrow genetic base making them vulnerable to biotic stresses. Four
chickpea varieties from diverse geographic origins were chosen and
arranged in a randomized complete block design. Mesorhizobium sp.
cicer strain SW7 was added to all the chickpea seeds. Chickpea seeds
were planted on October 9, 2013. Each genotype was sown 5 m in
length, with 35 cm inter-row spacing, in 3 rows. Weeds were
removed manually in all plots. Results showed that Analysis of
variance on the studied traits showed significant differences among
genotypes for N, P, K and Fe contents of chickpea, but there is not a
significant difference among Ca, Zn and Mg continents of chickpea.
The experimental coefficient of variation (CV) varied from 7.3 to
15.8. In general, the CV value lower than 20% is considered to be
good, indicating the accuracy of conducted experiments. The highest
grain N was observed in Hashem and Jam cultivars. The highest grain
P was observed in Jam cultivar. Phosphorus content (mg/100g)
ranged from 142.3 to 302.3 with a mean value of 221.3. The negative
correlation (-0.126) was observed between the N and P of chickpea
cultivars. The highest K and Fe contents were observed in Jam
cultivar.
Abstract: A simple adaptive voice activity detector (VAD) is
implemented using Gabor and gammatone atomic decomposition of
speech for high Gaussian noise environments. Matching pursuit is
used for atomic decomposition, and is shown to achieve optimal
speech detection capability at high data compression rates for low
signal to noise ratios. The most active dictionary elements found by
matching pursuit are used for the signal reconstruction so that the
algorithm adapts to the individual speakers dominant time-frequency
characteristics. Speech has a high peak to average ratio enabling
matching pursuit greedy heuristic of highest inner products to isolate
high energy speech components in high noise environments. Gabor
and gammatone atoms are both investigated with identical
logarithmically spaced center frequencies, and similar bandwidths.
The algorithm performs equally well for both Gabor and gammatone
atoms with no significant statistical differences. The algorithm
achieves 70% accuracy at a 0 dB SNR, 90% accuracy at a 5 dB SNR
and 98% accuracy at a 20dB SNR using 30d B SNR as a reference
for voice activity.
Abstract: Margin-Based Principle has been proposed for a long
time, it has been proved that this principle could reduce the
structural risk and improve the performance in both theoretical
and practical aspects. Meanwhile, feed-forward neural network is
a traditional classifier, which is very hot at present with a deeper
architecture. However, the training algorithm of feed-forward neural
network is developed and generated from Widrow-Hoff Principle that
means to minimize the squared error. In this paper, we propose
a new training algorithm for feed-forward neural networks based
on Margin-Based Principle, which could effectively promote the
accuracy and generalization ability of neural network classifiers
with less labelled samples and flexible network. We have conducted
experiments on four UCI open datasets and achieved good results
as expected. In conclusion, our model could handle more sparse
labelled and more high-dimension dataset in a high accuracy while
modification from old ANN method to our method is easy and almost
free of work.
Abstract: High density electrical prospecting has been widely
used in groundwater investigation, civil engineering and
environmental survey. For efficient inversion, the forward modeling
routine, sensitivity calculation, and inversion algorithm must be
efficient. This paper attempts to provide a brief summary of the past
and ongoing developments of the method. It includes reviews of the
procedures used for data acquisition, processing and inversion of
electrical resistivity data based on compilation of academic literature.
In recent times there had been a significant evolution in field survey
designs and data inversion techniques for the resistivity method. In
general 2-D inversion for resistivity data is carried out using the
linearized least-square method with the local optimization technique
.Multi-electrode and multi-channel systems have made it possible to
conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve
complex geological structures that were not possible with traditional
1-D surveys. 3-D surveys play an increasingly important role in very
complex areas where 2-D models suffer from artifacts due to off-line
structures. Continued developments in computation technology, as
well as fast data inversion techniques and software, have made it
possible to use optimization techniques to obtain model parameters to
a higher accuracy. A brief discussion on the limitations of the
electrical resistivity method has also been presented.
Abstract: Micro-electromechanical system (MEMS)
accelerometers and gyroscopes are suitable for the inertial navigation
system (INS) of many applications due to low price, small
dimensions and light weight. The main disadvantage in a comparison
with classic sensors is a worse long term stability. The estimation
accuracy is mostly affected by the time-dependent growth of inertial
sensor errors, especially the stochastic errors. In order to eliminate
negative effects of these random errors, they must be accurately
modeled. In this paper, the Allan variance technique will be used in
modeling the stochastic errors of the inertial sensors. By performing
a simple operation on the entire length of data, a characteristic curve
is obtained whose inspection provides a systematic characterization
of various random errors contained in the inertial-sensor output data.
Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.