Abstract: The sub-task pattern in terms of deviations and defects
should be identified and understood in order to improve the quality of
practices in construction projects. Therefore, sub-task susceptibility
to exposure to deviations and defects has been evaluated and
classified via six classifications proposed in this study. Thirty-four
case studies of specific sub-tasks (from compression members in
constructed concrete structures) were collected from seven
construction projects in order to examine the study’s proposed
classifications. The study revealed that the sub-task has a high
sensitivity to deviation, where 91% of the cases were recorded as
deviations; however, only 19% of cases were recorded as defects.
Other findings were that the actual work during the execution process
is a high source of deviation for this sub-task (74%), while only 26%
of the source of deviation was due to both design documentation and
the actual work. These findings significantly imply that the study’s
proposed classifications could be used to determine the pattern of
each sub-task and develop proactive actions to overcome issues of
sub-task deviations and defects.
Abstract: The paper deals with communication standards for
control and production system. The authors formulate the
requirements for communication security protection. The paper is
focused on application protocols of the industrial networks and their
basic classification. The typical attacks are analysed and the safety
protection, based on requirements for specific industrial network is
suggested and defined in this paper.
Abstract: This paper presents a real-time visualization technique
and filtering of classified LiDAR point clouds. The visualization is
capable of displaying filtered information organized in layers by the
classification attribute saved within LiDAR datasets. We explain the
used data structure and data management, which enables real-time
presentation of layered LiDAR data. Real-time visualization is
achieved with LOD optimization based on the distance from the
observer without loss of quality. The filtering process is done in two
steps and is entirely executed on the GPU and implemented using
programmable shaders.
Abstract: The article discusses multimodal mobility in
contemporary societies as a main planning and organization issue in
the functioning of administrative bodies, a problem which really
exists in the space of contemporary cities in terms of shaping modern
transport systems. The article presents classification of available
resources and initiatives undertaken for developing multimodal
mobility. Solutions can be divided into three groups of measures –
physical measures in the form of changes of the transport network
infrastructure, organizational ones (including transport policy) and
information measures. The latter ones include in particular direct
support for people travelling in the transport network by providing
information about ways of using available means of transport. A
special measure contributing to this end is a trip planner. The article
compares several selected planners. It includes a short description of
the Green Travelling Project, which aims at developing a planner
supporting environmentally friendly solutions in terms of transport
network operation. The article summarizes preliminary findings of
the project.
Abstract: Different strategies and tools are available at the oil
and gas industry for detecting and analyzing tension and possible
fractures in borehole walls. Most of these techniques are based on
manual observation of the captured borehole images. While this
strategy may be possible and convenient with small images and few
data, it may become difficult and suitable to errors when big
databases of images must be treated. While the patterns may differ
among the image area, depending on many characteristics (drilling
strategy, rock components, rock strength, etc.). In this work we
propose the inclusion of data-mining classification strategies in order
to create a knowledge database of the segmented curves. These
classifiers allow that, after some time using and manually pointing
parts of borehole images that correspond to tension regions and
breakout areas, the system will indicate and suggest automatically
new candidate regions, with higher accuracy. We suggest the use of
different classifiers methods, in order to achieve different knowledge
dataset configurations.
Abstract: The paper presents the results of clusterization by
Kohonen self-organizing maps (SOM) applied for analysis of array of
Raman spectra of multi-component solutions of inorganic salts, for
determination of types of salts present in the solution. It is
demonstrated that use of SOM is a promising method for solution of
clusterization and classification problems in spectroscopy of multicomponent
objects, as attributing a pattern to some cluster may be
used for recognition of component composition of the object.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: This paper presents the voltage problem location
classification using performance of Least Squares Support Vector
Machine (LS-SVM) and Learning Vector Quantization (LVQ) in
electrical power system for proper voltage problem location
implemented by IEEE 39 bus New- England. The data was collected
from the time domain simulation by using Power System Analysis
Toolbox (PSAT). Outputs from simulation data such as voltage, phase
angle, real power and reactive power were taken as input to estimate
voltage stability at particular buses based on Power Transfer Stability
Index (PTSI).The simulation data was carried out on the IEEE 39 bus
test system by considering load bus increased on the system. To verify
of the proposed LS-SVM its performance was compared to Learning
Vector Quantization (LVQ). The results showed that LS-SVM is faster
and better as compared to LVQ. The results also demonstrated that the
LS-SVM was estimated by 0% misclassification whereas LVQ had
7.69% misclassification.
Abstract: Consumer-to-Consumer (C2C) E-commerce has been
growing at a very high speed in recent years. Since identical or
nearly-same kinds of products compete one another by relying on
keyword search in C2C E-commerce, some sellers describe their
products with spam keywords that are popular but are not related to
their products. Though such products get more chances to be retrieved
and selected by consumers than those without spam keywords,
the spam keywords mislead the consumers and waste their time.
This problem has been reported in many commercial services like
ebay and taobao, but there have been little research to solve this
problem. As a solution to this problem, this paper proposes a method
to classify whether keywords of a product are spam or not. The
proposed method assumes that a keyword for a given product is
more reliable if the keyword is observed commonly in specifications
of products which are the same or the same kind as the given
product. This is because that a hierarchical category of a product
in general determined precisely by a seller of the product and so is
the specification of the product. Since higher layers of the hierarchical
category represent more general kinds of products, a reliable degree
is differently determined according to the layers. Hence, reliable
degrees from different layers of a hierarchical category become
features for keywords and they are used together with features only
from specifications for classification of the keywords. Support Vector
Machines are adopted as a basic classifier using the features, since
it is powerful, and widely used in many classification tasks. In
the experiments, the proposed method is evaluated with a golden
standard dataset from Yi-han-wang, a Chinese C2C E-commerce,
and is compared with a baseline method that does not consider
the hierarchical category. The experimental results show that the
proposed method outperforms the baseline in F1-measure, which
proves that spam keywords are effectively identified by a hierarchical
category in C2C E-commerce.
Abstract: In remote sensing, shadow causes problems in many
applications such as change detection and classification. It is caused
by objects which are elevated, thus can directly affect the accuracy of
information. For these reasons, it is very important to detect shadows
particularly in urban high spatial resolution imagery which created a
significant problem. This paper focuses on automatic shadow
detection based on a new spectral index for multispectral imagery
known as Shadow Detection Index (SDI). The new spectral index
was tested on different areas of WorldView-2 images and the results
demonstrated that the new spectral index has a massive potential to
extract shadows with accuracy of 94% effectively and automatically.
Furthermore, the new shadow detection index improved road
extraction from 82% to 93%.
Abstract: In the present study we evaluated the nutritional status of 214 institutionalized elderly residents of both genders, aged 65 years and older of 11 care homes located in the district of Viseu (center of Portugal). The evaluation was based on anthropometric measurements and the Mini Nutritional Assessment (MNA) score.
The mean age of the subjects was 82.3 ± 6.1 years-old. Most of the elderly residents were female (72.0%). The majority had 4 years of formal education (51.9%) and was widowed (74.3%) or married (14.0%).
Men presented a mean age of 81.2±8.5 years-old, weight 69.3±14.5 kg and BMI 25.33±6.5 kg/m2. In women, the mean age was 84.5±8.2 years-old, weight 61.2±14.7 kg and BMI 27.43±5.6 kg/m2.
The evaluation of the nutritional status using the MNA score showed that 24.0% of the residents show a risk of undernutrition and 76.0% of them were well nourished.
There was a high prevalence of obese (24.8%) and overweight residents (33.2%) according to the BMI. 7.5% were considered underweight.
We also found that according to their waist circumference measurements 88.3% of the residents were at risk for cardiovascular disease (CVD) and 64.0% of them presented very high risk for CVD (WC≥88 cm for women and WC ≥102 cm for men).
The present study revealed the coexistence of a dual form of malnutrition (undernourished and overweight) among the institutionalized Portuguese concomitantly with an excess of abdominal adiposity. The high prevalence of residents at high risk for CVD should not be overlooked.
Given the vulnerability of the group of institutionalized elderly, our study highlights the importance of the classification of nutritional status based on both instruments: the BMI and the MNA.
Abstract: Assistive robotics are playing a vital role in advancing the quality of life for disable people. There exist wide range of systems that can control and guide autonomous mobile robots. The objective of the control system is to guide an autonomous mobile robot using the movement of eyes by means of EOG signal. The EOG signal is acquired using Ag/AgCl electrodes and this signal is processed by a microcontroller unit to calculate the eye gaze direction. Then according to the guidance control strategy, the control commands of the wheelchair are sent. The classification of different eye movements allows us to generate simple code for controlling the wheelchair. This work was aimed towards developing a usable and low-cost assistive robotic wheel chair system for disabled people. To live more independent life, the system can be used by the handicapped people especially those with only eye-motor coordination.
Abstract: In this paper, we propose a smart music player that combines the musical genre classification and the spatial audio processing. The musical genre is classified based on content analysis of the musical segment detected from the audio stream. In parallel with the classification, the spatial audio quality is achieved by adding an artificial reverberation in a virtual acoustic space to the input mono sound. Thereafter, the spatial sound is boosted with the given frequency gains based on the musical genre when played back. Experiments measured the accuracy of detecting the musical segment from the audio stream and its musical genre classification. A listening test was performed based on the virtual acoustic space based spatial audio processing.
Abstract: In this paper, the comparison between k-Nearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alpha-α (8–13 Hz) and beta-β (13–30 Hz) were used as input signals for the classification model. The k-NN classification result is 88.46% accuracy. These results proved that k-NN can be used in order to predict the brain balancing application.
Abstract: This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system.
Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.
Abstract: Human face has a fundamental role in the appearance
of individuals. So the importance of facial surgeries is undeniable.
Thus, there is a need for the appropriate and accurate facial skin
segmentation in order to extract different features. Since Fuzzy CMeans
(FCM) clustering algorithm doesn’t work appropriately for
noisy images and outliers, in this paper we exploit Possibilistic CMeans
(PCM) algorithm in order to segment the facial skin. For this
purpose, first, we convert facial images from RGB to YCbCr color
space. To evaluate performance of the proposed algorithm, the
database of Sahand University of Technology, Tabriz, Iran was used.
In order to have a better understanding from the proposed algorithm;
FCM and Expectation-Maximization (EM) algorithms are also used
for facial skin segmentation. The proposed method shows better
results than the other segmentation methods. Results include
misclassification error (0.032) and the region’s area error (0.045) for
the proposed algorithm.
Abstract: This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.
Abstract: Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.
Abstract: We propose obstacle classification method based on 2D
LIDAR Database. The existing obstacle classification method based
on 2D LIDAR, has an advantage in terms of accuracy and shorter
calculation time. However, it was difficult to classifier the type of
obstacle and therefore accurate path planning was not possible. In
order to overcome this problem, a method of classifying obstacle type
based on width data of obstacle was proposed. However, width data
was not sufficient to improve accuracy. In this paper, database was
established by width and intensity data; the first classification was
processed by the width data; the second classification was processed
by the intensity data; classification was processed by comparing to
database; result of obstacle classification was determined by finding
the one with highest similarity values. An experiment using an actual
autonomous vehicle under real environment shows that calculation
time declined in comparison to 3D LIDAR and it was possible to
classify obstacle using single 2D LIDAR.