Abstract: We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.
Abstract: Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.
Abstract: Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.
Abstract: MicroRNAs are small non-coding RNA found in
many different species. They play crucial roles in cancer such as
biological processes of apoptosis and proliferation. The identification
of microRNA-target genes can be an essential first step towards to
reveal the role of microRNA in various cancer types. In this paper,
we predict miRNA-target genes for lung cancer by integrating
prediction scores from miRanda and PITA algorithms used as a
feature vector of miRNA-target interaction. Then, machine-learning
algorithms were implemented for making a final prediction. The
approach developed in this study should be of value for future studies
into understanding the role of miRNAs in molecular mechanisms
enabling lung cancer formation.
Abstract: In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.
Abstract: The effectiveness of microchannels in enhancing heat
transfer has been demonstrated in the semiconductor industry. In
order to tap the microscale heat transfer effects into macro
geometries, overcoming the cost and technological constraints,
microscale passages were created in macro geometries machined
using conventional fabrication methods. A cylindrical insert was
placed within a pipe, and geometrical profiles were created on the
outer surface of the insert to enhance heat transfer under steady-state
single-phase liquid flow conditions. However, while heat transfer
coefficient values of above 10 kW/m2·K were achieved, the heat
transfer enhancement was accompanied by undesirable pressure drop
increment. Therefore, this study aims to address the high pressure
drop issue using Constructal theory, a universal design law for both
animate and inanimate systems. Two designs based on Constructal theory were developed to study
the effectiveness of Constructal features in reducing the pressure drop
increment as compared to parallel channels, which are commonly
found in microchannel fabrication. The hydrodynamic and heat
transfer performance for the Tree insert and Constructal fin (Cfin)
insert were studied using experimental methods, and the underlying
mechanisms were substantiated by numerical results. In technical
terms, the objective is to achieve at least comparable increment in
both heat transfer coefficient and pressure drop, if not higher
increment in the former parameter. Results show that the Tree insert improved the heat transfer
performance by more than 16 percent at low flow rates, as compared
to the Tree-parallel insert. However, the heat transfer enhancement
reduced to less than 5 percent at high Reynolds numbers. On the
other hand, the pressure drop increment stayed almost constant at 20
percent. This suggests that the Tree insert has better heat transfer
performance in the low Reynolds number region. More importantly,
the Cfin insert displayed improved heat transfer performance along
with favourable hydrodynamic performance, as compared to Cfinparallel
insert, at all flow rates in this study. At 2 L/min, the
enhancement of heat transfer was more than 30 percent, with 20
percent pressure drop increment, as compared to Cfin-parallel insert.
Furthermore, comparable increment in both heat transfer coefficient
and pressure drop was observed at 8 L/min. In other words, the Cfin
insert successfully achieved the objective of this study. Analysis of the results suggests that bifurcation of flows is
effective in reducing the increment in pressure drop relative to heat
transfer enhancement. Optimising the geometries of the Constructal
fins is therefore the potential future study in achieving a bigger stride
in energy efficiency at much lower costs.
Abstract: Cloud computing is a business model which provides
an easier management of computing resources. Cloud users can
request virtual machine and install additional softwares and configure
them if needed. However, user can also request virtual appliance
which provides a better solution to deploy application in much faster
time, as it is ready-built image of operating system with necessary
softwares installed and configured. Large numbers of virtual
appliances are available in different image format. User can
download available appliances from public marketplace and start
using it. However, information published about the virtual appliance
differs from each providers leading to the difficulty in choosing
required virtual appliance as it is composed of specific OS with
standard software version. However, even if user choses the
appliance from respective providers, user doesn’t have any flexibility
to choose their own set of softwares with required OS and
application. In this paper, we propose a referenced architecture for
dynamically customizing virtual appliance and provision them in an
easier manner. We also add our experience in integrating our
proposed architecture with public marketplace and Mi-Cloud, a cloud
management software.
Abstract: Nowadays, food safety is a great public concern;
therefore, robust and effective techniques are required for detecting
the safety situation of goods. Hyperspectral Imaging (HSI) is an
attractive material for researchers to inspect food quality and safety
estimation such as meat quality assessment, automated poultry
carcass inspection, quality evaluation of fish, bruise detection of
apples, quality analysis and grading of citrus fruits, bruise detection
of strawberry, visualization of sugar distribution of melons,
measuring ripening of tomatoes, defect detection of pickling
cucumber, and classification of wheat kernels. HSI can be used to
concurrently collect large amounts of spatial and spectral data on the
objects being observed. This technique yields with exceptional
detection skills, which otherwise cannot be achieved with either
imaging or spectroscopy alone. This paper presents a nonlinear
technique based on kernel Fukunaga-Koontz transform (KFKT) for
detection of fat content in ground meat using HSI. The KFKT which
is the nonlinear version of FKT is one of the most effective
techniques for solving problems involving two-pattern nature. The
conventional FKT method has been improved with kernel machines
for increasing the nonlinear discrimination ability and capturing
higher order of statistics of data. The proposed approach in this paper
aims to segment the fat content of the ground meat by regarding the
fat as target class which is tried to be separated from the remaining
classes (as clutter). We have applied the KFKT on visible and nearinfrared
(VNIR) hyperspectral images of ground meat to determine
fat percentage. The experimental studies indicate that the proposed
technique produces high detection performance for fat ratio in ground
meat.
Abstract: Ultrasonic metal welding has been the subject of ongoing research and development, most recently concentrating on metal joining in miniature devices, for example to allow solder-free wire bonding. As well as at the small scale, there are also opportunities to research the joining of thicker sheet metals and to widen the range of similar and dissimilar materials that can be successfully joined using this technology. This study presents the design, characterisation and test of a lateral-drive ultrasonic metal spot welding device. The ultrasonic metal spot welding horn is modelled using finite element analysis (FEA) and its vibration behaviour is characterised experimentally to ensure ultrasonic energy is delivered effectively to the weld coupon. The welding stack and fixtures are then designed and mounted on a test machine to allow a series of experiments to be conducted for various welding and ultrasonic parameters. Weld strength is subsequently analysed using tensile-shear tests. The results show how the weld strength is particularly sensitive to the combination of clamping force and ultrasonic vibration amplitude of the welding tip, but there are optimal combinations of these and also limits that must be clearly identified.
Abstract: Centrifugal-casting machine is used in manufacturing
special machine components like multi-layer journal bearing used in
all internal combustion engine, steam, gas turbine and air craft turboengine
where isotropic properties and high precisions are desired.
Moreover, this machine can be used in manufacturing thin wall hightech
machine components like cylinder liners and piston rings of IC
engine and other machine parts like sleeves, and bushes. Heavy-duty
machine component like railway wheel can also be prepared by
centrifugal casting. A lot of technological developments are required
in casting process for production of good casted machine body and
machine parts. Usually defects like blowholes, surface roughness,
chilled surface etc. are found in sand casted machine parts. But these
can be removed by centrifugal casting machine using rotating
metallic die. Moreover, die rotation, its temperature control, and good
pouring practice can contribute to the quality of casting because of
the fact that the soundness of a casting in large part depends upon
how the metal enters into the mold or dies and solidifies. Poor
pouring practice leads to variety of casting defects such as
temperature loss, low quality casting, excessive turbulence, over
pouring etc. Besides these, handling of molten metal is very
unsecured and dangerous for the workers. In order to get rid of all
these problems, the need of an automatic pouring device arises. In
this research work, a robot assisted pouring device and a centrifugal
casting machine are designed, developed constructed and tested
experimentally which are found to work satisfactorily. The robot
assisted pouring device is further modified and developed for using it
in actual metal casting process. Lot of settings and tests are required
to control the system and ultimately it can be used in automation of
centrifugal casting machine to produce high-tech machine parts with
desired precision.
Abstract: The rising demand for format-flexible packaging
machines is caused by current market changes. Increasing the formatflexibility
is a new goal for the packaging machine manufacturers’
product development process. There are no methodical or designorientated
tools for a comprehensive consideration of this target. This
paper defines the term format-flexibility in the context of packaging
machines and shows the state-of-the-art for improving the
changeover of production machines. The requirements for a new
approach and the concept itself will be introduced, and the method
elements will be explained. Finally, the use of the concept and the
result of the development of a format-flexible packaging machine
will be shown.
Abstract: Excessive vibration means increased wear, increased
repair efforts, bad product selection & quality and high energy
consumption. This may be sometimes experienced by cavitation or
suction/discharge recirculation which could occur only when net
positive suction head available NPSHA drops below the net positive
suction head required NPSHR. Cavitation can cause axial surging, if it
is excessive, will damage mechanical seals, bearings, possibly other
pump components frequently, and shorten the life of the impeller.
Efforts have been made to explain Suction Energy (SE), Specific
Speed (Ns), Suction Specific Speed (Nss), NPSHA, NPSHR & their
significance, possible reasons of cavitation /internal recirculation, its
diagnostics and remedial measures to arrest and prevent cavitation in
this paper. A case study is presented by the author highlighting that
the root cause of unwanted noise and vibration is due to cavitation,
caused by high specific speeds or inadequate net- positive suction
head available which results in damages to material surfaces of
impeller & suction bells and degradation of machine performance, its
capacity and efficiency too. Author strongly recommends revisiting
the technical specifications of CW pumps to provide sufficient NPSH
margin ratios >1.5, for future projects and Nss be limited to 8500 -
9000 for cavitation free operation.
Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: Journal bearings used in IC engines are prone to premature
failures and are likely to fail earlier than the rated life due to
highly impulsive and unstable operating conditions and frequent
starts/stops. Vibration signature extraction and wear debris analysis
techniques are prevalent in industry for condition monitoring of
rotary machinery. However, both techniques involve a great deal of
technical expertise, time, and cost. Limited literature is available on
the application of these techniques for fault detection in reciprocating
machinery, due to the complex nature of impact forces that
confounds the extraction of fault signals for vibration-based analysis
and wear prediction. In present study, a simulation model was developed to investigate
the bearing wear behaviour, resulting because of different operating
conditions, to complement the vibration analysis. In current
simulation, the dynamics of the engine was established first, based on
which the hydrodynamic journal bearing forces were evaluated by
numerical solution of the Reynold’s equation. In addition, the
essential outputs of interest in this study, critical to determine wear
rates are the tangential velocity and oil film thickness between the
journals and bearing sleeve, which if not maintained appropriately,
have a detrimental effect on the bearing performance. Archard’s wear prediction model was used in the simulation to
calculate the wear rate of bearings with specific location information
as all determinative parameters were obtained with reference to crank
rotation. Oil film thickness obtained from the model was used as a
criterion to determine if the lubrication is sufficient to prevent contact
between the journal and bearing thus causing accelerated wear. A
limiting value of 1 μm was used as the minimum oil film thickness
needed to prevent contact. The increased wear rate with growing
severity of operating conditions is analogous and comparable to the
rise in amplitude of the squared envelope of the referenced vibration
signals. Thus on one hand, the developed model demonstrated its
capability to explain wear behaviour and on the other hand it also
helps to establish a co-relation between wear based and vibration
based analysis. Therefore, the model provides a cost effective and
quick approach to predict the impending wear in IC engine bearings
under various operating conditions.
Abstract: Test automation allows performing difficult and time
consuming manual software testing tasks efficiently, quickly and
repeatedly. However, development and maintenance of automated
tests is expensive, so it needs a proper prioritization what to automate
first. This paper describes a simple yet efficient approach for such
prioritization of test cases based on the effort needed for both manual
execution and software test automation. The suggested approach is
very flexible because it allows working with a variety of assessment
methods, and adding or removing new candidates at any time. The
theoretical ideas presented in this article have been successfully
applied in real world situations in several software companies by the
authors and their colleagues including testing of real estate websites,
cryptographic and authentication solutions, OSGi-based middleware
framework that has been applied in various systems for smart homes,
connected cars, production plants, sensors, home appliances, car head
units and engine control units (ECU), vending machines, medical
devices, industry equipment and other devices that either contain or
are connected to an embedded service gateway.
Abstract: Maintenance and design engineers have great concern
for the functioning of rotating machineries due to the vibration
phenomenon. Improper functioning in rotating machinery originates
from the damage to rolling element bearings. The status of rolling
element bearings require advanced technologies to monitor their
health status efficiently and effectively. Avoiding vibration during
machine running conditions is a complicated process. Vibration
simulation should be carried out using suitable sensors/ transducers to
recognize the level of damage on bearing during machine operating
conditions. Various issues arising in rotating systems are interlinked
with bearing faults. This paper presents an approach for fault
diagnosis of bearings using neural networks and time/frequencydomain
vibration analysis.
Abstract: Result from the constant dwindle in natural resources,
the alternative way to reduce the costs in our daily life would be urgent
to be found in the near future. As the ancient technique based on the
theory of solar chimney since roman times, the double-skin façade are
simply composed of two large glass panels in purpose of daylighting
and also natural ventilation in the daytime. Double-skin façade is
generally installed on the exterior side of buildings as function as the
window, so there is always a huge amount of passive solar energy the
façade would receive to induce the airflow every sunny day. Therefore,
this article imposes a domestic double-skin window for residential
usage and attempts to improve the volume flow rate inside the cavity
between the panels by the frame geometry design, the installation of
outlet guide plate and the solar energy collection system. Note that the
numerical analyses are applied to investigate the characteristics of flow
field, and the boundary conditions in the simulation are totally based
on the practical experiment of the original prototype. Then we
redesign the prototype from the knowledge of the numerical results
and fluid dynamic theory, and later the experiments of modified
prototype will be conducted to verify the simulation results. The
velocities at the inlet of each case are increase by 5%, 45% and 15%
from the experimental data, and also the numerical simulation results
reported 20% improvement in volume flow rate both for the frame
geometry design and installation of outlet guide plate.
Abstract: Mineral product, waste concrete (fine aggregates),
waste in the optical field, industry, and construction employ separators
to separate solids and classify them according to their size. Various
sorting machines are used in the industrial field such as those operating
under electrical properties, centrifugal force, wind power, vibration,
and magnetic force. Study on separators has been carried out to
contribute to the environmental industry. In this study, we perform
CFD analysis for understanding the basic mechanism of the separation
of waste concrete (fine aggregate) particles from air with a machine
built with a rotor with blades. In CFD, we first performed
two-dimensional particle tracking for various particle sizes for the
model with 1 degree, 1.5 degree, and 2 degree angle between each
blade to verify the boundary conditions and the method of rotating
domain method to be used in 3D. Then we developed 3D numerical
model with ANSYS CFX to calculate the air flow and track the
particles. We judged the capability of particle separation for given size
by counting the number of particles escaping from the domain toward
the exit among 10 particles issued at the inlet. We confirm that
particles experience stagnant behavior near the exit of the rotating
blades where the centrifugal force acting on the particles is in balance
with the air drag force. It was also found that the minimum particle
size that can be separated by the machine with the rotor is determined
by its capability to stay at the outlet of the rotor channels.
Abstract: In this paper, we introduce a flexible box erecting
machine (BEM) that swiftly and automatically transforms cardboard
into a three dimensional box. Recently, the parcel service and
home-shopping industries have grown rapidly, and there is an
increasing need for various box types to ship various products.
However, workers cannot fold thousands of boxes manually in a day.
As such, automatic BEMs are garnering greater attention. This study
takes equipment operation into consideration as well as mechanical
improvements in order to design a BEM that is able to outperform its
conventional counterparts. We analyzed six dispatching rules – First In
First Out (FIFO), Shortest Processing Time (SPT), Earliest Due Date
(EDD), Setup Avoidance, EDD + SPT, and EDD + Setup Avoidance –
to determine which one was most suitable for BEM operation.
Consequently, SPT and Setup Avoidance were found to be the most
critical rules, followed by EDD + Setup Avoidance, EDD + SPT,
EDD, and FIFO. This hierarchy was valid for both our conventional
BEM and our new flexible BEM from the viewpoint of processing
time. We believe that this research can contribute to flexible BEM
management, which has the potential to increase productivity and
convenience.