Abstract: Compressor fans in modern aircraft engines are of considerate importance, as they provide majority of thrust required by the aircraft. Their challenging environment is frequently subjected to non-uniform inflow conditions. These conditions could be either due to the flight operating requirements such as take-off and landing, wake interference from aircraft fuselage or cross-flow wind conditions. So, in highly maneuverable flights regimes of fighter aircrafts affects the overall performance of an engine. Since the flow in compressor of an aircraft application is highly sensitive because of adverse pressure gradient due to different flow orientations of the aircraft. Therefore, it is prone to unstable operations. This paper presents the study that focuses on axial compressor response to inlet flow orientations for the range of angles as 0 to 15 degrees. For this purpose, NASA Rotor-37 was taken and CFD mesh was developed. The compressor characteristics map was generated for the design conditions of pressure ratio of 2.106 with the rotor operating at rotational velocity of 17188.7 rpm using CFD simulating environment of ANSYS-CFX®. The grid study was done to see the effects of mesh upon computational solution. Then, the mesh giving the best results, (when validated with the available experimental NASA’s results); was used for further distortion analysis. The flow in the inlet nozzle was given angle orientations ranging from 0 to 15 degrees. The CFD results are analyzed and discussed with respect to stall margin and flow separations due to induced distortions.
Abstract: This research aimed to study the influences of a soot blowing operation and geometrical variables to the stress characteristic of water wall tubes located in soot blowing areas which caused the boilers of Mae Moh power plant to lose their generation hour. The research method is divided into 2 parts (a) measuring the strain on water wall tubes by using 3-element rosette strain gages orientation during a full capacity plant operation and in periods of soot blowing operations (b) creating a finite element model in order to calculate stresses on tubes and validating the model by using experimental data in a steady state plant operation. Then, the geometrical variables in the model were changed to study stresses on the tubes. The results revealed that the stress was not affected by the soot blowing process and the finite element model gave the results 1.24% errors from the experiment. The geometrical variables influenced the stress, with the most optimum tubes design in this research reduced the average stress from the present design 31.28%.
Abstract: The discharge of dye in industrial effluents is of great concern because their presence and accumulation have a toxic or carcinogenic effect on living species. The removal of such compounds at such low levels is a difficult problem. The adsorption process is an effective and attractive proposition for the treatment of dye contaminated wastewater. Activated carbon adsorption in fixed beds is a very common technology in the treatment of water and especially in processes of decolouration. However, it is expensive and the powdered one is difficult to be separated from aquatic system when it becomes exhausted or the effluent reaches the maximum allowable discharge level. The regeneration of exhausted activated carbon by chemical and thermal procedure is also expensive and results in loss of the sorbent. The focus of this research was to evaluate the adsorption potential of the raw clay in removing rhodamine B from aqueous solutions using a laboratory fixed-bed column. The continuous sorption process was conducted in this study in order to simulate industrial conditions. The effect of process parameters, such as inlet flow rate, adsorbent bed height, and initial adsorbate concentration on the shape of breakthrough curves was investigated. A glass column with an internal diameter of 1.5 cm and height of 30 cm was used as a fixed-bed column. The pH of feed solution was set at 8.5. Experiments were carried out at different bed heights (5 - 20 cm), influent flow rates (1.6- 8 mL/min) and influent rhodamine B concentrations (20 - 80 mg/L). The obtained results showed that the adsorption capacity increases with the bed depth and the initial concentration and it decreases at higher flow rate. The column regeneration was possible for four adsorption–desorption cycles. The clay column study states the value of the excellent adsorption capacity for the removal of rhodamine B from aqueous solution. Uptake of rhodamine B through a fixed-bed column was dependent on the bed depth, influent rhodamine B concentration, and flow rate.
Abstract: The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.
Abstract: In this paper we presented a new method for tracking
flying targets in color video sequences based on contour and kernel.
The aim of this work is to overcome the problem of losing target in
changing light, large displacement, changing speed, and occlusion.
The proposed method is made in three steps, estimate the target
location by particle filter, segmentation target region using neural
network and find the exact contours by greedy snake algorithm. In
the proposed method we have used both region and contour
information to create target candidate model and this model is
dynamically updated during tracking. To avoid the accumulation of
errors when updating, target region given to a perceptron neural
network to separate the target from background. Then its output used
for exact calculation of size and center of the target. Also it is used as
the initial contour for the greedy snake algorithm to find the exact
target's edge. The proposed algorithm has been tested on a database
which contains a lot of challenges such as high speed and agility of
aircrafts, background clutter, occlusions, camera movement, and so
on. The experimental results show that the use of neural network
increases the accuracy of tracking and segmentation.
Abstract: This paper considers people’s driving skills
diagnosis under real driving conditions. In that sense, this research
presents an approach that uses GPS signals which have a direct
correlation with driving maneuvers. Besides, it is presented a novel
expert-driving-criteria approximation using fuzzy logic which
seeks to analyze GPS signals in order to issue an intelligent driving
diagnosis.
Based on above, this works presents in the first section the
intelligent driving diagnosis system approach in terms of its own
characteristics properties, explaining in detail significant
considerations about how an expert-driving-criteria approximation
must be developed. In the next section, the implementation of our
developed system based on the proposed fuzzy logic approach is
explained. Here, a proposed set of rules which corresponds to a
quantitative abstraction of some traffics laws and driving secure
techniques seeking to approach an expert-driving- criteria
approximation is presented.
Experimental testing has been performed in real driving
conditions. The testing results show that the intelligent driving
diagnosis system qualifies driver’s performance quantitatively with
a high degree of reliability.
Abstract: This paper presents an analytical study on the
behavior of reinforced concrete walls with rectangular cross section.
Several experiments on such walls have been selected to be studied.
Database from various experiments were collected and nominal shear
wall strengths have been calculated using formulas, such as those of
the ACI (American), NZS (New Zealand), Mexican (NTCC), and
Wood and Barda equations. Subsequently, nominal shear wall
strengths from the formulas were compared with the ultimate shear
wall strengths from the database. These formulas vary substantially in
functional form and do not account for all variables that affect the
response of walls. There is substantial scatter in the predicted values
of ultimate shear strength. Two new semi empirical equations are
developed using data from tests of 57 walls for transitions walls and
27 for slender walls with the objective of improving the prediction of
peak strength of walls with the most possible accurate.
Abstract: During manned exploration of space, missions will require astronaut crewmembers to perform Extra Vehicular Activities (EVAs) for a variety of tasks. These EVAs take place after long periods of operations in space, and in and around unique vehicles, space structures and systems. Considering the remoteness and time spans in which these vehicles will operate, EVA system operations should utilize common worksites, tools and procedures as much as possible to increase the efficiency of training and proficiency in operations. All of the preparations need to be carried out based on studies of astronaut motions. Until now, development and training activities associated with the planned EVAs in Russian and U.S. space programs have relied almost exclusively on physical simulators. These experimental tests are expensive and time consuming. During the past few years a strong increase has been observed in the use of computer simulations due to the fast developments in computer hardware and simulation software. Based on this idea, an effort to develop a computational simulation system to model human dynamic motion for EVA is initiated. This study focuses on the simulation of an astronaut moving the orbital replaceable units into the worksites or removing them from the worksites. Our physics-based methodology helps fill the gap in quantitative analysis of astronaut EVA by providing a multisegment human arm model. Simulation work described in the study improves on the realism of previous efforts, incorporating joint stops to account for the physiological limits of range of motion. To demonstrate the utility of this approach human arm model is simulated virtually using ADAMS/LifeMOD® software. Kinematic mechanism for the astronaut’s task is studied from joint angles and torques. Simulation results obtained is validated with numerical simulation based on the principles of Newton-Euler method. Torques determined using mathematical model are compared among the subjects to know the grace and consistency of the task performed. We conclude that due to uncertain nature of exploration-class EVA, a virtual model developed using multibody dynamics approach offers significant advantages over traditional human modeling approaches.
Abstract: In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.
Abstract: Background: Plantar pressure measurement is an effective method for assessing plantar loading and can be applied to evaluating movement performance of the foot. The purpose of this study is to explore the sprint athletes’ plantar loading characteristics and pain profiles in static standing. Methods: Experiments were undertaken on 80 first-division college sprint athletes and 85 healthy non-sprinters. ‘JC Mat’, the optical plantar pressure measurement was applied to examining the differences between both groups in the arch index (AI), three regional and six distinct sub-regional plantar pressure distributions (PPD), and footprint characteristics. Pain assessment and self-reported health status in sprint athletes were examined for evaluating their common pain areas. Results: Findings from the control group, the males’ AI fell into the normal range. Yet, the females’ AI was classified as the high-arch type. AI values of the sprint group were found to be significantly lower than the control group. PPD were higher at the medial metatarsal bone of both feet and the lateral heel of the right foot in the sprint group, the males in particular, whereas lower at the medial and lateral longitudinal arches of both feet. Footprint characteristics tended to support the results of the AI and PPD, and this reflected the corresponding pressure profiles. For the sprint athletes, the lateral knee joint and biceps femoris were the most common musculoskeletal pains. Conclusions: The sprint athletes’ AI were generally classified as high arches, and that their PPD were categorized between the features of runners and high-arched runners. These findings also correspond to the profiles of patellofemoral pain syndrome (PFPS)-related plantar pressure. The pain profiles appeared to correspond to the symptoms of high-arched runners and PFPS. The findings reflected upon the possible link between high arches and PFPS. The correlation between high-arched runners and PFPS development is worth further studies.
Abstract: Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.
Abstract: The objective of this study was to synthesize and characterize 5-acryloyloxy-3,4-dichlorocrotonolactone (a furanone derivative), use this derivative to modify a dental restorative, and study the effect of the derivative on the antibacterial activity and compressive strength of the formed restorative. In this study, a furanone derivative was synthesized, characterized, and used to formulate a dental restorative. Compressive strength (CS) and S. mutans viability were used to evaluate the mechanical strength and antibacterial activity of the formed restorative. The fabricated restorative specimens were photocured and conditioned in distilled water at 37oC for 24 h, followed by direct testing for CS or/and incubating with S. mutans for 48 h for antibacterial testing. The results show that the modified dental restorative showed a significant antibacterial activity without substantially decreasing the mechanical strengths. With addition of the antibacterial derivative up to 30%, the restorative kept its original CS nearly unchanged but showed a significant antibacterial activity with 68% reduction in the S. mutans viability. Furthermore, the antibacterial function of the modified restorative was not affected by human saliva. The aging study also indicates that the modified restorative may have a long-lasting antibacterial function. It is concluded that this experimental antibacterial restorative may potentially be developed into a clinically attractive dental filling restorative due to its high mechanical strength and antibacterial function.
Abstract: The present paper discusses the prediction of gas-liquid two-phase frictional pressure drop in a 2.12 mm horizontal circular minichannel using Artificial Neural Network (ANN). The experimental results are obtained with air as gas phase and water as liquid phase. The superficial gas velocity is kept in the range of 0.0236 m/s to 0.4722 m/s while the values of 0.0944 m/s, 0.1416 m/s and 0.1889 m/s are considered for superficial liquid velocity. The experimental results are predicted using different Artificial Neural Network (ANN) models. Networks used for prediction are radial basis, generalised regression, linear layer, cascade forward back propagation, feed forward back propagation, feed forward distributed time delay, layer recurrent, and Elman back propagation. Transfer functions used for networks are Linear (PURELIN), Logistic sigmoid (LOGSIG), tangent sigmoid (TANSIG) and Gaussian RBF. Combination of networks and transfer functions give different possible neural network models. These models are compared for Mean Absolute Relative Deviation (MARD) and Mean Relative Deviation (MRD) to identify the best predictive model of ANN.
Abstract: In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.
Abstract: In many practical situations, bubbles are dispersed in a
liquid phase. Understanding these complex bubbly flows is therefore
a key issue for applications such as shell and tube heat exchangers,
mineral flotation and oxidation in water treatment. Although a large
body of work exists for bubbles rising in an unbounded medium,
that of bubbles rising in constricted geometries has received less
attention. The particular case of a bubble sliding underneath an
inclined surface is common to two-phase flow systems. The current
study intends to expand this knowledge by performing experiments
to quantify the streamwise flow structures associated with a single
sliding air bubble under an inclined surface in quiescent water. This
is achieved by means of two-dimensional, two-component particle
image velocimetry (PIV), performed with a continuous wave laser
and high-speed camera. PIV vorticity fields obtained in a plane
perpendicular to the sliding surface show that there is significant bulk
fluid motion away from the surface. The associated momentum of the
bubble means that this wake motion persists for a significant time
before viscous dissipation. The magnitude and direction of the flow
structures in the streamwise measurement plane are found to depend
on the point on its path through which the bubble enters the plane.
This entry point, represented by a phase angle, affects the nature and
strength of the vortical structures. This study reconstructs the vorticity
field in the wake of the bubble, converting the field at different
instances in time to slices of a large-scale wake structure. This is, in
essence, Taylor’s ”frozen turbulence” hypothesis. Applying this to the
vorticity fields provides a pseudo three-dimensional representation
from 2-D data, allowing for a more intuitive understanding of the
bubble wake. This study provides insights into the complex dynamics
of a situation common to many engineering applications, particularly
shell and tube heat exchangers in the nucleate boiling regime.
Abstract: Fault diagnosis of Linear Parameter-Varying (LPV)
system using an adaptive Kalman filter is proposed. The LPV model
is comprised of scheduling parameters, and the emulator parameters.
The scheduling parameters are chosen such that they are capable of
tracking variations in the system model as a result of changes in the
operating regimes. The emulator parameters, on the other hand,
simulate variations in the subsystems during the identification phase
and have negligible effect during the operational phase. The nominal
model and the influence vectors, which are the gradient of the feature
vector respect to the emulator parameters, are identified off-line from
a number of emulator parameter perturbed experiments. A Kalman
filter is designed using the identified nominal model. As the system
varies, the Kalman filter model is adapted using the scheduling
variables. The residual is employed for fault diagnosis. The
proposed scheme is successfully evaluated on simulated system as
well as on a physical process control system.
Abstract: To increase the temperature contrast in thermal
images, the characteristics of the electrical conductivity and thermal
imaging modalities can be combined. In this experimental study, it is
objected to observe whether the temperature contrast created by the
tumor tissue can be improved just due to the current application
within medical safety limits. Various thermal breast phantoms are
developed to simulate the female breast tissue. In vitro experiments
are implemented using a thermal infrared camera in a controlled
manner. Since experiments are implemented in vitro, there is no
metabolic heat generation and blood perfusion. Only the effects and
results of the electrical stimulation are investigated. Experimental
study is implemented with two-dimensional models. Temperature
contrasts due to the tumor tissues are obtained. Cancerous tissue is
determined using the difference and ratio of healthy and tumor
images. 1 cm diameter single tumor tissue causes almost 40 °mC
temperature contrast on the thermal-breast phantom. Electrode
artifacts are reduced by taking the difference and ratio of background
(healthy) and tumor images. Ratio of healthy and tumor images show
that temperature contrast is increased by the current application.
Abstract: In this study, the influence of rolling process parameters such as the work roll cross angle and work roll shifting value on the strip shape and profile of aluminum have been investigated under dry conditions at a speed ratio of 1.3 using Hille 100 experimental mill. The strip profile was found to improve significantly with increase in work roll cross angle from 0o to 1o, with an associated decrease in rolling force. The effect of roll shifting (from 0 to 8mm) was not as significant as the roll cross angle. However, an increase in work roll shifting value achieved a similar decrease in rolling force as that of work roll cross angle. The effect of work roll shifting was also found to be maximum at an optimum roll speed of 0.0986 m/s for the desired thickness. Of all these parameters, the most significant effect of the strip shape profile was observed with variation of work roll cross angle. However, the rolling force can be a significantly reduced by either increasing the the work roll cross angle or work roll shifting.
Abstract: For briquetting of metal chips are used hydraulic and
mechanical presses. The density of the briquettes in this case is about
60% - 70 % on the density of solid metal. In this work are presented
the results of experimental studies for briquetting of metal chips, by
using a new technology for impact briquetting. The used chips are by
Armco iron, steel, cast iron, copper, aluminum and brass. It has been
found that: (i) in a controlled impact the density of the briquettes can
be increases up to 30%; (ii) at the same specific impact energy Es
(J/sm3) the density of the briquettes increases with increasing of the
impact velocity; (iii), realization of the repeated impact leads to
decrease of chips density, which can be explained by distribution of
elastic waves in the briquette.
Abstract: Texture is an important characteristic in real and
synthetic scenes. Texture analysis plays a critical role in inspecting
surfaces and provides important techniques in a variety of
applications. Although several descriptors have been presented to
extract texture features, the development of object recognition is still a
difficult task due to the complex aspects of texture. Recently, many
robust and scaling-invariant image features such as SIFT, SURF and
ORB have been successfully used in image retrieval and object
recognition. In this paper, we have tried to compare the performance
for texture classification using these feature descriptors with k-means
clustering. Different classifiers including K-NN, Naive Bayes, Back
Propagation Neural Network , Decision Tree and Kstar were applied in
three texture image sets － UIUCTex, KTH-TIPS and Brodatz,
respectively. Experimental results reveal SIFTS as the best average
accuracy rate holder in UIUCTex, KTH-TIPS and SURF is
advantaged in Brodatz texture set. BP neuro network works best in the
test set classification among all used classifiers.