Abstract: With the globalized production and logistics
environment, the need for reducing the product development interval
and lead time, having a faster response to orders, conforming to quality
standards, fair tracking, and boosting information exchanging
activities with customers and partners, and coping with changes in the
management environment, manufacturers are in dire need of an
information management system in their manufacturing environments.
There are lots of information systems that have been designed to
manage the condition or operation of equipment in the field but
existing systems have a decentralized architecture, which is not
unified. Also, these systems cannot effectively handle the status data
extraction process upon encountering a problem related to protocols or
changes in the equipment or the setting. In this regard, this paper will
introduce a system for processing and saving the status info of
production equipment, which uses standard representation formats, to
enable flexible responses to and support for variables in the field
equipment. This system can be used for a variety of manufacturing and
equipment settings and is capable of interacting with higher-tier
systems such as MES.
Abstract: This paper presents an investigation into the design of a flight control system, using a robust sliding mode control structure, designed using the exact feedback linearization procedure of the dynamic of a small-size autonomous helicopter in hover. The robustness of the controller in the context of stabilization and trajectory tracking with respect to small body forces and air resistance on the main and tail rotor, is analytically proved using Lyapunov approach. Some simulation results are presented to illustrate the performance and robustness of such controller in the presence of small body forces and air resistance.
Abstract: This study examined the role of driving experience in hazard perception and categorization using traffic scene pictures. Specifically, young-inexperienced, moderately experienced and very experienced (taxi) drivers observed traffic scene pictures while connected to an eye tracking system and were asked to rate the level of hazardousness of each picture and to mention the three most prominent hazards in it. Target pictures included nine, nearly identical, pairs of pictures where one picture in each pair included an actual hazard as an additional element. Altogether, 22 areas of interest (AOIs) were predefined and included 13 potential hazards and 9 actual hazards. Data analysis included both verbal reports and eye scanning patterns of these AOIs. Generally, both experienced and taxi drivers noted a relatively larger number of potential hazards than young inexperienced drivers Thus, by relating to less salient potential hazards, experienced drivers have demonstrated a better situation model of the traffic environment.
Abstract: Wireless Sensor networks have a wide spectrum of civil and military applications that call for secure communication such as the terrorist tracking, target surveillance in hostile environments. For the secure communication in these application areas, we propose a method for generating a hierarchical key structure for the efficient group key management. In this paper, we apply A* algorithm in generating a hierarchical key structure by considering the history data of the ratio of addition and eviction of sensor nodes in a location where sensor nodes are deployed. Thus generated key tree structure provides an efficient way of managing the group key in terms of energy consumption when addition and eviction event occurs. A* algorithm tries to minimize the number of messages needed for group key management by the history data. The experimentation with the tree shows efficiency of the proposed method.
Abstract: An adaptive neural network controller for
autonomous underwater vehicles (AUVs) is presented in this paper.
The AUV model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
In this regards, a nonlinear neural network is used to approximate the
nonlinear uncertainties of AUV dynamics, thus overcoming some
limitations of conventional controllers and ensure good performance.
The uniform ultimate boundedness of AUV tracking errors and the
stability of the proposed control system are guaranteed based on
Lyapunov theory. Numerical simulation studies for motion control of
an AUV are performed to demonstrate the effectiveness of the
proposed controller.
Abstract: In this paper the problem of estimating the time delay
between two spatially separated noisy sinusoidal signals by system
identification modeling is addressed. The system is assumed to be
perturbed by both input and output additive white Gaussian noise. The
presence of input noise introduces bias in the time delay estimates.
Normally the solution requires a priori knowledge of the input-output
noise variance ratio. We utilize the cascade of a self-tuned filter with
the time delay estimator, thus making the delay estimates robust to
input noise. Simulation results are presented to confirm the superiority
of the proposed approach at low input signal-to-noise ratios.
Abstract: Electronic nose (array of chemical sensors) are widely
used in food industry and pollution control. Also it could be used to
locate or detect the direction of the source of emission odors. Usually
this task is performed by electronic nose (ENose) cooperated with
mobile vehicles, but when a source is instantaneous or surrounding is
hard for vehicles to reach, problem occurs. Thus a method for
stationary ENose to detect the direction of the source and locate the
source will be required. A novel method which uses the ratio between
the responses of different sensors as a discriminant to determine the
direction of source in natural wind surroundings is presented in this
paper. The result shows that the method is accurate and easily to be
implemented. This method could be also used in movably, as an
optimized algorithm for robot tracking source location.
Abstract: In this paper, we consider the problem of tracking
multiple maneuvering targets using switching multiple target motion
models. With this paper, we aim to contribute in solving the problem
of model-based body motion estimation by using data coming from
visual sensors. The Interacting Multiple Model (IMM) algorithm is
specially designed to track accurately targets whose state and/or
measurement (assumed to be linear) models changes during motion
transition. However, when these models are nonlinear, the IMM
algorithm must be modified in order to guarantee an accurate track.
In this paper we propose to avoid the Extended Kalman filter because
of its limitations and substitute it with the Unscented Kalman filter
which seems to be more efficient especially according to the
simulation results obtained with the nonlinear IMM algorithm (IMMUKF).
To resolve the problem of data association, the JPDA
approach is combined with the IMM-UKF algorithm, the derived
algorithm is noted JPDA-IMM-UKF.
Abstract: The problem of manipulator control is a highly
complex problem of controlling a system which is multi-input, multioutput,
non-linear and time variant. In this paper some adaptive
fuzzy, and a new hybrid fuzzy control algorithm have been
comparatively evaluated through simulations, for manipulator
control. The adaptive fuzzy controllers consist of self-organizing,
self-tuning, and coarse/fine adaptive fuzzy schemes. These
controllers are tested for different trajectories and for varying
manipulator parameters through simulations. Various performance
indices like the RMS error, steady state error and maximum error are
used for comparison. It is observed that the self-organizing fuzzy
controller gives the best performance. The proposed hybrid fuzzy
plus integral error controller also performs remarkably well, given its
simple structure.
Abstract: The aim of this work is to study the elastic transfer
phenomenon which takes place in the elastic scattering of 16O on 12C
at energies near the Coulomb barrier. Where, the angular distribution
decrease steadily with increasing the scattering angle, then the cross
section will increase at backward angles due to the α-transfer process.
This reaction was also studied at different energies for tracking the
nuclear rainbow phenomenon. The experimental data of the angular
distribution at these energies were compared to the calculation
predictions. The optical potential codes such as SPIVAL and
Distorted Wave Born Approximation (DWUCK5) were used in
analysis.
Abstract: In this work a new platform for mobile-health systems is
presented. System target application is providing decision support to
rescue corps or military medical personnel in combat areas. Software
architecture relies on a distributed client-server system that manages a
wireless ad-hoc networks hierarchy in which several different types of
client operate. Each client is characterized for different hardware and
software requirements. Lower hierarchy levels rely in a network of
completely custom devices that store clinical information and patient
status and are designed to form an ad-hoc network operating in the
2.4 GHz ISM band and complying with the IEEE 802.15.4 standard
(ZigBee). Medical personnel may interact with such devices, that are
called MICs (Medical Information Carriers), by means of a PDA
(Personal Digital Assistant) or a MDA (Medical Digital Assistant),
and transmit the information stored in their local databases as well as
issue a service request to the upper hierarchy levels by using IEEE
802.11 a/b/g standard (WiFi). The server acts as a repository that
stores both medical evacuation forms and associated events (e.g., a
teleconsulting request). All the actors participating in the diagnostic
or evacuation process may access asynchronously to such repository
and update its content or generate new events. The designed system
pretends to optimise and improve information spreading and flow
among all the system components with the aim of improving both
diagnostic quality and evacuation process.
Abstract: This paper suggests a fast and stable Target Tracking
system in collaborative control of UAV and UGV. Wi-Fi communication range is limited in collaborative control of UAV and UGV. Thus, to secure a stable communications, UAV and UGV have
to be kept within a certain distance from each other. But existing
method which uses UAV Vertical Camera to follow the motion of
UGV is likely to lose a target with a sudden movement change.
Eventually, UGV has disadvantages that it could only move at a low
speed and not make any sudden change of direction in order to keep
track of the target. Therefore, we suggest utilizing AR Drone UAV front camera to track fast-moving and Omnidirectional Mecanum
Wheel UGV.
Abstract: This paper is concerned with studying the forgetting factor of the recursive least square (RLS). A new dynamic forgetting factor (DFF) for RLS algorithm is presented. The proposed DFF-RLS is compared to other methods. Better performance at convergence and tracking of noisy chirp sinusoid is achieved. The control of the forgetting factor at DFF-RLS is based on the gradient of inverse correlation matrix. Compared with the gradient of mean square error algorithm, the proposed approach provides faster tracking and smaller mean square error. In low signal-to-noise ratios, the performance of the proposed method is superior to other approaches.
Abstract: In this paper, we introduce a novel algorithm for object tracking in video sequence. In order to represent the object to be tracked, we propose a spatial color histogram model which encodes both the color distribution and spatial information. The object tracking from frame to frame is accomplished via center voting and back projection method. The center voting method has every pixel in the new frame to cast a vote on whereabouts the object center is. The back projection method segments the object from the background. The segmented foreground provides information on object size and orientation, omitting the need to estimate them separately. We do not put any assumption on camera motion; the proposed algorithm works equally well for object tracking in both static and moving camera videos.
Abstract: Shadow detection is still considered as one of the
potential challenges for intelligent automated video surveillance
systems. A pre requisite for reliable and accurate detection and
tracking is the correct shadow detection and classification. In such a
landscape of conditions, privacy issues add more and more
complexity and require reliable shadow detection.
In this work the intertwining between security, accuracy,
reliability and privacy is analyzed and, accordingly, a novel
architecture for Privacy Enhancing Video Surveillance (PEVS) is
introduced. Shadow detection and masking are dealt with through the
combination of two different approaches simultaneously. This results
in a unique privacy enhancement, without affecting security.
Subsequently, the methodology was employed successfully in a
large-scale wireless video surveillance system; privacy relevant
information was stored and encrypted on the unit, without
transferring it over an un-trusted network.
Abstract: As a part of the development of a numerical method of
close capture exhausts systems for machining devices, a test rig
recreating a situation similar to a grinding operation, but in a
perfectly controlled environment, is used. The properties of the
obtained spray of solid particles are initially characterized using
particle tracking velocimetry (PTV), in order to obtain input and
validation parameters for numerical simulations. The dispersion of a
tracer gas (SF6) emitted simultaneously with the particle jet is then
studied experimentally, as the dispersion of such a gas is
representative of that of finer particles, whose aerodynamic response
time is negligible. Finally, complete modeling of the test rig is
achieved to allow comparison with experimental results and thus to
progress towards validation of the models used to describe a twophase
flow generated by machining operation.
Abstract: Real-time object tracking is a problem which involves extraction of critical information from complex and uncertain imagedata. In this paper, we present a comprehensive methodology to design an artificial neural network (ANN) for a real-time object tracking application. The object, which is tracked for the purpose of demonstration, is a specific airplane. However, the proposed ANN can be trained to track any other object of interest. The ANN has been simulated and tested on the training and testing datasets, as well as on a real-time streaming video. The tracking error is analyzed with post-regression analysis tool, which finds the correlation among the calculated coordinates and the correct coordinates of the object in the image. The encouraging results from the computer simulation and analysis show that the proposed ANN architecture is a good candidate solution to a real-time object tracking problem.
Abstract: The colors of the human skin represent a special
category of colors, because they are distinctive from the colors of
other natural objects. This category is found as a cluster in color
spaces, and the skin color variations between people are mostly due
to differences in the intensity. Besides, the face detection based on
skin color detection is a faster method as compared to other
techniques. In this work, we present a system to track faces by
carrying out skin color detection in four different color spaces: HSI,
YCbCr, YES and RGB. Once some skin color regions have been
detected for each color space, we label each and get some
characteristics such as size and position. We are supposing that a face
is located in one the detected regions. Next, we compare and employ
a polling strategy between labeled regions to determine the final
region where the face effectively has been detected and located.
Abstract: Physiological control of a left ventricle assist device (LVAD) is generally a complicated task due to diverse operating environments and patient variability. In this work, a tracking control algorithm based on sliding mode and feed forward control for a class of discrete-time single input single output (SISO) nonlinear uncertain systems is presented. The controller was developed to track the reference trajectory to a set operating point without inducing suction in the ventricle. The controller regulates the estimated mean pulsatile flow Qp and mean pulsatility index of pump rotational speed PIω that was generated from a model of the assist device. We recall the principle of the sliding mode control theory then we combine the feed-forward control design with the sliding mode control technique to follow the reference trajectory. The uncertainty is replaced by its upper and lower boundary. The controller was tested in a computer simulation covering two scenarios (preload and ventricular contractility). The simulation results prove the effectiveness and the robustness of the proposed controller
Abstract: This paper introduces an intelligent system, which can be applied in the monitoring of vehicle speed using a single camera. The ability of motion tracking is extremely useful in many automation problems and the solution to this problem will open up many future applications. One of the most common problems in our daily life is the speed detection of vehicles on a highway. In this paper, a novel technique is developed to track multiple moving objects with their speeds being estimated using a sequence of video frames. Field test has been conducted to capture real-life data and the processed results were presented. Multiple object problems and noisy in data are also considered. Implementing this system in real-time is straightforward. The proposal can accurately evaluate the position and the orientation of moving objects in real-time. The transformations and calibration between the 2D image and the actual road are also considered.