Abstract: This case study features the Kuehne + Nagel PharmaChain solution for ‘cold chain’ pharmaceutical and biologic product shipments with IOT-enabled features for shipment temperature and location tracking. Using the case study method and content analysis, this research project investigates the application of the structurational model of technology theory introduced by Orlikowski in order to interpret the firm’s entry and participation in the IOT-impelled marketplace.
Abstract: In this paper, type-2 fuzzy logic control (T2FLC) and neuro-fuzzy control (NFC) for a doubly fed induction generator (DFIG) based on direct power control (DPC) with a fixed switching frequency is proposed for wind generation application. First, a mathematical model of the doubly-fed induction generator implemented in d-q reference frame is achieved. Then, a DPC algorithm approach for controlling active and reactive power of DFIG via fixed switching frequency is incorporated using PID. The performance of T2FLC and NFC, which is based on the DPC algorithm, are investigated and compared to those obtained from the PID controller. Finally, simulation results demonstrate that the NFC is more robust, superior dynamic performance for wind power generation system applications.
Abstract: Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.
Abstract: 3D model-based vehicle matching provides a new way
for vehicle recognition, localization and tracking. Its key is to
construct an evaluation function, also called fitness function, to
measure the degree of vehicle matching. The existing fitness functions
often poorly perform when the clutter and occlusion exist in traffic
scenarios. In this paper, we present a practical and efficient fitness
function. Unlike the existing evaluation functions, the proposed
fitness function is to study the vehicle matching problem from
both local and global perspectives, which exploits the pixel gradient
information as well as the silhouette information. In view of the
discrepancy between 3D vehicle model and real vehicle, a weighting
strategy is introduced to differently treat the fitting of the model’s
wireframes. Additionally, a normalization operation for the model’s
projection is performed to improve the accuracy of the matching.
Experimental results on real traffic videos reveal that the proposed
fitness function is efficient and robust to the cluttered background
and partial occlusion.
Abstract: A conventional optical coherence tomography (OCT) system has limited imaging depth, which is 1-2 mm, and suffers unwanted noise such as speckle noise. The motorized-stage-based OCT system, using a common-path Fourier-domain optical coherence tomography (CP-FD-OCT) configuration, provides enhanced imaging depth and less noise so that we can overcome these limitations. Using this OCT systems, OCT images were obtained from an onion, and their subsurface structure was observed. As a result, the images obtained using the developed motorized-stage-based system showed enhanced imaging depth than the conventional system, since it is real-time accurate depth tracking. Consequently, the developed CP-FD-OCT systems and algorithms have good potential for the further development of endoscopic OCT for microsurgery.
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: In this paper, autonomous performance of a small
manufactured unmanned helicopter is tried to be increased. For this
purpose, a small unmanned helicopter is manufactured in Erciyes
University, Faculty of Aeronautics and Astronautics. It is called as
ZANKA-Heli-I. For performance maximization, autopilot parameters
are determined via minimizing a cost function consisting of flight
performance parameters such as settling time, rise time, overshoot
during trajectory tracking. For this purpose, a stochastic optimization
method named as simultaneous perturbation stochastic approximation
is benefited. Using this approach, considerable autonomous
performance increase (around %23) is obtained.
Abstract: This paper describes a sliding mode controller for
autonomous underwater vehicles (AUVs). The dynamic of 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.
To address these difficulties, a nonlinear sliding mode controller is
designed to approximate the nonlinear dynamics of AUV and
improve trajectory tracking. Moreover, the proposed controller can
profoundly attenuate the effects of uncertainties and external
disturbances in the closed-loop system. Using the Lyapunov theory
the boundedness of AUV tracking errors and the stability of the
proposed control system are also guaranteed. Numerical simulation
studies of an AUV are included to illustrate the effectiveness of the
presented approach.
Abstract: In this paper, we present a four-step ortho-rectification
procedure for real-time geo-referencing of video data from a low-cost
UAV equipped with a multi-sensor system. The basic procedures for
the real-time ortho-rectification are: (1) decompilation of the video
stream into individual frames; (2) establishing the interior camera
orientation parameters; (3) determining the relative orientation
parameters for each video frame with respect to each other; (4)
finding the absolute orientation parameters, using a self-calibration
bundle and adjustment with the aid of a mathematical model. Each
ortho-rectified video frame is then mosaicked together to produce a
mosaic image of the test area, which is then merged with a well
referenced existing digital map for the purpose of geo-referencing
and aerial surveillance. A test field located in Abuja, Nigeria was
used to evaluate our method. Video and telemetry data were collected
for about fifteen minutes, and they were processed using the four-step
ortho-rectification procedure. The results demonstrated that the
geometric measurement of the control field from ortho-images is
more accurate when compared with those from original perspective
images when used to pin point the exact location of targets on the
video imagery acquired by the UAV. The 2-D planimetric accuracy
when compared with the 6 control points measured by a GPS receiver
is between 3 to 5 metres.
Abstract: Driver fatigue is an important factor in the increasing
number of road accidents. Dynamic template matching method was
proposed to address the problem of real-time driver fatigue detection
system based on eye-tracking. An effective vision based approach
was used to analyze the driver’s eye state to detect fatigue. The driver
fatigue system consists of Face detection, Eye detection, Eye
tracking, and Fatigue detection. Initially frames are captured from a
color video in a car dashboard and transformed from RGB into YCbCr
color space to detect the driver’s face. Canny edge operator was used
to estimating the eye region and the locations of eyes are extracted.
The extracted eyes were considered as a template matching for eye
tracking. Edge Map Overlapping (EMO) and Edge Pixel Count
(EPC) matching function were used for eye tracking which is used to
improve the matching accuracy. The pixel of eyeball was tracked
from the eye regions which are used to determine the fatigue state of
the driver.
Abstract: The paper presents a method for a simple and
immediate motion planning of a SCARA robot, whose end-effector
has to move along a given trajectory; the calculation procedure
requires the user to define in analytical form or by points the
trajectory to be followed and to assign the curvilinear abscissa as
function of the time. On the basis of the geometrical characteristics
of the robot, a specifically developed program determines the motion
laws of the actuators that enable the robot to generate the required
movement; this software can be used in all industrial applications for
which a SCARA robot has to be frequently reprogrammed, in order
to generate various types of trajectories with different motion times.
Abstract: Human motion capture has become one of the major
area of interest in the field of computer vision. Some of the major
application areas that have been rapidly evolving include the
advanced human interfaces, virtual reality and security/surveillance
systems. This study provides a brief overview of the techniques and
applications used for the markerless human motion capture, which
deals with analyzing the human motion in the form of mathematical
formulations. The major contribution of this research is that it
classifies the computer vision based techniques of human motion
capture based on the taxonomy, and then breaks its down into four
systematically different categories of tracking, initialization, pose
estimation and recognition. The detailed descriptions and the
relationships descriptions are given for the techniques of tracking and
pose estimation. The subcategories of each process are further
described. Various hypotheses have been used by the researchers in
this domain are surveyed and the evolution of these techniques have
been explained. It has been concluded in the survey that most
researchers have focused on using the mathematical body models for
the markerless motion capture.
Abstract: Due to the large amount of information in the World
Wide Web (WWW, web) and the lengthy and usually linearly
ordered result lists of web search engines that do not indicate
semantic relationships between their entries, the search for topically
similar and related documents can become a tedious task. Especially,
the process of formulating queries with proper terms representing
specific information needs requires much effort from the user. This
problem gets even bigger when the user's knowledge on a subject and
its technical terms is not sufficient enough to do so. This article
presents the new and interactive search application DocAnalyser that
addresses this problem by enabling users to find similar and related
web documents based on automatic query formulation and state-ofthe-
art search word extraction. Additionally, this tool can be used to
track topics across semantically connected web documents.
Abstract: We developed a prototype system for multiplayer RC-car driving in a collaborative augmented reality (AR) environment. The tele-existence environment is constructed by superimposing digital data onto images captured by a camera on an RC-car, enabling players to experience an augmented coexistence of the digital content and the real world. Marker-based tracking was used for estimating position and orientation of the camera. The plural RC-cars can be operated in a field where square markers are arranged. The video images captured by the camera are transmitted to a PC for visual tracking. The RC-cars are also tracked by using an infrared camera attached to the ceiling, so that the instability is reduced in the visual tracking. Multimedia data such as texts and graphics are visualized to be overlaid onto the video images in the geometrically correct manner. The prototype system allows a tele-existence sensation to be augmented in a collaborative AR environment.
Abstract: In this paper a comprehensive algorithm is presented to alleviate the undesired simultaneous effects of target maneuvering, observed glint noise distribution, and colored noise spectrum using online colored glint noise parameter estimation. The simulation results illustrate a significant reduction in the root mean square error (RMSE) produced by the proposed algorithm compared to the algorithms that do not compensate all the above effects simultaneously.
Abstract: This paper addresses the problem of determining the current 3D location of a moving object and robustly tracking it from a sequence of camera images. The approach presented here uses a particle filter and does not perform any explicit triangulation. Only the color of the object to be tracked is required, but not any precisemotion model. The observation model we have developed avoids the color filtering of the entire image. That and the Monte Carlotechniques inside the particle filter provide real time performance.Experiments with two real cameras are presented and lessons learned are commented. The approach scales easily to more than two cameras and new sensor cues.
Abstract: Motion detection is a basic operation in the selection of significant segments of the video signals. For an effective Human Computer Intelligent Interaction, the computer needs to recognize the motion and track the moving object. Here an efficient neural network system is proposed for motion detection from the static background. This method mainly consists of four parts like Frame Separation, Rough Motion Detection, Network Formation and Training, Object Tracking. This paper can be used to verify real time detections in such a way that it can be used in defense applications, bio-medical applications and robotics. This can also be used for obtaining detection information related to the size, location and direction of motion of moving objects for assessment purposes. The time taken for video tracking by this Neural Network is only few seconds.
Abstract: GSM has undoubtedly become the most widespread
cellular technology and has established itself as one of the most
promising technology in wireless communication. The next
generation of mobile telephones had also become more powerful and
innovative in a way that new services related to the user-s location
will arise. Other than the 911 requirements for emergency location
initiated by the Federal Communication Commission (FCC) of the
United States, GSM positioning can be highly integrated in cellular
communication technology for commercial use. However, GSM
positioning is facing many challenges. Issues like accuracy,
availability, reliability and suitable cost render the development and
implementation of GSM positioning a challenging task. In this paper,
we investigate the optimal mobile position tracking means. We
employ an innovative scheme by integrating the Kalman filter in the
localization process especially that it has great tracking
characteristics. When tracking in two dimensions, Kalman filter is
very powerful due to its reliable performance as it supports
estimation of past, present, and future states, even when performing
in unknown environments. We show that enhanced position tracking
results is achieved when implementing the Kalman filter for GSM
tracking.
Abstract: This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Abstract: This paper presents a particle swarm optimization
(PSO) based approach for multiple object tracking based on histogram
matching. To start with, gray-level histograms are calculated to
establish a feature model for each of the target object. The difference
between the gray-level histogram corresponding to each particle in the
search space and the target object is used as the fitness value. Multiple
swarms are created depending on the number of the target objects
under tracking. Because of the efficiency and simplicity of the PSO
algorithm for global optimization, target objects can be tracked as
iterations continue. Experimental results confirm that the proposed
PSO algorithm can rapidly converge, allowing real-time tracking of
each target object. When the objects being tracked move outside the
tracking range, global search capability of the PSO resumes to re-trace
the target objects.