Abstract: An adaptive dynamic cerebellar model articulation
controller (DCMAC) neural network used for solving the prediction
and identification problem is proposed in this paper. The proposed
DCMAC has superior capability to the conventional cerebellar model
articulation controller (CMAC) neural network in efficient learning
mechanism, guaranteed system stability and dynamic response. The
recurrent network is embedded in the DCMAC by adding feedback
connections in the association memory space so that the DCMAC
captures the dynamic response, where the feedback units act as
memory elements. The dynamic gradient descent method is adopted to
adjust DCMAC parameters on-line. Moreover, the analytical method
based on a Lyapunov function is proposed to determine the
learning-rates of DCMAC so that the variable optimal learning-rates
are derived to achieve most rapid convergence of identifying error.
Finally, the adaptive DCMAC is applied in two computer simulations.
Simulation results show that accurate identifying response and
superior dynamic performance can be obtained because of the
powerful on-line learning capability of the proposed DCMAC.
Abstract: Automotive suspension system is important part of car
comfort and safety. In this article automotive active suspension with
linear motor as actuator is designed using H-infinity control. This
paper is focused on comparison of different controller designed for
quart, half or full-car model (and always used for “full" car). Special
attention is placed on energy demand of the whole system. Each
controller configuration is simulated and then verified on the
hydraulic quarter car test bed.
Abstract: Requirements management is critical to software
delivery success and project lifecycle. Requirements management
and their traceability provide assistance for many software
engineering activities like impact analysis, coverage analysis,
requirements validation and regression testing. In addition
requirements traceability is the recognized component of many
software process improvement initiatives. Requirements traceability
also helps to control and manage evolution of a software system.
This paper aims to provide an evaluation of current requirements
management and traceability tools. Management and test managers
require an appropriate tool for the software under test. We hope,
evaluation identified here will help to select the efficient and
effective tool.
Abstract: This paper proposes a bi-objective model for the
facility location problem under a congestion system. The idea of the
model is motivated by applications of locating servers in bank
automated teller machines (ATMS), communication networks, and so
on. This model can be specifically considered for situations in which
fixed service facilities are congested by stochastic demand within
queueing framework. We formulate this model with two perspectives
simultaneously: (i) customers and (ii) service provider. The
objectives of the model are to minimize (i) the total expected
travelling and waiting time and (ii) the average facility idle-time.
This model represents a mixed-integer nonlinear programming
problem which belongs to the class of NP-hard problems. In addition,
to solve the model, two metaheuristic algorithms including nondominated
sorting genetic algorithms (NSGA-II) and non-dominated
ranking genetic algorithms (NRGA) are proposed. Besides, to
evaluate the performance of the two algorithms some numerical
examples are produced and analyzed with some metrics to determine
which algorithm works better.
Abstract: Nowadays companies strive to survive in a
competitive global environment. To speed up product
development/modifications, it is suggested to adopt a collaborative
product development approach. However, despite the advantages of
new IT improvements still many CAx systems work separately and
locally. Collaborative design and manufacture requires a product
information model that supports related CAx product data models. To
solve this problem many solutions are proposed, which the most
successful one is adopting the STEP standard as a product data model
to develop a collaborative CAx platform. However, the improvement
of the STEP-s Application Protocols (APs) over the time, huge
number of STEP AP-s and cc-s, the high costs of implementation,
costly process for conversion of older CAx software files to the STEP
neutral file format; and lack of STEP knowledge, that usually slows
down the implementation of the STEP standard in collaborative data
exchange, management and integration should be considered. In this
paper the requirements for a successful collaborative CAx system is
discussed. The STEP standard capability for product data integration
and its shortcomings as well as the dominant platforms for supporting
CAx collaboration management and product data integration are
reviewed. Finally a platform named LAYMOD to fulfil the
requirements of CAx collaborative environment and integrating the
product data is proposed. The platform is a layered platform to enable
global collaboration among different CAx software
packages/developers. It also adopts the STEP modular architecture
and the XML data structures to enable collaboration between CAx
software packages as well as overcoming the STEP standard
limitations. The architecture and procedures of LAYMOD platform
to manage collaboration and avoid contradicts in product data
integration are introduced.
Abstract: Surveillance system is widely used in the traffic
monitoring. The deployment of cameras is moving toward a
ubiquitous camera (UbiCam) environment. In our previous study, a
novel service, called GPS-VT, was firstly proposed by incorporating
global positioning system (GPS) and visual tracking techniques for
the UbiCam environment. The first prototype is called GODTA
(GPS-based Moving Object Detection and Tracking Approach). For a
moving person carried GPS-enabled mobile device, he can be
tracking when he enters the field-of-view (FOV) of a camera
according to his real-time GPS coordinate. In this paper, GPS-VT
service is applied to the tracking of vehicles. The moving speed of a
vehicle is much faster than a person. It means that the time passing
through the FOV is much shorter than that of a person. Besides, the
update interval of GPS coordinate is once per second, it is
asynchronous with the frame rate of the real-time image. The above
asynchronous is worsen by the network transmission delay. These
factors are the main challenging to fulfill GPS-VT service on a
vehicle.In order to overcome the influence of the above factors, a
back-propagation neural network (BPNN) is used to predict the
possible lane before the vehicle enters the FOV of a camera. Then, a
template matching technique is used for the visual tracking of a target
vehicle. The experimental result shows that the target vehicle can be
located and tracking successfully. The success location rate of the
implemented prototype is higher than that of the previous GODTA.
Abstract: Data warehousing success is not high enough. User
dissatisfaction and failure to adhere to time frames and budgets are
too common. Most traditional information systems practices are
rooted in hard systems thinking. Today, the great systems thinkers
are forgotten by information systems developers. A data warehouse
is still a system and it is worth investigating whether systems
thinkers such as Churchman can enhance our practices today. This
paper investigates data warehouse development practices from a
systems thinking perspective. An empirical investigation is done in
order to understand the everyday practices of data warehousing
professionals from a systems perspective. The paper presents a
model for the application of Churchman-s systems approach in data
warehouse development.
Abstract: The purpose of this paper is to analyze determinants of
information security affecting adoption of the Web-based integrated
information systems (IIS). We introduced Web-based information
systems which are designed to formulate strategic plans for Peruvian
government. Theoretical model is proposed to test impact of
organizational factors (deterrent efforts and severity; preventive
efforts) and individual factors (information security threat; security
awareness) on intentions to proactively use the Web-based IIS .Our
empirical study results highlight that deterrent efforts and deterrent
severity have no significant influence on the proactive use intentions
of IIS, whereas, preventive efforts play an important role in proactive
use intentions of IIS. Thus, we suggest that organizations need to do
preventive efforts by introducing various information security
solutions, and try to improve information security awareness while
reducing the perceived information security threats.
Abstract: When a small H/W IP is designed, we can develop an
appropriate verification environment by observing the simulated
signal waves, or using the serial test vectors for the fixed output. In the
case of design and verification of a massive parallel processor with
multiple IPs, it-s difficult to make a verification system with existing
common verification environment, and to verify each partial IP. A
TestDrive verification environment can build easy and reliable
verification system that can produce highly intuitive results by
applying Modelsim and SystemVerilog-s DPI. It shows many
advantages, for example a high-level design of a GPGPU processor
design can be migrate to FPGA board immediately.
Abstract: This paper presents the theoretical background and
the real implementation of an automated computer system to
introduce machine vision in flower, fruit and vegetable processing
for recollection, cutting, packaging, classification, or fumigation
tasks. The considerations and implementation issues presented in this
work can be applied to a wide range of varieties of flowers, fruits and
vegetables, although some of them are especially relevant due to the
great amount of units that are manipulated and processed each year
over the world. The computer vision algorithms developed in this
work are shown in detail, and can be easily extended to other
applications. A special attention is given to the electromagnetic
compatibility in order to avoid noisy images. Furthermore, real
experimentation has been carried out in order to validate the
developed application. In particular, the tests show that the method
has good robustness and high success percentage in the object
characterization.
Abstract: More and more natural disasters are happening every
year: floods, earthquakes, volcanic eruptions, etc. In order to reduce
the risk of possible damages, governments all around the world are
investing into development of Early Warning Systems (EWS) for
environmental applications. The most important task of the EWS is
identification of the onset of critical situations affecting environment
and population, early enough to inform the authorities and general
public. This paper describes an approach for monitoring of flood
protections systems based on machine learning methods. An
Artificial Intelligence (AI) component has been developed for
detection of abnormal dike behaviour. The AI module has been
integrated into an EWS platform of the UrbanFlood project (EU
Seventh Framework Programme) and validated on real-time
measurements from the sensors installed in a dike.
Abstract: Travelling salesman problem (TSP) is a combinational
optimization problem and solution approaches have been applied
many real world problems. Pure TSP assumes the cities to visit are
fixed in time and thus solutions are created to find shortest path
according to these point. But some of the points are canceled to visit
in time. If the problem is not time crucial it is not important to
determine new routing plan but if the points are changing rapidly and
time is necessary do decide a new route plan a new approach should
be applied in such cases. We developed a route plan transfer method
based on transfer learning and we achieved high performance against
determining a new model from scratch in every change.
Abstract: Nowadays social media are important tools for web
resource discovery. The performance and capabilities of web searches
are vital, especially search results from social research paper
bookmarking. This paper proposes a new algorithm for ranking
method that is a combination of similarity ranking with paper posted
time or CSTRank. The paper posted time is static ranking for
improving search results. For this particular study, the paper posted
time is combined with similarity ranking to produce a better ranking
than other methods such as similarity ranking or SimRank. The
retrieval performance of combination rankings is evaluated using
mean values of NDCG. The evaluation in the experiments implies
that the chosen CSTRank ranking by using weight score at ratio 90:10
can improve the efficiency of research paper searching on social
bookmarking websites.
Abstract: In this paper we proposed a novel method to acquire
the ROI (Region of interest) of unsupervised and touch-less palmprint
captured from a web camera in real-time. We use Viola-Jones
approach and skin model to get the target area in real time. Then an
innovative course-to-fine approach to detect the key points on the hand
is described. A new algorithm is used to find the candidate key points
coarsely and quickly. In finely stage, we verify the hand key points
with the shape context descriptor. To make the user much comfortable,
it can process the hand image with different poses, even the hand is
closed. Experiments show promising result by using the proposed
method in various conditions.
Abstract: Optical 3D measurement of objects is meaningful in
numerous industrial applications. In various cases shape acquisition
of weak textured objects is essential. Examples are repetition parts
made of plastic or ceramic such as housing parts or ceramic bottles as
well as agricultural products like tubers. These parts are often
conveyed in a wobbling way during the automated optical inspection.
Thus, conventional 3D shape acquisition methods like laser scanning
might fail. In this paper, a novel approach for acquiring 3D shape of
weak textured and moving objects is presented. To facilitate such
measurements an active stereo vision system with structured light is
proposed. The system consists of multiple camera pairs and auxiliary
laser pattern generators. It performs the shape acquisition within one
shot and is beneficial for rapid inspection tasks. An experimental
setup including hardware and software has been developed and
implemented.
Abstract: The objective of our work is to develop a new approach for discovering knowledge from a large mass of data, the result of applying this approach will be an expert system that will serve as diagnostic tools of a phenomenon related to a huge information system. We first recall the general problem of learning Bayesian network structure from data and suggest a solution for optimizing the complexity by using organizational and optimization methods of data. Afterward we proposed a new heuristic of learning a Multi-Entities Bayesian Networks structures. We have applied our approach to biological facts concerning hereditary complex illnesses where the literatures in biology identify the responsible variables for those diseases. Finally we conclude on the limits arched by this work.
Abstract: This paper compares Hilditch, Rosenfeld, Zhang-
Suen, dan Nagendraprasad Wang Gupta (NWG) thinning algorithms
for Javanese character image recognition. Thinning is an effective
process when the focus in not on the size of the pattern, but rather on
the relative position of the strokes in the pattern. The research
analyzes the thinning of 60 Javanese characters.
Time-wise, Zhang-Suen algorithm gives the best results with the
average process time being 0.00455188 seconds. But if we look at
the percentage of pixels that meet one-pixel thickness, Rosenfelt
algorithm gives the best results, with a 99.98% success rate. From the
number of pixels that are erased, NWG algorithm gives the best
results with the average number of pixels erased being 84.12%. It can
be concluded that the Hilditch algorithm performs least successfully
compared to the other three algorithms.
Abstract: Social bookmarking is an environment in which
the user gradually changes interests over time so that the tag
data associated with the current temporal period is usually more
important than tag data temporally far from the current period.
This implies that in the social tagging system, the newly tagged
items by the user are more relevant than older items. This study
proposes a novel recommender system that considers the users-
recent tag preferences. The proposed system includes the
following stages: grouping similar users into clusters using an
E-M clustering algorithm, finding similar resources based on
the user-s bookmarks, and recommending the top-N items to
the target user. The study examines the system-s information
retrieval performance using a dataset from del.icio.us, which is
a famous social bookmarking web site. Experimental results
show that the proposed system is better and more effective than
traditional approaches.
Abstract: In order to implement flexibility as well as survivable
capacities over passive optical network (PON), a new automatic
random fault-recovery mechanism with array-waveguide-grating
based (AWG-based) optical switch (OSW) is presented. Firstly,
wavelength-division-multiplexing and optical code-division
multiple-access (WDM/OCDMA) scheme are configured to meet the
various geographical locations requirement between optical network
unit (ONU) and optical line terminal (OLT). The AWG-base optical
switch is designed and viewed as central star-mesh topology to
prohibit/decrease the duplicated redundant elements such as fiber and
transceiver as well. Hence, by simple monitoring and routing switch
algorithm, random fault-recovery capacity is achieved over
bi-directional (up/downstream) WDM/OCDMA scheme. When error
of distribution fiber (DF) takes place or bit-error-rate (BER) is higher
than 10-9 requirement, the primary/slave AWG-based OSW are
adjusted and controlled dynamically to restore the affected ONU
groups via the other working DFs immediately.
Abstract: In this paper we propose a mixture of two different
distributions such as Exponential-Gamma, Exponential-Weibull and
Gamma-Weibull to model heterogeneous survival data. Various
properties of the proposed mixture of two different distributions are
discussed. Maximum likelihood estimations of the parameters are
obtained by using the EM algorithm. Illustrative example based on
real data are also given.