Abstract: During the past decade, pond aeration systems have
been developed which will sustain large quantities of fish and
invertebrate biomass. Dissolved Oxygen (DO) is considered to be
among the most important water quality parameters in fish culture.
Fishponds in aquaculture farms are usually located in remote areas
where grid lines are at far distance. Aeration of ponds is required to
prevent mortality and to intensify production, especially when
feeding is practical, and in warm regions. To increase pond
production it is necessary to control dissolved oxygen. Artificial
intelligence (AI) techniques are becoming useful as alternate
approaches to conventional techniques or as components of
integrated systems. They have been used to solve complicated
practical problems in various areas and are becoming more and more
popular nowadays. This paper presents a new design of diffused
aeration system using fuel cell as a power source. Also fuzzy logic
control Technique (FLC) is used for controlling the speed of air flow
rate from the blower to air piping connected to the pond by adjusting
blower speed. MATLAB SIMULINK results show high performance
of fuzzy logic control (FLC).
Abstract: In this paper, we explore a new scheme for filtering spoofed packets (DDOS attack) which is a combination of path fingerprint and client puzzle concepts. In this each IP packet has a unique fingerprint is embedded that represents, the route a packet has traversed. The server maintains a mapping table which contains the client IP address and its corresponding fingerprint. In ingress router, client puzzle is placed. For each request, the puzzle issuer provides a puzzle which the source has to solve. Our design has the following advantages over prior approaches, 1) Reduce the network traffic, as we place a client puzzle at the ingress router. 2) Mapping table at the server is lightweight and moderate.
Abstract: XML files contain data which is in well formatted manner. By studying the format or semantics of the grammar it will be helpful for fast retrieval of the data. There are many algorithms which describes about searching the data from XML files. There are no. of approaches which uses data structure or are related to the contents of the document. In these cases user must know about the structure of the document and information retrieval techniques using NLPs is related to content of the document. Hence the result may be irrelevant or not so successful and may take more time to search.. This paper presents fast XML retrieval techniques by using new indexing technique and the concept of RXML. When indexing an XML document, the system takes into account both the document content and the document structure and assigns the value to each tag from file. To query the system, a user is not constrained about fixed format of query.
Abstract: This article concerns with the accessibility of Business
process modelling tools (BPMo tools) and business process
modelling languages (BPMo languages). Therefore the reader will be
introduced to business process management and the authors'
motivation behind this inquiry. Afterwards, the paper will reflect
problems when applying inaccessible BPMo tools. To illustrate these
problems the authors distinguish between two different categories of
issues and provide practical examples. Finally the article will present
three approaches to improve the accessibility of BPMo tools and
BPMo languages.
Abstract: In this study, the use of silicon NAM (Non-Audible
Murmur) microphone in automatic speech recognition is presented.
NAM microphones are special acoustic sensors, which are attached
behind the talker-s ear and can capture not only normal (audible)
speech, but also very quietly uttered speech (non-audible murmur).
As a result, NAM microphones can be applied in automatic speech
recognition systems when privacy is desired in human-machine communication.
Moreover, NAM microphones show robustness against
noise and they might be used in special systems (speech recognition,
speech conversion etc.) for sound-impaired people. Using a small
amount of training data and adaptation approaches, 93.9% word
accuracy was achieved for a 20k Japanese vocabulary dictation
task. Non-audible murmur recognition in noisy environments is also
investigated. In this study, further analysis of the NAM speech has
been made using distance measures between hidden Markov model
(HMM) pairs. It has been shown the reduced spectral space of NAM
speech using a metric distance, however the location of the different
phonemes of NAM are similar to the location of the phonemes
of normal speech, and the NAM sounds are well discriminated.
Promising results in using nonlinear features are also introduced,
especially under noisy conditions.
Abstract: Evolutionary robotics is concerned with the design of
intelligent systems with life-like properties by means of simulated
evolution. Approaches in evolutionary robotics can be categorized
according to the control structures that represent the behavior and the
parameters of the controller that undergo adaptation. The basic idea
is to automatically synthesize behaviors that enable the robot to
perform useful tasks in complex environments. The evolutionary
algorithm searches through the space of parameterized controllers
that map sensory perceptions to control actions, thus realizing a
specific robotic behavior. Further, the evolutionary algorithm
maintains and improves a population of candidate behaviors by
means of selection, recombination and mutation. A fitness function
evaluates the performance of the resulting behavior according to the
robot-s task or mission. In this paper, the focus is in the use of
genetic algorithms to solve a multi-objective optimization problem
representing robot behaviors; in particular, the A-Compander Law is
employed in selecting the weight of each objective during the
optimization process. Results using an adaptive fitness function show
that this approach can efficiently react to complex tasks under
variable environments.
Abstract: Mixed Model Production is the practice of assembling
several distinct and different models of a product on the same
assembly line without changeovers and then sequencing those models
in a way that smoothes the demand for upstream components. In this
paper, we consider an objective function which minimizes total
stoppage time and total idle time and it is presented sequence
dependent set up time. Many studies have been done on the mixed
model assembly lines. But in this paper we specifically focused on
reducing the idle times. This is possible through various help policies.
For improving the solutions, some cases developed and about 40 tests
problem was considered. We use scatter search for optimization and
for showing the efficiency of our algorithm, experimental results
shows behavior of method. Scatter search and help policies can
produce high quality answers, so it has been used in this paper.
Abstract: Realistic 3D face model is desired in various
applications such as face recognition, games, avatars, animations, and
etc. Construction of 3D face model is composed of 1) building a face
shape model and 2) rendering the face shape model. Thus, building a
realistic 3D face shape model is an essential step for realistic 3D face
model. Recently, 3D morphable model is successfully introduced to
deal with the various human face shapes. 3D dense correspondence
problem should be precedently resolved for constructing a realistic 3D
dense morphable face shape model. Several approaches to 3D dense
correspondence problem in 3D face modeling have been proposed
previously, and among them optical flow based algorithms and TPS
(Thin Plate Spline) based algorithms are representative. Optical flow
based algorithms require texture information of faces, which is
sensitive to variation of illumination. In TPS based algorithms
proposed so far, TPS process is performed on the 2D projection
representation in cylindrical coordinates of the 3D face data, not
directly on the 3D face data and thus errors due to distortion in data
during 2D TPS process may be inevitable.
In this paper, we propose a new 3D dense correspondence algorithm
for 3D dense morphable face shape modeling. The proposed algorithm
does not need texture information and applies TPS directly on 3D face
data. Through construction procedures, it is observed that the proposed
algorithm constructs realistic 3D face morphable model reliably and
fast.
Abstract: The main idea behind in network aggregation is that,
rather than sending individual data items from sensors to sinks,
multiple data items are aggregated as they are forwarded by the
sensor network. Existing sensor network data aggregation techniques
assume that the nodes are preprogrammed and send data to a central
sink for offline querying and analysis. This approach faces two major
drawbacks. First, the system behavior is preprogrammed and cannot
be modified on the fly. Second, the increased energy wastage due to
the communication overhead will result in decreasing the overall
system lifetime. Thus, energy conservation is of prime consideration
in sensor network protocols in order to maximize the network-s
operational lifetime. In this paper, we give an energy efficient
approach to query processing by implementing new optimization
techniques applied to in-network aggregation. We first discuss earlier
approaches in sensors data management and highlight their
disadvantages. We then present our approach “Energy Efficient
Indexed Aggregation" (EEIA) and evaluate it through several
simulations to prove its efficiency, competence and effectiveness.
Abstract: In this paper an open agent-based modular framework
for personalized and adaptive curriculum generation in e-learning
environment is proposed. Agent-based approaches offer several
potential advantages over alternative approaches. Agent-based
systems exhibit high levels of flexibility and robustness in dynamic
or unpredictable environments by virtue of their intrinsic autonomy.
The presented framework enables integration of different types of
expert agents, various kinds of learning objects and user modeling
techniques. It creates possibilities for adaptive e-learning process.
The KM e-learning system is in a process of implementation in
Varna Free University and will be used for supporting the
educational process at the University.
Abstract: This paper presents modern vibration signalprocessing
techniques for vehicle gearbox fault diagnosis, via the
wavelet analysis and the Squared Envelope (SE) technique. The
wavelet analysis is regarded as a powerful tool for the detection of
sudden changes in non-stationary signals. The Squared Envelope
(SE) technique has been extensively used for rolling bearing
diagnostics. In the present work a scheme of using the Squared
Envelope technique for early detection of gear tooth pit. The pitting
defect is manufactured on the tooth side of a fifth speed gear on the
intermediate shaft of a vehicle gearbox. The objective is to
supplement the current techniques of gearbox fault diagnosis based
on using the raw vibration and ordered signals. The test stand is
equipped with three dynamometers; the input dynamometer serves as
the internal combustion engine, the output dynamometers introduce
the load on the flanges of output joint shafts. The gearbox used for
experimental measurements is the type most commonly used in
modern small to mid-sized passenger cars with transversely mounted
powertrain and front wheel drive; a five-speed gearbox with final
drive gear and front wheel differential. The results show that the
approaches methods are effective for detecting and diagnosing
localized gear faults in early stage under different operation
conditions, and are more sensitive and robust than current gear
diagnostic techniques.
Abstract: The approaches to make an agent generate intelligent actions in the AI field might be roughly categorized into two ways–the classical planning and situated action system. It is well known that each system have its own strength and weakness. However, each system also has its own application field. In particular, most of situated action systems do not directly deal with the logical problem. This paper first briefly mentions the novel action generator to situatedly extract a set of actions, which is likely to help to achieve the goal at the current situation in the relaxed logical space. After performing the action set, the agent should recognize the situation for deciding the next likely action set. However, since the extracted action is an approximation of the action which helps to achieve the goal, the agent could be caught into the deadlock of the problem. This paper proposes the newly developed hybrid architecture to solve the problem, which combines the novel situated action generator with the conventional planner. The empirical result in some planning domains shows that the quality of the resultant path to the goal is mostly acceptable as well as deriving the fast response time, and suggests the correlation between the structure of problems and the organization of each system which generates the action.
Abstract: Evaluation of contact pressure, surface and
subsurface contact stresses are essential to know the functional
response of surface coatings and the contact behavior mainly depends
on surface roughness, material property, thickness of layer and the
manner of loading. Contact parameter evaluation of real rough
surface contacts mostly relies on statistical single asperity contact
approaches. In this work, a three dimensional layered solid rough
surface in contact with a rigid flat is modeled and analyzed using
finite element method. The rough surface of layered solid is
generated by FFT approach. The generated rough surface is exported
to a finite element method based ANSYS package through which the
bottom up solid modeling is employed to create a deformable solid
model with a layered solid rough surface on top. The discretization
and contact analysis are carried by using the same ANSYS package.
The elastic, elastoplastic and plastic deformations are continuous in
the present finite element method unlike many other contact models.
The Young-s modulus to yield strength ratio of layer is varied in the
present work to observe the contact parameters effect while keeping
the surface roughness and substrate material properties as constant.
The contacting asperities attain elastic, elastoplastic and plastic states
with their continuity and asperity interaction phenomena is inherently
included. The resultant contact parameters show that neighboring
asperity interaction and the Young-s modulus to yield strength ratio
of layer influence the bulk deformation consequently affect the
interface strength.
Abstract: Model-based approaches have been applied successfully
to a wide range of tasks such as specification, simulation, testing, and
diagnosis. But one bottleneck often prevents the introduction of these
ideas: Manual modeling is a non-trivial, time-consuming task.
Automatically deriving models by observing and analyzing running
systems is one possible way to amend this bottleneck. To
derive a model automatically, some a-priori knowledge about the
model structure–i.e. about the system–must exist. Such a model
formalism would be used as follows: (i) By observing the network
traffic, a model of the long-term system behavior could be generated
automatically, (ii) Test vectors can be generated from the model,
(iii) While the system is running, the model could be used to diagnose
non-normal system behavior.
The main contribution of this paper is the introduction of a model
formalism called 'probabilistic regression automaton' suitable for the
tasks mentioned above.
Abstract: In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces
Abstract: This paper presents the fundamentals of Origami engineering and its application in nowadays as well as future industry. Several main cores of mathematical approaches such as Huzita- Hatori axioms, Maekawa and Kawasaki-s theorems are introduced briefly. Meanwhile flaps and circle packing by Robert Lang is explained to make understood the underlying principles in designing crease pattern. Rigid origami and its corrugation patterns which are potentially applicable for creating transformable or temporary spaces is discussed to show the transition of origami from paper to thick material. Moreover, some innovative applications of origami such as eyeglass, origami stent and high tech origami based on mentioned theories and principles are showcased in section III; while some updated origami technology such as Vacuumatics, self-folding of polymer sheets and programmable matter folding which could greatlyenhance origami structureare demonstrated in Section IV to offer more insight in future origami.
Abstract: This article presents the results using a parametric approach and a Wavelet Transform in analysing signals emitting from the sperm whale. The extraction of intrinsic characteristics of these unique signals emitted by marine mammals is still at present a difficult exercise for various reasons: firstly, it concerns non-stationary signals, and secondly, these signals are obstructed by interfering background noise. In this article, we compare the advantages and disadvantages of both methods: Auto Regressive models and Wavelet Transform. These approaches serve as an alternative to the commonly used estimators which are based on the Fourier Transform for which the hypotheses necessary for its application are in certain cases, not sufficiently proven. These modern approaches provide effective results particularly for the periodic tracking of the signal's characteristics and notably when the signal-to-noise ratio negatively effects signal tracking. Our objectives are twofold. Our first goal is to identify the animal through its acoustic signature. This includes recognition of the marine mammal species and ultimately of the individual animal (within the species). The second is much more ambitious and directly involves the intervention of cetologists to study the sounds emitted by marine mammals in an effort to characterize their behaviour. We are working on an approach based on the recordings of marine mammal signals and the findings from this data result from the Wavelet Transform. This article will explore the reasons for using this approach. In addition, thanks to the use of new processors, these algorithms once heavy in calculation time can be integrated in a real-time system.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.
Abstract: The work reported in this paper proposes
Swarm-Array computing, a novel technique inspired by swarm
robotics, and built on the foundations of autonomic and parallel
computing. The approach aims to apply autonomic computing
constructs to parallel computing systems and in effect achieve the
self-ware objectives that describe self-managing systems. The
constitution of swarm-array computing comprising four constituents,
namely the computing system, the problem/task, the swarm and the
landscape is considered. Approaches that bind these constituents
together are proposed. Space applications employing FPGAs are
identified as a potential area for applying swarm-array computing for
building reliable systems. The feasibility of a proposed approach is
validated on the SeSAm multi-agent simulator and landscapes are
generated using the MATLAB toolkit.
Abstract: Various mechanisms providing mutual exclusion and
thread synchronization can be used to support parallel processing
within a single computer. Instead of using locks, semaphores, barriers
or other traditional approaches in this paper we focus on alternative
ways for making better use of modern multithreaded architectures
and preparing hash tables for concurrent accesses. Hash structures
will be used to demonstrate and compare two entirely different
approaches (rule based cooperation and hardware synchronization
support) to an efficient parallel implementation using traditional
locks. Comparison includes implementation details, performance
ranking and scalability issues. We aim at understanding the effects
the parallelization schemes have on the execution environment with
special focus on the memory system and memory access
characteristics.