Abstract: Hazard rate estimation is one of the important topics
in forecasting earthquake occurrence. Forecasting earthquake
occurrence is a part of the statistical seismology where the main
subject is the point process. Generally, earthquake hazard rate is
estimated based on the point process likelihood equation called the
Hazard Rate Likelihood of Point Process (HRLPP). In this research,
we have developed estimation method, that is hazard rate single
decrement HRSD. This method was adapted from estimation method
in actuarial studies. Here, one individual associated with an
earthquake with inter event time is exponentially distributed. The
information of epicenter and time of earthquake occurrence are used
to estimate hazard rate. At the end, a case study of earthquake hazard
rate will be given. Furthermore, we compare the hazard rate between
HRLPP and HRSD method.
Abstract: This paper presents the adaptation of the knowledge management model and intellectual capital measurement NOVA to the needs of work or research project must be developed when conducting a program of graduate-level master. Brackets are added in each of the blocks which is represented in the original model NOVA and which allows to represent those involved in each of these.
Abstract: This paper proposes an efficient finite precision block floating point (BFP) treatment to the fixed coefficient finite impulse response (FIR) digital filter. The treatment includes effective implementation of all the three forms of the conventional FIR filters, namely, direct form, cascaded and par- allel, and a roundoff error analysis of them in the BFP format. An effective block formatting algorithm together with an adaptive scaling factor is pro- posed to make the realizations more simple from hardware view point. To this end, a generic relation between the tap weight vector length and the input block length is deduced. The implementation scheme also emphasises on a simple block exponent update technique to prevent overflow even during the block to block transition phase. The roundoff noise is also investigated along the analogous lines, taking into consideration these implementational issues. The simulation results show that the BFP roundoff errors depend on the sig- nal level almost in the same way as floating point roundoff noise, resulting in approximately constant signal to noise ratio over a relatively large dynamic range.
Abstract: The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
Abstract: With the exponentially increasing demand for
wireless communications the capacity of current cellular systems will
soon become incapable of handling the growing traffic. Since radio
frequencies are diminishing natural resources, there seems to be a
fundamental barrier to further capacity increase. The solution can be
found in smart antenna systems.
Smart or adaptive antenna arrays consist of an array of antenna
elements with signal processing capability, that optimize the
radiation and reception of a desired signal, dynamically. Smart
antennas can place nulls in the direction of interferers via adaptive
updating of weights linked to each antenna element. They thus cancel
out most of the co-channel interference resulting in better quality of
reception and lower dropped calls. Smart antennas can also track the
user within a cell via direction of arrival algorithms. This implies that
they are more advantageous than other antenna systems. This paper
focuses on few issues about the smart antennas in mobile radio
networks.
Abstract: Adapting various sensor devices to communicate
within sensor networks empowers us by providing range of
possibilities. The sensors in sensor networks need to know their
measurable belief of trust for efficient and safe communication. In this
paper, we suggested a trust model using fuzzy logic in sensor network.
Trust is an aggregation of consensus given a set of past interaction
among sensors. We applied our suggested model to sensor networks in
order to show how trust mechanisms are involved in communicating
algorithm to choose the proper path from source to destination.
Abstract: Linear convolutive filters are fast in calculation and in application, and thus, often used for real-time processing of continuous data streams. In the case of transient signals, a filter has not only to detect the presence of a specific waveform, but to estimate its arrival time as well. In this study, a measure is presented which indicates the performance of detectors in achieving both of these tasks simultaneously. Furthermore, a new sub-class of linear filters within the class of filters which minimize the quadratic response is proposed. The proposed filters are more flexible than the existing ones, like the adaptive matched filter or the minimum power distortionless response beamformer, and prove to be superior with respect to that measure in certain settings. Simulations of a real-time scenario confirm the advantage of these filters as well as the usefulness of the performance measure.
Abstract: A phenomenological model for species spreading which incorporates the Allee effect, a species- maximum attainable growth rate, collective dispersal rate and dispersal adaptability is presented. This builds on a well-established reaction-diffusion model for spatial spreading of invading organisms. The model is phrased in terms of the “hostility" (which quantifies the Allee threshold in relation to environmental sustainability) and dispersal adaptability (which measures how a species is able to adapt its migratory response to environmental conditions). The species- invading/retreating speed and the sharpness of the invading boundary are explicitly characterised in terms of the fundamental parameters, and analysed in detail.
Abstract: Source code retrieval is of immense importance in the software engineering field. The complex tasks of retrieving and extracting information from source code documents is vital in the development cycle of the large software systems. The two main subtasks which result from these activities are code duplication prevention and plagiarism detection. In this paper, we propose a Mohamed Amine Ouddan, and Hassane Essafi source code retrieval system based on two-level fingerprint representation, respectively the structural and the semantic information within a source code. A sequence alignment technique is applied on these fingerprints in order to quantify the similarity between source code portions. The specific purpose of the system is to detect plagiarism and duplicated code between programs written in different programming languages belonging to the same class, such as C, Cµ, Java and CSharp. These four languages are supported by the actual version of the system which is designed such that it may be easily adapted for any programming language.
Abstract: A procedural-animation-based approach which rapidly
synthesize the adaptive locomotion for quadruped characters that they
can walk or run in any directions on an uneven terrain within a
dynamic environment was proposed. We devise practical motion
models of the quadruped animals for adapting to a varied terrain in a
real-time manner. While synthesizing locomotion, we choose the
corresponding motion models by means of the footstep prediction of
the current state in the dynamic environment, adjust the key-frames of
the motion models relying on the terrain-s attributes, calculate the
collision-free legs- trajectories, and interpolate the key-frames
according to the legs- trajectories. Finally, we apply dynamic time
warping to each part of motion for seamlessly concatenating all desired
transition motions to complete the whole locomotion. We reduce the
time cost of producing the locomotion and takes virtual characters to
fit in with dynamic environments no matter when the environments are
changed by users.
Abstract: Modeling the behavior of the dialogue management in
the design of a spoken dialogue system using statistical methodologies
is currently a growing research area. This paper presents a work
on developing an adaptive learning approach to optimize dialogue
strategy. At the core of our system is a method formalizing dialogue
management as a sequential decision making under uncertainty whose
underlying probabilistic structure has a Markov Chain. Researchers
have mostly focused on model-free algorithms for automating the
design of dialogue management using machine learning techniques
such as reinforcement learning. But in model-free algorithms there
exist a dilemma in engaging the type of exploration versus exploitation.
Hence we present a model-based online policy learning
algorithm using interconnected learning automata for optimizing
dialogue strategy. The proposed algorithm is capable of deriving
an optimal policy that prescribes what action should be taken in
various states of conversation so as to maximize the expected total
reward to attain the goal and incorporates good exploration and
exploitation in its updates to improve the naturalness of humancomputer
interaction. We test the proposed approach using the most
sophisticated evaluation framework PARADISE for accessing to the
railway information system.
Abstract: Culture and family structure provide a sense security.
Further, the chrono, macro and micro contexts of development
influence developmental transitions and timetable particularly owing
to variations in the macrosystem associated with non normative life
events like migration. Migration threatens family links, security and
attachment bonds. Rising migratory trends have prompted an
increased interest in migration consequences on familial bonds,
developmental autonomy, socialization process, and sense of
security. This paper takes a narrative approach and applies the
attachment paradigm from a lifespan perspective, to examine the
settlement experiences of an India-born migrant student in Sydney,
Australia. It focuses on her quest to preserve family ties; her remote
secure base; her continual struggle to balance dependency and
autonomy, a major developmental milestone. As positional parental
power is culturally more potent in the Indian society, the paper
therefore raises some important concerns related to cultural
expectations, adaptation, acculturative stress and sense of security.
Abstract: Design Patterns have gained more and more
acceptances since their emerging in software development world last
decade and become another de facto standard of essential knowledge
for Object-Oriented Programming developers nowadays.
Their target usage, from the beginning, was for regular computers,
so, minimizing power consumption had never been a concern.
However, in this decade, demands of more complicated software for
running on mobile devices has grown rapidly as the much higher
performance portable gadgets have been supplied to the market
continuously. To get along with time to market that is business
reason, the section of software development for power conscious,
battery, devices has shifted itself from using specific low-level
languages to higher level ones. Currently, complicated software
running on mobile devices are often developed by high level
languages those support OOP concepts. These cause the trend of
embracing Design Patterns to mobile world.
However, using Design Patterns directly in software development
for power conscious systems is not recommended because they were
not originally designed for such environment. This paper
demonstrates the adapted Design Pattern for power limitation system.
Because there are numerous original design patterns, it is not possible
to mention the whole at once. So, this paper focuses only in creating
Energy Conscious version of existing regular "Builder Pattern" to be
appropriated for developing low power consumption software.
Abstract: In the context of spectrum surveillance, a new method
to recover the code of spread spectrum signal is presented, while the
receiver has no knowledge of the transmitter-s spreading sequence. In
our previous paper, we used Genetic algorithm (GA), to recover
spreading code. Although genetic algorithms (GAs) are well known
for their robustness in solving complex optimization problems, but
nonetheless, by increasing the length of the code, we will often lead
to an unacceptable slow convergence speed. To solve this problem we
introduce Particle Swarm Optimization (PSO) into code estimation in
spread spectrum communication system. In searching process for
code estimation, the PSO algorithm has the merits of rapid
convergence to the global optimum, without being trapped in local
suboptimum, and good robustness to noise. In this paper we describe
how to implement PSO as a component of a searching algorithm in
code estimation. Swarm intelligence boasts a number of advantages
due to the use of mobile agents. Some of them are: Scalability, Fault
tolerance, Adaptation, Speed, Modularity, Autonomy, and
Parallelism. These properties make swarm intelligence very attractive
for spread spectrum code estimation. They also make swarm
intelligence suitable for a variety of other kinds of channels. Our
results compare between swarm-based algorithms and Genetic
algorithms, and also show PSO algorithm performance in code
estimation process.
Abstract: We present a subband adaptive infinite-impulse response (IIR) filtering method, which is based on a polyphase decomposition of IIR filter. Motivated by the fact that the polyphase structure has benefits in terms of convergence rate and stability, we introduce the polyphase decomposition to subband IIR filtering, i.e., in each subband high order IIR filter is decomposed into polyphase IIR filters with lower order. Computer simulations demonstrate that the proposed method has improved convergence rate over conventional IIR filters.
Abstract: This paper explores an application of an adaptive learning mechanism for robots based on the natural immune system. Most of the research carried out so far are based either on the innate or adaptive characteristics of the immune system, we present a combination of these to achieve behavior arbitration wherein a robot learns to detect vulnerable areas of a track and adapts to the required speed over such portions. The test bed comprises of two Lego robots deployed simultaneously on two predefined near concentric tracks with the outer robot capable of helping the inner one when it misaligns. The helper robot works in a damage-control mode by realigning itself to guide the other robot back onto its track. The panic-stricken robot records the conditions under which it was misaligned and learns to detect and adapt under similar conditions thereby making the overall system immune to such failures.
Abstract: The recent trend in field oriented control (FOC) is towards the use of sensorless techniques that avoid the use of speed sensor and flux sensor. Sensors are replaced by estimators or observers to minimise the cost and increase the reliability. In this paper an anlyse of perfomance of a MRAS used in sensorless control of induction motors and sensitvity to machine parameters change are studied.
Abstract: Discrimination between different classes of environmental
sounds is the goal of our work. The use of a sound recognition
system can offer concrete potentialities for surveillance and
security applications. The first paper contribution to this research
field is represented by a thorough investigation of the applicability
of state-of-the-art audio features in the domain of environmental
sound recognition. Additionally, a set of novel features obtained by
combining the basic parameters is introduced. The quality of the
features investigated is evaluated by a HMM-based classifier to which
a great interest was done. In fact, we propose to use a Multi-Style
training system based on HMMs: one recognizer is trained on a
database including different levels of background noises and is used
as a universal recognizer for every environment. In order to enhance
the system robustness by reducing the environmental variability, we
explore different adaptation algorithms including Maximum Likelihood
Linear Regression (MLLR), Maximum A Posteriori (MAP)
and the MAP/MLLR algorithm that combines MAP and MLLR.
Experimental evaluation shows that a rather good recognition rate
can be reached, even under important noise degradation conditions
when the system is fed by the convenient set of features.
Abstract: This paper presents a new adaptive DMC controller
that improves the controller performance in case of plant-model
mismatch. The new controller monitors the plant measured output,
compares it with the model output and calculates weights applied to
the controller move. Simulations show that the new controller can
help improve control performance and avoid instability in case of
severe model mismatches.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.