Abstract: Global environmental changes lead to increased frequency and scale of natural disaster, Taiwan is under the influence of global warming and extreme weather. Therefore, the vulnerability was increased and variability and complexity of disasters is relatively enhanced. The purpose of this study is to consider the source and magnitude of hazard characteristics on the tourism industry. Using modern risk management concepts, integration of related domestic and international basic research, this goes beyond the Taiwan typhoon disaster risk assessment model and evaluation of loss. This loss evaluation index system considers the impact of extreme weather, in particular heavy rain on the tourism industry in Taiwan. Consider the extreme climate of the compound impact of disaster for the tourism industry; we try to make multi-hazard risk assessment model, strategies and suggestions. Related risk analysis results are expected to provide government department, the tourism industry asset owners, insurance companies and banking include tourist disaster risk necessary information to help its tourism industry for effective natural disaster risk management.
Abstract: The purpose of this study is to suggest energy efficient
routing for ad hoc networks which are composed of nodes with limited
energy. There are diverse problems including limitation of energy
supply of node, and the node energy management problem has been
presented. And a number of protocols have been proposed for energy
conservation and energy efficiency. In this study, the critical point of
the EA-MPDSR, that is the type of energy efficient routing using only
two paths, is improved and developed. The proposed TP-MESR uses
multi-path routing technique and traffic prediction function to increase
number of path more than 2. It also verifies its efficiency compared to
EA-MPDSR using network simulator (NS-2). Also, To give a
academic value and explain protocol systematically, research
guidelines which the Hevner(2004) suggests are applied. This
proposed TP-MESR solved the existing multi-path routing problem
related to overhead, radio interference, packet reassembly and it
confirmed its contribution to effective use of energy in ad hoc
networks.
Abstract: An Ad hoc wireless network comprises of mobile
terminals linked and communicating with each other sans the aid of
traditional infrastructure. Optimized Link State Protocol (OLSR) is a
proactive routing protocol, in which routes are discovered/updated
continuously so that they are available when needed. Hello messages
generated by a node seeks information about its neighbor and if the
latter fails to respond to a specified number of hello messages
regulated by neighborhood hold time, the node is forced to assume
that the neighbor is not in range. This paper proposes to evaluate
OLSR routing protocol in a random mobility network having various
neighborhood hold time intervals. The throughput and delivery ratio
are also evaluated to learn about its efficiency for multimedia loads.
Abstract: The experimental and theoretical results of a ZVS
(Zero Voltage Switching) isolated flyback DC-DC converter using
multilayered coreless PCB step down 2:1 transformer are presented.
The performance characteristics of the transformer are shown which
are useful for the parameters extraction. The measured energy
efficiency of the transformer is found to be more than 94% with the
sinusoidal input voltage excitation. The designed flyback converter
has been tested successfully upto the output power level of 10W,
with a switching frequency in the range of 2.7MHz-4.3MHz. The
input voltage of the converter is varied from 25V-40V DC.
Frequency modulation technique is employed by maintaining
constant off time to regulate the output voltage of the converter. The
energy efficiency of the isolated flyback converter circuit under ZVS
condition in the MHz frequency region is found to be approximately
in the range of 72-84%. This paper gives the comparative results in
terms of the energy efficiency of the hard switched and soft switched
flyback converter in the MHz frequency region.
Abstract: Restructured electricity markets may provide
opportunities for producers to exercise market power maintaining
prices in excess of competitive levels. In this paper an oligopolistic
market is presented that all Generation Companies (GenCos) bid in a
Cournot model. Genetic algorithm (GA) is applied to obtain
generation scheduling of each GenCo as well as hourly market
clearing prices (MCP). In order to consider network constraints a
multiperiod framework is presented to simulate market clearing
mechanism in which the behaviors of market participants are
modelled through piecewise block curves. A mixed integer linear
programming (MILP) is employed to solve the problem. Impacts of
market clearing process on participants- characteristic and final
market prices are presented. Consequently, a novel multi-objective
model is addressed for security constrained optimal bidding strategy
of GenCos. The capability of price-maker GenCos to alter MCP is
evaluated through introducing an effective-supply curve. In addition,
the impact of exercising market power on the variation of market
characteristics as well as GenCos scheduling is studied.
Abstract: This paper numerically investigates the effects of input
speed on the overall dynamic characteristics of a multi-body system
with differently located revolute clearance joints without friction. A
typical planar slider-crank mechanism is used as a demonstration case
in which the effects of the input speed on the dynamic performance
of the mechanism with a revolute clearance joint between the crank
and connecting rod, and between the connecting rod and slider are
separately investigated with comprehensive observations numerically
presented. It is observed that, changing the driving speed of a multibody
system makes the behavior of the system to change from
either periodic to chaotic, or chaotic to periodic depending on which
joint has clearance. The location of the clearance revolute joint and
the operating speed of a multi-body system play a crucial role in
predicting accurately the dynamic responses of the system. Therefore
the dynamic behavior of one clearance revolute joint cannot be used
as a general case for a mechanical system.
Abstract: In this paper, the design of a multiple U-slotted microstrip patch antenna with frequency selective surface (FSS) as a superstrate for WLAN and WiMAX applications is presented. The proposed antenna is designed by using substrate FR4 having permittivity of 4.4 and air substrate. The characteristics of the antenna are designed and evaluated the performance of modelled antenna using CST Microwave studio. The proposed antenna dual resonant frequency has been achieved in the band of 2.37-2.55 GHz and 3.4-3.6 GHz. Because of the impact of FSS superstrate, it is found that the bandwidths have been improved from 6.12% to 7.35 % and 3.7% to 5.7% at resonant frequencies 2.45 GHz and 3.5 GHz, respectively. The maximum gain at the resonant frequency of 2.45 and 3.5 GHz are 9.3 and 11.33 dBi, respectively.
Abstract: A highly optimized implementation of binary mixture
diffusion with no initial bulk velocity on graphics processors is
presented. The lattice Boltzmann model is employed for simulating
the binary diffusion of oxygen and nitrogen into each other with
different initial concentration distributions. Simulations have been
performed using the latest proposed lattice Boltzmann model that
satisfies both the indifferentiability principle and the H-theorem for
multi-component gas mixtures. Contemporary numerical
optimization techniques such as memory alignment and increasing
the multiprocessor occupancy are exploited along with some novel
optimization strategies to enhance the computational performance on
graphics processors using the C for CUDA programming language.
Speedup of more than two orders of magnitude over single-core
processors is achieved on a variety of Graphical Processing Unit
(GPU) devices ranging from conventional graphics cards to
advanced, high-end GPUs, while the numerical results are in
excellent agreement with the available analytical and numerical data
in the literature.
Abstract: In this paper, we proposed a novel receiver algorithm
for coherent underwater acoustic communications. The proposed
receiver is composed of three parts: (1) Doppler tracking and
correction, (2) Time reversal channel estimation and combining, and
(3) Joint iterative equalization and decoding (JIED). To reduce
computational complexity and optimize the equalization algorithm,
Time reversal (TR) channel estimation and combining is adopted to
simplify multi-channel adaptive decision feedback equalizer (ADFE)
into single channel ADFE without reducing the system performance.
Simultaneously, the turbo theory is adopted to form joint iterative
ADFE and convolutional decoder (JIED). In JIED scheme, the ADFE
and decoder exchange soft information in an iterative manner, which
can enhance the equalizer performance using decoding gain. The
simulation results show that the proposed algorithm can reduce
computational complexity and improve the performance of equalizer.
Therefore, the performance of coherent underwater acoustic
communications can be improved greatly.
Abstract: In this paper, multi-processors job shop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. By using the suitable hybrid of priority dispatching rules, the process of finding the best solution will be improved. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. Thus, the similar work has the same production lines. Other advantage of this algorithm is that the similar machines (not the same) can be considered. So, these machines are able to process a job with different processing and setup times. According to this capability and from this algorithm evaluation point of view, a number of test problems are solved and the associated results are analyzed. The results show a significant decrease in throughput time. It also shows that, this algorithm is able to recognize the bottleneck machine and to schedule jobs in an efficient way.
Abstract: A spatial classification technique incorporating a State of Art Feature Extraction algorithm is proposed in this paper for classifying a heterogeneous classes present in hyper spectral images. The classification accuracy can be improved if and only if both the feature extraction and classifier selection are proper. As the classes in the hyper spectral images are assumed to have different textures, textural classification is entertained. Run Length feature extraction is entailed along with the Principal Components and Independent Components. A Hyperspectral Image of Indiana Site taken by AVIRIS is inducted for the experiment. Among the original 220 bands, a subset of 120 bands is selected. Gray Level Run Length Matrix (GLRLM) is calculated for the selected forty bands. From GLRLMs the Run Length features for individual pixels are calculated. The Principle Components are calculated for other forty bands. Independent Components are calculated for next forty bands. As Principal & Independent Components have the ability to represent the textural content of pixels, they are treated as features. The summation of Run Length features, Principal Components, and Independent Components forms the Combined Features which are used for classification. SVM with Binary Hierarchical Tree is used to classify the hyper spectral image. Results are validated with ground truth and accuracies are calculated.
Abstract: Multi-Agent Systems (MAS) emerged in the pursuit to improve our standard of living, and hence can manifest complex human behaviors such as communication, decision making, negotiation and self-organization. The Social Network Services (SNSs) have attracted millions of users, many of whom have integrated these sites into their daily practices. The domains of MAS and SNS have lots of similarities such as architecture, features and functions. Exploring social network users- behavior through multiagent model is therefore our research focus, in order to generate more accurate and meaningful information to SNS users. An application of MAS is the e-Auction and e-Rental services of the Universiti Cyber AgenT(UniCAT), a Social Network for students in Universiti Tunku Abdul Rahman (UTAR), Kampar, Malaysia, built around the Belief- Desire-Intention (BDI) model. However, in spite of the various advantages of the BDI model, it has also been discovered to have some shortcomings. This paper therefore proposes a multi-agent framework utilizing a modified BDI model- Belief-Desire-Intention in Dynamic and Uncertain Situations (BDIDUS), using UniCAT system as a case study.
Abstract: In this paper an ant colony optimization algorithm is
developed to solve the permutation flow shop scheduling problem. In
the permutation flow shop scheduling problem which has been vastly
studied in the literature, there are a set of m machines and a set of n
jobs. All the jobs are processed on all the machines and the sequence
of jobs being processed is the same on all the machines. Here this
problem is optimized considering two criteria, makespan and total
flow time. Then the results are compared with the ones obtained by
previously developed algorithms. Finally it is visible that our
proposed approach performs best among all other algorithms in the
literature.
Abstract: This work deals with aspects of support vector machine learning for large-scale data mining tasks. Based on a decomposition algorithm for support vector machine training that can be run in serial as well as shared memory parallel mode we introduce a transformation of the training data that allows for the usage of an expensive generalized kernel without additional costs. We present experiments for the Gaussian kernel, but usage of other kernel functions is possible, too. In order to further speed up the decomposition algorithm we analyze the critical problem of working set selection for large training data sets. In addition, we analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our tests and conclusions led to several modifications of the algorithm and the improvement of overall support vector machine learning performance. Our method allows for using extensive parameter search methods to optimize classification accuracy.
Abstract: In this paper we propose new method for
simultaneous generating multiple quantiles corresponding to given
probability levels from data streams and massive data sets. This
method provides a basis for development of single-pass low-storage
quantile estimation algorithms, which differ in complexity, storage
requirement and accuracy. We demonstrate that such algorithms may
perform well even for heavy-tailed data.
Abstract: Predict daily global solar radiation (GSR) based on meteorological variables, using Multi-layer perceptron (MLP) neural networks is the main objective of this study. Daily mean air temperature, relative humidity, sunshine hours, evaporation, wind speed, and soil temperature values between 2002 and 2006 for Dezful city in Iran (32° 16' N, 48° 25' E), are used in this study. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data.
Abstract: This paper presents an approach for the design of
fuzzy logic power system stabilizers using genetic algorithms. In the
proposed fuzzy expert system, speed deviation and its derivative
have been selected as fuzzy inputs. In this approach the parameters of
the fuzzy logic controllers have been tuned using genetic algorithm.
Incorporation of GA in the design of fuzzy logic power system
stabilizer will add an intelligent dimension to the stabilizer and
significantly reduces computational time in the design process. It is
shown in this paper that the system dynamic performance can be
improved significantly by incorporating a genetic-based searching
mechanism. To demonstrate the robustness of the genetic based
fuzzy logic power system stabilizer (GFLPSS), simulation studies on
multimachine system subjected to small perturbation and three-phase
fault have been carried out. Simulation results show the superiority
and robustness of GA based power system stabilizer as compare to
conventionally tuned controller to enhance system dynamic
performance over a wide range of operating conditions.
Abstract: This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.
Abstract: Graph partitioning is a NP-hard problem with multiple
conflicting objectives. The graph partitioning should minimize the
inter-partition relationship while maximizing the intra-partition
relationship. Furthermore, the partition load should be evenly
distributed over the respective partitions. Therefore this is a multiobjective
optimization problem (MOO). One of the approaches to
MOO is Pareto optimization which has been used in this paper. The
proposed methods of this paper used to improve the performance are
injecting best solutions of previous runs into the first generation of
next runs and also storing the non-dominated set of previous
generations to combine with later generation's non-dominated set.
These improvements prevent the GA from getting stuck in the local
optima and increase the probability of finding more optimal
solutions. Finally, a simulation research is carried out to investigate
the effectiveness of the proposed algorithm. The simulation results
confirm the effectiveness of the proposed method.
Abstract: Experiments were carried out at the Faculty of Food
Technology of Latvia University of Agriculture (LLU). Soft cheese
Kleo produced in Latvia was packed in a biodegradable PLA without
barrierproperties and VC999 BioPack lidding film PLA, coated with
a barrier of pure silicon oxide (SiOx) and in combination with
modified atmosphere (MAP) the influence on the shelf life was
investigated and compared with some conventional (OPP, PE/PA,
PE/OPA and Multibarrier 60) polymer film impact. Modified
atmosphere consisted of carbon dioxide CO2 (E 290) 30% and
nitrogen N2 (E 941) 70%. The analyzable samples were stored at the
temperature of +4.0±0.5 °C up to 32 days- and analyzed before
packaging and in the 0, 5th, 11th, 15th, 18th, 22nd, 25th, 29th and 32nd
day of storage. The shelf life was extended along to 32 days, good
outside appearance and lactic acid aroma was observed.