Abstract: A new deployment of the multiple criteria decision
making (MCDM) techniques: the Simple Additive Weighting
(SAW), and the Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) for portfolio allocation, is demonstrated in
this paper. Rather than exclusive reference to mean and variance as in
the traditional mean-variance method, the criteria used in this
demonstration are the first four moments of the portfolio distribution.
Each asset is evaluated based on its marginal impacts to portfolio
higher moments that are characterized by trapezoidal fuzzy numbers.
Then centroid-based defuzzification is applied to convert fuzzy
numbers to the crisp numbers by which SAW and TOPSIS can be
deployed. Experimental results suggest the similar efficiency of these
MCDM approaches to selecting dominant assets for an optimal
portfolio under higher moments. The proposed approaches allow
investors flexibly adjust their risk preferences regarding higher
moments via different schemes adapting to various (from
conservative to risky) kinds of investors. The other significant
advantage is that, compared to the mean-variance analysis, the
portfolio weights obtained by SAW and TOPSIS are consistently
well-diversified.
Abstract: In this paper, at first we explain about negative
hypergeometric distribution and its properties. Then we use the w-function
and the Stein identity to give a result on the poisson
approximation to the negative hypergeometric distribution in terms of the total variation distance between the negative hypergeometric and
poisson distributions and its upper bound.
Abstract: The self-organizing map (SOM) model is a well-known neural network model with wide spread of applications. The main characteristics of SOM are two-fold, namely dimension reduction and topology preservation. Using SOM, a high-dimensional data space will be mapped to some low-dimensional space. Meanwhile, the topological relations among data will be preserved. With such characteristics, the SOM was usually applied on data clustering and visualization tasks. However, the SOM has main disadvantage of the need to know the number and structure of neurons prior to training, which are difficult to be determined. Several schemes have been proposed to tackle such deficiency. Examples are growing/expandable SOM, hierarchical SOM, and growing hierarchical SOM. These schemes could dynamically expand the map, even generate hierarchical maps, during training. Encouraging results were reported. Basically, these schemes adapt the size and structure of the map according to the distribution of training data. That is, they are data-driven or dataoriented SOM schemes. In this work, a topic-oriented SOM scheme which is suitable for document clustering and organization will be developed. The proposed SOM will automatically adapt the number as well as the structure of the map according to identified topics. Unlike other data-oriented SOMs, our approach expands the map and generates the hierarchies both according to the topics and their characteristics of the neurons. The preliminary experiments give promising result and demonstrate the plausibility of the method.
Abstract: The purpose of this paper is to detect human in images.
This paper proposes a method for extracting human body feature descriptors consisting of projected edge component series. The feature descriptor can express appearances and shapes of human with local
and global distribution of edges. Our method evaluated with a linear SVM classifier on Daimler-Chrysler pedestrian dataset, and test with
various sub-region size. The result shows that the accuracy level of
proposed method similar to Histogram of Oriented Gradients(HOG)
feature descriptor and feature extraction process is simple and faster than existing methods.
Abstract: Vibration of thin cylindrical shells made of a
functionally gradient material composed of stainless steel and nickel
is presented. The effects of the FGM configuration are studied by
studying the frequencies of FG cylindrical shells. In this case FG
cylindrical shell has Nickel on its outer surface and stainless steel on
its inner surface. The study is carried out based on third order shear
deformation shell theory. The objective is to study the natural
frequencies, the influence of constituent volume fractions and the
effects of configurations of the constituent materials on the
frequencies. The properties are graded in the thickness direction
according to the volume fraction power-law distribution. Results are
presented on the frequency characteristics, the influence of the
constituent various volume fractions on the frequencies.
Abstract: In North America, Most power distribution systems
employ a four-wire multi-grounded neutral (MGN) design. This paper has explained the inherent characteristics of multi-grounded three-phase four-wire distribution systems under unbalanced
situations. As a result, the mechanism of voltage swell and voltage sag in MGN feeders becomes difficult to understand. The simulation
tool that has been used in this paper is MATLAB under Windows software. In this paper the equivalent model of a full-scale multigrounded
distribution system implemented by MATLAB is
introduced. The results are expected to help utility engineers to understand the impact of MGN on distribution system operations.
Abstract: The statistical distributions are modeled in explaining
nature of various types of data sets. Although these distributions are
mostly uni-modal, it is quite common to see multiple modes in the
observed distribution of the underlying variables, which make the
precise modeling unrealistic. The observed data do not exhibit
smoothness not necessarily due to randomness, but could also be due
to non-randomness resulting in zigzag curves, oscillations, humps
etc. The present paper argues that trigonometric functions, which
have not been used in probability functions of distributions so far,
have the potential to take care of this, if incorporated in the
distribution appropriately. A simple distribution (named as, Sinoform
Distribution), involving trigonometric functions, is illustrated in the
paper with a data set. The importance of trigonometric functions is
demonstrated in the paper, which have the characteristics to make
statistical distributions exotic. It is possible to have multiple modes,
oscillations and zigzag curves in the density, which could be suitable
to explain the underlying nature of select data set.
Abstract: The competitive learning is an adaptive process in
which the neurons in a neural network gradually become sensitive to
different input pattern clusters. The basic idea behind the Kohonen-s
Self-Organizing Feature Maps (SOFM) is competitive learning.
SOFM can generate mappings from high-dimensional signal spaces
to lower dimensional topological structures. The main features of this
kind of mappings are topology preserving, feature mappings and
probability distribution approximation of input patterns. To overcome
some limitations of SOFM, e.g., a fixed number of neural units and a
topology of fixed dimensionality, Growing Self-Organizing Neural
Network (GSONN) can be used. GSONN can change its topological
structure during learning. It grows by learning and shrinks by
forgetting. To speed up the training and convergence, a new variant
of GSONN, twin growing cell structures (TGCS) is presented here.
This paper first gives an introduction to competitive learning, SOFM
and its variants. Then, we discuss some GSONN with fixed
dimensionality, which include growing cell structures, its variants
and the author-s model: TGCS. It is ended with some testing results
comparison and conclusions.
Abstract: This paper presents the design and implements the prototype of an intelligent data processing framework in ubiquitous sensor networks. Much focus is put on how to handle the sensor data stream as well as the interoperability between the low-level sensor data and application clients. Our framework first addresses systematic middleware which mitigates the interaction between the application layer and low-level sensors, for the sake of analyzing a great volume of sensor data by filtering and integrating to create value-added context information. Then, an agent-based architecture is proposed for real-time data distribution to efficiently forward a specific event to the appropriate application registered in the directory service via the open interface. The prototype implementation demonstrates that our framework can host a sophisticated application on the ubiquitous sensor network and it can autonomously evolve to new middleware, taking advantages of promising technologies such as software agents, XML, cloud computing, and the like.
Abstract: We studied the evolution of elliptic heavy SF6
gas cylinder surrounded by air when accelerated by a planar
Mach 1.25 shock. A multiple dynamics imaging technology has
been used to obtain one image of the experimental initial
conditions and five images of the time evolution of elliptic
cylinder. We compared the width and height of the circular and
two kinds of elliptic gas cylinders, and analyzed the vortex
strength of the elliptic ones. Simulations are in very good
agreement with the experiments, but due to the different initial
gas cylinder shapes, a certain difference of the initial density
peak and distribution exists between the circular and elliptic
gas cylinders, and the latter initial state is more sensitive and
more inenarrable.
Abstract: The paper describes a new approach for fingerprint
classification, based on the distribution of local features (minute
details or minutiae) of the fingerprints. The main advantage is that
fingerprint classification provides an indexing scheme to facilitate
efficient matching in a large fingerprint database. A set of rules based
on heuristic approach has been proposed. The area around the core
point is treated as the area of interest for extracting the minutiae
features as there are substantial variations around the core point as
compared to the areas away from the core point. The core point in a
fingerprint has been located at a point where there is maximum
curvature. The experimental results report an overall average
accuracy of 86.57 % in fingerprint classification.
Abstract: Consumer electronics are pervasive. It is impossible to
imagine a household or office without DVD players, digital cameras,
printers, mobile phones, shavers, electrical toothbrushes, etc. All
these devices operate at different voltage levels ranging from 1.8 to
20 VDC, in the absence of universal standards. The voltages
available are however usually 120/230 VAC at 50/60 Hz. This
situation makes an individual electrical energy conversion system
necessary for each device. Such converters usually involve several
conversion stages and often operate with excessive losses and poor
reliability. The aim of the project presented in this paper is to design
and implement a multi-channel DC/DC converter system,
customizing the output voltage and current ratings according to the
requirements of the load. Distributed, multi-agent techniques will be
applied for the control of the DC/DC converters.
Abstract: Throughput is an important measure of performance of production system. Analyzing and modeling of production throughput is complex in today-s dynamic production systems due to uncertainties of production system. The main reasons are that uncertainties are materialized when the production line faces changes in setup time, machinery break down, lead time of manufacturing, and scraps. Besides, demand changes are fluctuating from time to time for each product type. These uncertainties affect the production performance. This paper proposes Bayesian inference for throughput modeling under five production uncertainties. Bayesian model utilized prior distributions related to previous information about the uncertainties where likelihood distributions are associated to the observed data. Gibbs sampling algorithm as the robust procedure of Monte Carlo Markov chain was employed for sampling unknown parameters and estimating the posterior mean of uncertainties. The Bayesian model was validated with respect to convergence and efficiency of its outputs. The results presented that the proposed Bayesian models were capable to predict the production throughput with accuracy of 98.3%.
Abstract: The charge-exchange xenon (CEX) ion generated by ion thruster can backflow to the surface of spacecraft and threaten to the safety of spacecraft operation. In order to evaluate the effects of the induced plasma environment in backflow regions on the spacecraft, we designed a spherical single Langmuir probe of 5.8cm in diameter for measuring low-density plasma parameters in backflow region of ion thruster. In practice, the tests are performed in a two-dimensional array (40cm×60cm) composed of 20 sites. The experiment results illustrate that the electron temperature ranges from 3.71eV to 3.96eV, with the mean value of 3.82eV and the standard deviation of 0.064eV. The electron density ranges from 8.30×1012/m3 to 1.66×1013/m3, with the mean value of 1.30×1013/m3 and the standard deviation of 2.15×1012/m3. All data is analyzed according to the “ideal" plasma conditions of Maxwellian distributions.
Abstract: Antimosy-doped tin oxide (ATO) particles were
prepared via chemical coprecipitation and reverse emulsion. The size
and size distribution of ATO particles were obviously decreased via
reverse microemulsion method. At the relatively high yield the ATO
particles were nearly spherical in shape, meanwhile the crystalline
structure and excellent conductivity were reserved, which could satisfy
the requirement as composite fillers, such as dielectric filler of
polyimide film.
Abstract: The transient thermoelastic response of thick hollow cylinder made of functionally graded material under thermal loading is studied. The generalized coupled thermoelasticity based on the Green-Lindsay model is used. The thermal and mechanical properties of the functionally graded material are assumed to be varied in the radial direction according to a power law variation as a function of the volume fractions of the constituents. The thermal and elastic governing equations are solved by using Galerkin finite element method. All the finite element calculations were done by using commercial finite element program FlexPDE. The transient temperature, radial displacement, and thermal stresses distribution through the radial direction of the cylinder are plotted.
Abstract: The mineral having chemical compositional formula MgAl2O4 is called “spinel". The ferrites crystallize in spinel structure are known as spinel-ferrites or ferro-spinels. The spinel structure has a fcc cage of oxygen ions and the metallic cations are distributed among tetrahedral (A) and octahedral (B) interstitial voids (sites). The X-ray diffraction (XRD) intensity of each Bragg plane is sensitive to the distribution of cations in the interstitial voids of the spinel lattice. This leads to the method of determination of distribution of cations in the spinel oxides through XRD intensity analysis. The computer program for XRD intensity analysis has been developed in C language and also tested for the real experimental situation by synthesizing the spinel ferrite materials Mg0.6Zn0.4AlxFe2- xO4 and characterized them by X-ray diffractometry. The compositions of Mg0.6Zn0.4AlxFe2-xO4(x = 0.0 to 0.6) ferrites have been prepared by ceramic method and powder X-ray diffraction patterns were recorded. Thus, the authenticity of the program is checked by comparing the theoretically calculated data using computer simulation with the experimental ones. Further, the deduced cation distributions were used to fit the magnetization data using Localized canting of spins approach to explain the “recovery" of collinear spin structure due to Al3+ - substitution in Mg-Zn ferrites which is the case if A-site magnetic dilution and non-collinear spin structure. Since the distribution of cations in the spinel ferrites plays a very important role with regard to their electrical and magnetic properties, it is essential to determine the cation distribution in spinel lattice.
Abstract: Obtaining labeled data in supervised learning is often
difficult and expensive, and thus the trained learning algorithm tends
to be overfitting due to small number of training data. As a result,
some researchers have focused on using unlabeled data which may
not necessary to follow the same generative distribution as the labeled
data to construct a high-level feature for improving performance on
supervised learning tasks. In this paper, we investigate the impact of
the relationship between unlabeled and labeled data for classification
performance. Specifically, we will apply difference unlabeled data
which have different degrees of relation to the labeled data for
handwritten digit classification task based on MNIST dataset. Our
experimental results show that the higher the degree of relation
between unlabeled and labeled data, the better the classification
performance. Although the unlabeled data that is completely from
different generative distribution to the labeled data provides the lowest
classification performance, we still achieve high classification performance.
This leads to expanding the applicability of the supervised
learning algorithms using unsupervised learning.
Abstract: Boon Rawd Brewery is a beer company based in
Thailand that has an exemplary image, both as a good employer and a
well-managed company with a strong record of social responsibility.
The most famous of the company’s products is Singha beer. To study
the company’s marketing strategy, a case study analysis was
conducted together with qualitative research methods. The study
analyzed the marketing strategy of Boon Rawd Brewery before the
liberalization of the liquor market in 2000. The company’s marketing
strategies consisted of the following: product line strategy, product
development strategy, block channel strategy, media strategy, trade
strategy, and consumer incentive strategy. Additionally, the company
employed marketing mix strategy based on the 4Ps: product, price,
promotion and place (of distribution).
Abstract: Efficient preprocessing is very essential for automatic
recognition of handwritten documents. In this paper, techniques on
segmenting words in handwritten Arabic text are presented. Firstly,
connected components (ccs) are extracted, and distances among
different components are analyzed. The statistical distribution of this
distance is then obtained to determine an optimal threshold for words
segmentation. Meanwhile, an improved projection based method is
also employed for baseline detection. The proposed method has been
successfully tested on IFN/ENIT database consisting of 26459
Arabic words handwritten by 411 different writers, and the results
were promising and very encouraging in more accurate detection of
the baseline and segmentation of words for further recognition.