Abstract: The Model for Knowledge Base of Computational Objects
(KBCO model) has been successfully applied to represent the
knowledge of human like Plane Geometry, Physical, Calculus. However,
the original model cannot easyly apply in inorganic chemistry
field because of the knowledge specific problems. So, the aim of
this article is to introduce how we extend the Computional Object
(Com-Object) in KBCO model, kinds of fact, problems model, and
inference algorithms to develop a program for solving problems
in inorganic chemistry. Our purpose is to develop the application
that can help students in their study inorganic chemistry at schools.
This application was built successful by using Maple, C# and WPF
technology. It can solve automatically problems and give human
readable solution agree with those writting by students and teachers.
Abstract: Knowledge of an organization does not merely reside
in structured form of information and data; it is also embedded in
unstructured form. The discovery of such knowledge is particularly
difficult as the characteristic is dynamic, scattered, massive and
multiplying at high speed. Conventional methods of managing
unstructured information are considered too resource demanding and
time consuming to cope with the rapid information growth.
In this paper, a Multi-faceted and Automatic Knowledge
Elicitation System (MAKES) is introduced for the purpose of
discovery and capture of organizational knowledge. A trial
implementation has been conducted in a public organization to
achieve the objective of decision capture and navigation from a
number of meeting minutes which are autonomously organized,
classified and presented in a multi-faceted taxonomy map in both
document and content level. Key concepts such as critical decision
made, key knowledge workers, knowledge flow and the relationship
among them are elicited and displayed in predefined knowledge
model and maps. Hence, the structured knowledge can be retained,
shared and reused.
Conducting Knowledge Management with MAKES reduces work
in searching and retrieving the target decision, saves a great deal of
time and manpower, and also enables an organization to keep pace
with the knowledge life cycle. This is particularly important when
the amount of unstructured information and data grows extremely
quickly. This system approach of knowledge management can
accelerate value extraction and creation cycles of organizations.
Abstract: Radio wave propagation on the road surface is a major
problem on wireless sensor network for traffic monitoring. In this
paper, we compare receiving signal strength on two scenarios 1) an
empty road and 2) a road with a vehicle. We investigate the effect of
antenna polarization and antenna height to the receiving signal
strength. The transmitting antenna is installed on the road surface.
The receiving signal is measured 360 degrees around the transmitting
antenna with the radius of 2.5 meters. Measurement results show the
receiving signal fluctuation around the transmitting antenna in both
scenarios. Receiving signal with vertical polarization antenna results
in higher signal strength than horizontal polarization antenna. The
optimum antenna elevation is 1 meter for both horizon and vertical
polarizations with the vehicle on the road. In the empty road, the
receiving signal level is unvarying with the elevation when the
elevation is greater than 1.5 meters.
Abstract: Solar shading designs are important for reduction of building energy consumption and improvement of indoor thermal environment. This paper carried out a number of building simulations for evaluation of the energy performance of different shading devices based on incremental costs. The results show that movable shading devices lower incremental costs by up to 50% compared with fixed ones for the same building energy efficiency for residential buildings, and wing panel shadings are much more suitable in commercial buildings than baring screen ones and overhangs for commercial buildings.
Abstract: The implementation of Super-Ultra Low Emission
Vehicle standards requires more efficient exhaust gas purification. To
increase the efficiency of exhaust gas purification, an the adsorbent
capable of holding hydrocarbons up to 250-300 ОС should be
developed. The possibility to design such adsorbents by modification
of zeolites of mordenite type, ZSM-5 and NaY, using different
metals cations has been studied.
It has been shown that introducing Cr, Cs, Zn, Ni, Co, Li, Mn in
zeolites results in modification of the toluene TPD and toluene
sorption capacity.
5%LiZSM-5 zeolite exhibits the most attractive TPD curve, with
toluene desorption temperature ranging from 250 to 350ОС. The
sorption capacity of 5%Li-ZSM-5 is 0.4 mmol/g. NaY zeolite has the
highest sorption capacity, up to 2 mmol/g, and holds toluene up to
350ОС, but at 120ОС toluene desorption starts, which is not desirable,
since the adsorbent of cold start hydrocarbons should retain them
until 250-300ОС. Therefore 5%LiZSM-5 zeolite was found to be the
most promising to control the cold-start hydrocarbon emissions
among the samples studied.
Abstract: This paper is a numerical investigation of a laminar
isothermal plane two dimensional wall jet. Special attention has been
paid to the effect of the inlet conditions at the nozzle exit on the
hydrodynamic and thermal characteristics of the flow. The
behaviour of various fluids evolving in both forced and mixed
convection regimes near a vertical plate plane is carried out. The
system of governing equations is solved with an implicit finite
difference scheme. For numerical stability we use a staggered non
uniform grid. The obtained results show that the effect of the Prandtl
number is significant in the plume region in which the jet flow is
governed by buoyant forces. Further for ascending X values, the
buoyancy forces become dominating, and a certain agreement
between the temperature profiles are observed, which shows that the
velocity profile has no longer influence on the wall temperature
evolution in this region. Fluids with low Prandtl number warm up
more importantly, because for such fluids the effect of heat diffusion
is higher.
Abstract: This paper present an efficient and reliable technique of optimization which combined fuel cost economic optimization and emission dispatch using the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization algorithm (PSO) to reduce the cost of fuel and pollutants resulting from fuel combustion by keeping the output of generators, bus voltages, shunt capacitors and transformer tap settings within the security boundary. The performance of the proposed algorithm has been demonstrated on IEEE 30-bus system with six generating units. The results clearly show that the proposed algorithm gives better and faster speed convergence then linearly decreasing inertia weight.
Abstract: Missing data is a persistent problem in almost all
areas of empirical research. The missing data must be treated very
carefully, as data plays a fundamental role in every analysis.
Improper treatment can distort the analysis or generate biased results.
In this paper, we compare and contrast various imputation techniques
on missing data sets and make an empirical evaluation of these
methods so as to construct quality software models. Our empirical
study is based on NASA-s two public dataset. KC4 and KC1. The
actual data sets of 125 cases and 2107 cases respectively, without
any missing values were considered. The data set is used to create
Missing at Random (MAR) data Listwise Deletion(LD), Mean
Substitution(MS), Interpolation, Regression with an error term and
Expectation-Maximization (EM) approaches were used to compare
the effects of the various techniques.
Abstract: As computer network technology becomes
increasingly complex, it becomes necessary to place greater
requirements on the validity of developing standards and the
resulting technology. Communication networks are based on large
amounts of protocols. The validity of these protocols have to be
proved either individually or in an integral fashion. One strategy for
achieving this is to apply the growing field of formal methods.
Formal methods research defines systems in high order logic so that
automated reasoning can be applied for verification. In this research
we represent and implement a formerly announced multicast protocol
in Prolog language so that certain properties of the protocol can be
verified. It is shown that by using this approach some minor faults in
the protocol were found and repaired. Describing the protocol as
facts and rules also have other benefits i.e. leads to a process-able
knowledge. This knowledge can be transferred as ontology between
systems in KQML format. Since the Prolog language can increase its
knowledge base every time, this method can also be used to learn an
intelligent network.
Abstract: In this work, we present an automatic vehicle detection
system for airborne videos using combined features. We propose a
pixel-wise classification method for vehicle detection using Dynamic
Bayesian Networks. In spite of performing pixel-wise classification,
relations among neighboring pixels in a region are preserved in the
feature extraction process. The main novelty of the detection scheme is
that the extracted combined features comprise not only pixel-level
information but also region-level information. Afterwards, tracking is
performed on the detected vehicles. Tracking is performed using
efficient Kalman filter with dynamic particle sampling. Experiments
were conducted on a wide variety of airborne videos. We do not
assume prior information of camera heights, orientation, and target
object sizes in the proposed framework. The results demonstrate
flexibility and good generalization abilities of the proposed method on
a challenging dataset.
Abstract: ZnO nanostructures including nanowires, nanorods,
and nanoneedles were successfully deposited on GaAs substrates,
respectively, by simple two-step chemical method for the first time. A
ZnO seed layer was firstly pre-coated on the O2-plasma treated
substrate by sol-gel process, followed by the nucleation of ZnO
nanostructures through hydrothermal synthesis. Nanostructures with
different average diameter (15-250 nm), length (0.9-1.8 μm), density
(0.9-16×109 cm-2) were obtained via adjusting the growth time and
concentration of precursors. From the reflectivity spectra, we
concluded ordered and taper nanostructures were preferential for
photovoltaic applications. ZnO nanoneedles with an average diameter
of 106 nm, a moderate length of 2.4 μm, and the density of 7.2×109
cm-2 could be synthesized in the concentration of 0.04 M for 18 h.
Integrated with the nanoneedle array, the power conversion efficiency
of single junction solar cell was increased from 7.3 to 12.2%,
corresponding to a 67% improvement.
Abstract: Because support interference corrections are not properly
understood, engineers mostly rely on expensive dummy measurements
or CFD calculations. This paper presents a method based on uncorrected wind tunnel measurements and fast calculation techniques
(it is a hybrid method) to calculate wall interference, support interference and residual interference (when e.g. a support member
closely approaches the wind tunnel walls) for any type of wind tunnel and support configuration. The method provides with a simple formula
for the calculation of the interference gradient. This gradient is
based on the uncorrected measurements and a successive calculation
of the slopes of the interference-free aerodynamic coefficients. For the latter purpose a new vortex-lattice routine is developed that corrects
the slopes for viscous effects. A test case of a measurement on a wing proves the value of this hybrid method as trends and orders of
magnitudes of the interference are correctly determined.
Abstract: This paper presents a spectroscopic study on doping
of Vanadyl pathalocyanine (VOPc) by [6,6]-phenyl C61 butyric acid
methyl ester (PCBM). The films are characterized by UV/Vis/NIR
spectroscopy. A drastic increase in the absorption coefficient has
been observed with increasing dopant concentration. Optical
properties of VOPc:PCBM films deposited by spin coating technique
were studied in detail. Optical band gap decreased with the PCBM
incorporation in the VOPc film. Optical band gap calculated from the
absorption spectra decreased from 3.32 eV to 3.26 eV with a
variation of 0–75 % of PCBM concentration in the VOPC films.
Abstract: Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
Abstract: The paper presents a modelling methodology for
small scale multi-source renewable energy systems. Using historical
site-specific weather data, the relationships of cost, availability and
energy form are visualised as a function of the sizing of photovoltaic
arrays, wind turbines, and battery capacity. The specific dependency
of each site on its own particular weather patterns show that unique
solutions exist for each site. It is shown that in certain cases the
capital component cost can be halved if the desired theoretical
demand availability is reduced from 100% to 99%.
Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence technique for clustering. It produces higher
quality clusters compared to other population-based algorithms but with poor energy efficiency, cluster quality consistency and typically slower in convergence speed. Inspired by energy saving foraging behavior of natural honey bees this paper presents a Quality and Quantity Aware Artificial Bee Colony (Q2ABC) algorithm to improve quality of cluster identification, energy efficiency and convergence speed of the original ABC. To evaluate the performance of Q2ABC algorithm, experiments were conducted on a suite of ten benchmark UCI datasets. The results demonstrate Q2ABC outperformed ABC and K-means algorithm in the quality of clusters delivered.
Abstract: In biological and biomedical research motif finding tools are important in locating regulatory elements in DNA sequences. There are many such motif finding tools available, which often yield position weight matrices and significance indicators. These indicators, p-values and E-values, describe the likelihood that a motif alignment is generated by the background process, and the expected number of occurrences of the motif in the data set, respectively. The various tools often estimate these indicators differently, making them not directly comparable. One approach for comparing motifs from different tools, is computing the E-value as the product of the p-value and the number of possible alignments in the data set. In this paper we explore the combinatorics of the motif alignment models OOPS, ZOOPS, and ANR, and propose a generic algorithm for computing the number of possible combinations accurately. We also show that using the wrong alignment model can give E-values that significantly diverge from their true values.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: Robustness is one of the primary performance criteria for an Intelligent Video Surveillance (IVS) system. One of the key factors in enhancing the robustness of dynamic video analysis is,providing accurate and reliable means for shadow detection. If left undetected, shadow pixels may result in incorrect object tracking and classification, as it tends to distort localization and measurement information. Most of the algorithms proposed in literature are computationally expensive; some to the extent of equalling computational requirement of motion detection. In this paper, the homogeneity property of shadows is explored in a novel way for shadow detection. An adaptive division image (which highlights homogeneity property of shadows) analysis followed by a relatively simpler projection histogram analysis for penumbra suppression is the key novelty in our approach.
Abstract: In this paper, the two-dimension differential transformation method (DTM) is employed to obtain the closed form solutions of the three famous coupled partial differential equation with physical interest namely, the coupled Korteweg-de Vries(KdV) equations, the coupled Burgers equations and coupled nonlinear Schrödinger equation. We begin by showing that how the differential transformation method applies to a linear and non-linear part of any PDEs and apply on these coupled PDEs to illustrate the sufficiency of the method for this kind of nonlinear differential equations. The results obtained are in good agreement with the exact solution. These results show that the technique introduced here is accurate and easy to apply.