Abstract: This paper presents a multi-objective order allocation
planning problem with the consideration of various real-world
production features. A novel hybrid intelligent optimization model,
integrating a multi-objective memetic optimization process, a Monte
Carlo simulation technique and a heuristic pruning technique, is
proposed to handle this problem. Experiments based on industrial data
are conducted to validate the proposed model. Results show that (1)
the proposed model can effectively solve the investigated problem by
providing effective production decision-making solutions, which
outperformsan NSGA-II-based optimization process and an industrial
method.
Abstract: This article is a piece of the doctoral thesis
"Syncretism of traditional Kazakh culture in the light of the
innovation direction of circus and choreographic art of Kazakhstan
and its integration into the world civilization", and reveals the
features of the creative personalities of the traditional culture of
shamans, sals, seris, paluans in the comparative characteristic of the
European histriones and Russian skomorokhs.
Abstract: The purpose of this study is to understand how
consumers react to a company's promotional offers with mobile
applications (APP) as premiums. This paper presents the results of an
experimental study where five features of APP were involved: the
cost (free/discounted) for earning APP, the relationship between APP
and the promoted product, the perceived usefulness, the perceived
ease of use, and the perceived playfulness of APP in the context of
light foods purchase. The results support that the above features,
except perceived ease of use, have substantial influences on
consumers' intention to adopt the APP. Among the five features, the
cost for earning APP has the most impact on the adopting intention
of APP. The study also found a positive influence of adopting
intention of APP on the consumer's purchase intention of the
promoted product. Thus, APP-based premiums may enhance the
consumer's purchase intention of a company's promoted products.
Abstract: Extracting in-play scenes in sport videos is essential for
quantitative analysis and effective video browsing of the sport
activities. Game analysis of badminton as of the other racket sports
requires detecting the start and end of each rally period in an
automated manner. This paper describes an automatic serve scene
detection method employing cubic higher-order local auto-correlation
(CHLAC) and multiple regression analysis (MRA). CHLAC can
extract features of postures and motions of multiple persons without
segmenting and tracking each person by virtue of shift-invariance and
additivity, and necessitate no prior knowledge. Then, the specific
scenes, such as serve, are detected by linear regression (MRA) from
the CHLAC features. To demonstrate the effectiveness of our method,
the experiment was conducted on video sequences of five badminton
matches captured by a single ceiling camera. The averaged precision
and recall rates for the serve scene detection were 95.1% and 96.3%,
respectively.
Abstract: Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually the effects of blurred edges and jagged artifacts in the image to some extent. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to sharpen edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (“jaggies") along the tangent directions. In order to preserve image features such as edges, corners and textures, the nonlinear diffusion coefficients are locally adjusted according to the directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.
Abstract: In this paper we present a new approach to deal with
image segmentation. The fact that a single segmentation result do not
generally allow a higher level process to take into account all the
elements included in the image has motivated the consideration of
image segmentation as a multiobjective optimization problem. The
proposed algorithm adopts a split/merge strategy that uses the result
of the k-means algorithm as input for a quantum evolutionary
algorithm to establish a set of non-dominated solutions. The
evaluation is made simultaneously according to two distinct features:
intra-region homogeneity and inter-region heterogeneity. The
experimentation of the new approach on natural images has proved
its efficiency and usefulness.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: This paper presents the design and implementation of
the WebGD, a CORBA-based document classification and retrieval
system on Internet. The WebGD makes use of such techniques as Web,
CORBA, Java, NLP, fuzzy technique, knowledge-based processing
and database technology. Unified classification and retrieval model,
classifying and retrieving with one reasoning engine and flexible
working mode configuration are some of its main features. The
architecture of WebGD, the unified classification and retrieval model,
the components of the WebGD server and the fuzzy inference engine
are discussed in this paper in detail.
Abstract: Model Predictive Control (MPC) is increasingly being
proposed for real time applications and embedded systems. However
comparing to PID controller, the implementation of the MPC in
miniaturized devices like Field Programmable Gate Arrays (FPGA)
and microcontrollers has historically been very small scale due to its
complexity in implementation and its computation time requirement.
At the same time, such embedded technologies have become an
enabler for future manufacturing enterprises as well as a transformer
of organizations and markets. Recently, advances in microelectronics
and software allow such technique to be implemented in embedded
systems. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and
applied control technique in the industrial engineering. In fact in
this paper, we propose an efficient framework for implementation
of Generalized Predictive Control (GPC) in the performed STM32
microcontroller. The STM32 keil starter kit based on a JTAG interface
and the STM32 board was used to implement the proposed GPC
firmware. Besides the GPC, the PID anti windup algorithm was
also implemented using Keil development tools designed for ARM
processor-based microcontroller devices and working with C/Cµ
langage. A performances comparison study was done between both
firmwares. This performances study show good execution speed and
low computational burden. These results encourage to develop simple
predictive algorithms to be programmed in industrial standard hardware.
The main features of the proposed framework are illustrated
through two examples and compared with the anti windup PID
controller.
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: Metallic micro parts are playing an important role in micro-fabrication industry. Recently, we have demonstrated a new deformation mechanism for micro-formability of polycrystalline materials. Different depressed micro-features smaller than the grain size have been successfully fabricated on 6061 aluminum alloy (AA6061) substrates with good fidelity. To further verify this proposed deformation mechanism that grain size is not a limiting factor, we demonstrate here that in addition of depressed features, protruded micro-features on a polycrystalline substrate can similarly be fabricated.
Abstract: IMCS is Integrated Monitoring and Control System for
thermal power plant. This system consists of mainly two parts; controllers and OIS (Operator Interface System). These two parts are
connected by Ethernet-based communication. The controller side of communication is managed by CNet module and OIS side is managed
by data server of OIS. CNet module sends the data of controller to data
server and receives commend data from data server. To minimizes or
balance the load of data server, this module buffers data created by controller at every cycle and send buffered data to data server on request of data server. For multiple data server, this module manages
the connection line with each data server and response for each request
from multiple data server. CNet module is included in each controller
of redundant system. When controller fail-over happens on redundant system, this module can provide data of controller to data sever
without loss. This paper presents three main features – separation of get task, usage of ring buffer and monitoring communication status –of CNet module to carry out these functions.
Abstract: In Grid computing, a data transfer protocol called
GridFTP has been widely used for efficiently transferring a large volume
of data. Currently, two versions of GridFTP protocols, GridFTP
version 1 (GridFTP v1) and GridFTP version 2 (GridFTP v2), have
been proposed in the GGF. GridFTP v2 supports several advanced
features such as data streaming, dynamic resource allocation, and
checksum transfer, by defining a transfer mode called X-block mode.
However, in the literature, effectiveness of GridFTP v2 has not been
fully investigated. In this paper, we therefore quantitatively evaluate
performance of GridFTP v1 and GridFTP v2 using mathematical
analysis and simulation experiments. We reveal the performance
limitation of GridFTP v1, and quantitatively show effectiveness of
GridFTP v2. Through several numerical examples, we show that by
utilizing the data streaming feature, the average file transfer time of
GridFTP v2 is significantly smaller than that of GridFTP v1.
Abstract: As an adult man and woman love each other and come to have faith in each other as their spouse, they marry each other. Recently people-s economic life has become individualized and women are enjoying a high education level and increased participation in social activities, and these changes are creating environment favorable for single life. Thus, an increasing number of people are choosing celibacy, and many people prefer cohabitation to marriage. Nevertheless, marriage is still regarded as a must-to-do in our thought. Most of people throughout the world admit marriage as one of natural processes of life, and is an important passage rite in life that all people experience as we can see everywhere in the world despite the diversity of lifestyles. With regard to wedding ceremony, however, each country and culture has its own unique tradition and style of festival. It is not just a congratulatory ceremony but contains multiple concepts representing the age, country or culture. Moreover, the form and contents of wedding ceremony changes over time, and such features of wedding ceremony are well represented in films. This study took note of the fact that films reflect and reproduce each country-s historicity, culturality and analyzed four films, which are believed to show differences between Eastern and Western wedding ceremonies. The selected films are: A Perfect Match (2002), Marriage Is a Crazy Thing (2001), Bride Wars (2009) and 27 Dresses (2008). The author attempted to examine wedding ceremonies described in the four films, differences between the East and the West suggested by the films, and changes in their societies.
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: For most image fusion algorithms separate
relationship by pixels in the image and treat them more or less
independently. In addition, they have to be adjusted different
parameters in different time or weather. In this paper, we propose a
region–based image fusion which combines aspects of feature and
pixel-level fusion method to replace only by pixel. The basic idea is
to segment far infrared image only and to add information of each
region from segmented image to visual image respectively. Then we
determine different fused parameters according different region. At
last, we adopt artificial neural network to deal with the problems of
different time or weather, because the relationship between fused
parameters and image features are nonlinear. It render the fused
parameters can be produce automatically according different states.
The experimental results present the method we proposed indeed
have good adaptive capacity with automatic determined fused
parameters. And the architecture can be used for lots of applications.
Abstract: The article is devoted to Kazakh repatriates and their
migration to Kazakhstan as historical homeland, and also addresses
the problem of migrants- adaptation in the republic, particularly in
Almaty oblast (region). The authors used up-to-date statictics and
materials of the Department of Migration Committee to analyze the
newcomers- number and features of the repatriate-s location in this
oblast. Having studied this region they were able to identify the main
reasons why Kazakh Diaspora in Central Asia, Iran, Avganistana and
Turkey is eager to come back to their historic homeland along with
repatriates adaptation to the republic.
Abstract: A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.
Abstract: Smoothing or filtering of data is first preprocessing step
for noise suppression in many applications involving data analysis.
Moving average is the most popular method of smoothing the data,
generalization of this led to the development of Savitzky-Golay filter.
Many window smoothing methods were developed by convolving
the data with different window functions for different applications;
most widely used window functions are Gaussian or Kaiser. Function
approximation of the data by polynomial regression or Fourier
expansion or wavelet expansion also gives a smoothed data. Wavelets
also smooth the data to great extent by thresholding the wavelet
coefficients. Almost all smoothing methods destroys the peaks and
flatten them when the support of the window is increased. In certain
applications it is desirable to retain peaks while smoothing the data
as much as possible. In this paper we present a methodology called
as peak-wise smoothing that will smooth the data to any desired level
without losing the major peak features.
Abstract: This paper features the mathematical modeling of a single input single output based Timoshenko smart beam. Further, this mathematical model is used to design a multirate output feedback based discrete sliding mode controller using Bartoszewicz law to suppress the flexural vibrations. The first 2 dominant vibratory modes is retained. Here, an application of the discrete sliding mode control in smart systems is presented. The algorithm uses a fast output sampling based sliding mode control strategy that would avoid the use of switching in the control input and hence avoids chattering. This method does not need the measurement of the system states for feedback as it makes use of only the output samples for designing the controller. Thus, this methodology is more practical and easy to implement.