Open Access
ARTICLE
Usman Tariq
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 231-242, 2019, DOI:10.31209/2018.100000040
Abstract Wireless Sensor Networks (WSNs) empower the reflection of the environment
with an extraordinary resolve. These systems are combination of several
minuscule squat-cost, and stumpy-power on-chip transceiver sensing motes.
Characteristically, a sensing device comprises of four key gears: an identifying
element for data attainment, a microcontroller for native data dispensation, a
message component to permit the broadcast/response of data to/from
additional associated hardware, and lastly, a trivial energy source. Near field
frequency series and inadequate bandwidth of transceiver device drags to
multi-stage data transactions at minimum achievable requirements. State of
art, and prevailing operating systems, such as TinyOS (Levis, et.al. 2005),… More >
Open Access
ARTICLE
Fatma Mallouli
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 243-248, 2019, DOI:10.31209/2019.100000069
Abstract Density estimation via Gaussian mixture modelling has been successfully
applied to image segmentation. In this paper, we have learned distributions
mixture model to the pixel of an iris image as training data. We introduce the
proposed algorithm by adapting the Expectation-Maximization (EM) algorithm.
To further improve the accuracy for iris segmentation, we consider the EM
algorithm in Markovian and non Markovian cases. Simulated data proves the
accuracy of our algorithm. The proposed method is tested on a subset of the
CASIA database by Chinese Academy of Sciences Institute of Automation-IrisTwins. The obtained results have shown a significant improvement of our… More >
Open Access
ARTICLE
Lu Wu, Quan Liu, Ping Lou
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 249-257, 2019, DOI:10.31209/2018.100000010
Abstract The scheme of spatial pyramid matching (SPM) causes feature ambiguity near
dividing lines because it divides an image into different scales in a fixed manner.
A new method called soft SPM (sSPM) is proposed in this paper to reduce
feature ambiguity. First, an auxiliary area rotating around a dividing line in four
orientations is used to correlate the feature relativity. Second, sSPM is
performed to combine these four orientations to describe the image. Finally, an
optimized multiple kernel learning (MKL) algorithm with three basic kernels for
the support vector machine is applied. Specifically, for each level, a suitable
kernel is… More >
Open Access
ARTICLE
Farrukh Saleem1,2, Naomie Salim2, Abdulrahman H. Altalhi1, Abdullah AL‐Malaise AL‐Ghamdi1, Zahid Ullah1, Noor ul Qayyum1
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 259-277, 2019, DOI:10.31209/2018.100000002
Abstract Organizations are currently more dependent on Information and Communication
Technology (ICT) resources. The main purpose of this research is to help the
organization in order to maintain the quality of their ICT project based on
evaluation criteria presented in this research. This paper followed several steps
to support the methodology section. Firstly, an experimental investigation
conducted to explore the values assessment criterion, an organization may
realize from ICT project such as information systems, enterprise systems and IT
infrastructure. Secondly, the investigation is further based on empirical data
collected and analyzed from the respondents of six case studies using
questionnaire based… More >
Open Access
ARTICLE
Nour EL Yakine Koubaa, Mohamed Menaaa, Kambiz Tehranib, Mohamed Boudoura
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 279-294, 2019, DOI:10.31209/2018.100000007
Abstract This paper presents the use of a novel nature inspired meta-heuristic algorithm
namely Ant Lion Optimizer (ALO), which is inspired from the ant lions hunting
mechanism to enhance the frequency regulation and optimize the load
frequency control (LFC) loop parameters. The frequency regulation issue was
formulated as an optimal load frequency control problem (OLFC). The proposed
ALO algorithm was applied to reach the best combination of the PID controller
parameters in each control area to achieve both frequency and tie-line power
flow exchange deviations minimization. The control strategy has been tested
firstly with the standard two-area power system, followed by… More >
Open Access
ARTICLE
Rana Javed Masood1, DaoBo Wang1, Zain Anwar Ali2, Muhammad
Anwar2
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 295-304, 2019, DOI:10.31209/2018.100000009
Abstract In this article, adaptive hybrid control scheme is proposed for controlling the
position of a coaxial tri-rotor unmanned aerial system (UAS) in the presence of
input saturation and external wind disturbance. The adaptive hybrid controller
consists of model reference adaptive control with integral feedback (MRACI)
and proportional integral derivative (PID) controller. The adaptive controller
deals with the flight dynamics uncertainties and PID controller is used for tuning
the gains of MRACI whereas the stability of system is verified by Lyapunov
stability criterion. The integrator improves the order of the system thereby
improving the convergence rate by rejecting the noise and… More >
Open Access
ARTICLE
Nazan Çakmak Polat, Gözde Yaylali, Bekir Tanay
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 305-311, 2019, DOI:10.31209/2018.100000006
Abstract Soft set theory, which was defined by D. Molodtsov, has a rich potential for
applications in several fields of life. One of the successful application of the soft
set theory is to construct new methods for Decision Making problems. In this
study, we are introducing a method using graph representation of soft set
relations to solve Decision Making problems. We have successfully applied this
method to various examples. More >
Open Access
ARTICLE
Wen-Hsiang Hsieh, Jerzy W Rozenblit, Minvydas Ragulskis
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 313-314, 2019, DOI:10.31209/2019.100000092
Abstract This article has no abstract. More >
Open Access
EDITORIAL
Wen-Hsiang Hsieh, Jerzy W. Rozenblit, Minvydas Ragulskis
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 315-317, 2019, DOI:10.31209/2019.100000091
Abstract This article has no abstract. More >
Open Access
ARTICLE
Ching‐Yi Chen, Yi‐Jen Lin
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 319-327, 2019, DOI:10.31209/2019.100000093
Abstract In this study, a systematic data-driven adaptive neuro-fuzzy inference system
(ANFIS) modelling methodology is proposed. The new methodology employs an
unsupervised competitive learning scheme to build an initial ANFIS structure
from input-output data, and a high-performance PSO-LSE method is developed
to improve the structure and to identify the consequent parameters of ANFIS
model. This proposed modelling approach is evaluated using several nonlinear
systems and is shown to outperform other modelling approaches. The
experimental results demonstrate that our proposed approach is able to find the
most suitable architecture with better results compared with other methods
from the literature. More >
Open Access
ARTICLE
Chung Wen Hung, Wei Lung Mao, Han Yi Huang
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 329-341, 2019, DOI:10.31209/2019.100000093
Abstract Nonlinear system modeling and identification is the one of the most important
areas in engineering problem. The paper presents the recurrent fuzzy neural
network (RFNN) trained by modified particle swarm optimization (MPSO)
methods for identifying the dynamic systems and chaotic observation
prediction. The proposed MPSO algorithms mainly modify the calculation
formulas of inertia weights. Two MPSOs, namely linear decreasing particle
swarm optimization (LDPSO) and adaptive particle swarm optimization (APSO)
are developed to enhance the convergence behavior in learning process. The
RFNN uses MPSO based method to tune the parameters of the membership
functions, and it uses gradient descent (GD) based… More >
Open Access
ARTICLE
Byoung-Doo Oha,b, Hye-Jeong Songa,b, Jong-Dae Kima,b, Chan-Young Parka,b, Yu-Seop Kima,b
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 343-350, 2019, DOI:10.31209/2019.100000095
Abstract Accurate prediction of fine dust (PM10) concentration is currently recognized as
an important problem in East Asia. In this paper, we try to predict the
concentration of PM10 using Deep Neural Network (DNN). Meteorological
factors, yellow dust (sand), fog, and PM10 are used as input data. We test two
cases. The first case predicts the concentration of PM10 on the next day using
the day’s weather forecast data. The second case predicts the concentration of
PM10 on the next day using the previous day’s data. Based on this, we compare
the various performance results from the DNN model. In the… More >
Open Access
ARTICLE
Jihyuck Joa, Han‐Gyu Kimb, In‐Cheol Parka, Bang Chul Jungc, Hoyoung Yooc
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 351-358, 2019, DOI:10.31209/2019.100000096
Abstract A modified Viterbi scoring procedure is presented in this paper based on
Dijkstra’s shortest-path algorithm. In HMM-based speech recognition systems,
the Viterbi scoring plays a significant role in finding the best matching model,
but its computational complexity is linearly proportional to the number of
reference models and their states. Therefore, the complexity is serious in
implementing a high-speed speech recognition system. In the proposed
method, the Viterbi scoring is translated into the searching of a minimum path,
and the shortest-path algorithm is exploited to decrease the computational
complexity while preventing the recognition accuracy from deteriorating. In
addition, a two-phase comparison… More >
Open Access
ARTICLE
Nan Pan1, Yi Liu2, Dilin Pan2, Junbing Qian1, Gang Li3
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 359-366, 2019, DOI:10.31209/2019.100000097
Abstract It will face a lot of problems when using existing image-processing and 3D
scanning methods to do the similarity analysis of the line traces, therefore, an
effective comparison algorithm is put forward for the purpose of making
effective trace analysis and infer the criminal tools. The proposed algorithm
applies wavelet decomposition to the line trace 1-D detection signals to partially
reduce background noises. After that, the sequence comparison strategy based
on wavelet domain DTW is employed to do trace feature similarity matching.
Finally, using linear regression machine learning algorithm based on gradient
descent method to do constant iteration. The experiment… More >
Open Access
ARTICLE
Nan Pan1*, Dilin Pan2, Yi Liu2, Gang Li3
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 367-373, 2019, DOI:10.31209/2019.100000098
Abstract A set of laser detection system for shearing tools is developed, By holding
breakage of the cable, firstly, using single-point laser displacement sensors to
pick up surface features signal of line trace, then wavelet decomposition is used
to reduce the noise, and the signal after noise reduction is obtained. After that,
the threshold based sequence comparison method is used to achieve matches
of similar coincidence for trace features, and then using a gradient descent
method to getting the minimum cost of cost function value through continuous
iterative, and finally realizing the fast traceability of corresponding shearing
tool. More >
Open Access
ARTICLE
Ching‐Han Chena, Ching‐Yi Chenb, Nai‐Yuan Liua
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 375-384, 2019, DOI:10.31209/2019.100000099
Abstract This study introduces a dynamic gesture recognition system applicable in IPTV
remote control. In this system, we developed a hardware accelerator for realtime moving object detection. It is able to detect the position of hand block in
each frame at high speed. After acquiring the information of hand block, the
system can capture the robust dynamic gesture feature with the moving trail of
hand block in the continuous images, and input to FNN classifier for starting
recognition process. The experimental results show that our method has a good
recognition performance, and more applicable to real gesture-controlled
human-computer interactive environment. More >
Open Access
ARTICLE
Peizhong Liu1, Xiaofang Liu1, Yanming Luo2, Yongzhao Du1, Yulin Fan1, Hsuan‐Ming Feng3
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 385-394, 2019, DOI:10.31209/2019.100000100
Abstract Aiming at the drawback of artificial bee colony algorithm (ABC) with slow
convergence speed and weak exploitation capacity, an enhanced exploitation
artificial bee colony algorithm is proposed, EeABC for short. Firstly, a
generalized opposition-based learning strategy (GOBL) is employed when initial
population is produced for obtaining an evenly distributed population.
Subsequently, inspired by the differential evolution (DE), two new search
equations are proposed, where the one is guided by the best individuals in the
next generation to strengthen exploitation and the other is to avoid premature
convergence. Meanwhile, the distinction between the employed bee and the
onlooker bee is not… More >
Open Access
ARTICLE
Jia‐Shing Sheua, I‐Chen Chenb, Yi‐Syong Liaoa
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 395-404, 2019, DOI:10.31209/2019.100000101
Abstract This study proposed a household energy state monitoring system (HESMS) and
a household energy load monitoring system (HELMS) for monitoring smart
appliances. The HESMS applies reinforcement learning to receive changes in the
external environment and the state of an electrical appliance, determines if the
electrical appliance should be turned on, and controls the signals sent to the
HELMS according to these decisions. The HELMS implements an ON/OFF control
mechanism for household appliances according to the control signals and the
power consumption state. The proposed systems are based on the wireless
communication network and can monitor household appliances’ energy usage,
control… More >
Open Access
ARTICLE
Yuchi Kang1,2, Meihong Liu1, Sharon Kao‐Walter2,3, Jinbin Liu1, Qihong Cen4
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 405-411, 2019, DOI:10.31209/2019.100000102
Abstract A two-dimensional model of staggered tube banks of the bristle pack with
different pitch ratios was solved by computational fluid dynamics (CFD). The
pressure distribution along the gap centerlines and bristle surfaces were studied
for different upstream pressure from 0.2 to 0.6MPa and models. The results
show that the pressure is exponentially rather than strictly linearly decreasing
distributed inside the bristle pack. The pressure distribution is symmetry about
the circle’s horizontal line. The most obvious pressure drop occurred from about
60º to 90º. There is no stationary state reached between the kinetic energy
and the static pressure when the upstream… More >
Open Access
ARTICLE
Nan Pan1, Dilin Pan2, Yi Liu2
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 413-419, 2019, DOI:10.31209/2019.100000103
Abstract In order to solve the cutting tools classification problem, a crime tool
identification algorithm based on GVF-Harris-SIFT and KNN is put forward. The
proposed algorithm uses a gradient vector to smooth the gradient field of the
image, and then uses the Harris angle detection algorithm to detect the tool
angle. After that, the descriptors of the eigenvectors in corresponding feature
points were using SIFT to obtained. Finally, the KNN machine learning
algorithms is employed to for classification and recognition. The experimental
results of the comparison of the cutting tools show the accuracy and reliability
of the algorithm. More >
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