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 >

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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 >

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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 >

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947

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ARTICLE

Farrukh Saleem^1,2, 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 >

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ARTICLE

Nour EL Yakine Kouba^a, Mohamed Menaa^a, Kambiz Tehrani^b, Mohamed Boudour^a
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 >

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1121

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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 >

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898

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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 >

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1055

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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 >

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788

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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 >

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849

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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 >

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940

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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 >

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981

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ARTICLE

Byoung-Doo Oh^a,b, Hye-Jeong Song^a,b, Jong-Dae Kim^a,b, Chan-Young Park^a,b, Yu-Seop Kim^a,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 >

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803

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ARTICLE

Jihyuck Jo^a, Han‐Gyu Kim^b, In‐Cheol Park^a, Bang Chul Jung^c, Hoyoung Yoo^c
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 >

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839

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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 >

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797

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ARTICLE

Nan Pan^1*, 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 >

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856

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ARTICLE

Ching‐Han Chen^a, Ching‐Yi Chen^b, Nai‐Yuan Liu^a
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 >

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1087

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912

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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 >

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939

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ARTICLE

Jia‐Shing Sheu^a, I‐Chen Chen^b, Yi‐Syong Liao^a
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 >

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1278

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968

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ARTICLE

Yuchi Kang^1,2, Meihong Liu¹, Sharon Kao‐Walter^2,3, 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 >

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1108

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841

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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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