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. More >

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 More >

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 More >

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 More >

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 >

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 >

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 More >

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… More >

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 More >

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… More >