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  • Open Access


    Dynamic Task Assignment for Multi-AUV Cooperative Hunting

    Xiang Cao1,2,3, Haichun Yu1,3, Hongbing Sun1,3

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 25-34, 2019, DOI:10.31209/2018.100000038

    Abstract For cooperative hunting by a multi-AUV (multiple autonomous underwater vehicles) team, not only basic problems such as path planning and collision avoidance should be considered but also task assignments in a dynamic way. In this paper, an integrated algorithm is proposed by combining the self-organizing map (SOM) neural network and the Glasius Bio-Inspired Neural Network (GBNN) approach to improve the efficiency of multi-AUV cooperative hunting. With this integrated algorithm, the SOM neural network is adopted for dynamic allocation, while the GBNN is employed for path planning. It deals with various situations for single/multiple target(s) hunting in underwater environments with obstacles.… More >

  • Open Access


    Short-term Forecasting of Air Passengers Based on the Hybrid Rough Set and the Double Exponential Smoothing Model

    Haresh Kumar Sharma, Kriti Kumari, Samarjit Kar

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 1-14, 2019, DOI:10.31209/2018.100000036

    Abstract This article focuses on the use of the rough set theory in modeling of time series forecasting. In this paper, we have used the double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of the air transportation passenger data set in Australia and the results are compared with the classical DES model. It has been observed that the forecasting accuracy of the… More >

  • Open Access


    Surgical Outcome Prediction in Total Knee Arthroplasty Using Machine Learning

    Belayat Hossaina, Takatoshi Morookab, Makiko Okunob, Manabu Niia, Shinichi Yoshiyab, Syoji Kobashia

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 105-115, 2019, DOI:10.31209/2018.100000034

    Abstract This work aimed to predict postoperative knee functions of a new patient prior to total knee arthroplasty (TKA) surgery using machine learning, because such prediction is essential for surgical planning and for patients to better understand the TKA outcome. However, the main difficulty is to determine the relationships among individual varieties of preoperative and postoperative knee kinematics. The problem was solved by constructing predictive models from the knee kinematics data of 35 osteoarthritis patients, operated by posterior stabilized implant, based on generalized linear regression (GLR) analysis. Two prediction methods (without and with principal component analysis followed by GLR) along with… More >

  • Open Access


    An Accelerated Convergent Particle Swarm Optimizer (ACPSO) of Multimodal Functions

    Yasir Mehmood, Waseem Shahzad

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 91-103, 2019, DOI:10.31209/2018.100000017

    Abstract Particle swarm optimization (PSO) algorithm is a global optimization technique that is used to find the optimal solution in multimodal problems. However, one of the limitation of PSO is its slow convergence rate along with a local trapping dilemma in complex multimodal problems. To address this issue, this paper provides an alternative technique known as ACPSO algorithm, which enables to adopt a new simplified velocity update rule to enhance the performance of PSO. As a result, the efficiency of convergence speed and solution accuracy can be maximized. The experimental results show that the ACPSO outperforms most of the compared PSO… More >

  • Open Access


    Simulation of Real‐Time Path Planning for Large‐Scale Transportation Network Using Parallel Computation

    Jiping Liua,b, Xiaochen Kanga,*, Chun Donga, Fuhao Zhanga

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 65-77, 2019, DOI:10.31209/2018.100000013

    Abstract To guarantee both the efficiency and accuracy of the transportation system, the real-time status should be analyzed to provide a reasonable plan for the near future. This paper proposes a model for simulating the real-world transportation networks by representing the irregular road networks with static and dynamic attributes, and the vehicles as moving agents constrained by the road networks. The all pairs shortest paths (APSP) for the networks are calculated in a real-time manner, and the ever-changing paths can be used for navigating the moving vehicles with real-time positioning devices. In addition, parallel computation is used to accelerate the shortest… More >

  • Open Access


    Formal Modelling of Real-Time Self-Adaptive Multi-Agent Systems

    Awais Qasima, Syed Asad Raza Kazmib

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 49-63, 2019, DOI:10.31209/2018.100000012

    Abstract The paradigm of multi-agent systems is very expressive to model distributed real-time systems. These real-time multi-agent systems by their working nature have temporal constraints as they need to operate in pervasive, dynamic and unpredictable environments. To achieve better fault-tolerance, they need to have the ability of self-adaptivity making them adaptable to the failures. Presently there is a lack of vocabulary for the formal modelling of real-time multi-agent systems with self-adaptive ability. In this research we proposed a framework named SMARTS for the formal modelling of self-adaptive real-time multi-agent systems. Our framework integrates MAPE-K interfaces, reflection perspective and unification with distribution… More >

  • Open Access


    A Distributed Heterogeneous Inspection System for High Performance Inline Surface Defect Detection

    Yu-Cheng Chou1, Wei-Chieh Liao2, Yan-Liang Chen2, Ming Chang2, Po Ting Lin3

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 79-90, 2019, DOI:10.31209/2018.100000011

    Abstract This paper presents the Distributed Heterogeneous Inspection System (DHIS), which comprises two CUDA workstations and is equipped with CPU distributed computing, CPU concurrent computing, and GPU concurrent computing functions. Thirty-two grayscale images, each with 5,000× 12,288 pixels and simulated defect patterns, were created to evaluate the performances of three system configurations: (1) DHIS; (2) two CUDA workstations with CPU distributed computing and GPU concurrent computing; (3) one CUDA workstation with GPU concurrent computing. Experimental results indicated that: (1) only DHIS can satisfy the time limit, and the average turnaround time of DHIS is 37.65% of the time limit; (2) a… More >

  • Open Access


    An Improved K-nearest Neighbor Algorithm Using Tree Structure and Pruning Technology

    Juan Li

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 35-48, 2019, DOI:10.31209/2018.100000003

    Abstract K-Nearest Neighbor algorithm (KNN) is a simple and mature classification method. However there are susceptible factors influencing the classification performance, such as k value determination, the overlarge search space, unbalanced and multi-class patterns, etc. To deal with the above problems, a new classification algorithm that absorbs tree structure, tree pruning and adaptive k value method was proposed. The proposed algorithm can overcome the shortcoming of KNN, improve the performance of multi-class and unbalanced classification, reduce the scale of dataset maintaining the comparable classification accuracy. The simulations are conducted and the proposed algorithm is compared with several existing algorithms. The results… More >

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