Journals / IASC / Vol.26, No.5

  • ARTICLE

    A Hybrid Approach for the Lung(s) Nodule Detection Using the Deformable Model and Distance Transform

    Ayyaz Hussain1, Mohammed Alawairdhi2, Fayez Alazemi3, Sajid Ali Khan4, Muhammad Ramzan2,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 857-871, 2020, DOI:10.32604/iasc.2020.010120
    Abstract The Computer Aided Diagnosis (CAD) systems are gaining more recognition and being used as an aid by clinicians for detection and interpretation of diseases every passing day due to their increasing accuracy and reliability. The lung(s) nodule detection is a very crucial and difficult step for CAD systems. In this paper, a hybrid approach for the lung nodule detection using a deformable model and distance transform has been proposed. The proposed method has the ability to detect all major kinds of nodules such as the juxta-plueral, isolated, and the juxta-vescular, along with the non-solid nodules automatically and intelligently. Results show… More >

  • ARTICLE

    Causality Learning from Time Series Data for the Industrial Finance Analysis via the Multi-Dimensional Point Process

    Liangliang Shi1,2, Peili Lu3, Junchi Yan4,5,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 873-885, 2020, DOI:10.32604/iasc.2020.010121
    Abstract Causality learning has been an important tool for decision making, especially for financial analytics. Given the time series data, most existing works construct the causality network with the traditional regression models and estimate the causality by pairs. To fulfil a holistic one-shot inference procedure over the whole network, we propose a new causal inference method for the multidimensional time series data, specifically related to some case studies for the industrial finance analytics. Specifically, the time series are first converted to the event sequences with timestamps by fluctuation the detection, and then a multidimensional point process is used for learning the… More >

  • ARTICLE

    Multi-Scale Boxes Loss for Object Detection in Smart Energy

    Zhiyong Dai1,*, Jianjun Yi1, Yajun Zhang1, Liang He2
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 887-903, 2020, DOI:10.32604/iasc.2020.010122
    Abstract The rapid development of Internet of Things (IoT) technologies has boosted smart energy networks in recent years. However, power line surveillance systems still suffer from the low accuracy and efficiency of the power line area recognition and risk objects detection. This paper proposes a new customized loss function to tackle the disequilibrium of the size of objects on multi-scale feature maps in the deep learning-based detectors. To validate the new concept and improve the efficiency, we also presented a new object detection model. Experimental results are provided to exhibit the advantage of our proposed method in both accuracy and efficiency. More >

  • ARTICLE

    Design of an Observer-Based Controller for T-S Fuzzy Time-Delay Systems under Imperfect Premise Matching

    Zejian Zhang1, Dawei Wang2,*, Peng Li1, Xiao-Zhi Gao3
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 905-915, 2020, DOI:10.32604/iasc.2020.010123
    Abstract In this paper, the stabilization problem of an observer-based T-S fuzzy time-delay system under imperfect premise matching is studied, in which the T-S fuzzy observer model with time-delay and the fuzzy controller do not share the same membership functions. The objective is to design a state observer and unmatching fuzzy controller such that the closed-loop system with time-delay is asymptotically stable. A sufficient condition for the stabilization via observerbased state feedback under imperfect premise matching is presented, and an observer-based state feedback controller under imperfect premise is also constructed. The proposed control scheme is well capable of enhancing the design… More >

  • ARTICLE

    Video Preview Generation for Interactive Educational Digital Resources Based on the GUI Traversal

    Xiaohong Shi1,3, Chao Ma2, Yongsheng Rao3,*, Xiangping Chen4, Jingzhong Zhang3
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 917-932, 2020, DOI:10.32604/iasc.2020.010124
    Abstract The interactive educational digital resources (IEDRs) are more and more prevalent in all levels of education. With the proliferation of the Internet, more and more IEDRs are being shared online. How to aid users to explore helpful resources effectively and efficiently has become a challenge. The static preview of resources that current research has mainly focused on is ineffective for the IEDRs, because of the unique feature of the IEDRs that the knowledge is crucial for the users’ comprehension and is hidden in the process of interaction. To unfold the hidden crucial knowledge and ensure a users’ fast acquisition, we… More >

  • ARTICLE

    Wiener Model Identification Using a Modified Brain Storm Optimization Algorithm

    Tianhong Pan1,*, Ying Song2, Shan Chen2
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 934-946, 2020, DOI:10.32604/iasc.2020.010125
    Abstract The Wiener model is widely used in industrial processes. It is composed of a linear dynamic block and a nonlinear static block. Estimating the Wiener model is challenging because of the diversity of static nonlinear functions and the immeasurableness of intermediate signals owing to the series structure of the Wiener model. Existing optimization algorithms cannot satisfy the requirements of accuracy and efficiency of identification and often lose into a local optimum. Herein, a modified Brain Storm Optimization (mBSO) is proposed to estimate the parameters of the Wiener model. Many different combinations of individuals from intra or extra-groups ensure the diversity… More >

  • EDITORIAL

    Special Section on Data-Enabled Intelligence in Complex Agricultural Systems

    Long Wang1,*, Zhe Song2, Chao Huang1, Shancheng Jiang3, Jenq-Haur Wang4
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 947-948, 2020, DOI:10.32604/iasc.2020.010126
    Abstract This article has no abstract. More >

  • ARTICLE

    Effects of Different Salt Stress on Physiological Growth and Yield of Drip Irrigation Cotton (Gossypium hirsutum L.)

    Jiangchun Chen1, Zhenhua Wang1,2,*, Jinzhu Zhang1, Weibin Cao1
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 949-959, 2020, DOI:10.32604/iasc.2020.010127
    Abstract This study adopted the method of barrel planting to artificially set the salt content of six different soils (CK:1.5 g kg-1 , T1:3.0 g kg-1 , T2:4.0 g kg-1 , T3:5.3 g kg-1 , T4:6.2 g kg-1 , T5:7.3 g kg-1 ) to study the effects of different degrees of mild salt stress on photosynthetic physiology, growth index and yield of cotton under drip irrigation. The results showed that with the increasing salt stress and the prolongation of stress time, the photosynthetic physiological indexes of cotton showed a downward trend (P < 0.01), and the plant height and leaf area… More >

  • ARTICLE

    An Improved Algorithm of K-means Based on Evolutionary Computation

    Yunlong Wang1,2,3, Xiong Luo1,2,4,*, Jing Zhang1,2,3, Zhigang Zhao1, Jun Zhang5
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 961-971, 2020, DOI:10.32604/iasc.2020.010128
    Abstract K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a local optimum on the basis of several ideas from evolutionary computation, through the use of random and evolutionary processes. The experimental results show that the proposed algorithm is capable of improving the accuracy of K-means and decreasing the SSE of K-means, which indicates that the proposed algorithm can prevent K-means from falling into the local optimum to some extent. More >

  • ARTICLE

    An Apriori-Based Learning Scheme towards Intelligent Mining of Association Rules for Geological Big Data

    Maojian Chen1,2,3, Xiong Luo1,2,3,*, Yueqin Zhu4, Yan Li1,2,3, Wenbing Zhao5, Jinsong Wu6
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 973-987, 2020, DOI:10.32604/iasc.2020.010129
    Abstract The past decade has witnessed the rapid advancements of geological data analysis techniques, which facilitates the development of modern agricultural systems. However, there remains some technical challenges that should be addressed to fully exploit the potential of those geological big data, while gathering massive amounts of data in this application field. Generally, a good representation of correlation in the geological big data is critical to making full use of multi-source geological data, while discovering the relationship in data and mining mineral prediction information. Then, in this article, a scheme is proposed towards intelligent mining of association rules for geological big… More >

  • ARTICLE

    A PSO-XGBoost Model for Estimating Daily Reference Evapotranspiration in the Solar Greenhouse

    Jingxin Yu1,3, Wengang Zheng1,*, Linlin Xu3, Lili Zhangzhong1, Geng Zhang2, Feifei Shan1
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 989-1003, 2020, DOI:10.32604/iasc.2020.010130
    Abstract Accurate estimation of reference evapotranspiration (ET0) is a critical prerequisite for the development of agricultural water management strategies. It is challenging to estimate the ET0 of a solar greenhouse because of its unique environmental variations. Based on the idea of ensemble learning, this paper proposed a novel ET0i estimation model named PSO-XGBoost, which took eXtreme Gradient Boosting (XGBoost) as the main regression model and used Particle Swarm Optimization (PSO) algorithm to optimize the parameters of XGBoost. Using the meteorological and soil moisture data during the two-crop planting process as the experimental data, and taking ET0i calculated based on the improved… More >

  • EDITORIAL

    Industrial Informatics-Based Applications and Techniques in Intelligent Automation

    Zheng Xu1,*, Qingyuan Zhou2
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1005-1006, 2020, DOI:10.32604/iasc.2020.010132
    Abstract This article has no abstract. More >

  • ARTICLE

    Research on the Dynamic Compensation System of the Cathode Electrode Wear for a Short Electric Arc Machine Tool

    Bo Li1,3, Di Xiong2,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1007-1021, 2020, DOI:10.32604/iasc.2020.010133
    Abstract A methodology of on-line monitoring of the diameter of the cathode electrode is put forward in light of the issue that the diameter of the cathode electrode cannot be detected in an accurate manner in short electric arc machine tools. The monitoring methodology is capable of precisely determining the wear amount of the cathode electrode and uses the bus module in the numerical control machine tool to feed the wear amount of the cathode electrode back to the numerical control system (CNC system). The CNC system dynamically adjusts the position of the cathode electrode by driving the compensation shaft to… More >

  • ARTICLE

    Mathematical Interpolation and Correction of Three-Dimensional Modelling of High-Speed Railway

    Jun Gao1,2,*, Xiao Lin3
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1023-1034, 2020, DOI:10.32604/iasc.2020.010134
    Abstract Three-dimensional (3D) modelling of high-speed railways, bad geology, and special geotechnical engineering inferences may involve problems, such as inaccurate geological data, hidden underground geological phenomena, and complex geological processes. In this study, surface geological boundaries, drainage, transportation networks, covers, lenses, and small geological units are established using topographic surveying and mapping data, geological data, and geological exploration data acquisition. The 3D model of the karst system combines geological and mathematical interpolation curved surface 3D model simulation analysis, trend surface fitting, and interpolation of the NURBS surface and correct analysis. The model is used to describe the properties of objects, including… More >

  • ARTICLE

    Research on Agent-Based Economic Decision Model Systems

    Chenxi Liu1, Suchun Yang2,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1035-1046, 2020, DOI:10.32604/iasc.2020.010135
    Abstract Based on an analysis of the development of economic decision support systems, agents are applied to construct intelligent economic decision support systems. This paper proposes a task-oriented agent design concept and designs multiple types of agents to complete the decision-making tasks with the task as the core. The structure of multi-agent based systems is provided, and the concrete realization structure of different types of agents in the system is also provided. Additionally, this study discusses the operational mechanism of the whole system and the cooperation between multiple agents in the system. Finally, these functions are implemented through a combination of… More >

  • ARTICLE

    Numerical Simulation Study on the Regularity of CIS Bedding Hydraulic Fracturing Based on 3D Penny-Shape Model

    JiangtaoLi1,2,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1047-1061, 2020, DOI:10.32604/iasc.2020.010136
    Abstract In view of poor permeability of coal seam and soft coal quality in China's coal mining area, a point hydraulic fracturing method suitable for the occurrence characteristics of coal seam is put forward based on the characteristics of coal seam hydraulic fracturing and the field practical experience of coal seam hydraulic fracturing for many years. A theoretical and mathematical model of hydraulic fracturing is established. Based on the large-scale finite element software ABAQUS, numerical simulation of two dimensional and three dimensional hydraulic fracturing is carried out, and the fracture propagation law and its parameter sensitivity of coal seam point hydraulic… More >

  • ARTICLE

    Low-Carbon Efficiency Model Evaluation of China’s Iron and Steel Enterprises Based on Data and Empirical Evidence

    Xuesong Xu, Hongyan Shao, Shengjie Yang*, Rongyuan Chen
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1063-1072, 2020, DOI:10.32604/iasc.2020.010137
    Abstract The aim of this study is to consider the economic, resource, energy and environmental factors in a low-carbon economic efficiency evaluation system and to analyze the factors affecting iron and steel enterprises. A combined data envelopment analysis and Malmquist index model have been used in this paper. We empirically investigate the low-carbon efficiency of the Chinese steel industry using observations of 17 listed enterprises from 2009 to 2013. The results show that the economic efficiency of China’s iron & steel enterprises is generally low. The Malmquist productivity index also shows a decreasing trend. Based on our findings, some policies are… More >

  • ARTICLE

    Study on the Application of an Improved Intelligent Group Algorithm

    Fengjuan Wang1,2,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1073-1080, 2020, DOI:10.32604/iasc.2020.010138
    Abstract The Particle swarm optimization algorithm (PSO) and the simulated annealing algorithm (SA) are two well-known stochastic and intelligent methods used for optimization. Both methods have some shortcomings. On the basis of the shortcomings of PSO and SA, this paper offers an enhanced intelligent group algorithm on the basis of the roulette rule to improve the parameter velocity ν of (PSO) and the initial temperature of SA algorithm. This paper gives a detailed introduction to the principle and flow of the new algorithm and introduces the application status of the new algorithm. More >

  • ARTICLE

    Design and Application Research of a Digitized Intelligent Factory in a Discrete Manufacturing Industry

    Yefeng Liu1,2,*, Yuan Zhao1,2, Kangju Li1,2, Shengping Yu3, Shaowu Li4
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1081-1096, 2020, DOI:10.32604/iasc.2020.010139
    Abstract The intelligent flexible system is a powerful means for manufacturing enterprises to achieve high efficiency and high output. The physical structure of the flexible system is constructed in this paper. The three systems; workshop intelligent logistics system, management and control system and three flexible processing that produce four products are given by the deeper design. The local workshop net which contains; position sensors, radio frequency identification (RFID), AGVs, intelligent machine, robots, raw materials and operation devices is constructed in this paper. The software, which includes gages, real-time workshops, logistics trolley, and information of all kinds of logistics process and processing,… More >

  • ARTICLE

    Ontology-Supported Double-Level Model Construction for International Disaster Medical Relief Resource Forecasting

    Min Zhu1,2,3,#, Huiyu Jin1,#, Ruxue Chen1, Quanyi Huang2,3, Shaobo Zhong4, Guang Tian1,*
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1097-1109, 2020, DOI:10.32604/iasc.2020.010140
    Abstract In a disaster, mass casualties lead to a surge in demand for medical services. Some relief actions have been criticized for being ill-adapted to dominating medical needs. This research established a disaster medical relief planning model in 3 steps. 1. Establishing the two-level conceptual model. 2. Using the ontology method to describe the hierarchy and relating rules of the terms and concepts associated with the model. 3. Using an ontology-support casebased reasoning approach to build the case similarity matching process, which can provide a more efficient system for decision support. A case study validated the model and demonstrated its usage. More >

  • ARTICLE

    Blockchain Queuing Model with Non-Preemptive Limited-Priority

    Tianmu Li1,2, Yongjun Ren1,2,*, Jinyue Xia3
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1111-1122, 2020, DOI:10.32604/iasc.2020.012531
    Abstract Blockchain technology has recently obtained widespread attention. And it is being regarded as potentially even more disruptive than the Internet, whose usage includes large areas of applications ranging from crypto currency, financial services, reputation system, Internet of Things, sharing economy to public and social services. The existing works of blockchain primarily are focused on key components and potential applications. However, in the existing blockchain systems, the waiting time of transactions is too long. Furthermore, it may produce serious consequences because many important transactions are not handled timely. To solve the problem, in the paper, the blockchain Queuing model with non-preemptive… More >

  • ARTICLE

    Design and Development of Unmanned Surface Vehicle for Meteorological Monitoring

    Dongli Wu1, Yunping Liu2,3,*, Ze Xu2,3, Weiyan Shang4
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1123-1138, 2020, DOI:10.32604/iasc.2020.012757
    Abstract In view of the paucity of hydrometeorological data, inefficiency and high cost of manual detection, a design scheme of meteorological monitoring unmanned surface vehicle (USV) based on STM32 MCU (Microcontroller Unit, which is also known as Single Chip Microcomputer) is proposed in this paper. The path planning is designed by combining the image data acquired by camera with improved RRT-path algorithm (rapidly-exploring random tree algorithm based on auxiliary path), and then motors are controlled so as to realize autonomous cruise control of USV using the incremental PID algorithm. In addition, the design can also realize real-time monitoring of meteorological data… More >

  • ARTICLE

    Oversampling Methods Combined Clustering and Data Cleaning for Imbalanced Network Data

    Yang Yang1,*, Qian Zhao1, Linna Ruan2, Zhipeng Gao1, Yonghua Huo3, Xuesong Qiu1
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1139-1155, 2020, DOI:10.32604/iasc.2020.011705
    Abstract In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes an oversampling method for imbalanced… More >

  • ARTICLE

    Blockchain-Based Data Storage Mechanism for Industrial Internet of Things

    Jin Wang1,2, Wencheng Chen1, Lei Wang3, Yongjun Ren4,*, R. Simon Sherratt5
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1157-1172, 2020, DOI:10.32604/iasc.2020.012174
    Abstract With the development of the Industrial Internet of Things and the continuous expansion of application scenarios, many development bottlenecks have followed. Its data security issue has become an obstacle to its widespread application. It has attracted substantial attention from both academia and industry. Blockchain technology has the characteristics of decentralization, openness and transparency and non-tampering. It has natural advantages in solving the security of the Industrial Internet of Things. Accordingly, this paper first analyzes the security risks associated with data storage in the Industrial Internet of Things and proposes the use of blockchain technology to ensure the secure storage of… More >

  • ARTICLE

    Pricing Method for Big Data Knowledge Based on a Two-Part Tariff Pricing Scheme

    Chuanrong Wu1,*, Huayi Yin1, Xiaoming Yang2, Zhi Lu3, Mark E. McMurtrey4
    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1173-1184, 2020, DOI:10.32604/iasc.2020.014961
    Abstract Nowadays big data knowledge is being bought and sold online for market research, new product development, or other business decisions, especially when customer demands and consumer preferences knowledge for new product development are needed. Previous studies have introduced two commonly used pricing schemes for big data knowledge transactions (e.g., cloud services): Subscription pricing and pay-per-use pricing from a big data knowledge provider’s standpoint. However, few studies to date have investigated a two-part tariff pricing scheme for big data knowledge transactions, albeit this pricing scheme may increasingly attract the big data knowledge providers in this hyper-competitive market. Also, little research has… More >

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