Peng Mei¹, Gangyi Ding¹, Qiankun Jin¹, Fuquan Zhang^2,*, Yeh-Cheng Chen³

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 159-171, 2021, DOI:10.32604/iasc.2021.012813

Abstract Community structure is a key component in complex network systems. This paper aims to improve the effectiveness of community detection and community discovery in complex network systems by providing directions for the reconstruction and optimization of community structures to expand the application of intelligent optimization algorithms in community structures. First, deep learning algorithms and ant colony algorithms are used to elaborate the community detection and community discovery in complex networks. Next, we introduce the technology of transfer learning and propose an algorithm of deep self-encoder modeling based on transfer learning (DSEM-TL). The DSEM-TL algorithm’s indicators… More >

Zejian Zhang¹, Dawei Wang^2,*, Xiao-Zhi Gao³

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 173-189, 2021, DOI:10.32604/iasc.2021.012805

Abstract The interval type-2 fuzzy sets can describe nonlinear plants with uncertain parameters. It exists in nonlinearity. The parameter uncertainties extensively exist in the nonlinear practical Networked Control Systems (NCSs), and it is paramount to investigate the stabilization of the NCSs on account of the section type-2 fuzzy systems. Notice that most of the existing research work is only on account of the convention Parallel Distribution Compensation (PDC). For overcoming the weak point of the PDC and acquire certain guard stability conditions, the state tickling regulator under imperfect premise matching can be constructed to steady the… More >

Chunhua Zhou^*, Jianjing Shen, Xiaofeng Guo, Zhenyu Zhou

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 127-141, 2021, DOI:10.32604/iasc.2021.012532

Abstract In the process of interaction between users and battlefield situation information, combat tasks are the key factors that affect users’ information selection. In order to solve the problems of battlefield situation information recommendation (BSIR) for combat tasks, we propose a task-oriented battlefield situation information hybrid recommendation model (TBSI-HRM) based on tensor factorization and deep learning. In the model, in order to achieve high-precision personalized recommendations, we use Tensor Factorization (TF) to extract correlation relations and features from historical interaction data, and use Deep Neural Network (DNN) to learn hidden feature vectors of users, battlefield situation More >

Tao Ni¹, Lingtao Huang^1,*, Huanfei Zheng², Hongyan Zhang¹

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 143-158, 2021, DOI:10.32604/iasc.2021.012268

Abstract To overcome the problem that a teleoperation system loses robustness when the target moves outside the robot’s visual field or it is far away from the desired position, and to improve the operability and controllability of a master-slave teleoperation system, we present an image servo based haptic feedback (ISBHF) control method for teleoperating. The ISBHF control method involves extracting target feature points and constructing image servo based virtual force. First, the image characteristics of the environment and targets are identified and extracted by a 3D reconstruction method. A composite image Jacobian matrix is used to… More >

Qi Chen, Wei Cui^*, Jianqiu Sun, Xingguang Li, Xuyu Tian

Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 115-126, 2021, DOI:10.32604/iasc.2021.011617

Abstract The existing range-Doppler algorithms for SAR imaging are affected by a fast-time Doppler effect so they cannot be directly applied to FMCW SAR. Moreover, range migration is more evident in squint mode. To reveal the influence of the continuous motion of FMCW SAR in the squint mode on the echo signal and optimize the imaging process, an improved range-Doppler algorithm is based on squint FMCW SAR imaging is proposed in this paper. Firstly, the imaging geometry model and echo signal model of FMCW SAR are analyzed and deduced. The problem of Doppler center offset under… More >

Rizwan Munir^1,*, Yifei Wei¹, Rahim Ullah², Iftikhar Hussain³, Kaleem Arshid⁴, Umair Tariq¹

Journal of Quantum Computing, Vol.2, No.4, pp. 193-202, 2020, DOI:10.32604/jqc.2020.016151

Abstract A smart grid is the evolved form of the power grid with the integration of sensing, communication, computing, monitoring, and control technologies. These technologies make the power grid reliable, efficient, and economical. However, the smartness boosts the volume of data in the smart grid. To obligate full benefits, big data has attractive techniques to process and analyze smart grid data. This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid. The offered framework comprises of the following four layers: (i) Data source layer, (ii) More >

Saptarshi Sahoo^1,*, Amit Kumar Mandal¹, Pijus Kanti Samanta², Indranil Basu¹, Pratik Roy¹

Journal of Quantum Computing, Vol.2, No.4, pp. 181-192, 2020, DOI:10.32604/jqc.2020.015688

Abstract Quantum Computing and Quantum Information Science seem very promising and developing rapidly since its inception in early 1980s by Paul Benioff with the proposal of quantum mechanical model of the Turing machine and later By Richard Feynman and Yuri Manin for the proposal of a quantum computers for simulating various problems that classical computer could not. Quantum computers have a computational advantage for some problems, over classical computers and most applications are trying to use an efficient combination of classical and quantum computers like Shor’s factoring algorithm. Other areas that are expected to be benefitted More >

Dandan Peng, Le Sun^*

Journal of Quantum Computing, Vol.2, No.4, pp. 171-179, 2020, DOI:10.32604/jqc.2020.015402

Abstract The purpose of this work aims is to automatically build top-k (the number of suggested results) light weight service based systems (LitSBSs) on the basis of user-given keywords. Compared with our previous work, we use a score (oscore) to evaluate the keyword matching degree and QoS performance of a service so that we could find top-k LitSBSs with both high keyword matching degree and great QoS performance at the same time. In addition, to guarantee the quality of found top-k LitSBSs and improve the time efficiency, we redesign the database-driven algorithm (LitDB). We add the More >

Seth Aishwarya¹, Vaishnav Abeer^1,*, Babu B. Sathish¹, K. N. Subramanya²

Journal of Quantum Computing, Vol.2, No.4, pp. 157-170, 2020, DOI:10.32604/jqc.2020.015018

Abstract The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems. The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body. Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind. One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables, which provide… More >

Mohammad Aldossary^*

Computer Systems Science and Engineering, Vol.36, No.2, pp. 353-368, 2021, DOI:10.32604/csse.2021.014974

Abstract With the expansion of cloud computing, optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important, since it directly affects providers’ revenue and customers’ payment. Thus, providing prediction information of the cloud services can be very beneficial for the service providers, as they need to carefully predict their business growths and efficiently manage their resources. To optimize the use of cloud services, predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs. However, such mechanisms need to be provided with energy awareness not only at the level of More >