Forthcoming and Online First Articles

International Journal of Cybernetics and Cyber-Physical Systems

International Journal of Cybernetics and Cyber-Physical Systems (IJCCPS)

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International Journal of Cybernetics and Cyber-Physical Systems (6 papers in press)

Regular Issues

  • A review of methods for detection and segmentation of kidney stones from CT scan images using image processing method   Order a copy of this article
    by Vahid Nazmdeh, Somayeh Saraf Esmili 
    Abstract: Kidney stone disease is on the rise today, Kidney stones are hard deposits often due to the high concentration of minerals and salts in the urine occur. Computed tomography (CT) has become the gold standard for diagnosing kidney stones. The aim of this study was to review computer-aided detection (CAD) algorithms for the detection of kidney stones in CT images. Due to the presence of different organs in CT images, Image segmentation and Region of Interest (ROI) selection is one of the challenges in this field, and choosing a suitable method for image segmentation can increase the accuracy, sensitivity, and efficiency of the system. In this article, we provide a brief overview of recent work in the diagnosis of kidney stones using image processing techniques.
    Keywords: kidney stone; computed tomography; Medical imaging; image processing; segmentation; detection; Region of Interest; Feature extraction; Classification.
    DOI: 10.1504/IJCCPS.2022.10047145
  • Research on fast video mapping method of borehole in front view   Order a copy of this article
    by Qunpo Liu, Qijing Wang, Ruxin Gao, Junjia Bi, Naohiko Hanajima 
    Abstract: Camera borehole technology is widely used in borehole engineering, which can obtain the structure of deep-buried rock mass. However, when people recognise these images, there will be problems such as heavy workload, low efficiency and easy recognition errors. In order to solve these problems, this paper proposes a fast unfolding mosaic and fusion method of looped image in the forward view video. This paper proposes a borehole image expansion method with autonomous positioning of the borehole centre and adaptive adjustment of the expansion radius to achieve rapid and automatic image expansion. On this basis, an adaptive adjustment algorithm of image stitching points based on grey difference registration is proposed, which solves the defect of determining stitching points by artificial experience. The practical results show that the image mosaic technology is simple, the processing is flexible and efficient, and the forward view borehole expansion map obtained has an ideal effect.
    Keywords: image expansion; image mosaic; CLAHE; image processing.
    DOI: 10.1504/IJCCPS.2022.10047531
  • Apple surface defect identification based on image analysis   Order a copy of this article
    by Qunpo Liu, Yuxi Zhao, Jianjun Zhang, Ruxin Gao 
    Abstract: The apple fruit defect detection is a necessary step before apple entered the market. When using deep learning to detect apple defects, apple defects are prone to miss detection and inaccurate positioning due to multiple convolutions and down-sampling. Therefore, this paper proposes YOLO-APPLE model. Three residual blocks in YOLOV3 were replaced with three dense blocks, and feature transfer between dense connected blocks was strengthened by combining average pooling to improve feature reuse, so as to reduce the rate of missed detection. Complete-IOU is used as the regression loss to locate the prediction frame more accurately. Secondly, K-means clustering algorithm was used for clustering apple defect dataset to obtain anchor boxes more consistent with apple defect and raise the efficiency of precision of the model. The results showed that the average precision of YOLO-APPLE model is 93.53%, and the detection speed is 43FPS, which can detect in real time.
    Keywords: apple defect; YOLO-APPLE model; dense block; complete-IOU; K-means clustering algorithm.
    DOI: 10.1504/IJCCPS.2022.10047532
  • Electromagnetic failure analysis of control system processors in the internet of things   Order a copy of this article
    by Varghese Mathew Vaidyan, Akhilesh Tyagi 
    Abstract: Adverse operating conditions in which control system processors operate in networked control systems cause failures and are susceptible to malware attacks Decoupled distance monitoring of stuck-at faults and malware is more robust and can likely be integrated into Internet of Things (IoT) frameworks The key differentiator of our failure/performance analysis and Machine Learning (ML) modelling from traditional methods is that we propose an Electromagnetic (EM) Spectral domain-based analysis approach entirely decoupled from the controller processor It can analyse the causes of failure/performance issues even on a complex 6-stage pipelined microarchitecture Since traditional malware cannot travel across EM side-channels, our monitor is safe from malware attacks already afflicting the IoT/Computer control system The technique was evaluated using Support Vector Machines (SVM), AdaBoost (AB) and other classification algorithms Our results on controller implementations on a six-stage pipelined processor demonstrated accuracy of more than 80% in predicting control system stuck-at faults and malware.
    Keywords: IoT devices; fault analysis; machine learning; computer architecture; electromagnetics.
    DOI: 10.1504/IJCCPS.2022.10047533
  • A summary of the stability of several types of neural network   Order a copy of this article
    by Wenbo Fei, Jianhua Zhang, Yang Li 
    Abstract: Owing to the influence of factors such as signal transmission delay, external interference and parameter deviation, the stability of neural networks has always been the focus of scholars, and many related literatures have been published. This article mainly summarizes and analyses the stability research of several types of neural networks (Hopfield neural network, BAM neural network, cellular neural network, Cohen-Grossberg neural network). In the study of neural network stability, in addition to the common methods such as Lyapunov-Krasovskii method and LMI technology, other more advantageous solutions are also analysed. Finally, the conclusion and prospect of neural network stability analysis are given.
    Keywords: neural networks; stability.
    DOI: 10.1504/IJCCPS.2022.10047534
  • U-model-based sliding mode control for manipulator control systems   Order a copy of this article
    by Changyi Lei, Quanmin Zhu, Chenguang Yang 
    Abstract: This paper investigates U-model based sliding mode control (SMC) of multi-input-multi-output (MIMO) uncertain manipulator systems with external disturbance. The proposed controller is composed of a U-model controller and a sliding mode controller. The U-model controller is implemented to simplify the design procedure, which applies linear techniques to nonlinear systems through cancelling the nonlinearity and dynamics of original plant. The sliding mode controller is designed based on Lyapunov theorem to improve the robustness of U-model based control. The simulation on tracking task of a 2 degree-of-freedom (DoF) nonlinear manipulator with disturbance demonstrates the enhanced robustness in U-control system operation.
    Keywords: U-model; sliding mode control; manipulator; uncertain nonlinear system.
    DOI: 10.1504/IJCCPS.2022.10048386