International Journal of Systems, Control and Communications (19 papers in press)

Regular Issues

• Fault diagnosis method for substation relay protection equipment based on CNN-SVM model  
  by Bing Tang, Zhenguo Ma, Tianlei Xia, Yuming Huang 
  Abstract: With the increasing complexity of power systems, diagnosing equipment faults has become increasingly challenging. Traditional methods often struggle to handle noise and nonlinear issues in power data effectively. To address these limitations, a fault diagnosis model for substation relay protection equipment was developed using a support vector machine (SVM), enhanced with a convolutional neural network (CNN) and a channel attention mechanism for further performance optimisation. Experimental results demonstrated that with a dataset size of 2,000, the proposed model achieved an accuracy of 97.2% and a false positive rate of 2.8%. Additionally, the model effectively diagnosed various fault types, attaining an average accuracy of 85% with a diagnosis time of approximately 1.3 seconds. These findings highlight the models superior fault diagnosis capabilities, including reduced false alarm rates and stable performance with large-scale data, providing robust technical support for the reliable and efficient operation of power systems.
  Keywords: relay protection equipment; RPE; fault diagnosis; convolutional neural network; CNN; support vector machine; SVM.
  DOI: 10.1504/IJSCC.2025.10071737
   
  
• High resolution network combined with PnP algorithm for pose estimation of aerobics robot  
  by Yan Liu, Yan Zhao, Bingyan Yu 
  Abstract: The traditional pose estimation method for aerobics robots has problems of low accuracy and computational efficiency. This study utilises high-resolution networks to extract local features and uses a transformer for multi-scale feature fusion to design a pose estimation method for aerobics robots. This method achieves an adaptive fusion of multi-scale features by constructing a transformer-enhanced HRNets feature extraction module, effectively capturing local and global features of robot joints. At the same time, a deformable attention mechanism is introduced to reduce the complexity of feature processing, and the EPnP algorithm with sparse control point constraints is used to establish the mapping relationship between 3D point clouds and 2D images. The precise solution of pose parameters is achieved through Levenberg-Marquardt optimisation. The proposed fitness robot pose estimation based on a high-resolution network and PnP algorithm can effectively improve the precision and efficiency of robot pose estimation, and reduce computational costs. This study is meaningful for the practical application of robot vision and attitude control.
  Keywords: high-resolution network; PnP algorithm; transformer; aerobics robot; feature fusion.
  DOI: 10.1504/IJSCC.2025.10071785
   
  
• Development of an integrated intelligent burnishing tool holder  
  by Ruize Tan, Zhipeng Yuan, Xuanyi Lin, Jialiang Zhu, Zhongyu Piao 
  Abstract: Cutting force measurement and vibration measurement are fundamental requirements in the burnishing process. Hence, various methods of measuring the cutting force and vibration have been proposed by many researchers. In this study, a novel integrated intelligent burnishing tool holder was designed to measure three-dimensional cutting forces and vibrations during the burnishing process. Using the positive piezoelectric effect of quartz crystal, a cutting force sensing unit was designed on a standard CNC tool holder to measure cutting forces, while three-axis vibrations were simultaneously monitored using a commercial A27F100 three-axis piezoelectric accelerometer during the burnishing process. Then the cross-sectional dimensions of the handle are determined by calculating the dimensions of the quartz crystal group. Finally, the three-way cutting force measurement part of the tool holder was calibrated. The calibration results show that the tool holder has high sensitivity and low nonlinear error, repeatability error and cross-axis interference.
  Keywords: cutting force measurement; vibration signal measurement; piezoelectric quartz crystal force measuring unit; integrated intelligent burnishing tool holder.
  DOI: 10.1504/IJSCC.2025.10071811
   
  
• Product surface defect detection algorithm based on transfer learning and DL  
  by Xitao Sun, Shuo Xue 
  Abstract: This study addresses the critical challenge of enhancing surface defect detection accuracy in industrial manufacturing through an optimised deep learning framework. We propose a hybrid model integrating convolutional block attention module (CBAM) and embedded inverse residual block (EIRB) into the U-Net architecture, combined with meta-transfer learning for structural optimisation. The enhanced network demonstrates superior performance: achieving 93.6% classification accuracy, 81% recall rate, and 8.98-second average detection time. Cross-part testing on six industrial components shows 92.5% accuracy for gears and 90.3% for valves. Notably, under varying lighting conditions, it maintains the highest F1-score of 0.89 compared to conventional models. This approach balances computational efficiency with detection robustness, making it suitable for real-time industrial applications requiring high precision and adaptability. The results confirm that our method provides both technical superiority and practical feasibility for advanced defect detection systems in manufacturing environments.
  Keywords: industrial products; surface defect detection; transfer learning; meta transfer learning; MTL; U-Net.
  DOI: 10.1504/IJSCC.2025.10071976
   
  
• Mobile network data collection based on A2S-Det and DSANS algorithms  
  by Zeyang Xu 
  Abstract: Aiming at the problems of high energy consumption and delay in sensor data collection in mobile networks, the proposed optimisation algorithm in the research achieves the optimisation of energy consumption and delay through adaptive anchor point selection and detection, combined with the non uniform step-size distributed sub-gradient algorithm. When the number of anchor points is 9, the energy consumption is as low as 1.63 J, and when the coverage parameter is 4, the transmission delay is as low as 125 s. The transmission delays of static and dynamic data collection in winter are 149 s and 123 s respectively, and the energy consumption is 0.48 J and 0.31 J. Under 200 nodes, the energy consumption of the algorithm is 1.51 J, which is superior to 1.99 J of the greedy algorithm. This algorithm effectively reduces latency and energy consumption and improves the efficiency of data acquisition in mobile networks.
  Keywords: wireless sensor network; WSN; data collection; A2S-Det; DSANS; transmission delay; energy consumption.
  DOI: 10.1504/IJSCC.2025.10072073
   
  
• Decentralised adaptive fuzzy sliding mode control for robotic arms using a voltage control approach in workspace  
  by Li Wang 
  Abstract: The paper presents a novel decentralised adaptive fuzzy sliding mode control (AFSMC) strategy with voltage-based control for robotic arms operating in the workspace. Traditional torque-based methods require precise dynamic modelling and are often too computationally intensive for real-time or embedded applications. The proposed approach directly manipulates motor voltage inputs, simplifying control law derivation while ensuring robustness against uncertainties, unknown dynamics, and external disturbances. By integrating fuzzy logic approximators within the sliding mode framework, the method effectively compensates for structural and non-structural uncertainties, eliminating the need for accurate dynamic models. A hyperbolic tangent function is employed to reduce chattering and achieve smoother control signals. Furthermore, the workspace-based design addresses end-effector trajectory limitations inherent in joint-space controllers. Simulation results for a three-degree-of-freedom manipulator demonstrate high tracking precision, excellent disturbance rejection, and lower computational demand, making the proposed voltage-based AFSMC highly suitable for real-time industrial and collaborative robotic applications.
  Keywords: robotic arm control; voltage control tactic; adaptive fuzzy sliding mode control; AFSMC.
  DOI: 10.1504/IJSCC.2025.10072074
   
  
• A study on classification of small sample based on stochastic configuration networks  
  by Saixian Yuan, Xuemei Yao, Yan Tang, Hongmei Zou 
  Abstract: Due to the limited availability of samples and the high cost of annotation, small sample classification presents significant challenges. Traditional models often struggle with poor generalisation and inadequate inter-class separability. To tackle these issues, this paper introduces an ensemble model called RS-SCN, which combines stochastic configuration networks (SCN) with the random subspace (RS) method. In this approach, the feature space is partitioned into random subspaces, each used to train an independent SCN model. The outputs of these models are then integrated through majority voting. This strategy reduces dependence on large-scale datasets while enhancing generalisation performance. Experimental results demonstrate that RS-SCN surpasses traditional methods in both generalisation and robustness. As a result, this approach enhances the applicability of SCN to both small-sample classification and function approximation tasks, offering an effective solution to the generalisation challenges posed by limited data.
  Keywords: stochastic configuration networks; SCN; classification of small sample; random subspace method; subset of features; majority voting.
  DOI: 10.1504/IJSCC.2025.10072175
   
  
• Parallel grid considering weight parameters and displacement projection  
  by Yuan Sun 
  Abstract: Parallel grids are essential for large-scale numerical simulations, but their generation accuracy and efficiency are often inadequate. This article proposes a parallel grid optimisation method that incorporates weight parameters and displacement projections, featuring grid subdivision, three displacement algorithms, and a distance-weighted approach. Results indicate that this method achieves approximately 90% grid optimisation accuracy, surpassing other methods, with computation times under 200 seconds much lower than comparative techniques. Additionally, its F-value approaches 100%, showing minimal fluctuations. This approach effectively reduces computing resource consumption and computation time while maintaining accuracy, making it highly significant for engineering applications and scientific research.
  Keywords: parallel grid subdivision; displacement projection calculation; weight parameters; grid interpolation algorithm; RBF.
  DOI: 10.1504/IJSCC.2025.10072344
   
  
• A novel obstacle avoidance strategy for autonomous vehicle based on dynamic safety boundary  
  by Wenbo Li, Han Jin, Jun-Guo Lu, Hongyi Kang, Qing-Hao Zhang 
  Abstract: Obstacle avoidance is a pivotal function in autonomous driving to ensure vehicle safety and operational efficiency, particularly in complex dynamic scenarios. However, traditional methods often fall short of effectively addressing the variability and high-risk conditions inherent in real-world environments, thereby limiting their practical applicability. To address these limitations, this paper proposes a novel obstacle avoidance strategy based on a dynamic safety boundary. Specifically, candidate paths are first generated through local path planning and subsequently evaluated for safety using a collision detection algorithm. The static safety boundary is then extended into a dynamic model, enabling real-time adjustments to safety distances and facilitating precise obstacle avoidance. Experimental results across multiple real-world scenarios demonstrate that the proposed approach significantly improves obstacle avoidance performance with only a marginal increase in response time in complex dynamic scenarios, thus highlighting its superior reliability and practical applicability.
  Keywords: obstacle avoidance; autonomous driving; safety boundary; local path planning; collision detection algorithm; complex dynamic scenario.
  DOI: 10.1504/IJSCC.2025.10072358
   
  
• Industrial internet of things data monitoring algorithm based on improved graph convolutional network  
  by Bin Hu, Changyi Jin 
  Abstract: Monitoring and analysis of industrial internet of things (IIoT) data require high spatiotemporal correlation, heterogeneity, and periodicity. Traditional methods fail to effectively capture these properties. This study proposes an improved IIoT data monitoring model based on a graph convolutional network (GCN), integrating temporal convolutional networks (TCNs) and dilated convolutions to enhance spatiotemporal feature extraction. A federated learning mechanism is also incorporated to ensure data security. Experimental results show the model achieves an average accuracy of 0.88072, a coefficient of determination of 0.94431, and an explanatory variance score of 0.96651 outperforming existing methods. Additionally, the FL-TGCN model attains a lower average RMSE of 1.76 compared to the long short-term memory model in time series optimisation. These findings confirm that the proposed model exhibits strong robustness, effective component integration, and stable generalization in multi-sensor IIoT environments, indicating significant application potential.
  Keywords: graph convolutional network; GCN; industrial internet of things; IIoT; time series; dilated convolution; federated learning.
  DOI: 10.1504/IJSCC.2025.10072359
   
  
• Edge computing and dynamic scheduling control of internet of things based on DQN reinforcement learning scheduling algorithm  
  by Pengkang Xing, Changjie Wu 
  Abstract: This study proposes a dynamic scheduling control method for internet of things (IoT) systems based on a deep Q-network (DQN) algorithm, combining convolutional neural networks and Q-learning. Feature information is extracted via a convolutional neural network, and its parameters are optimised through Q-learning to develop a deep Q-learning algorithm for IoT edge computing and dynamic orchestration. The goal is to improve resource utilisation and reduce energy consumption. Experiments show the proposed method achieves a low error rate of 0.31%, a computation speed of 6.7 bps, and a space occupancy rate of 27.8%, outperforming weighted fair queuing (1.22%, 37.9%) and highest response ratio next (1.73%, 53.2%). IoT resource utilisation reached 92.1% and system stability 93.8%. These results demonstrate the algorithms superior accuracy, efficiency, and reliability, offering a cost-effective solution for dynamic scheduling and optimisation in IoT edge computing environments.
  Keywords: internet of things; convolutional neural networks; Q-learning algorithms; QLAs; deep Q-network algorithms; edge computing; dynamic scheduling control.
  DOI: 10.1504/IJSCC.2025.10072360
   
  
• Output power fluctuation control method for power storage system based on adaptive wavelet packet decomposition  
  by Wenxuan Liu 
  Abstract: In order to improve the stability of the output power of the power storage system and shorten the control time, a power storage system output power fluctuation control method based on adaptive wavelet packet decomposition is proposed. Firstly, a mathematical model of SOC is constructed for the battery of the power storage system, introducing SOC constraints and charging and discharging power boundary limitations. Secondly, a three-level wavelet packet decomposition tree architecture is constructed, which utilises adaptive wavelet packet basis function selection and signal reconstruction. Finally, an adaptive PID controller integrating fuzzy logic and neural networks is designed to achieve coordinated suppression and precise control of output power fluctuations in the energy storage system through dynamic parameter adjustment. The experimental results show that the proposed method has a smoother output power curve and significantly shorter control response time, with a maximum control response time of 20.5 ms.
  Keywords: adaptive wavelet packet decomposition; electric energy storage system; output power; wave control.
  DOI: 10.1504/IJSCC.2025.10073002
   
  
• Intelligent feedback control for enhanced accuracy and stability in Michelson interferometer  
  by Asmaa A.E.H. Ali, A.A. Abouelsoud 
  Abstract: Michelson interferometers (MIs) are frequently utilised in scientific and industrial applications that require high precision measurements. However, their effectiveness is frequently hampered by external disturbances such as temperature variations and mechanical vibrations, resulting in measurement inaccuracies and instability. This work studies the use of intelligent feedback controllers (IFCs), such as adaptive control systems (ACS) and proportional-integral-derivative (PID) control methods, to improve the accuracy and stability of MIs. We show that using a MI configuration with spectrum analysis for validation, we can enhance measurement accuracy, system stability, and response times. The development of an Arduino-based control system with stepper motors for real-time MI adjustment and spectrum analysis is described in detail, offering a low-cost and practical solution for dynamic situations. Experimental results show that ACS reduces measurement errors by 50% and improves response times by 30% compared to traditional PID control, making it a superior choice for applications requiring high precision and robustness. This study highlights the potential of intelligent feedback controllers in advancing interferometric systems, particularly in environments with unpredictable disturbances.
  Keywords: Michelson interferometer; intelligent feedback controller; IFC; adaptive control system; ACS; proportional-integral-derivative; PID control; measurement accuracy; system stability; spectrum analysis; Arduino.
  DOI: 10.1504/IJSCC.2025.10073041
   
  
• Chattering free fractional-order sliding mode control for a buck converter based on an extended state observer: design, analysis and experiments  
  by Tao Zheng, Juan Li, Yi Xiao, Kaiwen Cao, Shengquan Li 
  Abstract: This paper presents a fractional-order non-singular terminal sliding mode control (SMC), based on an extended state observer (ESO) to mitigate load resistance disturbances in the buck converter. First, a novel modelling method is developed to convert the matched and mismatched disturbances caused by load resistance variations into a uniform matched total disturbance. An extended state observer is then designed to estimate and compensate the system state variables and total disturbance. Second, to overcome the issues of slow convergence and chattering phenomenon in traditional sliding mode control, a fractional-order non-singular terminal sliding mode control method is designed to achieve stable tracking of the reference voltage. Third, the stability of the closed-loop system is proved by Lyapunov theorem. Finally, both simulation and experimental results show that the proposed fractional-order SMC exhibits excellent voltage tracking performance and effective rejection of load disturbances.
  Keywords: buck converter; fractional-order calculus; disturbance estimation; non-singular terminal sliding mode; extended state observer; ESO.
  DOI: 10.1504/IJSCC.2025.10071127
   
  
• A power image autonomous recognition method based on improved regional full convolution network  
  by Shuhua Liang, Yansong Sun, Dalei Wu, Xian Yang, Jiaying Li, Lei Gao 
  Abstract: In view of the poor recognition effect caused by the interference of electromagnetic wave, external environment and other factors in the process of power image acquisition, an autonomous recognition method of power image based on improved regional full convolution network is proposed. Firstly, the power image is collected and the interference factors are analysed. Based on this, the image pre-processing is completed. Secondly, the offset and weight are introduced to increase the receptive field of the standard grid to improve the regional full convolution network. Then, the improved regional full convolution network is applied to construct the power image autonomous recognition model to realise the power image recognition function. Ultimately, empirical trials are conducted to substantiate the progressiveness of the suggested approach. The outcomes reveal that the detection precision of the proposed method for power imagery surpasses 94.32%, and it exhibits superior accuracy and recall rates.
  Keywords: full convolutional neural network; power image; autonomous recognition; smart grid.
  DOI: 10.1504/IJSCC.2025.10071258
   
  
• Sign language recognition using improved 3D convolutional neural networks  
  by Hrithik Paul, Soubhik Acharya, Priti Paul, Bitan Misra, Nilanjan Dey 
  Abstract: Sign language recognition (SLR) plays an important role in enabling communication for those who are hard to hear or deaf. SLR involves recognising and translating signs into natural language, and this task can be enhanced by employing deep learning methods. The proposed approach uses 3D convolutional neural networks (3D CNNs) to extract features. Through this method, improvements in the accuracy and real-time performance of SLR can be achieved. In this experimental study, a 3D CNN along with an LSTM architecture is implemented for feature extraction in SLR systems, and their advantages and limitations over 3D CNN and 2D CNN and 3D CNN combined models are highlighted. Compared with the traditional 3D CNN architecture, the 3D CNN-LSTM model can effectively interpret the spatiotemporal features of sign language expression, which is crucial for accurately recognising signs. Additionally, various strategies for optimising the architecture of 3D CNN-LSTM to achieve better performance are discussed in this article. Finally, some remaining challenges and future research directions in this area are highlighted. The analysis of the outcomes indicates that the 3D CNN-LSTM architecture has excellent potential for enhancing the accessibility of SLR systems and facilitating communication for individuals who communicate via sign language.
  Keywords: 3D convolutional neural network; 3D CNN; deep learning; sign language recognition; SLR; long short-term memory; LSTM; 2D convolutional neural network; 2D CNN.
  DOI: 10.1504/IJSCC.2025.10072174
   
  
• A smart home system for elderly living alone based on an improved DS algorithm and multi-sensor data fusion  
  by Qianqian Hu, Heguang Wang, Bing Wang 
  Abstract: A smart home system design method is proposed to address the challenges faced by the elderly living alone using smart home systems. By introducing a fuzzy control algorithm, the stability and fault tolerance of the system are enhanced, and the accuracy of device coordination is improved. The experimental results show that the proposed method has an average absolute error of 1.483 and a root mean square error of 1.976, which are lower than the traditional algorithms by 1.012 and 1.486, respectively. The data processing efficiency is improved by 23%, and the system response time is shorter than that of other algorithms, and the overall operation efficiency is enhanced. In addition, the system operates stably with a packet loss rate of 0.174% and a high data transmission speed, which proves that the method has a large potential in practical applications.
  Keywords: multi-sensor; data fusion technology; DS; fuzzy control algorithm; smart home system.
  DOI: 10.1504/IJSCC.2025.10071099
   
  
• An efficient image enhancement method for transformer internal defect recognition via autonomous robotic fish  
  by Liqing Liu, Chun He, Chi Zhang, He Zhang, Jun Yao, Youwei Wang, Yunze Tong, Xuebo Zhang 
  Abstract: Currently, detecting the internal status of large transformers often involves labour-intensive methods like manual core drilling or hanging cover inspection. To promote the use of robot fish to replace manual inspection of the operating conditions inside the transformer, this paper designs a transformer internal image enhancement, defect detection and segmentation system, and explores the feasibility of robot fish replacing manual inspection. Initially, a self-calibrated illumination network is combined with histogram equalisation to improve the internal image quality of the transformer. Then, an image super-resolution network is introduced to restore the lost details in low-resolution transformer interior images, enabling technicians to better judge the operating status of the transformer through the images taken by the robot fish. Furthermore, we study the possible defect types inside the transformer, then build and augment the existing defect database. Subsequently, transformer internal defects are detected through the state-of-the-art YOLOv10 model, and SAM is introduced to perform instance-level segmentation on images within the defect object box. Experimental results demonstrate that our method significantly enhances the visual quality and resolution of transformer internal images and excels in detecting defects, achieving a mAP 50 of 98% on the augmented defect dataset.
  Keywords: transformer internal image; image enhancement; defect detection; robot fish.
  DOI: 10.1504/IJSCC.2025.10072699
   
  
• Intelligent tea picking model integrating YOLOV5 and Fast R-CNN algorithm  
  by Yafei Li, Xuanzhang Zhu 
  Abstract: Due to the limitations of traditional manual tea picking, an intelligent tea picking model is proposed to enhance efficiency. The model pre-processes images using Gaussian filtering and two colour spaces for tender leaf and background segmentation, optimised by Otsu's algorithm. An improved watershed algorithm segments the tea leaves, while the Zhang refinement algorithm and Shi-Tomasi corner detection determine picking points. Combining YOLOv5 and Fast R-CNN, with ResNet-50 and CBAM for feature extraction, ensures accurate tea recognition. A binocular vision system provides 3D coordinates, and a robotic arm performs precise picking. Results show the YOLOv5s model achieved over 0.8 in accuracy, recall, and average precision, with 97.2% segmentation accuracy, and CBAM enhanced model performance. This model offers a robust solution for intelligent, automated tea picking, supporting the mechanisation of tea production.
  Keywords: tea; YOLOV5; Fast R-CNN; RGB; image; binocular vision.
  DOI: 10.1504/IJSCC.2025.10071914