International Journal of Advanced Mechatronic Systems (6 papers in press)

Regular Issues

• Modern usage in representation reconstruction methods: an empirical way of GAN to provide solutions for multiple sectors  
  by Rohit Rastogi, Vineet Rawat, Sidhant Kaushal 
  Abstract: Image restoration poses a formidable challenge in the field of computer vision, endeavouring to restore high-quality images from degraded or corrupted versions. This research paper conducts a comprehensive comparison of three prominent image restoration methodologies: GFP GAN, DeOldify, and MIRNet. GFP GAN, featuring a specialised GAN architecture designed for image restoration tasks, introduces an AI-centric approach. DeOldify, a deep learning-based method, focuses on colourising and restoring old images using advanced AI techniques, while MIRNet offers a lightweight network specifically crafted for image restoration within an AI framework. The comparative analysis involves training and testing each method on a diverse dataset comprising both degraded and ground truth images. Employing a confusion matrix, precision, accuracy, recall, and other evaluation metrics are computed to comprehensively assess the performance of these AI-based methods. The matrix affords insights into the strengths and weaknesses of each AI-driven approach, providing a nuanced understanding of their respective performances.
  Keywords: GFP GAN; DeOldify; MIRNet; confusion matrix; restoration; image enhancement; computer vision; AI techniques; precision; accuracy; recall; noise; blur.
  
  
• Automatic voltage regulator design based on multi-objective optimisation concept  
  by Nadir Fergani 
  Abstract: In this paper, a novel analytical two degrees of freedom (2DOF) controller for the automatic voltage regulator (AVR) system is proposed, with an explicit tuning formula derived based on a desired appropriate disturbance transfer function to enable precise control of the closed-loop performance and sensitivities. Unlike the conventional formulation of AVR control as a constrained single objective optimisation problem, the proposed approach optimises the controller through the multi-objective particle swarm optimisation (MOPSO) algorithm, using the best-desired disturbance transfer function. Three conflicting control objectives are considered, represented by disturbance integral absolute error (IAEd), maximum sensitivity (MS) and maximum noise sensitivity (MN). The resulting Pareto optimal sets clarify, for the first time, the trade-off on the AVR controller design. A comparative analysis with the most recent 2 DOF controllers has been performed where the role of the controller structure appears clearly in both performance and robustness. A new approach to AVR controller optimisation is introduced in this work, emphasising a prioritised balance between performance and robustness while leveraging analytical tuning.
  Keywords: automatic voltage regulator; AVR; multi-objective particles swarm optimisation; MOPSO; controller tuning; uncertainty; high order system.
  
  
• Efficient combined non-singular fast terminal sliding mode control for robust balancing of inverted pendulum cart  
  by Iman Faraj, Jasim Khawwaf 
  Abstract: The inverted pendulum cart system is widely recognised as a challenging benchmark in control systems and robotics due to its inherent instability and significant nonlinearity. This paper proposes a control methodology based on non-singular fast terminal sliding mode (NFTSM) to stabilise this system. A comparative analysis of the proposed NFTSM approach against various nonlinear techniques such as classical sliding mode control (SMC), terminal sliding mode (TSM), fast terminal sliding mode (FTSM), and non-terminal sliding mode (NTSM) has been conducted. These methods have been evaluated based on their effectiveness in controlling the inverted pendulum cart. Simulation results demonstrate that the proposed NFTSM offers superior accuracy, rapid response, resilience to uncertainties, disturbance rejection, reduced chattering, and elimination of singularities. The results further reveal that the proposed NFTSM yields lower RMS error, highlighting its efficiency over the compared techniques.
  Keywords: SIMO underactuated system; inverted pendulum cart; combined non-singular fast terminal sliding mode; nonlinear systems; robust control.
  
  
• Enhancing drone performance: using reinforcement learning for active pan and tilt rotor fault tolerance strategy  
  by Zairil Zaludin 
  Abstract: Quadcopter drones rely on their four rotors to maintain control of altitude and attitude. However, if one rotor fails, the drones ability to stay airborne is compromised. This article proposes a solution that improves attitude control during a single rotor failure by minimising roll, pitch, and yaw deviations. This was done by actively panning and tilting one of the functioning motors. The controller for the pan and tilt was designed using the reinforcement learning method. The solution was obtained after the agent accumulated as many reward points as possible through 5,000 training episodes. The study demonstrated the feasibility of improving the uncontrollable rolling, pitching, and yawing usually seen in a drone after a single rotor total failure during flight.
  Keywords: unmanned aerial vehicles; UAV; fault tolerance; quadrotor; quadcopter; quadcopter malfunction; drone; reinforcement learning; RL; DDPG agent; drone rotor pan-tilt.
  
  
• Generation of multiple N shares based on deep learning protocol for efficient storage of patient health records data splitting in cloud framework  
  by J.R. Ancy Jero, D.S. Misbha 
  Abstract: This work proposes a novel deep learning protocol for efficient storage of patient health records (PHRs) in a cloud framework. It mainly consists of uploading PHR to the cloud and retrieving PHR from the cloud. Before uploading the PHR, the text data encryption is done using Rivest, Shamir, and Adelman (RSA) algorithm. The PHR image encryption is carried out using the image encryption algorithm and the discrete cosine transform (DCT) is utilised for the compression. Then the PHR is shared to the cloud using the dense deep belief network (DenseBN)-based multiple N sharing protocol. For the retrieval, the requested files are obtained by combining all partitioned parts of the shared data, followed by a decryption process. The DenseBN offers a key complexity of 0.765, key sharing time of 0.465, key retrieval time of 0.555, Jaccard similarity of 0.909, and memory of 0.374 for considering the key size as 64.
  Keywords: patient health record; PHR; multiple ‘N’ sharing protocol; Rivest; Shamir; and Adelman; RSA; dense belief network; DenseBN; cloud.
  
  

Special Issue on: Application of Robotic Process Automation for Industry 4.0

• Autism spectrum disorder detection using convolutional neural network with transfer learning  
  by Rakhee Kundu, Sunil Kumar 
  Abstract: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, which can vary widely among individuals, making it difficult to establish a uniform set of criteria for diagnosis, leads to underdiagnosis or misdiagnosis in previous researches. This research developed the green anaconda one-to-one-based optimiser-based convolutional neural network with transfer learning (GAOOBO_CNN_TL) for ASD classification utilising multimodal data. Firstly, the image pre-processing process is performed by Kuwahara filters and RoI extraction. Then, the input autism data is normalised by using Z-score. Then, the selection of features is conducted by the Hubert index and data augmentation is done by employing bootstrapping. Then, the ASD classification is done by CNN_TL. Then, weight optimisation is executed using GAOOBO, which is the incorporation of green anaconda optimisation (GAO) and one-to-one-based optimiser (OOBO). The GAOOBO_CNN_TL gained accuracy with 92.823%, specificity with 93.127% and sensitivity with 91.997%.
  Keywords: autism spectrum disorder; ASD; green anaconda optimisation; GAO; GoogLeNet; Xception; convolutional neural network; CNN.