International Journal of Engineering Systems Modelling and Simulation (20 papers in press)

Regular Issues

• Rayleigh wave in rotating thermoelastic half-space under impedance boundary conditions, two-temperature, diffusion  
  by Heena Sharma, Sangeeta Kumari, Bharti Thakur 
  Abstract: In the present work, the governing equation of Rayleigh wave is considered with rotation, diffusion, and two-temperature under impedance boundary conditions for generalised thermoelastic half-space. The surface wave technique is used to solve the governing equation of Rayleigh wave to obtain the frequency equation. It also satisfies radiation conditions. Impact of rotation, initial stress, two-temperature, magnetic field, and diffusion with impedance boundary conditions numerically calculated. The effect of different parameters against dimensionless speed is presented graphically.
  Keywords: Rayleigh wave; frequency equation; generalised thermoelasticity; relaxation time; initial stress; two-temperature; magnetic field; rotation; diffusion; impedance boundary conditions; IBCs.
  DOI: 10.1504/IJESMS.2024.10063678
   
  
• Induction motor speed control performance enhancement using fractional order filters  
  by Saida Hassainia, Samir Ladaci, Sihem Kechida, Khaled Khelil 
  Abstract: This paper proposes a novel methodology for performance enhancement of simple feedback control by means of fractional order filters applied to the speed drive of an induction machine. Three performance indexes are used for this aim namely the integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE). A first order filter in different positions of the control loop is considered in presence of a noise source in order to design the filter configuration. Then, an optimisation of the fractional order filter is realised. Comparative simulation results are given to illustrate the superiority and robustness of the proposed control scheme with regard to integer order filters and unfiltered controllers.
  Keywords: fractional order filter; fractional order systems; induction machine; pi controller; performance index; speed control.
  DOI: 10.1504/IJESMS.2024.10064607
   
  
• A versatile biomedical device employed for diverse applications in minimally invasive surgical procedures  
  by Md. Abdul Raheem Junaidi 
  Abstract: The research revolutionises to introduce a new design of an instrument that combines the functionality of Maryland forceps with that of a suction irrigation device. Currently, the above two operations have to be carried out sequentially, which adds to the amount of time and effort spent by the surgeon. Integrating both these features within the same device can ensure that both processes can take place simultaneously or one after the other as many times as required, without unnecessary removal of the device from the incision. Thus the article has modelled the instrument which can potentially benefit in all other various laparoscopic procedures.
  Keywords: laparoscopic instruments; irrigation; suction; forceps; mechanism; multi-functional.
  DOI: 10.1504/IJESMS.2024.10064909
   
  
• Optimisation of deep learning-based models for the diagnosis of heart disease through ODTH method  
  by Monali Gulhane, T. Sajana 
  Abstract: In middle- and low-income countries, cardiovascular illnesses (CVDs) constitute the leading cause of death, with heart attacks and strokes accounting for around 80% of CVD-related fatalities. Enabling early intervention and treatment planning, effective cardiac irregularity prediction and the design of trustworthy heart disease prediction systems eventually lower death rates. This research investigated the viability of predicting cardiac disease using tabular data and convolutional neural networks (CNN). We first retrieved pertinent data from the collection of records, which was then abridged to 14 characteristics; each record is converted into heatmaps, and PNG files of the heatmaps are stored for further CNN processing and visualisation to DenseNet121, ResNet50 and VGG19. Using 10-fold cross-validation, we discovered that DenseNet121, in addition to the optimisation method stochastic gradient descent (SGD), performed better with 97% accuracy while the other two VGG19 54.39% and ResNet50is 51.00% models, performed low as compared to DenseNet121 in addition with the use of accuracy of 54.39% and 51.00%, respectively. Our research demonstrates that deep learning models are capable to correctly forecast heart disease from tabular data. In this paper, it is concluded that tabular data can be given as input to deep learning models to achieve better accuracy and good results can be observed for further study in the field of disease prediction.
  Keywords: machine learning; deep learning;DenseNet121; ResNet50; VGG19; optimisation.
  DOI: 10.1504/IJESMS.2024.10066247
   
  
• Modelling an SEIR model using saturated treatment function and analysing its stability: the effect of treating H3N2V affected patients by medicines  
  by A. Joshua Cyril Yagan, D. Jasmine 
  Abstract: Swine flu is a respiratory illness characterised by its intense spread during specific seasons, leading to concerns about the potential challenges caused by limited drug availability. This article presents a susceptible-exposed-infected-recovered (SEIR) model incorporating a saturated treatment function to address these concerns. Emphasising the crucial role of early medication in managing the infection, the model serves as a mathematical representation of the disease’s dynamics, featuring the novel inclusion of a saturated treatment function to better manage swine flu’s transmission challenges. This study emphasises early prescription medicine treatment for infected patients. A thorough methodology verifies the model’s positivity and boundedness to ensure it appropriately represents real-world disease dynamics. To better comprehend disease propagation, calculate the reproduction number and find the model’s equilibrium locations. The Gershgorin Circle theorem is used to test model stability, showing its capacity to capture disease transmission’s complicated dynamics. The essay uses numerical simulations to emphasise the need of timely and proper medicine in preventing illness development. This model-driven technique can avert swine flu pandemics by predicting pharmaceutical needs and reducing supply bottlenecks.
  Keywords: variant influenza; swine flu; H3N2; respiratory infection; pandemic; epidemic; SEIR compartmental model; saturated treatment function; reproduction number; model stability and implications.
  DOI: 10.1504/IJESMS.2024.10065300
   
  
• Multi-period planning of fish breeding chains and investigation of its efficiency under demand uncertainty  
  by Sajad Moradi 
  Abstract: This article studies an issue in the fish farming industry, aiming to find the best multi-period plan for managing various chains, including spawning, breeding, harvesting, and selling trout over a given time horizon. It provides a new mixed integer linear programming model that efficiently finds the optimum solution. In the proposed model, some intermediate stages of the breeding chains that do not affect key decisions are ignored, thereby reducing the size and complexity of the proposed model without compromising the optimality of the answers. When weekly demands are considered uncertain data, by simulating weekly demand, it is shown that using the first-in, first-out policy during the selling season, the schedule provided by the deterministic model, in which average value is considered for the weekly demand, will still be effective relatively. By analysing the obtained results, some approaches are suggested to improve the proposed program.
  Keywords: fishing industry; mathematical modelling; demands uncertainty; sales management; simulation.
  DOI: 10.1504/IJESMS.2024.10066291
   
  
• Fortifying cyber defence: unveiling the power of convolutional neural networks and cutting-edge data preprocessing methods for DDoS attack detection in the digital frontier  
  by Chris Harry Kandikattu, Sam Sangeeth Panguluri, Sandeep Kumar, Suneetha Bulla, Abdul Raheem Shaik 
  Abstract: With a global increase in the frequency of cyberattacks in the internet space, the digital sphere faces a significant upheaval in danger to an individual’s online presence and corporate entities. The work put forward in this paper takes advantage of deep learning techniques to improve security against DDoS attacks. The research paper provides a holistic approach to detecting DDoS attacks using convolutional neural networks (CNNs) combined with advanced data preprocessing methods. The proposed work in this research paper has been evaluated using two widely known and publicly available datasets, namely NSL-KDD and CSE-CIC-IDS208. The proposed work demonstrates that the proposed methodology consistently outperforms both datasets, achieving impressive accuracy scores of 97.46% and 98.53%. These findings underscore the promising potential of the proposed approach in enhancing the accuracy and effectiveness of intrusion detection systems.
  Keywords: distributed denial of service; DDoS; cloud attacks; cloud environment; cloud security.
  
  
• Quantum vs. classical methods in information security, computing and machine learning domains: an empirical study  
  by Kriti Srivastava, Akshit Gabhane, Ankit Ladva, Pushkar Waykole, S. Suman Rajest 
  Abstract: The rise in big data has necessitated the development of new computing technologies that can process large volumes of data in a faster and more efficient manner. In recent years, quantum computing has emerged as a promising candidate for this purpose due to its ability to work with high dimensionality data and its potential for solving complex problems that classical computers struggle with. This research work conducts a comparative analysis of quantum computing and classical computing in the fields of information security, computing, and machine learning, which are all critical fields in the modern world. The study uses a variety of methods, including theoretical analysis, simulation, and experimental implementation to demonstrate the benefits of quantum computing. This study serves as a basis for future research in the field of quantum computing and its applications, which could lead to significant advancements in various areas of science and technology.
  Keywords: comparative analysis; Grover’s algorithm; Shor’s algorithm; quantum computing; quantum SVM; quantum CNN; information security.
  
  
• Character classification enhancement through hybrid feature fusion in challenging scripts systems  
  by Sobia Habib, Manoj Kumar Shukla, Rajiv Kapoor 
  Abstract: One of the most intriguing research problems is to achieve high accuracy in character recognition of degraded scripts, which is essential for applications such as document digitisation, language translation, and text-to-speech systems. We aim to recognise two degraded scripts of Devanagari and Urdu languages, which have unique difficulties, mainly due to the presence of broken and merged dots. Traditional character recognition techniques, including template matching and feature-based methods, have been widely used but need to be more efficient to handle the complexities of Urdu and Devanagari scripts. We propose classifying damaged scripts using zone-based power curve fitting and a pre-trained VGG19 model that trains on script degradation patterns. Using 6,250 printed examples with distortions from damaged Devanagari and Urdu manuscripts, we fine-tune the VGG19 model. It helps the proposed model understand these characters’ intricate features and minimises overfitting. Our changes improve accuracy and strengthen the script damage detection system. Our results show that the VGG19 architecture works well across most feature extraction strategies, with accuracy scores ranging from 89.26% to 93.42%, while combining the power curve fitting methodology with VGG19 improves classification accuracy to 97.42%.
  Keywords: power curve fitting; VGG19; challenging scripts systems; broken characters; merged dots characters; deep learning features.
  
  
• Machine learning-driven innovations for energy efficiency engineering systems empower greener technologies  
  by R. Regin, K. Selvamani, S. Kanimozhi, Pallavi Ahire, Swakantik Mishra, Sukhwinder Sharma, Sushma Rani 
  Abstract: The research investigates the role of high-energy electronics as a key player in the strength efficiency and sustainability sector. In addition, we look at recent developments in power electronics, including advanced semiconductor materials and novel topologies with machine learning-enhanced control strategies to bring technological innovations towards climate-smart technology. Our process combines a complete literature research and architectural analysis to illuminate innovative power electronics through machine learning and data-driven optimisation. Where the consequences of this study not only show substantial enhancements in power performance and sustainability, but also strengthen the case for embedding advanced energy electronics across myriad programs perfectly aligned with eco-green tech. The discussion extends to how our results may influence the integration of renewable electricity, industrial strategies, and environmental sustainability through transformational system learning-driven innovations. This paper outlines a scenario where green technology meets machine learning to usher in a new era of energy efficiency for a greener planet, highlighting power electronics’ immense potential and future direction. Current constraints are noted as side comments.
  Keywords: sustainable power systems; machine learning optimisation; advanced power electronics; renewable energy integration; energy efficiency solutions; green technology innovations; smart grid technologies; eco-friendly semiconductor materials.
  DOI: 10.1504/IJESMS.2025.10068738
   
  
• Transforming electrical simulation and management with smart grid technologies  
  by K. Chitra, S.Silvia Priscila, Edwin Shalom Soji, R. Rajpriya, B. Gayathri, A. Chitra 
  Abstract: Electrical simulation and management are essential for ensuring reliable, efficient, and sustainable power supply to various consumers. However, the traditional power grid faces many challenges, such as ageing infrastructure, increasing demand, integration of renewable energy sources, power quality issues, and cyber-attacks. Smart grid technologies offer a promising solution to overcome these challenges and transform the electrical distribution and management system. Intelligent grid systems encompass sophisticated sensing technology, advanced metering devices, communications infrastructure, control mechanisms, data interpretation tools, and automation components. These elements facilitate two-way information and electrical power exchange between those responsible for grid management and end-users. This article reviews intelligent grid technologies’ impact on electrical distribution and operational management. A case study of a mid-sized urban region informs the article’s organised approach to creating and deploying an intelligent grid network. The findings show that intelligent grid technologies improve electrical distribution system dependability, operational efficiency, environmental sustainability, and security. This article explores the problems and opportunities of intelligent grid systems and provides guidance for future research and technology.
  Keywords: smart grid; electrical simulation; power management; smart meter and microgrid; systems’ reliability; operational efficiency; environmental sustainability; grid management.
  
  
• Modelling and optimisation of structural parameters of main landing gear during touchdown and taxing  
  by Mantesh Basappa Khot, Abhijit Prekash, R. Gopalakrishna, Karan Nanda, Hriday Ghosh 
  Abstract: Runway irregularities induce vibrations in the fuselage of aircraft during take-off, taxiing, and landing, leading to fatigue stresses in the airframe. These vibrations impacts passenger comfort and affect the functioning of instruments. To reduce fuselage vibrations in the Fokker-70 aircraft, an optimisation of parameters is conducted, aiming to lower the peak of the frequency response at resonant conditions and minimise the time difference between the fuselage and tire stabilisation after touchdown. This prevents airframe failure due to excessive vibration at resonance. MATLAB s Nelder-Mead simplex algorithm is used for optimisation. Additionally, a PID controller is implemented in the landing gear model to further mitigate vibrations. The controller s effectiveness is tested using a runway model with various bumps, adhering to Boeing s runway roughness criteria. Results show the controller smoothens fuselage response to runway excitations, reducing vibration and enhancing the airframe s fatigue life.
  Keywords: complex modal analysis; Nelder-Mead simplex method; optimisation; Oleo pneumatic shock absorber; PID controller.
  
  
• Exploring algorithmic solutions and network modelling to address optimisation challenges in IoT environments  
  by Rashmi Prava Das, Debendra Muduli, Ashish Kr. Luhach 
  Abstract: Internet of things (IoT) has a transformative technology, reshaping the landscape of connectivity and information exchange. It represents an intricate network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity, enabling them to collect and exchange data effortlessly. The paper focuses on measuring the efficacy of optimisation algorithms, namely the hybrid simulated annealing-local search algorithm (SA-LSA), genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA), in addressing multi-objective optimisation challenges and complex function minimisation scenarios. It aims to provide a comprehensive understanding of selecting appropriate algorithms for diverse optimisation challenges, considering factors such as solution space complexity, exploration-exploitation trade-off preferences, and convergence speed. The potential of this work lies in contributing valuable insights into the performances of optimisation algorithms, specifically in navigating trade-offs and converging towards optimal solutions. This work conducts a comparative analysis of algorithms, evaluating the overall performance to provide insights into their strengths and weaknesses, facilitating the selection of optimisation approaches for specific applications spanning multi-objective scenarios and complex function minimisation tasks.
  Keywords: energy consumption modelling; particle swarm optimisation; PSO; hybrid simulated annealing-local search algorithm; SA-LSA; genetic algorithm; GA; multi-objective optimisation.
  
  
• Optimal magnetorheological damper for two-wheeled vehicle using analytical method  
  by Keshav Manjeet, Dhawade Eashan, C. Sujatha 
  Abstract: Magnetorheological (MR) fluid technology has gained significant development in effectively isolating undesirable vibrations by use of MR fluids acting as a dissipating medium in a damper. The current work aims at optimally designing an MR damper for a commercial 110 cc two-wheeled vehicle; this damper could replace the conventional passive damper. The geometric dimension of the valve is designed in such a way that it should match the performance of the passive damper in field-off state and includes dimensional constraints from the geometry of the vehicle. After the dimensions of the damper are decided on, valve geometry is optimised in the MATLAB environment for multiple objective functions using an analytical method of solving a magnetic circuit. Further, the optimised MR damper is implemented in a 5-DOF half-car vibration model developed and the response is then compared with that of the vehicle model with conventional passive dampers at both ends.
  Keywords: MR damper; two-wheeled vehicle model; passive damper; optimisation; analytical magnetostatic model; 5-DOF vibrating model.
  
  
• A novel keyframe extraction technique systems using deep reinforcement learning simulation  
  by M. Dhanushree, R. Priya, P. Aruna, R. Bhavani 
  Abstract: Smartphones and wearables make video capture easy, increasing video generation. The abundance of videos led to the development of a video summarisation study. The most important moments of a longer input video are selected to minimise its length while maintaining context. Video summary using keyframe extraction outputs key frames of key events. Its uses include anomaly detection, efficient video storage, indexing, and retrieval. Extracting semantically meaningful frames is difficult, and a big research gap exists. The keyframe extraction using deep reinforcement learning (KE_DRL) approach extracts representative and distinct semantically relevant keyframes. Frame-level and video-level characteristics are extracted. Frame-level characteristics are extracted using modified ResNet50 and I3Dnet. They are aggregated to generate a feature vector and global average pooled to get video-level features. attention-based video summariser network (AVSumnet) uses semantic video attributes as input. It is trained via reinforcement learning. A new summariser network training reward mechanism is proposed. Experimental findings show that the KE_DRL method creates better video keyframes than existing methods.
  Keywords: keyframe extraction; video summarisation; bi-directional gated recurrent unit; BDGRU; deep reinforcement learning; attention mechanism; squeeze and excitation block; policy gradient; reward function; representativeness.
  
  
• Research on steady speed control strategy of direct-drive pump-controlled hydraulic motor based on improved extended state observer  
  by Guoshuai Li, Yong Sang, Qingli Qi 
  Abstract: This paper introduces the direct-drive volumetric control (DDVC), focusing on the direct-drive pump-controlled hydraulic motor electro-hydraulic system as the primary subject of research. Aiming to address the problem of parameter uncertainties and unknown nonlinear disturbances in the system. A robust backstepping controller based on the extended state observer (ESO) is designed to improve the systems dynamic performance and robustness. In the controller, the ESO is used to estimate the total uncertainties in the system. Then, the robust backstepping controller is designed to enhance both the dynamic and robust performance. Moreover, Lyapunov theory is utilised to rigorously demonstrate the stability of both the ESO and the closed-loop control system. Finally, the results indicate the designed controller has excellent robustness and dynamic performance.
  Keywords: direct drive; electro-hydraulic system; nonlinear disturbances; robust control; extended state observer.
  
  
• Impact analysis and control of EV charging on grid connected to optimal structure hybrid wind-solar PV system  
  by Abdul Zeeshan, Swapnil Srivastava 
  Abstract: A detailed review of research work has been done for wind-PV hybrid generation system (WPVHGS) to identify suitable plant structure. There is scope of optimisation of WPVHGS when feeding to grid at unbalanced voltage conditions. In addition to this, optimal sizing of battery energy storage (BES) and supercapacitor (SC) is required for the same. To support the review, simulations are carried out on MATLAB Simulink for comparative analysis of impact on grid due to EV charging, where a centralised charger is charging battery equivalent to 10 EVs. Using simulations, severity of grid current imbalance is observed, when battery charger is connected to 575-V bus. When doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) is introduced then currents and voltages are balanced but severe third harmonics of current is introduced. By replacing proportional-integral (PI) controller with adaptive neuro-fuzzy inference system (ANFIS), its observed that voltage and current total harmonic distortion (THD) gets mitigated.
  Keywords: hybrid; wind; solar PV; battery; supercapacitor; doubly-fed induction generator; DFIG; electric vehicle; battery energy storage; BES; adaptive neuro-fuzzy inference system; ANFIS; total harmonic distortion; THD; wind energy conversion system; WECS.
  
  
• An optimised AES algorithm and its FPGA implementation for secure information  
  by G. Mallikharjuna Rao, K. Deergha Rao 
  Abstract: Security algorithms play a crucial role across various communication networks, encompassing both wired and wireless infrastructures. As technology rapidly evolves, particularly in the realm of 5G communications, the demand for more robust security measures is becoming increasingly prominent. Research to date has focused on the AES 128-bit encryption standard, with its implementation being extensively tested, synthesised, and applied to different FPGA platforms such as Spartan, Virtex, and Kintex. Nevertheless, existing studies fall short in providing an AES algorithm optimised for minimising power consumption, reducing latency, and conserving space, all of which are critical for effective security. This study introduces an enhanced AES algorithm tailored for FPGA implementation, specifically designed to meet the stringent criteria of reduced latency, decreased power usage, and lower spatial requirements for the purposes of simulation and synthesis, using Xilinx-ISE v14.7 tool.
  Keywords: advanced encryption standard; AES; FPGA; information security; 5G communication; IoT.
  
  
• H controller synthesis for multiple time-varying delays systems with application to double diabetes mellitus  
  by S. Syafiie, F. Tadeo 
  Abstract: Many physical, biological, chemical, electrical, and industrial systems exhibit time-varying delays in their inner dynamics, caused by aftereffects or dead-time phenomena. As the expectation is that the mathematical models of these systems behave like the real process, the techniques to develop control systems should consider these multiple delays. In this context, this paper aims to synthesise a memory-less controller satisfying H performance. More precisely, the controller gain is selected to guarantee closed-loop stability in the presence of delays, by using a Lyapunov-Krasovskii functional (LKF) and a reciprocally convex approach to upper bound integration functions. The closed-loop stability condition is presented as linear matrix inequalities (LMI), solving the stability to extract the optimal controller gain after minimisation of the H performance. The approach is illustrated numerically for a double diabetes mellitus (DDM) system. It is shown that the proposed controller synthesis is simple and the controller gain is able to drive the blood glucose concentration to the desired level upon periodic glucose intakes.
  Keywords: time-delay systems; multiple delays; H control; glycemic regulation; double diabetes mellitus.
  
  
• Earthquake prediction using deep learning based-recurrent neural network technique  
  by J. Sahaya Ruben, M. Adams Joe, M. Anand, M.Prem Anand 
  Abstract: This article explores the application of deep learning techniques to enhance earthquake prediction and detection, addressing the critical need for improved disaster preparedness and risk mitigation. With natural disasters like earthquakes causing widespread harm to people, and ecosystems, the study focuses on leveraging machine learning methodologies, including recurrent neural networks and reinforcement learning, to develop an innovative earthquake prediction framework. The proposed approach begins with feature extraction using CNN to capture essential seismic data patterns. Subsequently, the RNN model is trained to analyse time series seismic data, allowing for the prediction of earthquake events with enhanced precision. In addition, Q-learning is integrated into the process to make informed decisions based on the current state, potentially reducing false alarms and improving overall prediction accuracy. The promising results and potential impact of this research underscore the importance of ongoing efforts to harness technology for more effective earthquake prediction and mitigation strategies.
  Keywords: convolutional neural network; CNN; recurrent neural network; RNN; Q-learning; deep learning.