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Archives: Paper Submission

Single-Stage PV-Grid Integrated Multilevel Inverter Driven Induction Motor Drive for Water Pumping

This paper presents an innovative single-stage grid-integrated solar photovoltaic (PV) system for water-pumping applications using an induction motor drive (IMD). The proposed system employs a seven-level diode-clamped multilevel inverter (MLI) to convert DC power from the PV array to AC power for the motor, eliminating the need for an intermediate DC-DC converter. A perturb and observe (P&O) algorithm is utilized for the PV array’s maximum power point tracking (MPPT). Direct torque control (DTC) with space vector modulation (SVM) provides precise speed regulation of the induction motor. The system enables bidirectional power flow between the PV array, motor load, and utility grid, optimizing energy utilization under varying irradiance and demand conditions. The proposed configuration exhibits enhanced efficiency and improved power quality compared to conventional two-stage topologies, offering a promising solution for grid-connected solar-powered water pumping systems.

Identifying Silent Mutations for the Introduction of Restriction Sites in Open Reading Frames

For many applications in molecular biology, restriction sites need to be engineered into an open reading frame (ORF), a part of the genetic material that codes for a protein. Importantly, silent mutations need to be performed because only these do not alter the amino acid sequence of the protein. However, finding such silent mutations is a very time-consuming process. We have developed a program which recognizes all silent mutations in an open reading frame (ORF) that each lead to a new restriction site. Our program uses python technologies comprising web crawlers and data analysis libraries to deduce the amino acid sequence coded by an ORF and convert the ORF nucleotide sequence into the amino acid single letter sequence. In doing so, reverse translation back into the nucleotide sequence allows the consideration of all possible nucleotide sequences coding for the same amino acid sequence, which are then compared with the restriction recognition sites of commercially available restriction enzymes, such as from New England Biolabs (e.g., https://www.neb.com/). This allows the identification of restriction sites that can be engineered via silent mutations within the provided DNA input sequences. The output is presented in a user-friendly tabular format that can be examined or downloaded (as a CSV file) for ongoing evaluations.

Next Level Chatbot: Expert Advisory Solution

The proposed research aims to enhance ticket creation efficiency by utilising a chatbot embedded with automation capabilities, allowing tickets to be completed without human intervention. This will significantly improve overall processing time and workflow from both a customer service and technical efficiency perspective.

Next Level Chatbot: Expert Advisory Solution

The proposed research aims to enhance ticket creation efficiency by utilising a chatbot embedded with automation capabilities, allowing tickets to be completed without human intervention. This will significantly improve overall processing time and workflow from both a customer service and technical efficiency perspective.

Next Level Chatbot: Expert Advisory Solution

The proposed research aims to enhance ticket creation efficiency by utilising a chatbot embedded with automation capabilities, allowing tickets to be completed without human intervention. This will significantly improve overall processing time and workflow from both a customer service and technical efficiency perspective.

North Atlantic Offshore Wind Characteristics: Modeling and Comparison with Field Measurements and Industry Standards

Wind characteristics are critical to offshore wind resource development. The power output from a wind turbine is very sensitive to the local wind speed. Wind speed measurement is often limited to surface area close to Lidar buoys or meteorological stations and up to 200m due to the range of remote sensing devices. On the other hand, wind fields from ground level and up to 20000m above ground level can be simulated using Weather Research & Forecasting (WRF) model. In this study, WRF simulations are performed for the North Atlantic offshore waters to obtain wind speed time and spatial properties. Statistics of wind speeds for selected sites are derived and validated with field measurements. Wind vertical profiles are compared with ISO and IEC standards, and a power law profile is further derived to find the best fit. It is also demonstrated that the WRF model is reliable to forecast wind data, optimize prediction and improve reliability for coastal and offshore energy development. The wind modeling and characterizing can be extended to global regions to identify prospects with the most renewable energy potentials.

Enhancing Routing Efficiency and Performance in Mobile Ad-Hoc Networks Using Deep Learning Techniques

Abstract— MANET stands for Mobile Ad-hoc Network also
called wireless Ad-hoc Network or Ad-hoc Wireless Network. It is
a decentralized wireless network consisting of mobile devices
(nodes) that communicate with each other without relying on a
fixed infrastructure. MANET forms a highly dynamic
autonomous topology with the presence of one or multiple
different transceivers between modes. MANETs consist of a peer
to-peer, self-configuring and self-healing modes. Mobile Ad-hoc
Network has wide range of applications such as military and
defense operations, healthcare, sensor networks, wireless sensor
networks, Internet of Things (IoT) etc. In order to enhance the
routing efficiency and performance in Mobile Ad-Hoc Networks
(MANETs) this paper proposing different deep learning
techniques.
Keywords— Mobile Ad-Hoc Networks, Wireless Network
Topology, Wireless Communication, Deep Learning.

Enhancing Routing Efficiency and Performance in Mobile Ad-Hoc Networks Using Deep Learning Techniques

Abstract— MANET stands for Mobile Ad-hoc Network also
called wireless Ad-hoc Network or Ad-hoc Wireless Network. It is
a decentralized wireless network consisting of mobile devices
(nodes) that communicate with each other without relying on a
fixed infrastructure. MANET forms a highly dynamic
autonomous topology with the presence of one or multiple
different transceivers between modes. MANETs consist of a peer
to-peer, self-configuring and self-healing modes. Mobile Ad-hoc
Network has wide range of applications such as military and
defense operations, healthcare, sensor networks, wireless sensor
networks, Internet of Things (IoT) etc. In order to enhance the
routing efficiency and performance in Mobile Ad-Hoc Networks
(MANETs) this paper proposing different deep learning
techniques.
Keywords— Mobile Ad-Hoc Networks, Wireless Network
Topology, Wireless Communication, Deep Learning.

Machine Learning based Multi-Variate MBB-User Growth Prediction and Worst-Cell Clustering in Cellular Network

The Paper herein, introduces the algorithm and the model of machine learning where Multiple variable linear regression model, support vector machine, K-Means clustering method is used to predict the mean user number depending of some other variables. This analysis will help a market operation and planning team of a telecom network, to design and optimize the network and achieve maximum users under a telecom network. The worst cells which we obtained from the clustering methods will help them to work with the worst cells and solve network issues or capacity issues to get more subscriber to improve their profit. This analysis will help to plan and design cluster by cluster which is also very important for a telecom operator. The final result correctly leads the company to predict the user number of a network, which definitely provides great commercial value and help to build a good and customer-centric mobile network. The predictive models and clustering algorithms provide actionable insights and recommendations for improv- ing network efficiency, QoS, and user satisfaction. Despite the promising results, the research faces several limitations and challenges, including data quality issues, model interpretability, and algorithm scalability. Future research directions may focus on addressing these challenges and exploring new techniques for enhancing predictive accuracy, model explain ability, and computational efficiency

Robust Cascade PID-based Controller Design for Brushless DC Motor using Antlion Optimization Algorithm

Brushless DC (BLDC) motors are widely utilized in various fields, including high-speed drives, artificial heart pumps, and electric vehicles, due to their superior torque, compact size, and enhanced efficiency. However, it is very difficult to obtain satisfactory control
performance for BLDC motors using conventional PID
controller, because of the difficulty in tuning the proper PID parameters.
In this paper, an optimal cascade PID controller is designed for
controlling the BLDC motor. The proposed cascade controller consist of an inner loop and an outer loop, each responsible for different aspects of the control process. The inner loop handles fast dynamics, namely current control, while the outer loop deals with slower dynamics, as speed control. This separation allows each loop to be optimized individually, resulting in improved overall system performance and stability. By quickly responding to disturbances in the inner loop, cascade controllers can effectively overcome oscillations and enhance the stability of the motor. Additionally, cascade controllers can better handle non-linearities and parameter variations, leading to more accurate and reliable control of the output. However, tuning PID controller gains is crucial for achieving optimal performance and stability in control systems, and metaheuristic algorithms offer significant benefits by efficiently searching for the best gain values, even in complex and high-dimensional parameter spaces. The proposed cascade PID controller’s gains are optimized using the Antlion Optimization (ALO) algorithm, a modern metaheuristic algorithm known for its effectiveness in constrained problems and diverse search spaces. This optimization enhances the controller’s robustness against disturbances, particularly supply voltage variations. To validate the system’s performance, Hardware-in-Loop (HIL) Typhon technology is employed, allowing real-time testing of the BDCM and controller under various conditions. This ensures the reliability and effectiveness of the system before actual implementation.