Applied Science and Engineering Journal for Advanced Research

Design of a Trajectory Tracking Controller for Coreless Tubular Linear Motor Using Model Predictive Controller

2025-02-26T06:43:07+0530

This paper presents a cascaded control structure for a coreless tubular linear motor. The system includes position and speed loops employing PI controllers, and a current loop using Finite Control Set Model Predictive Control (FCS-MPC). This structure addresses challenges associated with low stator inductance, specifically its impact on current control. A simulation model was developed using MATLAB/Simulink. The simulation results demonstrate the effectiveness of the proposed solution in tracking the desired trajectory and minimizing the negative effects of low stator inductance on the current loop.

Alkali Interaction with Expansive and Non Expansive Soils

2025-03-30T12:42:41+0530

This research examines the interaction of alkali contaminants with both expansive and non-expansive soils, specifically black cotton soil and red soil, while assessing the effectiveness of sulphur and gypsum in restoring soil pH for geotechnical stability. Alkaline contamination, commonly caused by industrial effluents, construction activities, and agricultural practices, alters soil behavior by influencing its strength, swelling-shrinkage properties, and overall suitability for construction and infrastructure development. Laboratory experiments are conducted to evaluate the impact of alkalinity on key soil properties, including Atterberg limits, swelling potential, shear strength, permeability, and consolidation characteristics.

Expansive soils, such as black cotton soil, exhibit significant volume changes with moisture fluctuations, whereas non-expansive soils like red soil respond differently to alkali exposure. The presence of alkali contaminants can lead to reduced cohesion, increased dispersibility, and diminished bearing capacity, which pose risks to foundations, pavements, and embankments. This study investigates the potential of sulphur and gypsum as chemical stabilizers to counteract the negative effects of alkalinity. Controlled soil treatment trials are conducted to systematically assess variations in soil pH, structural integrity, and overall engineering performance.

The results offer valuable insights into the geotechnical consequences of alkaline contamination and the effectiveness of remediation techniques in stabilizing affected soils. By addressing the challenges posed by contaminated soils in civil engineering applications, this research contributes to sustainable ground improvement strategies, enhancing the durability and safety of infrastructure projects in impacted areas.

Gesture Control Revolution: Enhancing Automotive Infotainment through Advanced Hand Gesture Recognition

2025-04-01T13:13:07+0530

In the ever-developing industry of automobiles, a focus should be made on the innovation of the car’s user experience while keeping the driver safe. The following paper therefore aims at proposing a new hand gesture recognition system to be implemented in car infotainment, which employs a modified CNN model enhanced with KNN for enhanced gesture mapping. The efficiency of the system was tested on a data of samples consisting of 10000 images of 10 different gestures performed by different users under different lighting conditions. The results obtained for the experimental evaluation proved that the used CNN reached the accuracy of 92,5% with the validation set and the further use of KNN for post-processing increased the classification accuracy up to 95,2%. Resource consumption was low, the CNN occupied roughly 50 MB of memory, that is why it is possible to use it for the in-vehicle system. A similar survey that targeted users showed that 85% of them were comfortable with the system as it was easy to learn and did not interfere with the control of infotainment functions. This research discusses the possibility of using gesture recognition technology to improve the user experience in vehicles making infotainment systems safer and more efficient.

Leveraging Microservices and Serverless Architectures for Enhanced Enterprise Agility

2025-04-13T11:12:56+0530

This study investigates the impact of serverless and microservice architectures on the agility, scalability, and cost efficiency of an enterprise. Modern digital enterprises can no longer rely on traditional monolithic architectures due to constraints on deployment velocity, flexibility, and scalability. The research underlines the far-reaching benefits of microservices in modularity, resource consumption, and development cycle time optimization, while also noting the benefits of serverless computing in infrastructure expenditure, auto-scaling capabilities, and performance enhancement. Moreover, the integration of services was analyzed with a focus on security hygiene through policy enforcement, authentication, and workload distribution techniques. It is indisputable that the shift towards microservices and serverless structures provides enterprises with the ability to rapidly achieve innovations, operational agility, and scalability. This study has proven that the adoption of cloud-native architectures is imperative for enterprise modernization and attaining competitiveness within the ever-evolving realm of information technologies.

Forecasting Performance: Leveraging Machine Learning on Earned Value Data for Proactive Control

2025-04-25T10:16:56+0530

This study investigates the utilisation of machine learning methodologies in the context of earned value management (EVM) data, aiming for the anticipatory prediction and regulation of project outcomes. This research seeks to utilise machine learning frameworks, including regression analysis, decision trees, and neural networks, to forecast upcoming project outcomes, pinpoint possible risks, and improve the decision-making process. The study illustrates how the incorporation of sophisticated algorithms alongside conventional EVM data can yield enhanced, immediate insights into cost and schedule effectiveness. The document further explores the ramifications of this methodology for project leaders, providing a comprehensive structure for enhancing project oversight and regulation via insights derived from data.

Intent-Based Networking with AI: Towards Fully Autonomous Network Operations

2025-05-06T11:20:26+0530

This research explored the integration of Artificial Intelligence (AI) into Intent-Based Networking (IBN) systems with the goal of enabling fully autonomous network operations. Utilizing a qualitative research design, the study involved expert interviews and system behavior observations within simulated environments. The AI-driven IBN prototype was developed with components for natural language intent recognition, policy translation, and autonomous fault management. The findings indicated high accuracy in interpreting user intents (93.7%), efficient policy deployment, and significant reductions in both configuration and recovery times compared to traditional Software-Defined Networking (SDN) systems. Experts validated the system's operational advantages while also noting challenges in handling ambiguous inputs and adapting to diverse network configurations. Overall, the research highlighted the feasibility and benefits of AI-enhanced IBN while recommending further real-world testing and security considerations to achieve truly autonomous network infrastructures.

Experimental and Regression-Based Wear Analysis of MWCNT Reinforced AA7075 Using Box-Behnken Design

2025-05-05T20:18:04+0530

The research analyzes the wear characteristics of MWCNT-reinforced AA7075 metal matrix composites under different combinations of MWCNT volume fraction (2–6 wt%), operating temperature (80–120°C) and applied force (40–60 N). The wear resistance of composites produced by stir-casting fabrication received analysis through ANOVA combined with regression modeling after testing their wear resistance properties. A combination of 6% reinforcement with 100°C temperature under 40 N load proved to be the optimal conditions according to the desirability function approach which led to a wear rate of 3.349 Nm/mm³ and 0.826 in desirability. The studies reveal that reinforcement percentage served as the key variable (p = 0.004) which decreased wear by 25% when using 2% MWCNTs. Performance outcomes were most significantly improved through moderation of temperature conditions at 100°C combined with loading at 40 N. A developed regression model demonstrated the capability to predict wear rates with less than 5% error accuracy following validation through experimental confirmation. The obtained results can directly help engineers build high-wear-resistant composites for industries focused on aerospace and automotive manufacturing.