Dr Edward Ofoegbu

The integration of advanced technologies into aerospace systems, par- particularly within hydraulic systems such as landing gear mechanisms, has introduced new dimensions of complexity and vulnerability. This paper discusses the methods of fault detection from a historical perspective to the current state of the art adopted in industry for real-time detection of faults in hydraulic systems deployed in aerospace. A review of fault detection methods ranging from the manual method, the threshold method, the statistical method, the model-based method, the signal-based method, the knowledge-based method and time frequency analysis method, and the artificial intelligence method, Explainable AI (XAI) was explored and discussed in this paper. The review reiterated that while detection methods, such as manual inspection and threshold-based monitoring, are straightforward to implement, they fail to deliver precise results when detecting complex faults. Model-based and AI-driven advanced techniques enhance precision at the expense of demanding greater computational power and sufficient data availability.

The need to accomplish net zero objectives of 2030, has increased consumers' appetite for electric vehicles and their variants. While consumers' interest soars high, so also have their apprehension for the safety issues relating to fire incidents in battery-operated cars. Battery management systems are tasked with maintaining the health of the battery system which the accurate estimation of state of charge (SOC) is a key parameter used for state-of-health (SOH) estimation. Hence, this study explores a data-driven estimation of a battery's charge state while in a discharge state using simple linear regression, ensemble methods, and neural networks. A known dataset consisting of 835248 records was used to train and test each model to determine the best fit. This study determined that neural networks are the models of choice for SOC prediction as opposed to linear and ensemble regression, but also that wide feed-forward neural networks have a high accuracy of prediction similar to or better than what is obtainable in a deep neural network while having the advantage of less technical and computation costs required for thin client devices such as a battery management system.

Energy conservation and its efficient utilization especially in cloud data centers has been a subject of discourse amongst numerous stakeholders. Advancement in information technology tools provides a solution to automating the process of electricity metering as well as remote load control alternatives. This paper presents an energy meter reader implemented with a microcontroller based logic methodology fused with a building automation system to implement remote load control by home owners using SMS from a GSM phone. A password based relay circuit was incorporated to ensure secure switching by the user. The system when deployed can enable users query and set energy consumption rates remotely so as to reduce the cost on final consumers as well as conserve energy. This is could be a useful system in the green-based design of cloud data centers.

Battery management systems (BMS) are critical in ensuring the performance, reliability, and safety of battery systems through accurate estimation of the State of Charge (SOC) of batteries. As on-board SOC estimation, together with other functionalities by the BMS can result in its high design complexity, high cost, and high energy consumption, this study explores a data-driven estimation of a Lithium battery state of charge (SOC) while discharging, using simple linear regression, ensemble methods, and neural networks respectively to ensure an accurate low time complexity solution as compared to existing methods. A known dataset of 835,248 records from Li [NiMnCo]O2 (H-NMC)/Graphite + SiO battery was used to train and test each model to determine the best fit. This study determined that neural networks are the models of choice for SOC prediction instead of linear and ensemble regression. Still, also the wide tri-layered feed-forward neural network proposed in this study showed great results by having a maximum error percentage of less than 1 %, and a mean squared error (MSE) of 1e-08, which is similar to or better than what is obtainable in other more complex deep neural network variants such as the Gated recurrent unit recurrent neural network (GRU-RNN), with an MSE of 1e-06 and similar load classifying neural network models with an error percentage of 3.8 %. The FFNN proposed in this study also has the advantage of having lower technical and time complexity computational costs required for active fault estimation in thin client devices such as a BMS.

The transformation of the power grid from traditional methods into smart grids for the generation, transmission, distribution, and utilization of electric energy reduces the dependency on fossil fuels, thus taking a step towards environmental sustainability. This research paper simulates a smart grid's behavior when integrated with a renewable energy source. The grid network topology of a power system was modeled and simulated in MATLAB/SIMULINK, where the model consisted of renewable energy resources (RER's) having a series of solar panels, redundant power generating stations, a transmission infrastructure, and a power utilization section for 3-phase high voltage industrial load and low voltage domestic load, respectively. The results of the study showed that there is power loss and signal distortion in the output voltage when multiple generators are integrated. These harmonics can be easily filtered out when the integration is done between the generator and a renewable energy resource (RER) using properly designed Resistor-Inductor-capacitor filters and booster circuits. The results further demonstrated that when integration was accomplished between the generator and the RERs, a stable output voltage of 0.6e4 V was obtained, given a generator capacity of 6600 V. The industrial and domestic load also showed minimal instability by maintaining an output of 1100/440 V, respectively, before and after integration. The study also showed that RERs can be used to support the power supply to the grid to maintain its supply voltage and the overall stability of the system. However, RERs cannot withstand supplying a power grid on their own.

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized autonomous driving (AD), enabling vehicles to perceive, reason, and act with unprecedented precision in dynamic environments. This review synthesises advancements across core AD domains, such as perception systems that leverage convolutional neural networks (CNNs) and multimodal sensor fusion techniques to improve object detection accuracy. Advancements in complex traffic collision-free navigation using reinforcement learning (RL), hierarchical planning, and the adoption of model predictive control (MPC) and adaptive algorithm applications to reduce tracking errors in autonomous driving were considered in this review. However, critical challenges persist that affect the efficiency of AD systems, such as data scarcity, adversarial vulnerabilities, ethical dilemmas in life-critical scenarios, and inconsistent regulatory frameworks. Emerging solutions such as edge computing, physics-aware sensor fusion, and explainable AI (XAI) promise to enhance real-time reliability and public trust. This review determined that future progress hinges on cross-disciplinary collaboration to address technical robustness, societal equity, and global standardization. Therefore, charting a roadmap bridging AI innovation and ethical governance for safety, scalability, and socially responsible autonomous mobility.

Model Predictive Control (MPC) has become a revolutionary control method in current engineering practice, offering explicit benefits over previous approaches. It explicitly considers models of the system, constraints, and interactions among multivariable systems as an integral part of the decision-making process. The review’s subject provides context on the theoretical underpinning of MPC, its subsequent methodology development, and meta-areas of application since its inception as a control paradigm in the 1970s, up to today, where it is viewed as a flexible and sophisticated control paradigm. The Nonlinear, Robust, Stochastic, Adaptive, and Explicit variants of MPC are discussed here in terms of their optimisation models, computing requirements, and uncertainty computational needs. Specific consideration is given to computational issues, such as model accuracy and real-time optimisation, as well as newer methods, including sequential quadratic programming, real-time iteration schemes, model reduction, and integrating machine learning. In addition, the concepts of Distributed MPC and Economic MPC are introduced as the new paradigm dealing with the achievement of scalability goals and performance economy when working in large-scale and cost-effective systems. MPC flexibility and cost potential are shown through case studies in process industries, energy systems, aerospace, robotics, and manufacturing. With its advantages, much of the computational intensity, modelling, and implementation complexity is an active research frontier. In the future, connecting to machine learning, high-performance computing, and distributed control architectures will likely increase the importance and potential of MPC. In general, MPC remains a foundation of control engineering, and provides powerful, flexible, and cost-effective solutions to what have become more challenging systems.

Applications in harsh environments greatly suffer from intermittency faults in their interconnections/wirings. Due to the erratic behavior of intermittency that causes signal irregularities, it is tough to distinguish irregularities from an actual transmitted signal, particularly in the earlier stages where signal abnormalities mainly resemble noise. This paper explores step changes in the resistance of a wire caused by broken strands as a failure parameter. Thus, a test rig was designed to emulate the ageing mechanism of the wire. with results of the study highlighting that resistance step changes could effectively be used to locate intermittency faults in low-power cable applications.

In recent years, the application of Indoor Positioning Systems (IPS) has experienced a significant increase in demand, with the aging of the world’s population and their changing lifestyles. While outdoor positioning systems, such as the Global Positioning System (GPS), have significantly advanced over the years, indoor positioning has been restrained by the limitations of the employed technologies. This paper presents a hybrid wireless positioning system able to locate wearables indoor accurately. It is based on the Wi-Fi and Bluetooth technologies, by using trilateration to determine the position of Bluetooth low energy (BLE) wearable devices with an accuracy of up to 1.8 meters. A graphical user interface (GUI) was used to illustrate the performance of the proposed systems, by allowing users to visualize the captured data in two dimensions and three- dimensions in real-time.

Gas turbines are a key player in the energy generation sector and thus form a key component in energy systems as a critical infrastructure. The determination of key parameters in optimization and efficiency of the gas turbines are of utmost importance to increase their power conversion efficiency. This paper presents a simple power estimation model for a gas turbine considering all its parameters. 7412 multivariate data records from the UCI Machine Learning Repository were used in the development of a linear prediction model for estimating the turbine energy yield for a combined cycle power plant. Simulation results show that the inlet temperature of the turbine is the most critical parameter that predicts its energy yield capacity, while ambient atmospheric conditions of temperature, humidity and pressure do not predict its energy yield capacity.

Autonomous vehicles otherwise regarded as self-driving vehicles are poised to be the next generation of technological advancement in the transportation sector globally. They offer superior value for money with regard to the cost of operation, excellent safety records, and many other benefits. Cities around the globe have adopted it even as research and development efforts are ongoing. This study investigates the role autonomous vehicles could play in Africa, especially as it relates to transportation inclusivity. The study determined that there are lots of inclusivity issues beguiling African nations ranging from religious, financial, educational, and cultural issues and was able to highlight how the adoption of autonomous vehicles can aid to solve issues relating to stigmatization and social exclusion.

Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.

The high rate of vehicle theft and the loss of lives occasioned by drunk driving has caused irreparable losses to people and businesses, from a personal, commercial and reputation perspective. Existing systems deployed to mitigate against vehicle theft have all been breached by the ever-adaptive criminals. Drunk driving has been estimated to be a leading cause of deaths on highways and motorways, through preventable accidents. Technology has provided the tools that can aid in mitigating the vices aforementioned with the aim of provisioning lasting solutions. This paper proposes a new architecture for adaptive user authentication in order to mitigate drunk driving and vehicle theft. It considered user authentication in three (3) phases and proposed an authentication architecture for each identified phase, with a step by step description of the implementation method and tools for each phase. The architecture proposed in this study can aid in real time prevention of vehicular theft, unauthorized vehicular access and usage, while also utilizing the benefits of the latest technologies in machine vision and alcohol breadth analyzers to detect and prevent drunk driving, and the associated accidents it causes.