Yazar "Moveh, Samuel" seçeneğine göre listele

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    Evaluating the Performance of Fuzzy-PID Control for Lane Recognition and Lane-Keeping in Vehicle Simulations
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2023) Moveh, Samuel; Yahya, Khalid O. Moh.; Attar, Hani; Amer, Ayman; Mohamed, Mahmoud; Badmos, Tajudeen Adeleke
    This study presents the use of a vision-based fuzzy-PID lane-keeping control system for the simulation of a single-track bicycle model. The lane-keeping system (LKS) processes images to identify the lateral deviation of the vehicle from the desired reference track and generates a steering control command to correct the deviation. The LKS was compared to other lane-keeping control methods, such as Ziegler–Nichols proportional derivative (PD) and model predictive control (MPC), in terms of response time and settling time. The fuzzy-PID controller had the best performance, with fewer oscillations and a faster response time compared to the other methods. The PD controller was not as robust under various conditions due to changing parameters, while the MPC was not accurate enough due to similar reasons. However, the fuzzy-PID controller showed the best performance, with a maximum lateral deviation of 2 cm, a settling time of 12 s, and Kp and Kd values of 0.01 and 0.06, respectively. Overall, this work demonstrates the potential of using fuzzy-PID control for effective lane recognition and lane-keeping in vehicles.
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    Fuel Characterisation of the Physicochemical, Thermal and Kinetic Properties of Corn Cob Biomass Wastes for Potential Energy Recovery
    (Slovnaft VURUP a.s, 2018) Otitolaiye, Victor O.; Dodo, Yakubu A.; Jagun, Zainab T.; Bashir, Faizah M.; Moveh, Lawrence P.; Ajibade, Samuel-Soma M.; Moveh, Samuel
    This study presents insights into the solid biofuel properties of corn cob biomass (CCB) wastes for sustainable energy recovery. The physicochemical, thermal, and kinetic properties of CCB were charac-terised through ultimate, proximate, heating value, and thermogravimetric (TGA) analyses. Results showed that CCB contains high carbon (41.88 wt.%), hydrogen (6.33 wt.%), volatile matter (68.21 wt.%), and higher heating (15.70 wt.%) values for potential energy recovery. However, the high ash (16.56 wt.%) content could pose bed agglomeration, fouling, and sintering problems during high-temperature conversion. Thermal analysis resulted in 55.84%-59.51% loss of mass and residual mass of 40.49%-44.17%. Kinetic analyses revealed that CCB is highly reactive as characterised by the average activation energy, Ea=134.58 kJ/mol and pre-exponential factor, ko=2.53×l008/min. In conclusion, CCB is a potentially practical feedstock for sustainable energy recovery through thermo-chemical conversion. © 2020. All rights reserved.
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    Mechanical and Fresh Properties of Sustainable Kenaf Fibrous Concrete Incorporating Sorghum Husk Ash
    (Univ Tun Hussein Onn Malaysia, 2021) Ogunbode, Ezekiel Babatunde; Dodo, Yakubu Aminu; Moveh, Samuel
    This article describes the findings of an experimental investigation on the performance of concrete using Kenaf Fiber (KF) and Sorghum Husk Ash (SHA) (CEM 1). To characterised the SHA (EDS), microstructural studies such as X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray fluorescence (XRF), and Energy-dispersive X-ray spectroscopy were performed. CEM 1 was used with the KBF (length, L = 50 mm) and five volume fractions ranging from 0 to 1.0 percent (= 0.25 percent). Following that, five concrete mixtures were cast with 10 percent SHA as a substitute for CEM 1. The samples were cured in water and their characteristics were evaluated in both the fresh and hardened stages. In new concrete, the use of Kenaf fibre and SHA lowered slump values while increased VeBe time. When Kenaf fibre was added to either CEM 1 or SHA concrete mixes, it resulted in a good interaction with high tensile and flexural strengths, as well as increased concrete ductility and crack dispersion. When 0.5 percent Kenaf Fibre was added to dry concrete at the age of 56 days, it resulted in the largest increase in tensile and flexural strengths. The research found that utilising KF and SHA to manufacture sustainable green concrete is both technologically and environmentally viable.
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    Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques
    (MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Aliero, Muhammad S.; Pasha, Muhammad F.; Smith, David T.; Ghani, Imran; Asif, Muhammad; Jeong, Seung Ryul; Moveh, Samuel
    Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the datasets and data pre-processing. The study compiles a new comprehensive public set of training datasets for building occupancy profile prediction with over 40,000 records. To the best of our knowledge, it is the largest dataset to date, with the most realistic and challenging setting in building occupancy prediction. Furthermore, to the best of our knowledge, this is the first study that attained a robust occupancy count by considering a multimodal input to a single output regression model through the mining and mapping of feature importance, which has advantages over statistical approaches. The proposed solution is tested in a living room with a prototype system integrated with various sensors to obtain occupant-surrounding environmental datasets. The model’s prediction results indicate that the proposed solution can obtain data, and process and predict the occupants’ presence and their number with high accuracy values of 99.7% and 99.35%, respectively, using random forest.
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    Review of AI-Based Vision Detection Algorithms for Autonomous Mobile Robots
    (Springer Science and Business Media Deutschland GmbH, 2024) Moveh, Samuel; Merchán-Cruz, Emmanuel Alejandro
    This study presents a comprehensive review of AI-based vision detection algorithms for autonomous mobile robots. Over the years, research on autonomous mobile robotics, artificial intelligence (AI), and vision detection algorithms has significantly advanced. Furthermore, an in-depth analysis of some AI-based image detection algorithms for autonomous mobile robots is presented, demonstrating the complicated web of factors that go into creating an algorithm, including the algorithmic approach, methodology, data training, performance standards, real-time implications, and potential for improvement. This study acts as a roadmap for the development of improved AI-driven robotic vision systems as the algorithm’s capabilities continue to change the field of artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    The Role of Smart Environment Initiatives on Environmental Degradation: Consolidating the Resilient Built Landscape
    (Institute of Electrical and Electronics Engineers Inc., 2022) Agboola, Oluwagbemiga Paul; Moveh, Samuel; Yahya, Khalid; Attar, Hani; Amer, Ayman
    Primarily in industrialized and some developing nations, the adoption of the smart city approach as a sustainable approach to the management and implementation of infrastructure developments has been rising. Initiatives to build resilience are critical for successful cities in these developing nations, like Nigeria. This research explores the various advantages of building resilience in Nigeria's smart cities in light of this growth. Few scholars have examined the building and smart city efforts aimed at enhancing Nigeria's built environment's sustainability in the context of the current global environmental issues. Thus, this study closes the gaps by evaluating the various aspects of building and the city's resilience with a focus on Lagos, the most populated metropolis in Africa. The following issues are covered with reviews of the literature: (i) the current Lagos Smart City projects; (ii) Smart City and Building initiatives in Nigeria; and (iii) the goal of robust resilience. By succinctly describing the strategic planning for smart city development, in addition to the opportunities discovered in the smart city and building initiatives, this paper contributes to the conversation around smart cities. This will aid in the documentation, forecasting, and future decision-making processes for Nigeria's Smart Cities. © 2022 IEEE.