This paper presents an investigation into the thermal behaviour of a copper coil embedded into a block of resin that is bound to the asphalt in the roadway. The roadway is made up of three layers of 50mm asphalt and t...
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ISBN:
(数字)9798350349139
ISBN:
(纸本)9798350349146
This paper presents an investigation into the thermal behaviour of a copper coil embedded into a block of resin that is bound to the asphalt in the roadway. The roadway is made up of three layers of 50mm asphalt and the resin block surrounding the coil was placed within the surface layer. The thermal behaviour of the coil and the surrounding environment is presented when the coil is energised at 90A DC to be within the electromagnetic radiation limits. Based on the exerpimental results, simulations are conducted in ANSYS Maxwell and Icepak to predict the temperature rise using AC energisation at 85kHz. The coil is energised in the simulation with a range of currents to predict the thermal stress the resin block will withstand at steady-state conditions. The simulation findings predict the thermal behaviour of the resin block and the surrounding asphalt when the coil is transferring 10kW.
Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concep...
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The rise of security concerns has spurred on-going research into Brain-computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can en...
The rise of security concerns has spurred on-going research into Brain-computer Interfaces (BCI) based authentication. These applications utilize electroencephalogram (EEG) signals, due to their properties that can enhance security systems. In previous studies, EEG data has been incorporated into various authentication systems to compare the performance of new and existing classification methods. However, using EEG data to compare the performance of distance metrics in a P300-based BCI authentication system has not been explored yet. In this study, EEG data is used to determine the most effective distance metric for authenticating users in a closed-loop system. To accomplish this task, we conducted a longitudinal study to evaluate three distance metrics (Cosine, Correlation and Chebyshev) while participants interacted with our BCI authentication system. Our results indicated that the Cosine similarity outperformed all other distance metrics for each user.
The increasing preference for text-based communication on online chat applications has caused the number of social interactions to increase rapidly. However, text-based communication usually results in misunderstandin...
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This paper addresses the problems of output feedback stabilization for a class of nonlinear systems with a kind of truncation protocol. Based on the sampled measurements, an output feedback controller is proposed to g...
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It is quite common for medical drugs and prescriptions to be misidentified by hospitals and after drugs are being dispensed to the patients. Misidentification of medical drugs is more common among elderly and visually...
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Cardiovascular diseases (CVDs) persist as a primary cause of mortality on a global scale, necessitating effective prediction methods. This study presents a novel modelling approach utilizing the Self-Organizing Map (S...
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ISBN:
(数字)9798331528553
ISBN:
(纸本)9798331528560
Cardiovascular diseases (CVDs) persist as a primary cause of mortality on a global scale, necessitating effective prediction methods. This study presents a novel modelling approach utilizing the Self-Organizing Map (SOM), an unsupervised machine learning approach, for CVD prediction. The SOM model employed to analyze and cluster patient data based on intrinsic patterns without predefined labels, enabling the identification of high-risk individuals. Results demonstrated that the SOM model effectively differentiates between healthy and at-risk patients, offering a robust approach for early detection. The SOM presented to be the best predictive model for the CVD data prediction with the highest accuracy (84.44%); precision (89.13%); recall (82.00%); F1-score (85.42%). The U-Matrix of SOM as a visualization tool provided insightful representations that highlighted two discernible clusters, effectively illustrating the health status of individuals with similar health conditions concerning CVD. This facilitates a better understanding and management of CVD. The research findings suggested the potential of integrating SOM into clinical workflows, offering a potent tool for healthcare professionals in the fight against CVD.
Unmanned aerial vehicles can improve short- term weather forecasting by acquiring information from weather sensors and other sensors. With this information, there is the possibility of making relevant maps like solar ...
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ISBN:
(数字)9798350364293
ISBN:
(纸本)9798350364309
Unmanned aerial vehicles can improve short- term weather forecasting by acquiring information from weather sensors and other sensors. With this information, there is the possibility of making relevant maps like solar radiation, pollen, emissions, particles, and others. Another advantage of this acquisition system is the high rate of flights, compared to the classic measurements made with the weather balloon, which is launched twice a day. In addition to the advantages listed above, we discuss the multiplication factor of the acquired data, these systems being able to operate in various geographical locations.
The IoT based Smart farmland using deep learning is a system for tracking animals on agricultural land combines surveillance cameras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally t...
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ISBN:
(数字)9798350349221
ISBN:
(纸本)9798350349238
The IoT based Smart farmland using deep learning is a system for tracking animals on agricultural land combines surveillance cameras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally the animal's presence. Upon detection, the system utilizes AI methods to categorize the animal type and send immediate SMS notifications to farmers through a GSM module. It incorporates an animal-repellant buzzer and a gate control mechanism. Through the use of transfer learning with CNN models, it accurately identifies four specific animal species: elephants, cows, goats, and pigs. Continuous updates and training with new data maintain its precision, serving the agricultural and wildlife protection domains. This technology significantly aids farmers in safeguarding their crops, offering crucial insights into intrusions and amplifying crop yield. Its primary purpose revolves around simplifying the identification of animals on agricultural land.
In this paper, a brief overview of converter topologies used in stationary battery energy storage systems is given. A simulation model of converter was developed in MATLAB Simulink. A simulation is conducted to provid...
In this paper, a brief overview of converter topologies used in stationary battery energy storage systems is given. A simulation model of converter was developed in MATLAB Simulink. A simulation is conducted to provide a preliminary understanding of the converter's bidirectional nature. The results indicate that the state of charge (SOC) increases during battery charging and decreases during discharging. The topology of non-isolated two-stage bidirectional converter is presented for proposed real-world prototype, including the high-level electrical schematic and mechanical layout. An 18-kW prototype converter for stationary battery energy storage systems was developed and presented. The converter housing with all installed ancillary components for proper operation is also described. At the time of writing this paper, the actual converter is still in the development phase.
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