Conference Publications
Permanent URI for this collection
Browse
Recent Submissions
1 - 5 of 66
-
PublicationSuperpixels-based automatic density peaks and fuzzy clustering approach in COVID-19 lung segmentation(IEEE, 2023-12)Clustering algorithms that rely on minimizing an objective function suffer from the drawback of requiring manual setting of the number of clusters. This limitation becomes particularly evident when applied to image segmentation, where the large number of pixels can lead to memory overflow issues. To overcome this challenge, a reference of Automatic Fuzzy Clustering Framework (AFCF) for image segmentation method has been used as the comparison to the Density Peaks Clustering (DPC) algorithm. AFCF used superpixel algorithm to reduce the spatial information of data during computation, DPC algorithm to generate decision graph, and prior entropy-based fuzzy clustering (PEFC) algorithm to achieve fully automatic segmentation method in determining the number of cluster and the clustering result. In this study, 50 open-source healthy, COVID-19 and pneumonia infected radiographs dataset are acquired from the Kaggle and Github. The radiographs dataset that segmented by DPC is down sampling to 100*100 pixels due to overloading computation. At the end of the image segmentation, a segmentation performance evaluation is conducted based on sensitivity, specificity, accuracy, precision, recall, F-score and time consumed. The result shows that AFCF algorithm has the better overall performance with higher accuracy of 92.48% and F-score 0.9455. Meanwhile, the most highlighted evaluation index is drop to the time consume comparison, AFCF has around 2.7 times faster processing speed compare to DPC algorithm.
-
PublicationPerformance evaluation of different devices and algorithms for modelling small artefact(IOP Publishing, 2023)3D reconstruction and modelling play important roles in various applications, specifically in heritage preservation. With the aid of suitable hardware like the 3D sensors as well as respective data processing methods, the work has become more feasible in realizing the aim to conserve and preserve more artefacts. However, too many choices and alternatives might lead to different results which may affecting the preservation purpose. The objective of this work is to analyze and evaluate the performance of different devices and algorithms for small artefact modelling. Two 3D sensors, iPhone 13 Pro Max LiDAR and Structure sensor were selected to collect data of small artefact to be reconstructed and modelled. Two main, important surface reconstruction algorithms which are Poisson and Ball-Pivoting methods were also selected to be tested. Specifications of the sensors’ capabilities as well as modelling results of the artefact are examined. Different parameters of the algorithms were selected to study their effect. These findings will help to learn more about 3D sensors and the suitable modelling methods in making them better for usage in a variety of areas, including archaeology, architecture, and the protection of cultural heritage.
-
PublicationIndoor automated fire extinguisher system using computer vision(IOP Publishing, 2023)This paper presents the development of an automated fire extinguisher robot by employing fire recognition using computer vision. This project aims to develop a robot that can search, detect and extinguish small flames for indoor purposes. The robot is developed by applying open-source image processing to recognize the presence of fire. A control program was built to control the movement of the robot’s servo motor. The performance of the fire recognition was analysed in different light intensities and angles. The development of this project makes use of a camera and computer vision in place of various sensors, such as gas sensors, temperature sensors, and infrared sensors, to detect fires. Furthermore, a microprocessor is employed to operate the water pump and servo motor, which drive the nozzle to the position specified by the microcontroller. The results reveal that the proposed vision-based fire detection system has a high classification accuracy correspond to fire recognition.
-
PublicationPerformance evaluation of deep learning techniques for human activity recognition system(IOP Publishing Ltd., 2023)Human Activity Recognition (HAR) is crucial in various applications, such as sports and surveillance. This paper focuses on the performance evaluation of a HAR system using deep learning techniques. Features will be extracted using 3DCNN, and classification will be performed using LSTM. Meanwhile, 3DCNN and RNN are two additional, well-known classification techniques that will be applied in order to compare the effectiveness of the three classifiers. The 3DCNN-LSTM approach contributes the highest overall accuracy of 86.57%, followed by 3DCNN-3DCNN and 3DCNN-RNN with the overall accuracy of 86.07% and 79.60%, respectively. Overall, this paper contributes to the field of HAR and provides valuable insights for the development of activity recognition systems.
-
PublicationEffect of load capacitance on sympathetic inrush current(IEEE, 2023-12)In electrical systems, transformers are used to step up or step down the voltage to meet the needs of different loads. When a transformer is energised for the first times, it draws a high current or also known as inrush current. The inrush current can cause a several issues such as voltage dips, mal-operation and other in the electrical network. Besides, when the sympathetic phenomena are occurred, the magnitude and duration of the current changed dramatically. One of the factors that can affect the magnitude and duration of inrush current is the presence of load capacitance. This study uses a simulation model to investigate the impact of load capacitance on sympathetic inrush current that drawn by two-parallel connected transformers under different circuit breaker operating times. The goals of this project to investigate the effect of load capacitance on sympathetic inrush current as well as by varying the value of load capacitance to observe how severe it is. In this paper, the effect of load capacitances is analysed by modelling 100 kVA, 11 kV/415 V of wye-delta of single line (circles) transformer and all the schematics model and simulation results are performed by PSCAD software. As a result, it is expected to observe the magnitude and duration of sympathetic inrush current to make a comparison within ten different values. According to simulation results, the higher value of load capacitance can produce the lower magnitude of sympathetic inrush current as well as shorten the duration of the current appear which is the time taken to reach steady state. The findings of this study can aid in the design of the transformers to minimize the impact of sympathetic inrush current. In conclusion, this project contributes to new research contribution and expected to be completed within the given time frame.