Pusat Pengajian Kejuruteraaan Elektrik dan Elektronik - Tesis
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- PublicationCharacterising micro-cracks in crystalline silicon solar cells using transelection imaging(2020-05-01)Teo, Teow Wee
- PublicationCyber-power reliability assessment of dynamic thermal rating enhanced system integrity protection systems(2021-11-01)Jimada-Ojuolape, BilkisuInformation and Communication Technology (ICT) systems have become a vital part of every aspect of our daily lives, and their integration into the electric power system has become paramount. The ICT enhanced power system referred to as smart grid exhibits intelligent monitoring and control, bidirectional communication between stakeholders and power system elements, security and safety of supply and self-healing qualities. The presence of smart grid infrastructure within the power system provides an avenue by which the power systems can more efficiently manage their overall increased complexity and size while also operating within narrower security limits. Dynamic thermal rating systems (DTR) and System integrity protection scheme (SIPS) are two technologies that are used to boost network reliability. Synchrophasors from phasor measurement units (PMU) can improve wide-area monitoring and protection (WAMP) capabilities such as those required and enabled by DTR and SIPS. Although DTR allows the existing transmission lines to be operated much closer to their limits, line outages after DTR implementation could threaten network security. Thus, the SIPS, designed to ensure network security, is helpful to forestall impending contingencies that could arise from DTR implementation. This thesis presents novel models for a composite reliability assessment of a synchrophasor-based DTR, and SIPS enhanced smart grid. In contrast to existing research, which considers these two technologies independently, this thesis proposes a joint assessment of these two technologies. The proposed system offers WAMP functions to the network and simultaneously considers the effects of component failures and communication network availability on system-widereliability. The proposed sequential Monte-Carlo simulation (SMCS) methods which are analysed in several case studies, successfully simulate different contingencies that stem from component failures and communication network outages like cascading line outages, leading to load curtailment. Results show that while DTR increases overall network ageing, the ageing process can be slowed down by a reliable SIPS while minimally affecting load curtailment. The result shows a 98.92% decrease in load curtailment value and network ageing is reduced by 11.95% when the SMCS method for component failures are tested and analysed. Results also show that the network topologies with more redundant communication paths offer significant improvements to network reliability. This result shows 90.05% improvement in reliability when the single mesh and the most reliable double mesh topology are compared. The study also revealed that SIPS deployment offers more improvement to reliability than scenarios where SIPS is not deployed by 99.15%. These results imply that DTR does increase network ageing and that the use of PMUs and implementation of SIPS alongside DTR on a single network does slow down the ageing process, thereby, improving the general reliability of the network.
- PublicationDesign implementation of 4 × 4 butler matrix with bandwidth enhancement and size reduction for fifth generation (5g) wireless communication system(2022-05-01)Mohd Suhaimi, Nazleen SyahiraSignals are susceptible to reflections from various obstacles resulting in multipath signal propagation delay and degrading the quality of the received signal. As the number of subscribers to wireless communication is increasing at an alarming rate, future wireless communication requires a high data rate, better coverage, improved capacity and higher quality of service. Implementing a Butler matrix is one of the solutions to overcome these issues. However, the conventional 4 × 4 Butler matrix has a bulky size which consists of 10 individual components such as four 3 dB branch-line couplers with 90° output phase differences, two 0° Schiffman phase shifters, two 45° Schiffman phase shifters, two conventional 0 dB crossovers with 90° output phase differences. Besides that, the conventional Butler matrix has high insertion loss due to the additional path from the phase shifters and crossovers. Owing to these issues, an alternative way of implementing the proposed 4 × 4 Butler matrix in a single layer by eliminating four phase shifters and one crossover is proposed in this work. The center frequency is set to 6.5 GHz which lies in the 5G frequency band between 5.9 GHz and 7.1 GHz. The design utilizes two 3 dB cross-slotted patch couplers with 45° output phase difference as a replacement of two conventional 3 dB/90° couplers with two 45° Schiffman phase shifters. This approach contributes to physical size reduction and better performance. The -3 dB ± 1 dB fractional bandwidth enhancement of coupling coefficient and physical size reduction for the proposed 3 dB/90° cross-slotted patch coupler are 18.77% and 26.32%, whereas 22.52% and 45.72% for the proposed 3 dB/45° cross-slotted patch coupler compared to their conventional designs, respectively. The proposed 4 × 4 Butler matrix exhibits overall fractional bandwidth enhancement of 9.7% and overall physical size reduction of 51.56% compared to the conventional 4 × 4 Butler matrix. The beam scanning of the conventional 4 × 4 switched-beam Butler matrix is covered from -45° to +45°, whereas from -50° to +50° for the proposed 4 × 4 switched-beam Butler matrix. The highest gain of the conventional and proposed 4 × 4 switched-beam Butler matrices are 9.32 dBi and 9.84 dBi, respectively. Therefore, the proposed 4 × 4 switched-beam Butler matrix shows good performances in terms of compactness and beam scanning angles at the output ports.
- PublicationDesign, modeling, and analysis of 100kw two-stage three-phase grid-connected pv generation system(2021-12-01)Mohamed Hariri, Muhammad HafeezThe grid-connected PV (GPV) generation system has become a focal interest nowadays due to the fact that it offers abundant opportunities to harvest free energy sources from sunlight. Solar irradiation and the PV cell surface temperature are thetwo significant atmospheric variables that directly affect the total generation of the PV current. The main issues lie in how to deliver the maximum available power from the PV arrays where the types of its electrical parameters are in DC quantities form which later converted into the symmetrical three-phase AC utility grid without compromising the quality of the injected power. The operating point of PV oscillates in the region of the maximum power point (MPP) giving rise to the waste of energy. In addition, the existing conventional PLL synchronization mechanisms of the GPV generation system faced difficulties in providing the accurate value of grid information during fault conditions. The appropriate maximum power point tracking (MPPT) algorithms technique against absurd atmospheric conditions, proper converter switching and its topologies, suitable power filter arrangements, and the robustness of synchronization scheme in encountered the grid-line disturbances are vital for the effectiveness of the designated 100𝑘𝑊 two-stage three-phase GPV generation system. Based on the design, system model structure, and the analysis that has been carried out in this research work, it can be concluded that the most applicable GPV generation system is the system arrangement which incorporated the Cuckoo Search (SC) MPPT technique, DC-DC boost converter, three-phase space vector pulse-width modulation (SVPWM) voltage source inverter, LCL power filters,and the CDSC grid synchronization scheme. This system is capable of providing a maximum active power of 100𝑘𝑊 from the PV arrays with the solar irradiation level of 1000𝑊/𝑚2 to the utility grid and generates low total harmonics distortions (𝑇𝐻𝐷𝑖) of 2.06% from the rated inverter input power. The introduction of the proposed controller mechanism has optimized the maximum power transfer as well as improved the dynamic response of the designated GPV generation system during unpredictable atmospheric and various types of grid fault conditions.
- PublicationEnhanced dense space attention network for single image super-resolution(2022-02-01)Ooi, Yoong KhangThe development of deep learning has received much attention in the single image super-resolution reconstruction application. The first convolutional neural network (CNN)-based image super-resolution model was the Super-resolution Convolutional Neural Network (SRCNN). Since then, many researchers have put efforts into developing the CNN-based model for image super-resolution to improve the accuracy and reduce the running time of the model. Until today, some models still suffer from the vanishing-gradient problem and rely on a large number of layers that result in a long-running time. Therefore, an enhanced dense space attention network (EDSAN) model is proposed to overcome the problems. The objectives of this projectare to improve the accuracy of the model and reduce the running time by reducing the number of layers required. This project developed a Local Wider Dense Space Attention Block (LWDSAB) in the EDSAN model that adopted a dense connection and residual network to utilize all the features to correlate the low-level feature and high-level feature. Besides, the convolutional block attention module (CBAM) layer and multiscale block (MSB) are deployed in the model to reduce the running timewithout affecting the model’s performance. The model is evaluated through peak signal-to-noise ratio (PNSR) and structural similarity index measure (SSIM) metrics. For state-of-the-art comparison purposes, a total of 4 recent models were taken for results benchmarking. Besides, a total of 4 different types of datasets will be used forperformance evaluation. Results show EDSAN made a different amount of improvement respective to different datasets and different scale factors when compared to different models. EDSAN model performed the best for the Set5 dataset. The greatest improvement made for the PSNR value was 8.96% relative to the DRDN model at a scale factor of 4. For other datasets, EDSAN showed a positive result in the PSNR value at a scale factor of 2 and 3. Although EDSAN is not the top performer at a scale factor of 4, the percentage of the PSNR improvement is more significant than those models that outperform the EDSAN. In terms of SSIM, the EDSAN model showed a positive result for all datasets and all scale factors compared to other models. The highest achievement made was 10.29% relative to the DRDN model for the Urban100 dataset at a scale factor of 4. In conclusion, EDSAN successfully solved the vanishing-gradient problem and long-running time issue.
- PublicationExtended nearest centroid neighbor method with training set reduction for classification(2020-06-01)Mukahar, NordianaThe k - Nearest Centroid Neighbor (kNCN) is a well-known non-parametric classifier that shows remarkable performance in classification. Nevertheless, this technique suffers from slow classification time and one-sided selection of nearest centroid neighbors which leads to the poor performance of classification accuracy. This thesis first presents four variants of the training data set reduction techniques termed Reduced Set k - Nearest Centroid Neighbor.v1 (RSkNCN.v1), Reduced Set k - Nearest Centroid Neighbor.v2 (RSkNCN.v2), Reduced Set k - Nearest Centroid Neighbor.v3 (RSkNCN.v3) and Reduced Set k - Nearest Centroid Neighbor.v4 (RSkNCN.v4) to reduce the classification time of the kNCN. Atypical samples are removed first by using Wilson’s Edited kNCN and the fraction of training set is computed by using the maximum or optimum rank of training samples (that agrees with the majority of its k - nearest centroid neighbors). Experimental results carried out with 30 sets of the Real-world data from UCI Repository and FV-USM image database show that the proposed training set reduction techniques obtain the best performance in terms of reduction ratio and classification time compared to the benchmark techniques (Wilson’s Edited, Iterative and Limited-kNCNs). All the proposed techniques give satisfying results in terms of classification accuracy except for the RSkNCN.v4 that shows a poor result. This technique performs such aggressive training samples removal strategy and there is a possibility that the training samples with useful information might be removed leading to poor classification performance accuracy. Regarding the second problem of the kNCN, this thesis proposes a new Reduced Set Extended k - Nearest Centroid Neighbor (RSENCN) classifier to improve the classification accuracy of the kNCN classifier. The proposed RSENCN classifier captures more class information by considering nearest centroid neighbors from the views of training and test samples. Experimental comparisons and statistical significant analysis have confirmed that the proposed RSENCN classifier outperforms the other benchmark classifiers (kNCN, DWkNCN, kNN, DWkNN, FkNN, ENN, MkNN, kGNN) by yielding the highest classification accuracy of 88.56%, 89.20% and 83.90% on 30 sets of the Real-world data, I-4I data set and FV-USM image database. In conclusion, the findings reveal that a small subset that produces a high reduction ratio and fast classification time can be obtained by using the maximum or optimum rank of training samples (that agrees with the majority of its k - nearest centroid neighbors). The findings also reveal that the factors of spatial distribution and two-sided consideration of nearest centroid neighbors result in consistent improvement of classification performance of the RSENCN classifier.
- PublicationFlex sensors precompensator via hammerstein-wiener modelling approach for improved automated goniometric measurements(2020-04-01)Ali, Syed Afdar Ali Syed MubarakThis research introduces a new approach to model the characteristic of flex sensors on a goniometric glove, which is designed to capture the user hand gesture that can be used to control a bionic hand. The main technique employs a constrained control strategy which is aimed to provide an approximate linear mapping between the raw sensor output and the dynamic finger goniometry. In order to smoothly recover the goniometry on the bionic hand's side during the transmission, the precompensator is restructured into a Hammerstein-Wiener model, which contains of a linear dynamic system and two static nonlinearities. A series of real-time experiments involving several hand gestures have been conducted to analyse the performance of the proposed method. The performance is evaluated in terms of the integral of absolute error between the glove's and the bionic hand's dynamic goniometry. Comparisons are made with the raw sensor data, which has been preliminarily calibrated with the finger goniometry, and the Hammerstein-Wiener model. Experimental results show that the raw sensor data result in average percentage errors between 7.1% and 20.193%, whereas for the Wiener model, the average percentage errors vary between 2.8% and 3.32%, which are well below the range from the raw data. A clear error reduction is obtained via the Hammerstein-Wiener precompensator where the resulting average percentage errors are no greater than 1.53%. This concludes that the proposed strategy can remarkably improve the dynamic goniometry of the glove.
- PublicationHigh efficiency air substrate microstrip antennas for millimeter wave applications(2022-06-01)Shahed, Kamal ShahanawazThe microstrip antenna is gaining popularity because to its simple production process, mechanical robustness, and ease of integration into a system's surface. Initial research demonstrated that a reduction in the antenna’s extent outcomes in a straightforward reduction in its efficiency and bandwidth. Suspending dielectric substrates between air, on the other hand, proved essential in achieving wide bandwidth and high efficiency, but at the expense of increased manufacturing costs. Furthermore, the thickness of the metal radiator/ ground and the height of the substrate of a microstrip antenna are normally determined by the manufacturer's specifications and cannot be altered by the designer. As a result, employing the air as a substrate allows the designer to adjust the thickness of the sheet metal/ substrate to achieve the desired antenna properties. This research aimed to design, investigate, and validate the influence of the air substrate-based sheet metal microstrip on various antenna parameters for the millimeter wave applications. Novel antenna structures were ascertained by performing parametric simulation studies on the thickness of the sheet metal/ substrate and the design of radiator with the Computer Simulation Technology Microwave Studio ® software. The simulated antenna designs were validated by manufacturing and evaluating their performance in the anechoic chamber. Significantly, for operating in the 24 GHz frequency band, a simple wheel-shaped copper microstrip antenna is described. The vertical polarization was generated using a coaxial feed probe, while the horizontal polarization was generated using a wheel-shaped microstrip with four coupling arms. An antenna made of two copper sheets placed together between two air substrates was built and tested in the anechoic chamber. The field vectors from the feed probe and the radiating arms were properly combined, resulting in stable in-band omnidirectional radiation patterns. The antenna had an overall footprint of 8 mm, which allowed it to achieve a −10 dB impedance bandwidth > 3 GHz and a radiation efficiency of >95%. The axial ratio < 2.7 dB and the gain < 6 dBi were maintained in the operational frequency band of 22.5 to 25.7 GHz. Furthermore, the antenna system's fabrication cost was greatly decreased due to the integration of simple geometry and an air-substrate. As a result, the antenna is a strong contender for the millimeter wave applications in the fifth generation.
- PublicationImplementation of mp3 decoder with parallel technique(2020-06-01)Esther, Ong Tze ShinThis thesis describes the mp3 decoding algorithm implemented in Matlab. The algorithm will read the mp3 file from the input, decode the mp3 bitstream into pulse code modulated (PCM) outputs using a standard mp3 decoding algorithm and written into wav file. One option to improve the performance is by using parallel processing. An implementation of mp3 decoding with parallel technique in Matlab is presented. It focuses on software-based, with different number of tasks. The Parallel Computing Toolbox in Matlab is among several available tool that offer this capability. Three decoding methods are used in the implementation including sequential decoding, two-task parallel decoding and three-task parallel decoding. Six songs in mp3 file format have been used to test the different approaches of audio decoding. Equality of two variances based on Bonett’s method, T-test analysis and PSNR test have been employed to check the validity of the PCM output obtained from the sequential and parallel decoding compared to the PCM output generated in Matlab. Results of the tests can be observed from p-value and PSNR value respectively. The time taken to decode the mp3 file by using the three methods mentioned above are compared and their performances are evaluated. From the experimental results, two-task parallel decoding has improved the decoding time by 13.97% with speedup of 1.163 while three-task parallel decoding has 5.06% improvement in the decoding time with speedup of 1.053. This is because as the number of tasks increases, so does the overhead which comes from the communication during the parallel decoding. It shows that two-task parallel decoding is optimum to perform the mp3 decoding.
- PublicationImproved framework for balanced truncation based model reduction of second order structured systems(2022-08-01)Ali, SadaqatModel order reduction (MOR) techniques approximate the behavior of the large systems with lower order system information. The need to formulate algorithms for linear second order structured systems (SOSSs), bilinear SOSSs and nonlinear system arose, as there was not much literary support to encounter MOR especially for unstable systems. Two structure preserving second order balanced truncation (SOBT) techniques for unstable SOSSs as well as bilinear SOSSs along with an improved balanced truncation technique for nonlinear systems are implemented. Bernoulli feedback stabilization is applied and gramians are computed by solving infinite/finiteinterval algebraic Lyapunov equations. The gramians are partitioned into position and velocity portions for structure preservation and retention of original interpretation in reduced order model. Then gramians are balanced with different combinations to obtain SOBT techniques. To verify the correctness of the developed frameworks, all the proposed methods have been tested on several benchmark (building model, Piezoelectric and distillation column systems) and simulated systems. Results depict that limited interval techniques are much better than infinite interval techniques with average reduction error of 50% less. Moreover, techniques for combined time-frequency limited applications are also presented where average reduction error has been found 25% less than infinite interval techniques. The proposed frameworks can be applied to model order reduction applications related to unstable linear SOSSs, bilinear SOSSs, and nonlinear systems over infinite and finite time/frequency intervals.
- PublicationImproved indoor localization method for a mobile robot using the fusion of zigbee-based rssi and odometry(2020-08-01)Loganathan, AnbalaganIndoor localization is by definition is the method of acquiring the location of a device or user in an indoor setting. Localizing a mobile node in an indoor setting is comparatively more difficult than localizing a static node because it changes its position over time continuously. In addition, for RSSI-based localization, due to the environmental occlusion and the communication modules limitation such as sudden transceiver failures, or restricted energy and bandwidth, at a certain time instance a node can only obtain two RSSI values when moving. Besides, choosing the right communication module is another challenge to be faced when using the RSSI-based localizing method. There is always a trade-off with cost, efficiency, and convenience. Odometry-based localization, on the other hand, suffers from accumulated errors due to the wheel slippage, sensor drift, and other environmental factors. Thus a new method of fusion of localization result of Zigbee-based RSSI values and the odometry is proposed in this work. Initially, an improved path-loss propagation model is generated by using a curve-fitting graph. Then, a preliminary set of experiments was conducted to investigate the accuracy of each method and to identify the optimal weighting parameters before both localization techniques are fused which will compensate for the deficiencies of individual methods. The RSSI received by the mobile node, Node M is smoothed through the Curve Smoothness Index and pass through a median filter with before the coordinates were calculated and used for the fusion with odometry-based results. The results were recorded by allowing the mobile robot to move through three different trajectories. Both numerical and experimental results revealed that the use of the Curve Smoothness index and median filter on the RSSI values improved the RSSI-based localization significantly. The fusion of both odometry and Zigbee-based localization outperformed their individual component’s results. From the experimental results, it was shown that the proposed methods provided significant improvements for all trajectories considered, which ranges from 30% to 36%.
- PublicationLocal contrast enhancement methods based on modified histogram equalization(2022-03-01)Majeed, Samer HameedImages with poor contrast might be acquired under some circumstances, such as poor capturing environment and insufficient illumination. These captured images could be a significant challenge to computer vision researchers especially the ones with poor contrast. The conventional contrast enhancement techniques including variants of histogram equalization (HE) techniques have several main drawbacks such as manual parameter adjustment, introduction of artifacts and noises, corruption of image’s details, insufficient illumination enhancement, and formation of over enhanced pixels and regions. To reduce these limitations, this study proposes two new HE-based contrast enhancement techniques, which are Iterated Adaptive Entropy-Clip Limit Histogram Equalization (IAECHE) and Adaptive Entropy Index Histogram Equalization (AEIHE). The proposed IAECHE technique divides the image into multi sub-images based on the dimensions of an input image. Each sub-image will then be divided into contextual regions before individually enhanced using the conventional Contrast Limited Adaptive Histogram Equalization (CLAHE). Different to CLAHE, the value of the clip limit is adaptively and automatically set. On the other hand, the proposed AEIHE technique divides the input image into three sub-images. Then, the sub-images will be enhanced individually by applying an adaptive and automatic window size with adaptive and automatic clip limit depending on the distribution of the pixels over the gray levels of these sub-images. The optimum values of the window size and the clip limit are obtained by combining the Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), and Particle Swarm Optimization (PSO) with the conventional CLAHE. Both techniques have been compared with 10 state-of-the-art HE-based contrast enhancement techniques. For qualitative analysis, the proposed IAECHE has produced better enhanced images by improving the local contrast and highlighting the local details of the resultant images, while the proposed AEIHE has successfully improved the images’ local contrast, preserved the images’ structure, and highlighted the images’ hidden details. This qualitative analysis has been supported by quantitative analysis. The results strongly indicate that the proposed IAECHE and AEIHE techniques could possibly be used to pre-process images in many applications such as surveillance, medical and security purposes.
- PublicationMicrowave nondestructive testing for defect evaluation using modified k-means clustering algorithm(2022-02-01)Mohammed Mohsen Shrifan, Nawaf HassanMicrowave nondestructive testing is an effective technique to detect underneath defects in dielectric composites. However, the presence of outliers in the microwave measurement data due to insulation surface roughness, porosity irregularity, and inconsistent stand-off distance influence the sensitivity of defect detection, sizing, and depth estimation. Machine learning approaches have been proposed to assess defects under insulation to overcome the aforementioned limitations of the conventional microwave nondestructive testing techniques. In this research, published public domain datasets of ceramic-based insulations (Macor) are utilized to validate the proposed method. The utilized data is based on the inspection of Macor samples using a Ka band open-ended rectangular waveguide, operating from 26.5 to 40 GHz. A total of 101 reflection coefficients is obtained at each inspected location in the frequency domain. The ambient noise in the reflection coefficients is mitigated using a Gaussian filter. Inverse fast Fourier transform then converts the reflection coefficients into a time domain with 1500 time steps. The dimensionality of 1500 time steps is reduced using principal component analysis into three dominant attributes. In defect detection, a modified k-means algorithm is developed to cluster the microwave attributes into defect and defect-free. The outliers in microwave attributes are eliminated prior to centroid measurement using an adaptive Tukey’s rule to improve defect detection. Moreover, a new distance metric is developed, enhancing the defect size evaluation to properlyassign every inspected location into a defect or defect-free over 2D imaging. Furthermore, the variation in Chebychev distance between microwave attributes and the centroid of the defect-free is proposed for defect depth estimation with an acceptable degree of accuracy. The proposed method has significantly superior defect detection accuracy between 93.89% and 96.28%. Moreover, a minimum error rate between 12.07% and 25.14% is achieved in defect sizing evaluation. Meanwhile, an error rate between 4.47% and 9.24% is achieved in terms of defect depth estimation. In addition, the modified k-means is validated on nine multivariate datasets with an average clustering accuracy of 81%.
- PublicationModel predictive control of single-phase split-source inverter(2022-05-01)Adrian Tan, Soon TheamSingle-phase split-source inverter (SSI) is an emerging and attractive topology for a boost DC–AC power conversion system. Existing SSI suffers from high frequency commutations across the diodes and the implementation of conventional sinusoidal pulse-width modulation (PWM) control result in variable inductor charging duty cycle which produces undesirable low-frequency harmonics. In order to overcome aforementioned drawbacks, hybrid modulation of quasi-sinusoidal and constant PWM is adopted but harmonics in the output voltage are obliged to concentrate around the switching frequency and its multiples. Addressing the aforementioned concerns, this thesis proposes a simplified SSI (S3I) topology which retains the attractive features of existing SSI with the added benefits of reduced switch count, enhanced voltage-boosting gain, reduced output filter requirement, and enhanced power efficiency. For the proposed S3I, a new PWM strategy is derived and implemented which ensures the inductor is being charged with constant duty cycle while the harmonics in the output voltage is concentrated around twice the switching frequency and its multiples. In addition, model predictive control is developed and implemented for the proposed S3I as an alternative control that offers easy inclusion of nonlinearities and constraints such as output current and DC-link voltage. Comprehensive steady-state analysis is discussed while simulation and experimental results are presented to validate of the proposed topology and both of the control strategies. Experimental results of proposed S3I with new PWM strategy achieved good output voltage gain of 1.66 to 5.89 and DC-link voltage gain of 1.85 to 2.82 for modulation index of 0.5 to 0.7. The proposed S3I are able to achieve 88% to 92% efficiency for full range of input voltage. Meanwhile, experimental results for proposed S3I controlled with MPC strategy shows good tracking of measured DC-link voltage of 58V and measured peak output current of 0.61A was obtained with the predetermined reference value of 60V and 0.5A respectively.
- PublicationModeling and control of v-groove rotary impact driver(2020-05-01)Leong, Chi HoeRotary-impact screw driver which is categorized as hand-held cordless power tool raised the opportunities of use in many applications. However, there are some shortfalls in this type of tool such as unsynchronized impact mechanism, too much power for more precise jobs and it makes a lot of vibration. The objective of this study is to model the physical system of v-groove type of rotary impact driver and its application’s load. A state-flow control algorithm is developed on the model to improve the mentioned shortfalls. The speed is optimized by using state-flow algorithm with fixed-point data types, thus reduced hardware computational requirement with acceptable accuracy. An exploratory study was also made to evaluate the complexity of the virtual simulation between impact driver and drill driver model to perform real-time hardware verification in virtual simulation. The purpose of doing the latter is to verify the developed algorithm so that it does not have risk of overrun on defined solver fixed time step. Finally, prototype was built based on the software simulation model for actual human test and validation. From results, there were 10% improvement in system synchronization, 33% reduction of impact speed on small delicate screwing, and 19% reduction of vibration from motor’s reaction torque to user’s arms. Valuable understanding was gained from experiments and optimization through simulation.
- PublicationModified particle swarm optimization for single objective problems in continuous space(2022-04-01)Abdul Karim, AasamParticle Swarm Optimization (PSO) is a prominent Swarm Intelligence (SI) algorithm which achieves significant optimization performance for global optimization problems. However, slow convergence, local optima entrapment and improper balance in exploration and exploitation searches may occur if it is applied to solve complex problems. To overcome these limitations, this research has proposed three improved PSO variants, namely, Vital Information Selector PSO (VISPSO), Effective Guide PSO (EGPSO) and Hovering Swarm PSO (HSPSO). To strengthen the exploration and exploitative searches of the swarm, the VISPSO guides its swarm using an optimized guide particle created using the directional information contained in the two nearest neighbors of the global best particle. The EGPSO is proposed to improve the VISPSO by intelligently providing an alternate search trajectory to those particles stuck in local optima. The HSPSO further improves the VISPSO and EGPSO by using a two swarm approach and creating a cooperative mechanism which realizes an effective division of labor among the particles of the two subswarms. The optimization performances of these three proposed PSO variants have been compared with six conventional PSO variants using 2014 IEEE Congress on Evolutionary Computation (CEC 2014) and four real-world engineering design problems. The experimental results obtained by each proposed PSO variant are also thoroughly evaluated and verified via the non-parametric statistical analyses. Based on the experiment results, the VISPSO exhibits better search accuracy, search reliability, and search efficiency in solving different benchmark functions. The EGPSO shows significant performance improvement over the VISPSO, however, the computational cost of the algorithm is compromised. On the other hand, the robustness of the HSPSO is not effected by the challenging optimization problems with different characteristics. The study concludes that the optimization performance of HSPSO is much better than VISPSO, EGPSO and other conventional PSO variants as it is able to improve the exploration and exploitation search capabilities with lower processing time.
- PublicationNew practical repeater and clock network design methodologies for complex system-on-chip (soc) using hybrid meta heuristic technique(2022-02-01)Teh, Eng KeongWith the advent of deep-submicron technology, the delay of interconnects, especially global signals and clock networks which connect the intellectual property (IP) blocks, have become the key performance limiting factors to a System-on-Chip (SoC). Furthermore, with significant increases in SoC design complexity, such as larger die size, more digital and analog mixed signals IPs, higher clock frequency, more clock domains, lower power consumption requirements, lower development cost, and shorter design schedule, global repeater and clock network design havebecome nondeterministic polynomial-time hardness (NP-Hard) problems that arecomputationally hard problems where the best algorithms known so far have exponential time complexity. As a result, most of the prior works are either no longer practical or not sufficient for the recent complex SoC. This thesis presents new practical repeater and clock network design methodologies that deploy hybrid meta heuristic (HM) techniques. The proposed methodologies combine meta-heuristic algorithms with different artificial intelligence algorithms such as exact and heuristic algorithms, to make correct guesses for certain decisions and subsequently achieve near global optima results in shorter turn-around time. Specifically, this thesis proposed HM techniques which use a Genetic Algorithm (GA) based flow to find near optimum repeater recipe as well as a Mean-shift Algorithm based flow to optimize floorplan pin before correctly insert global buffer and flop repeaters into a complex SoC. Based on results from buffer repeater insertion experiments on a 14 nm SoC, it showed that the proposed techniques did further improve 43% of total timing path and reduce 9.09% of total power consumption with 83.33% less design convergence turn-around-time. In terms of flop repeater insertion, the proposed techniques had successfully inserted 539k flop repeaters into a 10 nm SoC and saved approximately 30 men-month efforts. Other than global signals repeater insertion techniques, the thesis also introduces a flexible full chip (FC) clock network topology and a HM flow which utilizes a k-mean based synthesis algorithm to search for near optimum global clock distribution solution in shorter turn-around time. With these techniques, clock repeaters were inserted into two SoCs built in 10 nm and 7 nm technology nodes with averagely 16.98% better FC clock skew for 10 nm SoC and 28.89% for 7 nm SoC compares to a conventional ASIC technique. On top of skew improvement, the proposed technique had achieved 64.5% less turn-around-time in FC clock balancing phase of the 10 nm SoC. Based on the results, it was depicted that the practicality and effectiveness of the proposed hybrid meta-heuristic algorithm-based repeater insertion and clock distribution methodologies in reducing design turn-around-time were proven. In conclusion, it is suggested that while industry computer aided design (CAD) tool vendors continue improving local optimisation heuristic algorithms, SoC designers are recommended to invest in hybrid meta-heuristic techniques, which combine the industry heuristic algorithms with meta-heuristic algorithms from academic knowledge pool and exact algorithms from subject matter experts, for the global optimisation.
- PublicationNonlinear exposure intensity-based histogram equalization for non-uniform illumination image(2022-09-01)Saad, Nor HidayahNon-uniform illumination image consists of different illumination regions. Applying the same contrast enhancement concept to the whole image can over or under enhance the image. Thus, different enhancement concepts should be applied to different illumination regions, necessitating region determination. Almost all existing region determination methods only consider intensity to determine the regions, so they can only detect two different regions, dark and bright, which does not represent the real exposure condition. To solve this, Local Neighbourhood Exposure Region Determi nation (LNERD) is proposed by considering the intensity, entropy and contrast which can better represent the details in the image. The three attributes are combined with a rule-based method for identifying illumination regions before enhancement process is applied. Due to over-enhancement, the existing Histogram Equalization (HE)-based methods produce washed-out effects and unnatural appearance, limiting the ability to achieve illumination uniformity of an image. Therefore, this study proposes a modified HE method named as Nonlinear Exposure Intensity Modification Histogram Equaliza tion (NEIMHE). The proposed NEIMHE method divides exposure regions into five sub-regions and modifies each sub-histogram region’s by adding a nonlinear weight to its cumulative density function (CDF). The modified HE equations provide intensity expansion and mapping directions for under- and overexposed regions. Using 600 non-uniform illuminated sample images from three databases, the proposed LNERD method detects over-exposed, well-exposed, and under-exposed regions more accurately than existing methods. The proposed NEIMHE method improves image unifor mity, detail, and naturalness by achieving the highest scores in Discrete Entropy (DE),Measure of Enhancement (EME), and Peak Signal to Noise Ratio (PSNR) and secondbest in Absolute Mean Brightness Error (AMBE) and Lightness Order Error (LOE).Those findings prove that NEIMHE can improve non-uniform illumination images’exposure.
- PublicationOptimal cell utilisation in three-phase battery energy storage systems using a hybrid modular multilevel converter(2022-05-01)Bani Ahmad, Ashraf MohammadBattery energy storage systems (BESSs) are a promising technology for power grid applications due to their dynamic behaviours. However, cell state-of-charge (SoC) balancing is crucial to overcome the inability to fully utilise the available capacity of a BESS. In addition, using a high number of switches in a cascaded H-bridge BESS can potentially increase cost and power losses. Moreover, the conventional topology does not have the ability to take advantage of its idle cells, at least one-third of its cells are constantly idling in a typical three-phase BESS. This thesis proposes a novel circuit topology and a SoC balancing control for a three-phase grid-scale BESS using a three cascaded hybrid modular multilevel converter (TCHMMC) without redundant cells. The proposed TCHMMC is constructed with one branch instead of three branches to take advantage of its idle cells/modules and to eliminate the need of SoC balancing among the branches. The reduction in component count is achieved by integrating each individual cell into L-bridge compared with H-bridge. The simulation results indicate that at least 140 out of 333 modules (3996 cells) are in an idle state during the BESS operation. Using 333 modules, the three-phase output power has increased by 58.8 % in the proposed TCHMMC (306 kW) compared with the conventional topology (180 kW). In addition, SoC balancing among 3996 cells and 2664 cells is achieved within 53 min and 48 min respectively. An experimental validation has been performed to demonstrate the effectiveness of the proposed TCHMMC and the SoC balancing control. The experimental results indicate that the three-phase output voltage of the proposed TCHMMC is increased by 33.3 % compared to the conventional topology. Moreover, the SoC balancing among 9 modules is achieved in 73 min.
- PublicationRobust observer-based residual generation for fault detection and estimation in linear uncertain discrete-time system(2022-08-01)Ahmad, MasoodWith the ongoing increase in system complexity, less tolerance to performance degradation and safety requirements of many industrial systems have increased the demand of Fault Detection (FD) because fault leads to reduced efficiency or even complete failure of the entire system. In this thesis, fault detection and estimation problem for the class of Linear Discrete-Time Invariant (LDTI) systems subject to model uncertainties and unknown inputs are addressed using observer-based technique. Two types of model uncertainties (i.e., norm bounded uncertainty and stochastic uncertainty) are considered in the linear dynamical system states, input, and output matrices simultaneously. 𝐻𝐻∞ disturbance attenuation filter, adaptive threshold, model matching technique-based Fault Detection Filter (FDF) and Proportional Integral (PI) observer are designed to detect and estimate the fault in the monitored system. The core of all the proposed techniques is 𝐻𝐻∞ norm minimization using Bounded Real Lemma (BRL) in Linear Matrix Inequality (LMI) framework. From 𝐻𝐻∞ minimization, residual is further processed using signal norms and a suitable threshold is designed for successful fault detection. Several kinds of faults are tested in the Direct Current (DC) motor and three tank system. From results, all the proposed methods are capable to generate a robust residual that is insensitive to disturbance and model uncertainties, but sensitive to fault. All the tested faults are successfully detected and estimated that confirm the effectiveness of the proposed methods.