Ground resistance ( Rg) plays an important role in designing, operating, and maintaining transmission lines (TLs). This paper investigates the impact of ground resistance along the TL route on the performance of a dis...
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ISBN:
(纸本)9798350371635;9798350371628
Ground resistance ( Rg) plays an important role in designing, operating, and maintaining transmission lines (TLs). This paper investigates the impact of ground resistance along the TL route on the performance of a distance relay with Mho characteristics. The performance of the distance relay has been investigated for various types of faults and a wide range of Rg changes. The effect of soil resistivity on impedance trajectory in single-line-to-ground faults in boundary point faults is investigated. The results show that Rg can lead to alterations in the line's electrical characteristics, affecting signal transmission, power losses, and overall system efficiency that is not considered in distance relay settings. A simple 2-bus network is used in PSCAD/EMTDC software to simulate and implement distance relay settings. Also, MATLAB software has been used to calculate phase sequence impedances due to Rg changes. The simulations performed in the 2-bus test system show a significant effect of Rg on the performance of the distance relay.
Autonomous vehicle control is an important sub-field of autonomous vehicle research. Many challenges remain to improve the safety and performance of autonomous vehicle control systems in urban driving environments. On...
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ISBN:
(纸本)9798350371635;9798350371628
Autonomous vehicle control is an important sub-field of autonomous vehicle research. Many challenges remain to improve the safety and performance of autonomous vehicle control systems in urban driving environments. One such urban driving environment is the roundabout junction, which presents its own unique challenges to potential solutions to autonomous vehicle control. This paper proposes and tests a vehicle control agent as a candidate solution for urban roundabout navigation. The vehicle control agent is based on a hierarchical deep reinforcement learning architecture with a superior network selecting short-term lane-change behaviour and a subordinate network selecting longitudinal acceleration values. The road sequence followed by the agent is selected by a route planner based on Dijkstra's algorithm. The proposed agent learns to navigate the roundabout environment safely, reaching the goal state in 100% of validation scenarios after training. The agent also outperforms an agent based on the Krauss-following model in 2 out of 5 tested metrics and matches the performance of the Krauss-following model in the remaining 3 metrics.
Prostate cancer is a prevalent malignancy in men, often diagnosed using magnetic resonance imaging (MRI). Accurate segmentation of prostate MRI is crucial for early cancer diagnosis, yet the challenges arise from limi...
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ISBN:
(纸本)9798350371635;9798350371628
Prostate cancer is a prevalent malignancy in men, often diagnosed using magnetic resonance imaging (MRI). Accurate segmentation of prostate MRI is crucial for early cancer diagnosis, yet the challenges arise from limited annotated data and variations in prostate shape, appearance, and size. This study introduces an innovative U-shaped approach called UMC-Net that combines an encoder of MaxVIT and a novel convolution block, enabling the fusion of global and local features to enhance prostate MRI segmentation. The Atrous convolution extracts spatial dimension information, followed by point-wise convolution, which reduces parameters and computations. The Global and local features fusion (GLFF) module performs a better fusion of features to improve the segmentation performance. The performance of the proposed method is evaluated on a publicly available dataset using the dice similarity coefficient (DSC) and Hausdorff distance (HD), which demonstrate remarkable results, with DSC and HD values of 0.887 and 0.761, 4.8, and 9.6, respectively. These findings highlight the superior performance of UMC-Net compared to the state-of-the-art methods in prostate MRI segmentation, paving the way for more accurate early cancer diagnosis.
To address the challenges of mountain fire navigation in uneven environments, this paper proposes a collision-free path planning method for UAVs to perform forest fire rescue tasks in uneven terrain. Drawing on previo...
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ISBN:
(纸本)9798350370058;9798350370164
To address the challenges of mountain fire navigation in uneven environments, this paper proposes a collision-free path planning method for UAVs to perform forest fire rescue tasks in uneven terrain. Drawing on previous research, our approach utilises an Unmanned Aerial Vehicle (UAV) for disaster area rescue and a reactive algorithm coupled with RRT for obstacle avoidance to ensure efficient monitoring and real-time rescue routing around obstacles. This approach combines advanced fire modelling with state-of-the-art navigation techniques to improve hill fire response strategies by focusing on hill fire surveillance missions in uneven environments.
Effective handling and modelling of time series data play a crucial role in enhancing the quality of derived information during the research process which includes addressing missing values. Meticulous attention to th...
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ISBN:
(纸本)9798350370058;9798350370164
Effective handling and modelling of time series data play a crucial role in enhancing the quality of derived information during the research process which includes addressing missing values. Meticulous attention to these data-related tasks is paramount, as the outcomes of the research are directly influenced by the quality and integrity of the processed data. This study employs a non-parametric Gaussian Process Regression (GPR) prediction algorithm in machine learning to investigate the predictive performance of Malaysian demographic data for 1960 to 2021. For robust results, the traditional parametric models were introduced for comparison. The reliability and efficiency of the algorithm are presented. The results show that the GPR with squared exponential covariance function can give the most accurate prediction on the data based on the low mean absolute deviation (MAD) and root mean squared error values (RMSE).
Stalling of critical induction motors in process control industries can bring significant financial losses to industrial facilities. Static Synchronous Compensators (STATCOMs) and Static Var Compensators (SVCs) are ty...
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ISBN:
(纸本)9798350371635;9798350371628
Stalling of critical induction motors in process control industries can bring significant financial losses to industrial facilities. Static Synchronous Compensators (STATCOMs) and Static Var Compensators (SVCs) are typically used for stabilizing such motors. With the tremendous growth of Battery Energy Storage Systems (BESS) it is quite likely that BESS will be installed in distribution networks where such critical motors are connected. This paper presents the laboratory implementation of a new cost-effective control of a BESS as STATCOM, termed BESS-STATCOM, to stabilize a critical induction motor which may be connected either locally at BESS terminals or remotely from it in a distribution network. The hardware results of the performance of BESS-STATCOM are compared with PSCAD software simulation results of the same system. Motor stabilization is successfully demonstrated during both charging and discharging modes of BESS operation. The BESS-STATCOM can provide dynamic voltage control utilizing the entire BESS converter capacity for reactive power modulation. The proposed technique opens a new revenue making opportunity for BESS to provide a 24/7 dedicated critical motor stabilization service through reactive power control while performing its normal active power based ancillary services. The proposed BESS-STATCOM control will soon be field demonstrated in the network of Elexicon Energy, Canada.
In the context of industrial process modelling and fault diagnosis, deep neural networks (DNNs) face challenges such as long training times, high computational costs, and limited interpretability, hampering their effi...
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ISBN:
(纸本)9798350371635;9798350371628
In the context of industrial process modelling and fault diagnosis, deep neural networks (DNNs) face challenges such as long training times, high computational costs, and limited interpretability, hampering their efficiency and applicability. Meanwhile, various multivariate data analysis techniques, nonlinear regression methods, and shallow neural networks have found wide applications, from anomaly detection to root cause fault diagnosis. In this paper, the application of extreme learning machine (ELM, a type of single feed-forward layer network) based algorithms in process modeling and diagnostics will be explored. A recursive transformation will be derived for the domain-adaptation ELM (DAELM) to present the sequential DAELM (S-DAELM). The proposed sequential DAELM can easily transform into the Regularized ELM (RELM) and DAELM form. The ridge parameter and sequential switch in the proposed S-DAELM algorithm will be set according to the problem statement, and correspondingly the RELM, DAELM or Sequential DAELM mode can be realized. Finally the S-DAELM is used as an unsupervised modeling tool to reconstruct the process variables. Once the reconstruction is completed, the PCA algorithm is applied after the S-DAELM layer to automatically analyze the reconstruction residuals for efficient fault detection and root cause analysis.
The rise of 6G technology will enable various innovative applications to deliver transformative experiences and services with unprecedented speed, reliability, and interactivity. On one hand, the realization of such i...
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ISBN:
(纸本)9798350371635;9798350371628
The rise of 6G technology will enable various innovative applications to deliver transformative experiences and services with unprecedented speed, reliability, and interactivity. On one hand, the realization of such innovative applications relies on the processing of large volumes of data generated at the Extreme Edge of the network, requiring time-critical and resourceintensive processing. On the other hand, these applications require handling multi-modal data or inputs from several sensing data sources, and as a result, the resulting computing tasks encompass multiple subtasks that are crucial to service delivery. Conventional offloading schemes overlook the complexity of these applications, jeopardizing the task success rate and application QoS. In this work, we highlight the dire need for a computational offloading scheme that addresses the intricate nature of such applications and their computing tasks, and present a preliminary problem formulation to tackle these needs.
Quadrupedal mobile robots can traverse a wider range of terrain types than their wheeled counterparts but do not perform the same on all terrain types. These robots are prone to undesirable behaviours like sinking and...
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ISBN:
(纸本)9798350371635;9798350371628
Quadrupedal mobile robots can traverse a wider range of terrain types than their wheeled counterparts but do not perform the same on all terrain types. These robots are prone to undesirable behaviours like sinking and slipping on challenging terrains. To combat this issue, we propose a terrain classifier that provides information on terrain type that can be used in robotic systems to create a traversability map to plan safer paths for the robot to navigate. The work presented here is a terrain classifier developed for a Boston Dynamics Spot robot. Spot provides over 100 measured proprioceptive signals describing the motions of the robot and its four legs (e.g., foot penetration, forces, joint angles, etc.). The developed terrain classifier combines dimensionality reduction techniques to extract relevant information from the signals and then applies a classification technique to differentiate terrain based on traversability. In representative field testing, the resulting terrain classifier was able to identify three different terrain types with an accuracy of approximately 97%
Incorporating embedded energy resources (EERs) into demand-side management (DSM) has the potential to optimize the operational efficiency of distributed energy systems. It can be achieved by leveraging the reserve cap...
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ISBN:
(纸本)9798350371635;9798350371628
Incorporating embedded energy resources (EERs) into demand-side management (DSM) has the potential to optimize the operational efficiency of distributed energy systems. It can be achieved by leveraging the reserve capacity of these resources. The effective use of this reserved capacity results in enhanced planning, scheduling, and operation optimization. Forecasting the power consumption of EERs is an essential part of reserved capacity calculation. This research presents a review of machine learning (ML)-based techniques utilized for short-term power consumption forecasting for a category of EERs specifically identified as thermostatically controlled loads (TCLs). Given the complicated characteristics and the difficulty in forecasting these resources, the existing research in this area is quite limited and there is still space for further studies. Moreover, this review underscores the importance of dedicating resources to further research, including developing replicable case studies and exploring novel technologies in this field.
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