Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better. In this work, we provide a definitio...
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Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better. In this work, we provide a definition and novel measure of interference for value-based reinforcement learning methods such as Fitted Q-Iteration and DQN. We systematically evaluate our measure of interference, showing that it correlates with instability in controlperformance, across a variety of network architectures. Our new interference measure allows us to ask novel scientific questions about commonly used deep learning architectures and study learning algorithms which mitigate interference. Lastly, we outline a class of algorithms which we call online-aware that are designed to mitigate interference, and show they do reduce interference according to our measure and that they improve stability and performance in several classic control environments.
Developing two intrusion detection systems (IDS) to identify grey hole attacks in wireless ad hoc networks is the goal of this project. For this, the Random Forest (RF) and Decision Tree algorithms were used. The NS2 ...
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This paper presents the phasor measurement Unit (PMU) based monitoring and management which is suitable for medium voltage networks that will have significant distributed energy resources (DER) integration. Its fundam...
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ISBN:
(纸本)9798350395082;9798350395075
This paper presents the phasor measurement Unit (PMU) based monitoring and management which is suitable for medium voltage networks that will have significant distributed energy resources (DER) integration. Its fundamental technology, its application, and its benefits to distribution networks were described. This included the differences between PMU compared to conventional supervisory control and data acquisition (SCADA) systems which are currently deployed in the distribution network. The research trend on this technology application is also surveyed and discussed. The pilot demonstration project on PMU-based monitoring and management, which is currently ongoing at one of the medium voltage level distribution systems in Qatar, is also presented. The experience of planning, installing, and commissioning the system is shared. Highlight of the capability and performance of this PMU monitoring system is also commented on.
Transient stability analysis (TSA) is crucial for maintaining the stability of power systems. Compared to traditional dynamic simulations, neural network-based power system TSA models have been widely applied in recen...
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ISBN:
(纸本)9798350377477;9798350377460
Transient stability analysis (TSA) is crucial for maintaining the stability of power systems. Compared to traditional dynamic simulations, neural network-based power system TSA models have been widely applied in recent years due to their strong nonlinear mapping capability and fast prediction speed. In this paper, a new TSA approach is proposed based on the Transformer neural network, which combines the encoder-decoder architecture and the attention mechanism. A multichannel feature extraction structure and a supervised contrastive learning algorithm are utilized to mitigate the overfitting phenomenon and enhance the generalization capability of the proposed model, resulting in competitive performance. The efficacy of the proposed TSA method is validated by the superior performance in the standard IEEE 39-bus test case.
The partitioning problem is a key problem for distributed control techniques. The problem consists in the definition of the subnetworks of a dynamical system that can be considered as individual control agents in the ...
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The proceedings contain 25 papers. The special focus in this conference is on . The topics include: Denoising of ECG Signal Using Optimized iiR Filter Architecture—A CSD-Based Design;deep Learning Analysis for Skin C...
ISBN:
(纸本)9789819944439
The proceedings contain 25 papers. The special focus in this conference is on . The topics include: Denoising of ECG Signal Using Optimized iiR Filter Architecture—A CSD-Based Design;deep Learning Analysis for Skin Cancer Detection;Design and Development of a BCI Framework to control a UTM Using EEG Headset;approximate Compressors-Based Multiplier for Image Processing and Neuromorphic Modeling;integration of Particle Swarm Optimization and Sliding Mode control: A Comprehensive Review;design and Testing of a Solar Powered Automated Fruit and Vegetable Sorter;characterization of Dust Particles and Their Impact on the performance of Photovoltaic Panels: A Laboratory Investigation;Design and Analysis of DC-DC Boost Converter;energy Management Analysis on Smart Street Lighting for Smart Cities;design and Implementation of Smart Waste Management System;Design and Development of Efficient Feeding network Structure for Patch Antenna Array Modules in UAV Communication Applications;E—RiCoBiT—ii: A High Performing RiCoBiT (Ring Connected Binary Tree) Topology with Fully Adaptive Routing Algorithm;Design and Development of Autonomous VTOL for Medicine Deliveries in Hilly Areas;Implementation and Design of Agile and Multipurpose Autonomous Robot Using ROS;prediction of Chronic Pain Onset in Patients Experiencing Tonic Pain: A Survey;design and Analysis of Miniaturized Broadband Microstrip Patch Antenna for Aircraft Surveillance Applications;Multiplier Design for the Modulo Set {2n- 1, 2n, 2n+1- 1 } and Its Application in DCT for HEVC;Modelling performance Analysis in VLSI Testing Methodologies;Verification of AHB2APB Bridge Protocol Using UVM;Design and Verification of AMBA AHB Protocol Using UVM.
In FDD massive MIMO (mMIMO) systems, although assisted with Rel.16 eType ii feedback, Channel State Information (CSI) accuracy is limited by overhead, quantization error and user mobility. In this paper, we propose Mu...
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ISBN:
(纸本)9798350310900
In FDD massive MIMO (mMIMO) systems, although assisted with Rel.16 eType ii feedback, Channel State Information (CSI) accuracy is limited by overhead, quantization error and user mobility. In this paper, we propose Multimodal Hetero-frequency Channel Extrapolation (M-HCE) based CSI reconstruction, where channel model with enhanced Levy-Ito decomposition path structure and Karhuen-Loeve expansion are introduced. Downlink channel is reconstructed from the multimodal transformation of uplink Sounding Reference Signal (SRS) channel and CSI feedback, by exploring both partial downlink-uplink reciprocity between hetero-frequency channels and reported downlink CSI. The performance is verified in commercial evaluation platform, which shows 23%-66% throughput gain compared with eType ii CSI for user mobility 10-60 km/h and typical FDD operating bands.
The sense of agency (SoA), the feeling of recognizing that the observed movement is caused by oneself, which is important in robot teleoperation, is reduced by shared control, in which the robot and the human cooperat...
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ISBN:
(纸本)9798350388039;9798350388046
The sense of agency (SoA), the feeling of recognizing that the observed movement is caused by oneself, which is important in robot teleoperation, is reduced by shared control, in which the robot and the human cooperate to control the robot. In this study, we developed a system that uses a recurrent neural network with parametric biases (RNNPB) trained on expert operational data to predict the next input from non-experts and convert it into robot commands in real time. Through an experiment with a pouring task, it was confirmed that the proposed method outputs predicted values that spatially and temporally interpolate the operational inputs, gradually correcting the robot's movements to align with the experts' trajectories. The proposed method showed a high SoA comparable to direct control;however, no statistically significant difference in task performance was observed. Future work aims to improve the generality of the model to accommodate a wider variety of input trajectories.
Photovoltaic (PV) system performance is influenced by environmental factors such as irradiation, temperature, and shading effects, which we cannot control. However, to address this aspect, maximum power point tracking...
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Photovoltaic (PV) system performance is influenced by environmental factors such as irradiation, temperature, and shading effects, which we cannot control. However, to address this aspect, maximum power point tracking (MPPT) algorithms come into play. They keep an eye on the working point and modify it in real-time to comply with the Maximum Power Point (MPP), which assists in reducing the impact of environmental fluctuations. This, in turn, results in steady and ideal power generation from the PV system, increasing its dependability and efficiency regardless of the unpredictable climate conditions. This research compares three commonly used approaches for tracking the MPP in PV systems: the perturb and observe (P&O) method, fuzzy logic control (FLC), and Artificial Neural networks (ANN). This comparison is based on a simulation using MATLAB/Simulink to carry out an in-depth analysis of these three methods. This simulation enables a complete examination of all the three methods, considering parameters like complexity, stability, and efficiency. This study intends to give a comprehensive review of the performance and applicability of the P&O method, fuzzy logic control, and ANN in PV systems by assessing these parameters. The present research adds useful insights to current information by thoroughly examining three popular MPPT approaches with simulations in MATLAB/Simulink The findings can help researchers and professionals choose the best strategy for their PV systems based on their specific requirements. Furthermore, the comparison study highlights prospective possibilities for future research and MPPT technique improvements. Copyright (c) 2024 The Authors.
With the rapid development of autonomous driving technology, the autonomous driving ability of vehicles has become an important research direction in intelligent transportation systems. Convolutional Neural network (C...
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ISBN:
(纸本)9781510685710
With the rapid development of autonomous driving technology, the autonomous driving ability of vehicles has become an important research direction in intelligent transportation systems. Convolutional Neural network (CNN) has become one of the core technologies in autonomous driving vision systems due to its excellent performance in image recognition and classification. Embedded systems also play an important role in the realization of autonomous vehicle driving due to their high efficiency and real-time nature. In this study, a vehicle autonomous driving scheme based on Convolutional Neural network (CNN) and embedded system is proposed. Firstly, the convolutional neural network was used to perform real-time image processing and feature extraction on the road scene. Specifically, through the superposition of multi-layer convolutional layers and pooling layers, features such as edges, textures, and objects in the image are extracted layer by layer, so as to achieve efficient recognition of complex road environments. In this process, the convolutional layer is used to extract local features, while the pooling layer is used to reduce dimensionality and prevent overfitting to ensure the robustness and efficiency of the model. Secondly, an embedded system was designed and optimized, and the trained CNN model was deployed on the system to ensure real-time processing power and efficient energy consumption management. The design of the embedded system focuses on the optimal allocation of hardware resources and the effective control of energy consumption to meet the real-time operation needs of vehicles under different road conditions. Specifically, the embedded system uses high-performance processors and low-power hardware modules to ensure fast inference and real-time decision-making capabilities of CNN models. In addition, the overall performance and reliability of the system are further improved through the co-design of software and hardware. Combining the above two techn
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