This paper proposed a novel landing platform with wireless power transfer (WPT) to charge multiple unmanned aerial vehicles (UAVs) without restrictions on landing locations or alignment. A transmitter is designed usin...
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We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labe...
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We experimentally demonstrate reduced dimensionality in a interacting ensemble of emitters. The well-known stretched exponential decay dynamics, (Equation presented) with β = 0.5 in 3D geometries, is strikingly modif...
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Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slig...
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This paper presents a realistic power transformer condition assessment and Health Index (HI) determination technique. The real-time operating data of twenty-seven power transformers of Bhutan Power Corporation Limited...
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Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood agg...
Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall
The problems of high voltage equipment usually come from the imperfection of solid insulation, which may be caused by poor workmanship during cable installation, leading to multi-void inside solid insulation and subse...
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In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize...
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This paper aims to analyze the determinant parameters of Genetic Algorithm (GA) analysis for the optimized control performance of a closed control loop. The determinant parameters of GA optimization analysis cover pop...
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ISBN:
(纸本)9781665486644
This paper aims to analyze the determinant parameters of Genetic Algorithm (GA) analysis for the optimized control performance of a closed control loop. The determinant parameters of GA optimization analysis cover population size (nPop), mutation rate (mu) and iteration (iter.) are analyzed and justified. The control terminology covers the Proportional-Integral-Derivative (PID) controller, a prestigious solution for industrial control applications. Besides, the research proposed stability analysis to determine the upper and lower limit settings for the optimization analysis. The research has begun with model identification, stability analysis and is followed by determining the controller tunings. The performance indexes are applied to compare the response performance of GA with deterministic controller tunings. Analysis results and discussion shows that GA with proper determinant parameters’ settings are performing better than other tuning methods in the closed loop control performance.
Non-Hermitian optics provides a unique platform to take advantage of absorption losses in materials and control radiative properties. We demonstrate a non-Hermitian metasurface that exhibit directional suppression of ...
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