Heart disease is the highest cause of death in the world. Arrhythmia is an abnormality in the rhythm of the heartbeat. The heart beats too fast, too slow, or irregularly. Arrhythmias are not always dangerous, e.g., so...
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This article presents a novel method for point target detection in remote sensing imagery, focusing on the development of an innovative approach to enhance detection accuracy and reduce false positives. The core contr...
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A centralized real-time control based on recent guidelines and standards is presented in this work. The present study introduces a two-loop control capable of adapting to frequency changes, enabling a seamless transit...
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A centralized real-time control based on recent guidelines and standards is presented in this work. The present study introduces a two-loop control capable of adapting to frequency changes, enabling a seamless transition. Obtained results indicate that this approach achieves synchronization during frequency step change and sinusoidal variation of utilitys frequency following its restoration. Moreover, presence of DC-offset in grid voltages is considered. An enhanced second-order complex vector filter (ESOCVF)-based frequency locked loop (FLL) is designed to estimate input signal’s exact magnitude, frequency, and phase. Its performance during phase jump and frequency jump is considered and is compared with second order generalized integrator (SOGI), second-order-complex vector filter (SO-CVF) and third-order-complex vector filter (TO-CVF)-based FLL structures to demonstrate performance superiority of ESOCVF-based FLL in tracking of frequency and phase. Performance of various controls of solar photovoltaic (PV) array and battery energy storage system (BESS) based MG are studied in both grid-interfaced (GI) and standalone (SA) modes. IEEE
Purpose: Accurate depth estimation in surgical videos is a pivotal component of numerous image-guided surgery procedures. However, creating ground truth depth maps for surgical videos is often infeasible due to challe...
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Purpose: Accurate depth estimation in surgical videos is a pivotal component of numerous image-guided surgery procedures. However, creating ground truth depth maps for surgical videos is often infeasible due to challenges such as inconsistent illumination and sensor noise. As a result, self-supervised depth and ego-motion estimation frameworks are gaining traction, eliminating the need for manually annotated depth maps. Despite the progress, current self-supervised methods still rely on known camera intrinsic parameters, which are frequently unavailable or unrecorded in surgical environments. We address this gap by introducing a self-supervised system capable of jointly predicting depth maps, camera poses, and intrinsic parameters, providing a comprehensive solution for depth estimation under such constraints. Approach: We developed a self-supervised depth and ego-motion estimation framework, incorporating a cost volume based auxiliary supervision module. This module provides additional supervision for predicting camera intrinsic parameters, allowing for robust estimation even without predefined intrinsics. The system was rigorously evaluated on a public dataset to assess its effectiveness in simultaneously predicting depth, camera pose, and intrinsic parameters. Results: The experimental results demonstrated that the proposed method significantly improved the accuracy of ego-motion and depth prediction, even when compared with methods incorporating known camera intrinsics. In addition, by integrating our cost volume based supervision, the accuracy of camera parameter estimation, including intrinsic parameters, was further enhanced. Conclusions: We present a self-supervised system for depth, ego-motion, and intrinsic parameter estimation, effectively overcoming the limitations imposed by unknown or missing camera intrinsics. The experimental results confirm that the proposed method outperforms the baseline techniques, offering a robust solution for depth estimation
This study investigates the transition control problem for a double inverted pendulum system, which has one stable and three unstable equilibrium points. We propose a method for implementing transition control using a...
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Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC sy...
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This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(M...
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This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving ***,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation *** proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances ***/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure *** tests illustrate the efficiency of the proposed approach.
The Double Track Program prepares high school students for careers or entrepreneurship through skills training, aiming to reduce post-graduation unemployment in East Java. It offers seven skill areas: multimedia, culi...
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Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptogr...
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In this paper, we present a novel approach for joint activity detection (AD), channel estimation (CE), and data detection (DD) in uplink grant-free non-orthogonal multiple access (NOMA) systems. Our approach employs a...
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