With the dawn of modern digital civilization i.e., Industry 4.0, a need for bi-directional intelligent communicable devices, is welcomed to the maximum extent by commons. This demand of intelligent bi-directional comm...
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Recently, Power Consumption is one of The Most Important Researches on Cloud Computing Systems such as CPU central Processors, Drives, memory, and temperature that affect on consuming the amount of energy. But also th...
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Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performanc...
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Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performance of recurrent neural network (RNN) in voltage instability prediction. A recurrent neural network trained with Particle Swarm Optimization (PSO) is proposed in this paper. The proposed method is examined on 14-bus and 30-bus IEEE standard systems. These systems are simulated using MATLAB/Power System Toolbox program. Also, a detailed comparison between PSO algorithm and Backpropagation (BP) algorithm is discussed. The results proved the effectiveness of the proposed method.
In this paper, we present a cross-layer optimization scheme to improve the fairness and quality of service (QoS) of wireless ad-hoc network. As for the physical layer's information feedback, it is mainly the physi...
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The recent introduction of high dynamic range (HDR) video cameras has enabled the development of image based lighting techniques for rendering virtual objects illuminated with temporally varying real world illuminatio...
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ISBN:
(纸本)9781479946037
The recent introduction of high dynamic range (HDR) video cameras has enabled the development of image based lighting techniques for rendering virtual objects illuminated with temporally varying real world illumination. A key challenge in this context is that rendering realistic objects illuminated with video environment maps is computationally demanding. In this work, we present a GPU based rendering system based on the NVIDIA OptiX [1] framework, enabling real time raytracing of scenes illuminated with video environment maps. For this purpose, we explore and compare several Monte Carlo sampling approaches, including bidirectional importance sampling, multiple importance sampling and sequential Monte Carlo samplers. While previous work have focused on synthetic data and overly simple environment map sequences, we have collected a set of real world dynamic environment map sequences using a state-of-art HDR video camera for evaluation and comparisons. Based on the result we show that in contrast to CPU renderers, for a GPU implementation multiple importance sampling and bidirectional importance sampling provide better results than sequential Monte Carlo samplers in terms of flexibility, computational efficiency and robustness.
Machine-learning and soft computation methods are often used to adapt and modify control systems for robotic, aerospace, and other electromechanical systems. Most often, those who use such methods of selfadaptation fo...
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A neural control strategy for nonlinear processes with time-variant time-delay is proposed in this paper. In this strategy, a dynamic neural network based nonlinear Smith predictor is constructed to compensate for the...
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This paper uses renewable energy sources as a potential solution to meet energy demands, However, these sources suffer from intermittency, especially solar energy. This calls for an effective energy storage system. Hy...
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This paper explores the challenges and advancements in event-based visual sensing technology, focusing on the denoising of event streams generated by event cameras. Event cameras, inspired by biological visual systems...
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ISBN:
(数字)9798350350326
ISBN:
(纸本)9798350350333
This paper explores the challenges and advancements in event-based visual sensing technology, focusing on the denoising of event streams generated by event cameras. Event cameras, inspired by biological visual systems, offer significant advantages over traditional cameras in high-speed motion scenarios. However, they face challenges such as background activity noise (BA), which can degrade the quality of event stream data. In this study, we propose a novel denoising method based on both temporal and spatial correlations of event stream data. By setting different event processing windows, we effectively filter out noise while preserving genuine event information. We also introduce a visualization technique that converts event stream data into grayscale intensity images, enabling intuitive evaluation of denoising effectiveness. Experimental results demonstrate the superiority of our proposed method in reducing noise and improving image quality in various scenarios, including close-range moving targets and complex dataset environments.
This paper focuses on the study of MAC protocol in industrial wireless ad-hoc network, aiming at improving the fairness of industrial wireless ad-hoc network and quality of service (QOS). As a result, Hybrid Bi-channe...
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