In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-...
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In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.
The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's pha...
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This paper proposes a deep reinforcement learning-based power management method for mobile devices. By learning the load characteristics of the device under different usage scenarios and considering the influence of n...
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ISBN:
(纸本)9798350376975;9798350376968
This paper proposes a deep reinforcement learning-based power management method for mobile devices. By learning the load characteristics of the device under different usage scenarios and considering the influence of network conditions on power consumption, the CPU and GPU frequencies are dynamically adjusted for multiple application scenarios. At the same time, a "SLIDER" adjustment strategy is proposed, and combined with the system default adjustment strategy, which reduces the difficulty of adjustment and more fully utilizes the middle adjustable frequency of the *** proposed method reduces power consumption by 5.3%-18% compared to state-of-the-art method.
Gastric cancer has been one of the leading causes of death among oncological patients. Lymph node (LN) metastasis classification is an important task for computer-aided diagnosis (CAD) using abdominal CT images. Due t...
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Blockchain technology is a game-changing technology that can completely overhaul number of domains by proposing decentralized, transparent, and unchangeable transaction ledgers. The purpose of this overview of the lit...
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Although the current point cloud upsampling techniques are already capable of attaining arbitrary upsampling rates af-ter one training. However, during the upsampling process, it may be difficult to preserve the fine ...
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The present investigation proposes a Convolutional Neural network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic ...
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In order to solve the problem of foreign object detection in the cloud server center, this paper proposes an abnormal object detection system based on a transformer network, aiming to identify and warn abnormal object...
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Human pose estimation is crucial in computer vision for interaction, behavior analysis, and action recognition. This paper introduces EDUNet, a U-shaped key point detection network addressing information loss, redunda...
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In the dynamic landscape of GPON broadband access networks, the need for robust and efficient mechanisms to handle inconsistent data from crash reports is paramount. The first scenario is to create a similarity group ...
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