A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine

Khatab, Zahra Ezzati; Gazestani, Amirhosein Hajihoseini; Ghorashi, Seyed Ali; Ghavami, Mohammad

doi:10.1016/j.sigpro.2020.107915

Cited by 26 publications

(9 citation statements)

References 33 publications

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“…Autoencoder is a typical unsupervised network, which obtains the hidden expression of input data through coding compression and decoding reconstruction to achieve the purpose of feature extraction [33,34]. Compared with the basic autoencoder, denoising autoencoder (DAE) forces the network to generate robust intermediate variables by adding noise to the data to deal with the interference of noise [35,36].…”

Section: Denoising Network Model Based On Cdaementioning

confidence: 99%

RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Yao,

Zhu,

Jiang

et al. 2023

Meas. Sci. Technol.

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Remaining useful life (RUL) prediction of rolling bearing plays an important role in maintaining the safety of the equipment. However, the data collected from industrial scene often contains noises, which affects the RUL prediction precision of rolling bearing. To overcome the above problem, a data-driven scheme for RUL prediction of rolling bearing is proposed based on convolutional denoising autoencoder (CDAE) and bidirectional long short-term memory network (Bi-LSTM). In the proposed method, the vibration signal is directly used as input of the prognostics network model. Then, a denoising network model based on CDAE is built to reduce the effect of noise. Through stacking the convolutional autoencoder, the noise component is automatically removed from the raw data. Finally, the network model based on Bi-LSTM is established to extract the high-dimensional degradation characteristics of bearing and estimate the RUL of the rolling bearing. The experimental results on the Xi’an Jiaotong University bearing dataset show that the proposed method has satisfied performance of RUL prediction.

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Section: Denoising Network Model Based On Cdaementioning

confidence: 99%

RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Yao,

Zhu,

Jiang

et al. 2023

Meas. Sci. Technol.

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“…These are also selected for our ML based analysis of the five benchmark datasets. There are a few works on semi supervised apporaches [37] [38] and deep learning approaches [39] [40]. However, such approaches are specifically designed lacking the required generality for applying to a wide variety of benchmark sensor datasets for a comparitive analysis.…”

Section: Dataset 1: Ujiindoorloc Datasetmentioning

confidence: 99%

Characteristic analysis of fingerprint datasets from a pragmatic view of indoor localization using machine learning approaches

Mallik

Chowdhury

2023

J Supercomput

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“…Recognition of online training when a specific application performs ADL step by step is required to provide a person with home interventions or describe brief instructions on completing the task [ 27 ]. Each activity includes some continuous basic moves [ 64 , 65 ], and usually, human activity can last several seconds, and several basic movements can be involved in one second. From the point of view of sensor signals, continuous motions are more related to smooth signals, and changes between base continuing motions can create significant signal value changes.…”

Section: Har Analysismentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Serpush

Menhaj

Masoumi

et al. 2022

Computational Intelligence and Neuroscience

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Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

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A fingerprint technique for indoor localization using autoencoder based semi-supervised deep extreme learning machine

Cited by 26 publications

References 33 publications

RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

RUL prediction method for rolling bearing using convolutional denoising autoencoder and bidirectional LSTM

Characteristic analysis of fingerprint datasets from a pragmatic view of indoor localization using machine learning approaches

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

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