scholarly journals Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment

2020 ◽  
Vol 10 (14) ◽  
pp. 4721
Author(s):  
Gunwoo Lee
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Tracking Accuracy ◽  
Hybrid Positioning ◽  
Hybrid Localization ◽  
Positioning Method ◽  
Using Data

Accidents involving marine crew members and passengers are still an issue that must be studied and obviated. Preventing such accidents at sea can improve the quality of life on board by ensuring a safe ship environment. This paper proposes a hybrid indoor positioning method, an approach which is becoming common on land, to enhance maritime safety. Specifically, a recurrent neural network (RNN)-based hybrid localization system (RHLS) that provides accurate and efficient user-tracking results is proposed. RHLS performs hybrid positioning by receiving wireless signals, such as Wi-Fi and Bluetooth, as well as inertial measurement unit data from smartphones. It utilizes the RNN to solve the problem of tracking accuracy reduction that may occur when using data collected from various sensors at various times. The results of experiments conducted in an offshore environment confirm that RHLS provides accurate and efficient tracking results. The scalability of RHLS provides managers with more intuitive monitoring of assets and crews, and, by providing information such as the location of safety equipment to the crew, it promotes welfare and safety.

scholarly journals A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Changhui Jiang ◽  
Yuwei Chen ◽  
Shuai Chen ◽  
Yuming Bo ◽  
Wei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Measurement Unit ◽  
Short Term Memory ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Short Term ◽  
Inertial Measurement ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

2020 ◽  
Vol 17 (8) ◽  
pp. 3421-3426
Author(s):  
D. Deva Hema ◽  
J. Tharun ◽  
G. Arun Dev ◽  
N. Sateesh
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Online Reviews ◽  
Physical Contact ◽  
Proposed Model ◽  
Deep Recurrent Neural Network ◽  
Bayes Algorithm ◽  
Fake Reviews

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

scholarly journals Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1219
Author(s):  
Oseikhuemen Davis Ojie ◽  
Reza Saatchi
Keyword(s):  
Neural Network ◽  
Visual System ◽  
Inertial Measurement Unit ◽  
Healthy Adult ◽  
Sensory Systems ◽  
Measurement Unit ◽  
Clinical Test ◽  
Kohonen Neural Network ◽  
F Measure ◽  
Anterior Posterior

Kohonen neural network (KNN) was used to investigate the effects of the visual, proprioceptive and vestibular systems using the sway information in the mediolateral (ML) and anterior-posterior (AP) directions, obtained from an inertial measurement unit, placed at the lower backs of 23 healthy adult subjects (10 males, 13 females, mean (standard deviation) age: 24.5 (4.0) years, height: 173.6 (6.8) centimeter, weight: 72.7 (9.9) kg). The measurements were based on the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). KNN clustered the subjects’ time-domain sway measures by processing their sway’s root mean square position, velocity, and acceleration. Clustering effectiveness was established using external performance indicators such as purity, precision-recall, and F-measure. Differences in these measures, from the clustering of each mCTSIB condition with its condition, were used to extract information about the balance-related sensory systems, where smaller values indicated reduced sway differences. The results for the parameters of purity, precision, recall, and F-measure were higher in the AP direction as compared to the ML direction by 7.12%, 11.64%, 7.12%, and 9.50% respectively, with their differences statistically significant (p < 0.05) thus suggesting the related sensory systems affect majorly the AP direction sway as compared to the ML direction sway. Sway differences in the ML direction were lowest in the presence of the visual system. It was concluded that the effect of the visual system on the balance can be examined mostly by the ML sway while the proprioceptive and vestibular systems can be examined mostly by the AP direction sway.

scholarly journals Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 53
Author(s):  
Joohwan Sung ◽  
Sungmin Han ◽  
Heesu Park ◽  
Hyun-Myung Cho ◽  
Soree Hwang ◽  
...  
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Recurrent Neural Network ◽  
Joint Angle ◽  
Low Frequency ◽  
Measurement Unit ◽  
Mean Square ◽  
Overground Walking

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

scholarly journals RFID Hybrid Positioning Method of Phased Array Antenna Based on Neural Network

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 74953-74960 ◽  
Author(s):  
Junxuan Wang ◽  
Wei Wei ◽  
Weizhu Wang ◽  
Ruixin Li
Keyword(s):  
Neural Network ◽  
Phased Array ◽  
Array Antenna ◽  
Phased Array Antenna ◽  
Hybrid Positioning ◽  
Positioning Method

scholarly journals Multiattentive Recurrent Neural Network Architecture for Multilingual Readability Assessment

2019 ◽  
Vol 7 ◽  
pp. 421-436 ◽  
Author(s):  
Ion Madrazo Azpiazu ◽  
Maria Soledad Pera
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Architecture ◽  
Text Structure ◽  
Reading Level ◽  
Data Sets ◽  
Neural Network Architecture ◽  
Using Data ◽  
Word Attention ◽  
Readability Assessment

We present a multiattentive recurrent neural network architecture for automatic multilingual readability assessment. This architecture considers raw words as its main input, but internally captures text structure and informs its word attention process using other syntax- and morphology-related datapoints, known to be of great importance to readability. This is achieved by a multiattentive strategy that allows the neural network to focus on specific parts of a text for predicting its reading level. We conducted an exhaustive evaluation using data sets targeting multiple languages and prediction task types, to compare the proposed model with traditional, state-of-the-art, and other neural network strategies.

Sign in / Sign up

Export Citation Format

Share Document