scholarly journals Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 214269-214281
Author(s):  
Yu-Chun Wu ◽  
Chi-Wai Chow ◽  
Yang Liu ◽  
Yun-Shen Lin ◽  
Chong-You Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Ridge Regression ◽  
Learning Algorithm ◽  
Signal Strength ◽  
Data Preprocessing ◽  
Received Signal Strength ◽  
Machine Learning Algorithm ◽  
Sigmoid Function ◽  
Kernel Ridge Regression

scholarly journals Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6109 ◽  
Author(s):  
Willem Raes ◽  
Nicolas Knudde ◽  
Jorik De Bruycker ◽  
Tom Dhaene ◽  
Nobby Stevens
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Superior Performance ◽  
Dust Particles ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Set Up ◽  
Gp Model

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.

scholarly journals CMOS camera based visible light communication (VLC) using grayscale value distribution and machine learning algorithm

2020 ◽  
Vol 28 (2) ◽  
pp. 2427 ◽  
Author(s):  
Ke-Ling Hsu ◽  
Yu-Chun Wu ◽  
Yu-Cheng Chuang ◽  
Chi-Wai Chow ◽  
Yang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Learning Algorithm ◽  
Visible Light Communication ◽  
Value Distribution ◽  
Machine Learning Algorithm ◽  
Cmos Camera

scholarly journals Reconstruction of daily gridded snow water equivalent product for the Pan-Arctic region based on a ridge regression machine learning approach

2021 ◽  
Author(s):  
Donghang Shao ◽  
Hongyi Li ◽  
Jian Wang ◽  
Xiaohua Hao ◽  
Tao Che ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Regression Model ◽  
Spatial Resolution ◽  
Ridge Regression ◽  
Snow Water Equivalent ◽  
Learning Algorithm ◽  
Arctic Region ◽  
Machine Learning Algorithm ◽  
Snow Water

Abstract. Snow water equivalent is an important parameter of the surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing snow water equivalent products. In the Pan-Arctic region, the existing snow water equivalent products are limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of snow water equivalent data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing snow water equivalent (SWE) products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare snow water equivalent products on a global scale. We evaluated the accuracy of the RRM SWE product using Global Historical Climatology Network (GHCN) data and Russian snow survey data. The MAE, RMSE, R, and R2; between the RRM SWE products and observed snow water equivalents are 0.24, 30.29 mm, 0.87, and 0.76, respectively. The accuracy of the RRM SWE dataset is improved by 24 %, 25 %, 32 %, 7 %, and 10 % compared with the original AMSR-E/AMSR2 snow water equivalent dataset, ERA-Interim SWE dataset, Global Land Data Assimilation System (GLDAS) SWE dataset, GlobSnow SWE dataset, and ERA5-land SWE dataset, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely too much on an independent snow water equivalent product, it makes full use of the advantages of each snow water equivalent dataset, and it considers the altitude factor. The average MAE of RRM SWE product at different altitude intervals is 0.24 and the average RMSE is 23.55 mm, this method has good stability, it is extremely suitable for the production of snow datasets with large spatial scales, and it can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate snow water equivalent data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from the ‘A Big Earth Data Platform for Three Poles’ (http://dx.doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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