scholarly journals An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2784 ◽  
Author(s):  
Hongji Cao ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Hongxia Qi
Keyword(s):  
Gaussian Process ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Position Estimation ◽  
Relative Distance ◽  
Error Prediction ◽  
Estimation Result ◽  
Positioning Error ◽  
Positioning Method ◽  
Better Than

Trusted positioning data are very important for the fusion of Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP). This paper proposes an adaptive Bluetooth/Wi-Fi fingerprint positioning method based on Gaussian process regression (GPR) and relative distance (RD), which can choose trusted positioning results for fusion. In the offline stage, measurements of the Bluetooth and Wi-Fi received signal strength (RSS) were collected to construct Bluetooth and Wi-Fi fingerprint databases, respectively. Then, fingerprint positioning error prediction models were built with GPR and data from the fingerprint databases. In the online stage, online Bluetooth and Wi-Fi RSS readings were matched with the fingerprint databases to get a Bluetooth fingerprint positioning result (BFPR) and a Wi-Fi fingerprint positioning result (WFPR). Then, with the help of RD and fingerprint positioning error prediction models, whether the positioning results are trusted was determined. The trusted result is selected as the position estimation result when there is only one trusted positioning result among the BFPR and WFPR. The mean is chosen as the position estimation result when both the BFPR and WFPR results are trusted or untrusted. Experimental results showed that the proposed method was better than BFP and WFP, with a mean positioning error of 2.06 m and a root-mean-square error of 1.449 m.

Enhanced Kalman Filter using Noisy Input Gaussian Process Regression for Bridging GPS Outages in a POS

2017 ◽  
Vol 71 (3) ◽  
pp. 565-584 ◽  
Author(s):  
Wen Ye ◽  
Zhanchao Liu ◽  
Chi Li ◽  
Jiancheng Fang
Keyword(s):  
Kalman Filter ◽  
Gaussian Process ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Navigation Systems ◽  
Input Noise ◽  
Noisy Input ◽  
Precision Motion ◽  
Better Than

A Position and Orientation System (POS) integrating an Inertial Navigation Systems (INS) and the Global Positioning System (GPS) is a key component of remote sensing motion compensation. It can provide reliable and high-frequency high-precision motion information using a Kalman Filter (KF) during GPS availability. However, the performance of a POS significantly degrades during GPS outages. To maintain reliable POS outputs, this paper proposes a new hybrid predictor based on modelling the nonlinear time-series data-driven INS-errors using Noisy Input Gaussian Process Regression (NIGPR), which takes the input noise into account. The proposed approach is used to learn the nonlinear INS-errors model when GPS signals are available. When GPS outages occur, it starts to predict the observation measurement, and then feeds it to a KF as a virtual update to estimate all the INS errors. The proposed approach is verified in a real airplane, which combines a POS and Synthetic Aperture Radar (SAR). Experimental results show that the proposed approach significantly improves the performance of the POS, with improvements more than 90% better than a KF and 10% better than a Gaussian Process Regression (GPR/KF) combination during various GPS outages.

scholarly journals WiFi RTT Indoor Positioning Method Based on Gaussian Process Regression for Harsh Environments

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215777-215786
Author(s):  
Hongji Cao ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Shenglei Xu ◽  
Hongxia Qi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Indoor Positioning ◽  
Harsh Environments ◽  
Positioning Method

scholarly journals Gaussian Process-Based Hybrid Model for Predicting Oxygen Consumption in the Converter Steelmaking Process

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 352 ◽  
Author(s):  
Sheng-Long Jiang ◽  
Xinyue Shen ◽  
Zhong Zheng
Keyword(s):  
Oxygen Consumption ◽  
Gaussian Process ◽  
Hybrid Model ◽  
Prediction Models ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Converter Steelmaking ◽  
Data Set ◽  
Iron And Steel ◽  
Network Support

Oxygen is one of the most important energies used in converter steelmaking processes of integrated iron and steel works. Precisely forecasting oxygen consumption before processing can benefit process control and energy optimization. This paper assumes there is a linear relationship between the oxygen consumption and input materials, and random noises are caused by other unmeasurable materials and unobserved reactions. Then, a novel hybrid prediction model integrating multiple linear regression (MLR) and Gaussian process regression (GPR) is introduced. In the hybrid model, the MLR method is developed to figure the global trend of the oxygen consumption, and the GPR method is applied to explore the local fluctuation caused by noise. Additionally, to accelerate the computational speed on the practical data set, a K-means clustering method is devised to respectively train a number of GPR models. The proposed hybrid model is validated with the actual data collected from an integrated iron and steel work in China, and compared with benchmark prediction models including MLR, artificial neural network, support vector machine and standard GPR. The forecasting results indicate that the suggested model is able to not only produce satisfactory point forecasts, but also estimate accurate probabilistic intervals.

scholarly journals Indoor Positioning Method Using WiFi RTT Based on LOS Identification and Range Calibration

2020 ◽  
Vol 9 (11) ◽  
pp. 627
Author(s):  
Hongji Cao ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Shenglei Xu ◽  
Minghao Si ◽  
...  
Keyword(s):  
Gaussian Process Regression ◽  
Indoor Positioning ◽  
Line Of Sight ◽  
Calibration Model ◽  
Identification Algorithm ◽  
Recognition Model ◽  
Trip Time ◽  
Positioning Method ◽  
On Line ◽  
Better Than

WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m.

scholarly journals Comprehensive Modeling in Predicting Biodiesel Density Using Gaussian Process Regression Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bingxian Wang ◽  
Issam Alruyemi
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Fatty Acid Esters ◽  
Effective Parameters ◽  
Target Values ◽  
Comprehensive Modeling ◽  
First Time ◽  
Acid Esters ◽  
Better Than

In this study, four Gaussian process regression (GPR) approaches by various kernel functions have been proposed for the estimation of biodiesel density as the functions of pressure, temperature, molecular weight, and the normal melting point of fatty acid esters. Comparing the actual values with GPR outputs shows that these approaches have good accuracy, but the performance of the rational quadratic GPR model is better than others. In this GPR model, RMSE = 0.47 , MSE = 0.22 , MRE = 0.04 , R 2 = 1 , and STD is equal to 0.3. In addition, for the first time, this study shows that the effective parameters affect the biodiesel density. According to this analysis, it was shown that among the input parameters, pressure has the greatest effect on the target values with a relevancy factor of 0.59. This study can be used as a suitable and valuable work/tool for chemical and petroleum engineers who attempt environment protection and recovery improvement.

Analysis of Slope Stability Based on Gaussian Process Regression

2012 ◽  
Vol 170-173 ◽  
pp. 1330-1333 ◽  
Author(s):  
Yan Zhang ◽  
Guo Shao Su ◽  
Liu Bin Yan
Keyword(s):  
Slope Stability ◽  
Gaussian Process ◽  
Stability Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Support Vector ◽  
Test Study ◽  
Computation Process ◽  
Artificial Neural Network Ann ◽  
Better Than

Slope stability estimation is an engineering problem that involves several parameters. Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used in slope stability problem. However, there are some open issues for above-mentioned methods, which are very hard to overcome. For this reason, Gaussian Process Regression (GPR) which has a theoretical framework for obtaining the optimum hyperparameters self-adaptively has been used in slope stability problem. Without complicated mechanics computation process, through learning the empirical knowledge coming from real engineering, the complicated nonlinear mapping relationship between slope stability and its influencing factors was established easily using GPR. The results of test study indicate that the method is feasible, effective and simple to implement for slope stability evaluation. The results are better than previously published paper of ANN and SVM.

scholarly journals Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor

2021 ◽  
Vol 10 (10) ◽  
pp. 706
Author(s):  
Hongji Cao ◽  
Yunjia Wang ◽  
Jingxue Bi ◽  
Meng Sun ◽  
Hongxia Qi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Nearest Neighbor ◽  
Gaussian Process Regression ◽  
Initial Position ◽  
Reference Points ◽  
Dual Band ◽  
K Nearest Neighbor ◽  
Positioning Method ◽  
5 Ghz ◽  
Fingerprint Database

Since many Wi-Fi routers can currently transmit two-band signals, we aimed to study dual-band Wi-Fi to achieve better positioning results. Thus, this paper proposes a fingerprint positioning method for dual-band Wi-Fi based on Gaussian process regression (GPR) and the K-nearest neighbor (KNN) algorithm. In the offline stage, the received signal strength (RSS) measurements of the 2.4 GHz and 5 GHz signals at the reference points (RPs) are collected and normalized to generate the online dual-band fingerprint, a special fingerprint for dual-band Wi-Fi. Then, a dual-band fingerprint database, which is a dedicated fingerprint database for dual-band Wi-Fi, is built with the dual-band fingerprint and the corresponding RP coordinates. Each dual-band fingerprint constructs its positioning model with the GPR algorithm based on itself and its neighborhood fingerprints, and its corresponding RP coordinates are the label of this model. The neighborhood fingerprints are found by the spatial distances between RPs. In the online stage, the measured RSS values of dual-band Wi-Fi are used to generate the online dual-band fingerprint and the 5 GHz fingerprint. Due to the better stability of the 5 GHz signal, an initial position is solved with the 5 GHz fingerprint and the KNN algorithm. Then, the distances between the initial position and model labels are calculated to find a positioning model with the minimum distance, which is the optimal positioning model. Finally, the dual-band fingerprint is input into this model, and the output of this model is the final estimated position. To evaluate the proposed method, we selected two scenarios (A and B) as the test area. In scenario A, the mean error (ME) and root-mean-square error (RMSE) of the proposed method were 1.067 and 1.331 m, respectively. The ME and RMSE in scenario B were 1.432 and 1.712 m, respectively. The experimental results show that the proposed method can achieve a better positioning effect compared with the KNN, Rank, Coverage-area, and GPR algorithms.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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