scholarly journals Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1513
Author(s):  
Sameir A. Aziez ◽  
Nawar Al-Hemeary ◽  
Ahmed Hameed Reja ◽  
Tamás Zsedrovits ◽  
György Cserey
Keyword(s):  
Nearest Neighbor ◽  
Prediction Method ◽  
Data Synchronization ◽  
K Nearest Neighbor ◽  
Integration System ◽  
Gnss Receiver ◽  
Data Prediction ◽  
Update Rate ◽  
A Minor ◽  
Gnss Data

The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.

PREDICTION OF CORONARY ARTERY DISEASE BASED ON ENSEMBLE LEARNING APPROACHES AND CO-EXPRESSED OBSERVATIONS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640010 ◽  
Author(s):  
YING-TSANG LO ◽  
HAMIDO FUJITA ◽  
TUN-WEN PAI
Keyword(s):  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Nearest Neighbor ◽  
Prediction Method ◽  
Medical Decision ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Artery Disease ◽  
Voting Mechanism

Background: Coronary artery disease (CAD) is one of the most representative cardiovascular diseases. Early and accurate prediction of CAD based on physiological measurements can reduce the risk of heart attack through medicine therapy, healthy diet, and regular physical activity. Methods:Four heart disease datasets from the UC Irvine Machine Learning Repository were combined and re-examined to remove incomplete entries, and a total of 822 cases were utilized in this study. Seven machine learning methods, including Naïve Bayes, artificial neural networks (ANNs), sequential minimal optimization (SMO), k-nearest neighbor (KNN), AdaBoost, J48, and random forest, were adopted to analyze the collected datasets for CAD prediction. By combining co-expressed observations and an ensemble voting mechanism, we designed and evaluated a new medical decision classifier for CAD prediction. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm was applied to determine the best prediction method for CAD diagnosis. Results: Features of systolic blood pressure, cholesterol, heart rate, and ST depression are considered to be the most significant differences between patients with and without CADs. We show that the prediction capability of seven machine learning classifiers can be enhanced by integrating combinations of observed co-expressed features. Finally, compared to the use of any single classifier, the proposed voting mechanism achieved optimal performance according to TOPSIS.

scholarly journals AN INFECTIOUS DISEASE PREDICTION METHOD BASED ON K-NEAREST NEIGHBOR IMPROVED ALGORITHM

2019 ◽  
Vol 11 (01) ◽  
pp. 19-35
Author(s):  
Yaming Chen ◽  
Weiming Meng ◽  
Fenghua Zhang ◽  
Xinlu Wang ◽  
Qingtao Wu
Keyword(s):  
Infectious Disease ◽  
Nearest Neighbor ◽  
Prediction Method ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Improved Algorithm

scholarly journals MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features

2021 ◽  
Vol 21 (S1) ◽  
Author(s):  
Yu-Tian Wang ◽  
Qing-Wen Wu ◽  
Zhen Gao ◽  
Jian-Cheng Ni ◽  
Chun-Hou Zheng
Keyword(s):  
Computational Models ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Experimental Studies ◽  
Prediction Method ◽  
Disease Association ◽  
High Dimensionality ◽  
K Nearest Neighbor ◽  
Hypergraph Learning ◽  
Disease Associations

Abstract Background MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time‐consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. Methods This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. Result Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. Conclusion MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA–disease association predication.

scholarly journals Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL

2021 ◽  
Vol 11 (20) ◽  
pp. 9389
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Decai Xue ◽  
Shuqing Zhang
Keyword(s):  
Nearest Neighbor ◽  
Prediction Method ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Kernel Principal Component Analysis ◽  
Gear Pump ◽  
K Nearest Neighbor ◽  
Multiple Characteristic ◽  
Gear Pumps ◽  
Useful Life

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just-in-time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.

kNN-based Gas-bearing Prediction Using Local Waveform Similarity Gas-indication Attribute - An Application to a Tight Sandstone Reservoir

2021 ◽  
pp. 1-45
Author(s):  
Zhaohui Song ◽  
Sanyi Yuan ◽  
Zimeng Li ◽  
Shangxu Wang
Keyword(s):  
Field Data ◽  
Nearest Neighbor ◽  
Prediction Method ◽  
K Nearest Neighbor ◽  
Tight Sandstone ◽  
Unlabeled Sample ◽  
Sensitive Attribute ◽  
Waveform Similarity ◽  
Intelligent Models ◽  
Gas Bearing

Gas-bearing prediction of tight sandstone reservoirs is significant but challenging due to the relationship between the gas-bearing property and its seismic response being nonlinear and complex. Although machine learning (ML) methods provide potential for solving the issue, the major challenge of ML applications to gas-bearing prediction is that of generating accurate and interpretable intelligent models with limited training sets. The k Nearest neighbor ( kNN) method is a supervised ML method classifying an unlabeled sample according to its k neighboring labeled samples. We have introduced a kNN-based gas-bearing prediction method. The method can automatically extract a gas-sensitive attribute called the gas-indication local waveform similarity attribute (GLWSA) combining prestack seismic gathers with interpreted gas-bearing curves. GLWSA uses the local waveform similarity among the predicting samples and the gas-bearing training samples to indicate the existence of an exploitable gas reservoir. GLWSA has simple principles and an explicit geophysical meaning. We use a numerical model and field data to test the effectiveness of our method. The result demonstrates that GLWSA is good at characterizing the reservoir morphology and location qualitatively. When the method applies to the field data, we evaluate the performance with a blind well. The prediction result is consistent with the geologic law of the work area and indicates more details compared to the root-mean-square attribute.

scholarly journals Data-Driven Prediction of Load Curtailment in Incentive-Based Demand Response System

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2905 ◽  
Author(s):  
Jimyung Kang ◽  
Soonwoo Lee
Keyword(s):  
Energy Consumption ◽  
Demand Response ◽  
Nearest Neighbor ◽  
Service Providers ◽  
Prediction Method ◽  
Poor Performance ◽  
Data Driven ◽  
K Nearest Neighbor ◽  
Response System ◽  
Load Curtailment

Demand response, in which energy customers reduce their energy consumption at the request of service providers, is spreading as a new technology. However, the amount of load curtailment from each customer is uncertain. This is because an energy customer can freely decide to reduce his energy consumption or not in the current liberalized energy market. Because this uncertainty can cause serious problems in a demand response system, it is clear that the amount of energy reduction should be predicted and managed. In this paper, a data-driven prediction method of load curtailment is proposed, considering two difficulties in the prediction. The first problem is that the data is very sparse. Each customer receives a request for load curtailment only a few times a year. Therefore, the k-nearest neighbor method, which requires a relatively small amount of data, is mainly used in our proposed method. The second difficulty is that the characteristic of each customer is so different that a single prediction method cannot cover all the customers. A prediction method that provides remarkable prediction performance for one customer may provide a poor performance for other customers. As a result, the proposed prediction method adopts a weighted ensemble model to apply different models for different customers. The confidence of each sub-model is defined and used as a weight in the ensemble. The prediction is fully based on the electricity consumption data and the history of demand response events without demanding any other additional internal information from each customer. In the experiment, real data obtained from demand response service providers verifies that the proposed framework is suitable for the prediction of each customer’s load curtailment.

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