Random Forest Based Precise Localization Algorithm for UWB-Embedded Car Keys

2021 ◽  
Vol 46 (12) ◽  
pp. 2310-2318
Author(s):  
Sang-jin Lee ◽  
Ji-Woong Park ◽  
Young-Bae Ko
Keyword(s):  
Random Forest ◽  
Precise Localization ◽  
Localization Algorithm

A ROBUST IRIS LOCALIZATION ALGORITHM VIA RADIAL SYMMETRY FOR NONIDEAL CAPTURING CONDITION

2009 ◽  
Vol 23 (01) ◽  
pp. 45-57 ◽  
Author(s):  
WEN-CONG ZHANG ◽  
BIN LI ◽  
XUE-YI YE ◽  
ZHEN-QUAN ZHUANG ◽  
KONG-QIAO WANG
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Radial Symmetry ◽  
Precise Localization ◽  
Localization Algorithm ◽  
Time System ◽  
Iris Localization ◽  
Real Time System ◽  
The Ideal

Iris localization is a key component of practical iris recognition system. Previous algorithms show good localization performances for iris images captured in the ideal conditions. However, in practice, the quality of iris image is greatly influenced by luminance, eyelashes, hair or glasses frame, which will cause mislocalization. In order to improve the robustness of iris localization, this paper proposes a new localization algorithm based on the radial symmetry transform, in which the radial symmetry characteristic of the pupil is utilized to realize iris localization. Experimental results show that the proposed algorithm can efficiently avoid the interference of luminance and other bad conditions, and realize robust precise localization in a real-time system.

scholarly journals Research on Underground Location Algorithm Based on Random Forest and Environmental Factor Compensation

2020 ◽  
Author(s):  
Xin Qiao ◽  
Fei Chang
Keyword(s):  
Random Forest ◽  
Environmental Factor ◽  
Signal Transmission ◽  
Localization Algorithm ◽  
Similar Region ◽  
Strip Shape ◽  
Mine Roadway ◽  
Anchor Nodes ◽  
Kalman Filter Algorithm ◽  
Location Algorithm

Abstract In view of the complex and changeable underground environment of coal mine and the long strip shape of underground roadway, a new underground location algorithm based on Stochastic Forest and environmental factor compensation is proposed. Firstly, the underground AP network model and roadway environment are analyzed, and the fingerprint localization algorithm is constructed. At the same time, the Kalman filter algorithm is used to filter the RSS signal in the offline sampling and real-time positioning stage. Then the algorithm based on random forest and environmental factor compensation is proposed. Under the assumption that the attenuation factors between the two anchor nodes are the same, the signal strength ratio compensation algorithm is proposed, which optimizes the shortcomings of the similarity of the locality error of the similar region. The target speed constraint condition is introduced to reduces errors caused by the transmission of RSS signal transmission and environmental factors. Experiments were carried out by using a bomb shelter to simulate the real mine roadway environment. The results show that the proposed algorithm can meet the high-precision positioning of the well under the conditions of sparse anchor nodes and complex environment underground.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio

An Efficient Area-Based Localization Algorithm for Wireless Sensor Networks

2015 ◽  
Vol 10 (10) ◽  
pp. 1062
Author(s):  
A. Mesmoudi ◽  
Mohammed Feham ◽  
Nabila Labraoui ◽  
Chakib Bekara
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm
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