Sensor data anonymization based on genetic algorithm clustering with L-Diversity

2016 ◽  
Author(s):  
Ainur Abdrashitov ◽  
Anton Spivak
Keyword(s):  
Genetic Algorithm ◽  
Sensor Data ◽  
Data Anonymization

GENETIC ALGORITHM BASED VISUAL LOCALIZATION FOR A ROBOT PET IN HOME HEALTHCARE SYSTEM

2007 ◽  
Vol 04 (02) ◽  
pp. 141-160 ◽  
Author(s):  
FUNG-LING TONG ◽  
MAX Q.-H. MENG
Keyword(s):  
Genetic Algorithm ◽  
Home Healthcare ◽  
Sensor Data ◽  
Mapping Technique ◽  
Depth Information ◽  
Visual Localization ◽  
Legged Robot ◽  
Localization Algorithm ◽  
Localization Algorithms ◽  
Localization And Mapping

The simultaneous localization and mapping technique is an important requirement in the development autonomous robot. Many localization algorithms for wheeled robots using various sensors have been proposed. In this article, we present a visual localization algorithm for a small home-use robot pet (legged robot). A low-resolution camera is equipped on the robot as the only sensor for localization. Challenges of visual localization for legged robots include: (1) Unmodeled motion errors due to leg slippages are common in legged robot, (2) as the oscillated walking motion of robot leads to fluctuated sensor data, the high degree of freedom of legged robot increases the complexity of the localization problem and (3) camera has limited field of view and image points lack of depth information. In the proposed algorithm, the localization for high-dimensional movement robot is modeled as an optimization. The objective function is then solved by a genetic algorithm. Approaches to (1) increase the efficiency of the search and (2) weaken the influence of noisy feature points to the localization results are presented. Results from simulations show that the proposed algorithm is able to localize the legged robot accurately and efficiently.

scholarly journals Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3222 ◽  
Author(s):  
Di Wang ◽  
Lin Xie ◽  
Simon Yang ◽  
Fengchun Tian
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Near Infrared ◽  
Spectral Response ◽  
Sensor Data ◽  
Support Vector ◽  
True Negative ◽  
Svm Model ◽  
Tobacco Cultivation ◽  
Effective Principal

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.

A Model of Classification for E-Nose Based on Genetic Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 952-955 ◽  
Author(s):  
Min Jun Jiang ◽  
Yun Xiang Liu ◽  
Jing Xin Yang ◽  
Wan Jun Yu
Keyword(s):  
Genetic Algorithm ◽  
Electronic Nose ◽  
Sensor Data ◽  
Binary Codes ◽  
Classification Algorithms ◽  
Classification Rules ◽  
Fitness Evaluation ◽  
Mutation Operators ◽  
Paper Classification ◽  
F Measure

Electronic nose is an intelligent sensory analyzing instrument which simulates the biological olfaction system. Classification is very important for an electronic nose which is usually seen as the software of E-nose. In this paper, we present a model of classification based on genetic algorithm. Compared with common classification algorithms, genetic algorithm had more powerful flexibility and global searching capability. In this paper classification rules were represented in the form of chromosome by binary codes which are in accordance with the features of sensor data. F-measure was used as fitness evaluation. We also designed efficient crossover, mutation operators.

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