Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring

Huerta, Ramón; Mosqueiro, Thiago; Fonollosa, Jordi; Rulkov, Nikolai F.; Rodríguez-Luján, Irene

doi:10.1016/j.chemolab.2016.07.004

Cited by 104 publications

(65 citation statements)

References 46 publications

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“…To do this, we compare the performance of 3CRS with the RSP discovery method of two types of kmeans and DBSCAN based on SFS and SFS results, which are the most representative skyline discovery methods. For the objective analysis, a real-world dataset, Gas sensors for home activity monitoring dataset (below HT) [12] was used. The HT data consists of million data collected from 8 sensors related to the temperature and humidity.…”

Section: Experiments Resultsmentioning

confidence: 99%

A Study on Representative Skyline Using Connected Component Clustering

Choi¹,

Nasridinov²

2019

J Multimed Inf Syst

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Skyline queries are used in a variety of fields to make optimal decisions. However, as the volume of data and the dimension of the data increase, the number of skyline points increases with the amount of time it takes to discover them. Mainly, because the number of skylines is essential in many real-life applications, various studies have been proposed. However, previous researches have used the k-parameter methods such as top-k and k-means to discover representative skyline points (RSPs) from entire skyline point set, resulting in high query response time and reduced representativeness due to k dependency. To solve this problem, we propose a new Connected Component Clustering based Representative Skyline Query (3CRS) that can discover RSP quickly even in high-dimensional data through connected component clustering. 3CRS performs fast discovery and clustering of skylines through hash indexes and connected components and selects RSPs from each cluster. This paper proves the superiority of the proposed method by comparing it with representative skyline queries using k-means and DBSCAN with the real-world dataset.

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Section: Experiments Resultsmentioning

confidence: 99%

A Study on Representative Skyline Using Connected Component Clustering

Choi¹,

Nasridinov²

2019

J Multimed Inf Syst

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“…The real datasets are termed HT, Household, and PAMAP2, and are obtained from the UCI Machine Learning Repository [26]. First, HT is a ten-dimensional dataset that collects measured values of home sensors that monitor the temperature, humidity, and concentration levels of various gases produced in another project [27]. Second, Household is a seven-dimensional dataset that collects the measured values of active energy consumed by each electronic product in the home.…”

Section: Datasetsmentioning

confidence: 99%

A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph

et al. 2019

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OPTICS is a state-of-the-art algorithm for visualizing density-based clustering structures of multi-dimensional datasets. However, OPTICS requires iterative distance computations for all objects and is thus computed in O ( n 2 ) time, making it unsuitable for massive datasets. In this paper, we propose constrained OPTICS (C-OPTICS) to quickly create density-based clustering structures that are identical to those by OPTICS. C-OPTICS uses a bi-directional graph structure, which we refer to as the constraint graph, to reduce unnecessary distance computations of OPTICS. Thus, C-OPTICS achieves a good running time to create density-based clustering structures. Through experimental evaluations with synthetic and real datasets, C-OPTICS significantly improves the running time in comparison to existing algorithms, such as OPTICS, DeLi-Clu, and Speedy OPTICS (SOPTICS), and guarantees the quality of the density-based clustering structures.

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“…Gas sensors for Home Activity Monitoring Data Set [9]: this dataset contains readings from temperature, humidity and 8 metal-oxide (MOX) gas sensors of a contained environment whether or not a stimuli is presented. The collected data will be used in the Support Vector Regression (SVR) ML model with RBF kernel functions to assess all the policies.…”

Section: Performance Evaluation a Data Setsmentioning

confidence: 99%

Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach

Aleksandrova

Anagnostopoulos

Kolomvatsos

2019

2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)

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This work studies a sequential decision making methodology of when to update machine learning models in Edge Computing environments given underlying changes in the contextual data distribution. The proposed model focuses on updates scheduling and takes into consideration the optimal decision time for minimizing the network overhead. At the same time it preserves the prediction accuracy of models based on the principles of the Optimal Stopping Theory (OST). The paper reports on a comparative analysis between the proposed approach and other policies proposed in the respective literature while providing an evaluation of the performances using linear and support vector regression models. Our evaluation process is realized over real contextual data streams to reveal the strengths and weaknesses of the proposed strategy. Index Terms-Edge computing, machine learning model updates, communication efficiency, optimal stopping theory.

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Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring

Cited by 104 publications

References 46 publications

A Study on Representative Skyline Using Connected Component Clustering

A Study on Representative Skyline Using Connected Component Clustering

A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph

Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach

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