Cooperative Ad Hoc Node Localization with Ground-Level Suburban Measurements via Machine Learning Techniques

2018 ◽  
Author(s):  
Andreas Polydoros ◽  
Ioannis Dagres ◽  
Cenk Kose
Keyword(s):  
Machine Learning ◽  
Ad Hoc ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Node Localization ◽  
Learning Techniques

PATTERNS OF PREPOSITIONAL ATTACHMENTS — WHERE DICTIONARY SEMANTICS MEETS CORPUS STATISTICS

2000 ◽  
Vol 14 (06) ◽  
pp. 809-838 ◽  
Author(s):  
SANDA M. HARABAGIU
Keyword(s):  
Machine Learning ◽  
Information Extraction ◽  
Learning Process ◽  
Wall Street Journal ◽  
Ad Hoc ◽  
Machine Learning Techniques ◽  
Journal Articles ◽  
Wall Street ◽  
Web Documents ◽  
Learning Techniques

This paper presents a novel methodology of disambiguating prepositional phrase attachments. We create patterns of attachments by classifying a collection of prepositional relations derived from Treebank parses. As a by-product, the arguments of every prepositional relation are semantically disambiguated. Attachment decisions are generated as the result of a learning process, that builds upon some of the most popular current statistical and machine learning techniques. We have tested this methodology on (1) Wall Street Journal articles, (2) textual definitions of concepts from a dictionary and (3) an ad hoc corpus of Web documents, used for conceptual indexing and information extraction.

Bridge Response and Heavy Truck Classification Framework Based on a Two-Step Machine Learning Algorithm

2021 ◽  
pp. 036119812110520
Author(s):  
Fiorella Mete ◽  
David J. Corr ◽  
Michael P. Wilbur ◽  
Ying Chen
Keyword(s):  
Machine Learning ◽  
Ad Hoc ◽  
Learning Algorithm ◽  
Classification Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Classification Framework ◽  
Learning Techniques ◽  
Tree Methods ◽  
Step Algorithm

Collecting information on heavy trucks and monitoring the bridges which they regularly cross is important for many facets of infrastructure management. In this paper, a two-step algorithm is developed using bridge and truck data, by deploying sequentially unsupervised and supervised machine learning techniques. Longitudinal clustering of bridge data, concerning strain waveforms, is adopted to perform the first step of the algorithm, while image visual inspection and classification tree methods are applied to truck data concurrently in the second step. Both bridge and truck traffic must be monitored for a limited, yet significant, amount of time to calibrate the algorithm, which is then used to build a classification framework. The framework provides the same benefits of two data collection systems while only one needs to be operative. Depending on which monitoring system remains available, the framework enables the use of bridge data to identify the truck’s profile which generated it, or to estimate bridge response given the truck’s information. As a result, the present study aims to provide decision-makers with an effective way to monitor the whole bridge-traffic system, bridge managers to plan effective maintenance, and policymakers to develop ad hoc regulations.

scholarly journals Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5

2018 ◽  
Vol 242 ◽  
pp. 1417-1426 ◽  
Author(s):  
Yongming Xu ◽  
Hung Chak Ho ◽  
Man Sing Wong ◽  
Chengbin Deng ◽  
Yuan Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine Learning in Wireless Communication

2019 ◽  
pp. 141-161
Author(s):  
Neha Vaishnavi Sharma ◽  
Narendra Singh Yadav
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Ad Hoc ◽  
Ad Hoc Wireless Networks ◽  
Machine Learning Techniques ◽  
Energy Aware ◽  
Learning Techniques ◽  
Optimal Node ◽  
Self Learning ◽  
Direct Applicability

As the circumstances are changing, mankind has turned out to be more inclined to snappy and speedier correspondence and access to information. The correspondence happens in numerous structures (e.g., presently, this correspondence is all the more a virtual substance than a physical one). So as to keep up fast correspondence, the coming age will depend on exceptionally tried and true, canny and self-learning/self-modifying correspondence organizers. In this context, this chapter reviews the most important machine learning techniques with the direct applicability in wireless ad-hoc systems. A guide of machine learning methods and their relevance is also provided. Different applications of ad-hoc wireless networks are discussed in terms of energy-aware communications, optimal node deployment and localization, resource allocation, and scheduling.

Machine Learning in Wireless Communication

2021 ◽  
pp. 1979-1999
Author(s):  
Neha Vaishnavi Sharma ◽  
Narendra Singh Yadav
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Ad Hoc ◽  
Ad Hoc Wireless Networks ◽  
Machine Learning Techniques ◽  
Energy Aware ◽  
Learning Techniques ◽  
Optimal Node ◽  
Self Learning ◽  
Direct Applicability

As the circumstances are changing, mankind has turned out to be more inclined to snappy and speedier correspondence and access to information. The correspondence happens in numerous structures (e.g., presently, this correspondence is all the more a virtual substance than a physical one). So as to keep up fast correspondence, the coming age will depend on exceptionally tried and true, canny and self-learning/self-modifying correspondence organizers. In this context, this chapter reviews the most important machine learning techniques with the direct applicability in wireless ad-hoc systems. A guide of machine learning methods and their relevance is also provided. Different applications of ad-hoc wireless networks are discussed in terms of energy-aware communications, optimal node deployment and localization, resource allocation, and scheduling.

scholarly journals Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Amirhossein Mostajabi ◽  
Declan L. Finney ◽  
Marcos Rubinstein ◽  
Farhad Rachidi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Relative Humidity ◽  
Meteorological Parameters ◽  
Ground Level ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Predictive Skill ◽  
Learning Techniques ◽  
Lightning Discharges

Abstract Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.

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