Enabling Knowledge Extraction from Low Level Sensor Data

2011 ◽  
pp. 411-419
Author(s):  
Paolo Cappellari ◽  
Jie Shi ◽  
Mark Roantree ◽  
Crionna Tobin ◽  
Niall Moyna
Keyword(s):  
Knowledge Extraction ◽  
Sensor Data ◽  
Low Level ◽  
Level Sensor

Use of Low-Level Sensor Data to Improve the Accuracy of Bluetooth-Based Travel Time Estimation

2013 ◽  
Vol 2338 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Bahar Namaki Araghi ◽  
Lars Tørholm Christensen ◽  
Rajesh Krishnan ◽  
Jonas Hammershøj Olesen ◽  
Harry Lahrmann
Keyword(s):  
Travel Time ◽  
Time Estimation ◽  
Sensor Data ◽  
Travel Time Estimation ◽  
Low Level ◽  
Level Sensor

Efficient Multi-Level Human Motion Tracking Based on Compressive Infrared Sensing Paradigm

2013 ◽  
Vol 278-280 ◽  
pp. 988-993 ◽  
Author(s):  
Xiao Mu Luo ◽  
Dong Hui Liu ◽  
Hao Chen ◽  
Jia Ming Hong ◽  
Tong Liu ◽  
...  
Keyword(s):  
Motion Tracking ◽  
Large Scale ◽  
Human Motion ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Human Motion Tracking ◽  
Low Level ◽  
Infrared Sensing ◽  
Level Sensor ◽  
Multi Level

Multi-level human motion tracking and analysis is still an open question in person surveillance, especially with constrained computational and communication resources. In this paper, we propose a sensing paradigm which could address this challenge efficiently and effectively. The proposed paradigm mainly includes two components. First, we design a compressive infrared sensing model, which can sample and encode multi-level human motion into low-level sensor data directly, without the mediate process of scene recovery. Second, we employ lightweight data processing algorithms to detect and segment human motion at different levels, and decode the location information adaptively. We used self-developed pyroelectric infrared (PIR) sensor nodes to construct a wireless distributed network, and conducted experiments in real office environment. The experimental results showed that the proposed paradigm could track human motion at two levels robustly, and the computational and communication burden is low (5×1 sensor data stream at 5 Hz for processing). Our paradigm bridges the gap between the low-level sensor data and the high-level analysis for large-scale automated surveillance, and could serve as useful guidance for system design if needed.

A service-oriented context-awareness reasoning framework and its implementation

2018 ◽  
Vol 36 (6) ◽  
pp. 1114-1134 ◽  
Author(s):  
Xiufeng Cheng ◽  
Jinqing Yang ◽  
Lixin Xia
Keyword(s):  
Context Awareness ◽  
Service Providers ◽  
User Behavior ◽  
Contextual Information ◽  
Sensor Data ◽  
Context Information ◽  
Content Type ◽  
Low Level ◽  
Service Oriented ◽  
High Level

PurposeThis paper aims to propose an extensible, service-oriented framework for context-aware data acquisition, description, interpretation and reasoning, which facilitates the development of mobile applications that provide a context-awareness service.Design/methodology/approachFirst, the authors propose the context data reasoning framework (CDRFM) for generating service-oriented contextual information. Then they used this framework to composite mobile sensor data into low-level contextual information. Finally, the authors exploited some high-level contextual information that can be inferred from the formatted low-level contextual information using particular inference rules.FindingsThe authors take “user behavior patterns” as an exemplary context information generation schema in their experimental study. The results reveal that the optimization of service can be guided by the implicit, high-level context information inside user behavior logs. They also prove the validity of the authors’ framework.Research limitations/implicationsFurther research will add more variety of sensor data. Furthermore, to validate the effectiveness of our framework, more reasoning rules need to be performed. Therefore, the authors may implement more algorithms in the framework to acquire more comprehensive context information.Practical implicationsCDRFM expands the context-awareness framework of previous research and unifies the procedures of acquiring, describing, modeling, reasoning and discovering implicit context information for mobile service providers.Social implicationsSupport the service-oriented context-awareness function in application design and related development in commercial mobile software industry.Originality/valueExtant researches on context awareness rarely considered the generation contextual information for service providers. The CDRFM can be used to generate valuable contextual information by implementing more reasoning rules.

INTELLIGENT KNOWLEDGE EXTRACTION FROM THE WEB

2003 ◽  
Vol 11 (supp01) ◽  
pp. 117-134 ◽  
Author(s):  
JESÚS CARDEÑOSA ◽  
EDMUNDO TOVAR
Keyword(s):  
European Commission ◽  
Knowledge Extraction ◽  
Low Level ◽  
Content Provider ◽  
Imprecise Information ◽  
The Right ◽  
The Web

Many websites are in general poorly defined and its users are not able to find the information they need. That is the reason why many papers are addressed to propose techniques able to find the right information for a user. Most of these techniques focus on finding the required information in the whole Internet. Many times the owner of the website gives incomplete/imprecise information with low level of usefulness for the user. The re-structuring of the information is many times enough for detecting lacks of information, inconsistencies and imprecisions. However this work is normally very difficult without losing performances of the website. The authors have developed a novel application to exploit existing information in a website in a more profitable way restructuring the information without the intervention of the content provider. This paper describes the authors' experience during their participation in the European Commission ESPRIT 29158 FLEX Project.

Study on Sensor Signal Filtering Based on Wavelet Energy Entropy

2011 ◽  
Vol 63-64 ◽  
pp. 573-578
Author(s):  
Jian Guo Yan ◽  
Dong Li Yuan ◽  
Si Yuan Li ◽  
Xiao Jun Xing
Keyword(s):  
Maximum Entropy Principle ◽  
Noise Signal ◽  
Sensor Data ◽  
Sensor Signal ◽  
Signal Filtering ◽  
Wavelet Energy ◽  
Energy Entropy ◽  
Level Sensor ◽  
Wavelet Energy Entropy ◽  
Fuel Level

In order to increase the fuel level measurement accuracy in aircraft fuel system, the method of sensor signal filtering based on the wavelet energy entropy was put forward. Using the maximum entropy principle the wavelet energy entropy of high-frequency coefficient vector in each level was calculated while the output signal of sensor was analyzed in wavelet multi-resolution mode. Once the sum of wavelet energy entropy for filtered signal and noise signal is maximum, the filtering effect is much better. At the same time, the result of tests which use simulation signal and fuel level sensor data collected from fuel tank oscillation test are all satisfied, it is show that this method is available.

scholarly journals Information modeling and knowledge extraction for machine learning applications in industrial production systems

2020 ◽  
pp. 73-81
Author(s):  
Stefan Windmann ◽  
Christian Kühnert
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Structural Information ◽  
Semantic Annotation ◽  
Information Model ◽  
Knowledge Extraction ◽  
Information Modeling ◽  
Sensor Data ◽  
Tool Chain ◽  
Machine Learning Applications

AbstractIn this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.

Sign in / Sign up

Export Citation Format

Share Document