scholarly journals Online unsupervised state recognition in sensor data

2015 ◽  
Author(s):  
Julien Eberle ◽  
Tri Kurniawan Wijaya ◽  
Karl Aberer
Keyword(s):  
Sensor Data ◽  
State Recognition

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

Ground of mechanism of pay in a budget for the pipelinetransporting of hydrocarbon commodities

2019 ◽  
pp. 141-152
Author(s):  
Adam Bodіuk
Keyword(s):  
The State ◽  
Practical Significance ◽  
State Budget ◽  
Economic Interests ◽  
State Recognition ◽  
Main Pipelines ◽  
Business Entities ◽  
Combination Of Methods ◽  
Definition Of ◽  
Hydrocarbon Transport

The subject of the study is the mechanism for determining the fiscal fee forthe main transportation of hydrocarbon goods as a resource concept. The purposeof this article is to justify the nature and prospects of using, instead of currentrent, hydrocarbon fiscal-main income as a fiscal payment, which is brought intothe state budget by operators of the main hydrocarbon-transport system as business entities for their transportation of hydrocarbons and products of their processing through main pipelines appropriate to the economic requirements. Theresearch methodology is determined by a combination of methods: a) cognition:legal analysis (study of the regulatory framework for the use of rent); b) justification: abstract logical analysis (definition of the concepts of hydrocarbon fiscalmain income); c) generalization (substantiation of conclusions and proposals).Results of work. In the process of analyzing the regulatory legal acts that regulate the use of current annuity as payment to the budget for the main transportation of hydrocarbons, it was established that it is not a tax in the interpretationof PKU, since the essence does not meet the official definition of tax, does notmeet the accepted definition of the concept of rent. The accepted nature andmechanism of paying rent for the transportation of hydrogen resources and associated revenues of the state and users of the main hydrogen transport systemand the unpromising nature of its use as a fiscal payment are analyzed. Conclusions.It is proposed that the state pay for the territorial pumping of hydrocarbon resources according to our triple principle as hydrocarbon fiscal-main income, whichcorresponds to its essence, and accordingly change the mechanism for calculatingand depositing funds to treasury accounts. Since the funds come to the revenueside of the state budget, that is, inherently belong to state revenue. The creationof such a mechanism needs certain studies, justifications and government decisions. The same applies to land use, since the quality indicators of soils, wherethe laid pipelines are territorially different. In addition, there is a process ofchanging land for its intended purpose, for the property. The fee for movinghydrocarbon resources should be calculated depending on the type of transport,including pipelines, for a set of indicators: quantity and quality of goods, time,main tariffs and distance of its movement. The amount may be adjusted usingfactors officially established by the CMU. Since the pipelines are located in territorial lands, part of this fee should be transferred to the territorial local budgets.Theoretically, the economic use of trunk pipelines should be considered as a typeof economic environmental management. Therefore, this type of government revenue should be determined by a set of indicators, as well as taking into account the economic interests of business entities authorized by the CMU. Thus, theimplementation of our proposed fiscal payment is relevant, has scientific noveltyand promising practical significance, therefore, for state recognition it is proposedto include it in the Tax Code of Ukraine.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

PROBLEMATICS AND AFFILMATION OF KAHARINGAN RELIGION IDENTITY DURING INTEGRATION INTO HINDU DHARMA

Penamas ◽  
2020 ◽  
Vol 33 (2) ◽  
pp. 185
Author(s):  
Tiwi - Etika
Keyword(s):  
Religious Identity ◽  
Internal Conflict ◽  
Integration Theory ◽  
New Order ◽  
Religious Institutions ◽  
Religious Leaders ◽  
State Recognition ◽  
The Future ◽  
Future Challenges ◽  
Qualitative Descriptive

This article is the result of a research on the Kaharingan problematic issues of religious identity after being integrated into Hindu Dharma. During the ‘New Order’ (President Soeharto's government) Kaharingan religion was not included in one of the religions served by the state. The issue of state recognition and the ease of obtaining civil services for Kaharingan adherents are strong reasons for Kaharingan religious leaders to integrate Kaharingan as part of Hinduism. The research raises the issues: (1) how is the process of integrating Kaharingan religion into Hindu Dharma? (2) what are the implications of such integration? and, (3) how is the existence of Kaharingan religious identity as the original ‘Dayak tribe religion’ after integration into Hindu Dharma in the future? This study aims to portray the existence of Kaharingan religion during integration into Hindu Dharma. This type of research is qualitative-descriptive with the method of collecting data through observation and interviews with religious leaders and administrators of religious institutions namely the Hindu Kaharingan Grand Council (MB-AHK), as well as an analysis of documents related to the object of research. Theories used in this research are integration theory, identity theory and locality theory. The integration process has implications for various fields, ranging from education, social, religious, economic, political upto cultural identity. The future challenges of Kaharingan are: internal conflict, a dilemma of distortion from third parties and stigmatization as one of the Hindu Dharma sects.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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