Application of the hidden Markov models, kNN and SVM for a classification problem modes of power supply system

2021 ◽

Vol 15 (4) ◽

pp. 0-0

Keyword(s):

Markov Chains ◽

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Classification Problem ◽

Mathematical Tool ◽

Scene Classification ◽

Classification Problems ◽

Object Based ◽

Inference Algorithms

Multiclass classification problems such as document classification, medical diagnosis or scene classification are very challenging to address due to similarities between mutual classes. The use of reliable tools is necessary to get good classification results. This paper addresses the scene classification problem using objects as attributes. The process of classification is modeled by a famous mathematical tool: The Hidden Markov Models. We introduce suitable relations that scale the parameters of the Hidden Markov Model into variables of scene classification. The construction of Hidden Markov Chains is supported with weight measures and sorting functions. Lastly, inference algorithms extract most suitable scene categories from the Discrete Markov Chain. A parallelism approach constructs several Discrete Markov Chains in order to improve the accuracy of the classification process. We provide numerous tests on different datasets and compare classification accuracies with some state of the art methods. The proposed approach distinguishes itself by outperforming the other.

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Predicting Social Unrest Events with Hidden Markov Models Using GDELT

Discrete Dynamics in Nature and Society ◽

10.1155/2017/8180272 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 12

Author(s):

Fengcai Qiao ◽

Pei Li ◽

Xin Zhang ◽

Zhaoyun Ding ◽

Jiajun Cheng ◽

...

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Classification Problem ◽

Sequence Classification ◽

Social Unrest ◽

Event Prediction ◽

The Social ◽

Logistic Regression Method ◽

Burst Patterns

Proactive handling of social unrest events which are common happenings in both democracies and authoritarian regimes requires that the risk of upcoming social unrest event is continuously assessed. Most existing approaches comparatively pay little attention to considering the event development stages. In this paper, we use autocoded events dataset GDELT (Global Data on Events, Location, and Tone) to build a Hidden Markov Models (HMMs) based framework to predict indicators associated with country instability. The framework utilizes the temporal burst patterns in GDELT event streams to uncover the underlying event development mechanics and formulates the social unrest event prediction as a sequence classification problem based on Bayes decision. Extensive experiments with data from five countries in Southeast Asia demonstrate the effectiveness of this framework, which outperforms the logistic regression method by 7% to 27% and the baseline method 34% to 62% for various countries.

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Development of the algorithm for the selection of a wind generator-based autonomous power supply system

Power and Autonomous equipment ◽

10.32464/2618-8716-2019-2-1-8-16 ◽

2019 ◽

Vol 2 (1) ◽

pp. 8-16 ◽

Cited By ~ 1

Author(s):

P. A. Khlyupin ◽

G. N. Ispulaeva

Keyword(s):

Wind Turbines ◽

Power Supply ◽

Alternative Energy ◽

Power Supply System ◽

Supply System ◽

Power Generators ◽

Energy Devices ◽

Wind Generator ◽

Alternative Energy Sources ◽

Selection Of

Introduction: The co-authors provide an overview of the main types of wind turbines and power generators installed into wind energy devices, as well as advanced technological solutions. The co-authors have identified the principal strengths and weaknesses of existing wind power generators, if applied as alternative energy sources. The co-authors have proven the need to develop an algorithm for the selection of a wind generator-based autonomous power supply system in the course of designing windmill farms in Russia. Methods: The co-authors have analyzed several types of wind turbines and power generators. Results and discussions: The algorithm for the selection of a wind generator-based autonomous power supply system is presented as a first approximation. Conclusion: The emerging algorithm enables designers to develop an effective wind generator-based autonomous power supply system.

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