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Automated Self-trained System of Functional Control and State Detection of Railway Transport Nodes

Bakhytzhan Akhmetov Valeriy Lakhno Ayaulym Oralbekova Zhanat Kaskatayev Gulmira Mussayeva
International Journal of Electronics and Telecommunications | 2019 | vol. 65 | No 3 | 491-496 | DOI: 10.24425/ijet.2019.129804
Słowa kluczowe information intellectual technology of failure detection functional control learning matrix learning algorithm operator effectiveness criterion nodes and aggregates railway transport
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Abstrakt

Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions.

As the main way to improve the efficiency of state detection of the nodes and aggregates of RWT, we see the direction of giving the adaptability property for an automated data processing system from various contactless diagnostic information removal systems. The global purpose can be achieved, in particular, through the use of machine learning methods and failure recognition (recognition objects). In order to improve the operational reliability and service life of the main nodes and aggregates of RWT, there are proposed an appropriate model and algorithm of machine learning of the operator control system of nodes and aggregates. It is proposed to use the Shannon normalized entropy measure and the Kullback-Leibler distance information criterion as a criterion of the learning effectiveness of the automated detection system and operator node state control of RWT. The article describes the application of the proposed method on the example of an automatic detection system (ADS) of the state of a traction motor of an electric locomotive. There are given the test data of the model and algorithm in the MATLAB environment.

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Autorzy i Afiliacje

Bakhytzhan Akhmetov
Valeriy Lakhno
Ayaulym Oralbekova
Zhanat Kaskatayev
Gulmira Mussayeva

Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco

Kaoutar El Azhari Badreddine Abdallaoui Ali Dehbi Abdelaziz Abdalloui Hamid Zineddine
Journal of Water and Land Development | 2022 | No 54 | 13-20 | DOI: 10.24425/jwld.2022.141550
Słowa kluczowe Artificial Neural Network (ANN) learning algorithm multi-layer perceptron (MLP) modelling Rabat- Kenitra relative humidity
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Abstrakt

This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014.
It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg–Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error ( MSE) and a high correlation coefficient ( R), compared to the statistical indicators relating to the other models developed as part of this study.
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Autorzy i Afiliacje

Kaoutar El Azhari
1
ORCID: ORCID
Badreddine Abdallaoui
2
Ali Dehbi
1
ORCID: ORCID
Abdelaziz Abdalloui
1
ORCID: ORCID
Hamid Zineddine
1
  1. Moulay Ismail University, Faculty of Sciences, Zitoune, 50000, Meknes, Morocco
  2. University of Oxford, Mathematical Institute, Oxford, United Kingdom

The development of a meta-learning calibration network for low-cost sensors across domains

Feng Tianliang Xiong Xingchuang Jin Shangzhong
Metrology and Measurement Systems | 2023 | vol. 30 | No 4 | 617-635 | DOI: 10.24425/mms.2023.147957
Słowa kluczowe low-cost sensor array ozone pollutants timing characteristics meta-learning algorithm calibration
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Abstrakt

Low-cost sensor arrays are an economical and efficient solution for large-scale networked monitoring of atmospheric pollutants. These sensors need to be calibrated in situ before use, and existing data-driven calibration models have been widely used, but require large amounts of co-location data with reference stations for training, while performing poorly across domains. To address this problem, a meta-learningbased calibration network for air sensors is proposed, which has been tested on ozone datasets. The tests have proved that it outperforms five other conventional methods in important metrics such as mean absolute error, root mean square error and correlation coefficient. Taking Manlleu and Tona as the source domain and Vic as the target domain, the proposed method reduces MAE and RMSE by 17.06% and 6.71% on average, and improves R2 by an average of 4.21%, compared with the suboptimal pre-trained multi-source transfer calibration. The method can provide a new idea and direction to solve the problem of cross-domain and reliance on a large amount of co-location data in the calibration of sensors.
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Autorzy i Afiliacje

Feng Tianliang
1
Xiong Xingchuang
2
Jin Shangzhong
1
  1. College of Optical and Electronic Technology, China Jiliang University, Hangzhou, Zhejiang 310018, China
  2. National Institute of Metrology, Beijing 100029, China

Self-improving Q-learning based controller for a class of dynamical processes

Jakub Musial Krzysztof Stebel Jacek Czeczot
Archives of Control Sciences | 2021 | vol. 31 | No 3 | 527-551 | DOI: 10.24425/acs.2021.138691
Słowa kluczowe process control Q-learning algorithm reinforcement learning intelligent control on-line learning
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Abstrakt

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.
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Bibliografia

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[2] I.Carlucho, M. De Paula, S.A. Villar and G.G.Acosta: Incremental Qlearning strategy for adaptive PID control of mobile robots. Expert Systems With Applications, 80, (2017), 183–199, DOI: 10.1016/j.eswa.2017.03.002.
[3] K. Delchev: Simulation-based design of monotonically convergent iterative learning control for nonlinear systems. Archives of Control Sciences, 22(4), (2012), 467–480.
[4] M. Jelali: An overview of control performance assessment technology and industrial applications. Control Eng. Pract., 14(5), (2006), 441–466, DOI: 10.1016/j.conengprac.2005.11.005.
[5] M. Jelali: Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance. Springer-Verlag London, (2013)
[6] H.-K. Lam, Q. Shi, B. Xiao, and S.-H. Tsai: Adaptive PID Controller Based on Q-learning Algorithm. CAAI Transactions on Intelligence Technology, 3(4), (2018), 235–244, DOI: 10.1049/trit.2018.1007.
[7] D. Li, L. Qian, Q. Jin, and T. Tan: Reinforcement learning control with adaptive gain for a Saccharomyces cerevisiae fermentation process. Applied Soft Computing, 11, (2011), 4488–4495, DOI: 10.1016/j.asoc.2011.08.022.
[8] M.M. Noel and B.J. Pandian: Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach. Applied Soft Computing, 23, (2014), 444–451, DOI: 10.1016/j.asoc.2014.06.037.
[9] T. Praczyk: Concepts of learning in assembler encoding. Archives of Control Sciences, 18(3), (2008), 323–337.
[10] M.B. Radac and R.E. Precup: Data-driven model-free slip control of antilock braking systems using reinforcement Q-learning. Neurocomputing, 275, (2017), 317–327, DOI: 10.1016/j.neucom.2017.08.036.
[11] A.K. Sadhu and A. Konar: Improving the speed of convergence of multi-agent Q-learning for cooperative task-planning by a robot-team. Robotics and Autonomous Systems, 92, (2017), 66–80, DOI: 10.1016/j.robot.2017.03.003.
[12] N. Sahebjamnia, R. Tavakkoli-Moghaddam, and N. Ghorbani: Designing a fuzzy Q-learning multi-agent quality control system for a continuous chemical production line – A case study. Computers & Industrial Engineering, 93, (2016), 215–226, DOI: 10.1016/j.cie.2016.01.004.
[13] K. Stebel: Practical aspects for the model-free learning control initialization. in Proc. of 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR), Poland, (2015), DOI: 10.1109/MMAR.2015.7283918.
[14] R.S. Sutton and A.G. Barto: Reinforcement learning: An Introduction, MIT Press, (1998)
[15] S. Syafiie, F. Tadeo, and E. Martinez: Softmax and "-greedy policies applied to process control. IFAC Proceedings, 37, (2004), 729–734, DOI: 10.1016/S1474-6670(16)31556-2.
[16] S. Syafiie, F. Tadeo, and E. Martinez: Model-free learning control of neutralization process using reinforcement learning. Engineering Applications of Artificial Intelligence, 20, (2007), 767–782, DOI: 10.1016/j.engappai.2006.10.009.
[17] S. Syafiie, F. Tadeo, and E. Martinez: Learning to control pH processes at multiple time scales: performance assessment in a laboratory plant. Chemical Product and Process Modeling, 2(1), (2007), DOI: 10.2202/1934- 2659.1024.
[18] S. Syafiie, F. Tadeo, E. Martinez, and T. Alvarez: Model-free control based on reinforcement learning for a wastewater treatment problem. Applied Soft Computing, 11, (2011), 73–82, DOI: 10.1016/j.asoc.2009.10.018.
[19] P. Van Overschee and B. De Moor: RAPID: The End of Heuristic PID Tuning. IFAC Proceedings, 33(4), (2000), 595–600, DOI: 10.1016/S1474- 6670(16)38308-8.
[20] M. Wang, G. Bian, and H. Li: A new fuzzy iterative learning control algorithm for single joint manipulator. Archives of Control Sciences, 26(3), (2016), 297–310. DOI: 10.1515/acsc-2016-0017.
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Autorzy i Afiliacje

Jakub Musial
1
Krzysztof Stebel
1
ORCID: ORCID
Jacek Czeczot
1
  1. Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, Department of Automatic Control and Robotics, 44-100 Gliwice, ul. Akademicka 16, Poland

Support vector machine to criminal recidivism prediction

Olha Kovalchuk Ruslan Shevchuk Ludmila Babala Mykhailo Kasianchuk
International Journal of Electronics and Telecommunications | 2024 | vol. 70 | No 3 | 691-967 | DOI: 10.24425/ijet.2024.149598
Słowa kluczowe machine learning algorithm support vector machine risk assessment classification criminal recidivism prediction
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Abstrakt

Internal security of the state is one of the prerequisites for sustainable development. To ensure the public safety and personal security of citizens, it is necessary to develop effective measures to reduce crime and prevent crime in the future. The starting point for the development and practical implementation of an effective strategy to combat crime or prevent certain crimes is criminological forecasting. Individual forecasting is aimed at determining the possibility of committing a crime (crimes) in the future by a certain person or group of persons.
For risk assessment, the following are traditionally used machine learning models. Such models also provide qualitative assessments in the scientific prediction of the likelihood and possibilities of committing a repeat criminal offense. Knowledge gained from the application of machine learning algorithm, can provide justice authorities with anticipatory information that is essential for developing a general concept of combating crime. The development of applied models for crime analysis and forecasting can become a reliable tool to support decision-making in predicting likely criminal behavior in the future and ensuring the internal security of the state. In this paper, the results of the application are presented by the machine-learning algorithms Support Vector Machine (SVM) for assessment of the risk of recidivism of criminal offenses by persons who have already been convicted of such a crime in the past. The data set consisted of the 12,000 criminal defendants’ criminal profile information in Ukraine. The constructed classifier has a high precision (98.67%), recall (97.53%) and is qualitative (AUC is equal 0.981). The created SVM model can be applied to new data set to predict the risk of reoffending by convicted individuals in the future.
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Autorzy i Afiliacje

Olha Kovalchuk
1
Ruslan Shevchuk
2
Ludmila Babala
1
Mykhailo Kasianchuk
1
  1. West Ukrainian National University
  2. University of Bielsko-Biala and West Ukrainian National University

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