scholarly journals Electric Window Regulator Based on Intelligent Control

2021 ◽  
Author(s):  
Yang Xu ◽  
Yun Li ◽  
Chao Li
Keyword(s):  
Intelligent Control ◽  
Learning Algorithm ◽  
Control Function ◽  
Motor Current ◽  
Speed Regulation ◽  
Window Opening ◽  
Regulation Method ◽  
Window Regulator ◽  
The Stability ◽  
Opening And Closing

In order to effectively solve the problem of installation cost of automobile electric windows and the safety of passengers, the window regulator of the car must have an intelligent control function. For example, most automobile windows now have an anti-pinch function. In this paper, the model of DC brushed motor is analyzed, an intelligent control scheme for automotive power windows is proposed, and the relationship between current ripple and window travel, motor current and external resistance are verified. In the hardware design, S9S12G128 is the main control chip, and the motor current acquisition method is designed. In the software design, intelligent control methods such as current integration method, adaptive and self-learning algorithm and intelligent speed regulation method are proposed to realize functions such as automatic window opening and closing, intelligent anti-pinch and intelligent speed regulation. After many experiments, the results prove the feasibility of the above methods and the stability of the system.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

The Application of the Vector Control in Centrifuge

2012 ◽  
Vol 490-495 ◽  
pp. 2937-2941
Author(s):  
Feng Ou ◽  
Hong Chen ◽  
Xin Xiong
Keyword(s):  
Control System ◽  
Vector Control ◽  
Regulation System ◽  
Control Technology ◽  
Speed Regulation ◽  
Regulation Method ◽  
Very High

In order to ensure the stably operation for centrifuge, this paper presents a new speed regulation method based on the vector control technology, and designs the control system with the inverter for the centrifuge. The paper introduces the fundamental principal of vector control, and analyses the power and torque required for the centrifuge from the result of the calculation and simulation. At last, the paper shows the result the application of the vector control technology in centrifuge. The result shows that the vector control speed regulation system is simple, reliable, and its acceleration stability is very high. The analysis can also provide a reference for similar centrifuge design personnel

scholarly journals Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3276
Author(s):  
Szymon Szczęsny ◽  
Damian Huderek ◽  
Łukasz Przyborowski
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Waveform Analysis ◽  
Spiking Neural Network ◽  
Synaptic Connections ◽  
Practical Application ◽  
Accuracy Parameter ◽  
The Stability ◽  
Time Waveform ◽  
Network Mapping

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

A novel reinforcement learning method for improving occupant comfort via window opening and closing

2020 ◽  
Vol 61 ◽  
pp. 102247 ◽  
Author(s):  
Mengjie Han ◽  
Ross May ◽  
Xingxing Zhang ◽  
Xinru Wang ◽  
Song Pan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Learning Method ◽  
Window Opening ◽  
Occupant Comfort ◽  
Opening And Closing

scholarly journals APPLICATION OF LEARNING REINFORCEMENT METHOD IN ROBOTIZED AND AUTOMATED FORESTRY SYSTEMS

2020 ◽  
Vol 10 (1) ◽  
pp. 256-265
Author(s):  
Andrey Tolstyh ◽  
D Stupnikov ◽  
Sergey Malyukov ◽  
Aleksandr Luk'yanov ◽  
Yuriy Lunev
Keyword(s):  
Physical Environment ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Industrial Robots ◽  
Environment Model ◽  
Random Position ◽  
Learning Reinforcement ◽  
The Stability ◽  
Two Stages ◽  
The Cost

Abstract Currently, most large enterprises are actively using industrial robots and other automated solutions. This allows a significant increase in productivity and quality of work performed. This article gave a brief overview of modern industrial robots, their operating principle, basic components and systems. A reinforcement learning algorithm was developed and tested. The task of constructing a learning algorithm with reinforcement was divided into two stages: modeling the environment and description and optimization of the cost function. Since industrial robotic systems operate in the real world, the environment model should reflect basic physical laws. Therefore, the pyBullet library of the physical environment was chosen as the physical environment for testing. After modeling the manipulator in the selected physical medium, it was given the trivial task of touching a given object with the capture of the manipulator. An artificial neural network was used as an agent interacting with the environment. The inputs were the coordinates of the object and the existing angles of rotation of the articulated joints of the robot. Outputs - angle of rotation of joints at this step. This network was trained using the back propagation method, Adam modification. The system was trained for about 12 hours. Success is achieved in 95% of cases when testing the stability of the system (random position of the cylinder). In future, it is planned to test the obtained models on bench samples

scholarly journals A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Pengbo Zhang ◽  
Zhixin Yang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Approximation Accuracy ◽  
Weak Learner ◽  
Fast Learning ◽  
Learning Speed ◽  
Regression Problems ◽  
Learning Machine ◽  
The Stability ◽  
Stability And Accuracy

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.

Design of Light Control Curtain System in Smart Home

2014 ◽  
Vol 701-702 ◽  
pp. 1243-1246
Author(s):  
Jiu Hong Yang
Keyword(s):  
Automatic Control ◽  
Smart Home ◽  
Control Function ◽  
Basic Function ◽  
Frame Structure ◽  
Daily Lives ◽  
Control Programs ◽  
Position Detection ◽  
Light Sensor ◽  
Opening And Closing

To make a building structure beautiful and obtain a good daylighting, the windows become more and more big. It is inconvenience for the opening and closing of the bigger curtain. The frame structure of automatic control curtain is designed and manufactured, as well as DC motor drive, light detection, preventing over-wind, curtain position detection circuit. The corresponding control programs realize the automatic control function of curtain. Automatic curtain system contains three kinds of mode. Manual mode is the basic function of automatic curtain system. The wireless remote control function provides convenience for the user to control the opening and closing of curtain. Automatic light sensor model realizes the automatic control of curtain, according to the brightness of the light. The sensitivity of light sensor can be adjusted by artificial to make the customer satisfied. The open degree of curtain can be manual adjusted to meet the requirements of the various length of the curtain. The system is simple and practical. Its energy consumption and cost is low. As a part of the smart home, the system is suitable for opening and closing control of the curtains and provides the convenience for people's daily lives.

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