Climatic Temperature Data Forecasting in Nineveh Governorate Using the Recurrent Neutral Network Method

Based on remote sensing image, the spectral information of urban Greenbelt in Guangzhou City was extracted from TM image by ENVI4.7. After finishing image preprocessing, then used 4 methods (such as principal component analysis, tasseled cap transformation, the normalized difference vegetation index(NDVI) method, SOFM artifical neutral network method) extract greenbelt information of Guangzhou City, and compared the images produced by four methods, according to the actual situation of the study area, we find that SOFM neutral network has the best classification effect.

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A state-of-art method for solar irradiance forecast via using fisheye lens

International Journal of Low-Carbon Technologies ◽

10.1093/ijlct/ctaa087 ◽

2020 ◽

Author(s):

Lei Chen ◽

Yangluxi Li

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Solar Irradiance ◽

Weather Condition ◽

Neutral Network ◽

Fisheye Lens ◽

Network Method ◽

Forecast Function ◽

State Of Art ◽

Irradiance Forecast

Abstract The purpose of this investigation is to enable the solar irradiance forecast function implementing a common camera devise instead of specialized instrument thereby serve for other researches. Development of various simulated tools requires higher accuracy surrounding weather condition data. Previous studies mainly focus on the improvement of precision for professional monitor equipment i.e. total sky imager, which is limited to the scope of users. In this research, a fisheye lens graph is rectified following a particular algorithm based on the image forming principle. Moreover, solar irradiance prediction adopts the advanced BP neutral network method being proved to be valid. Final results indicate that after rectifying the special perspective images under fisheye direction, colour threshold configuration could remarkably recognize the cloud image. The conclusion shows that common camera fisheye lens coupled with BP neural network successfully predict the solar irradiance.

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Vibration Control of Cylindrical Shell Panel With Shape Memory Alloy Actuators

Volume 8: 22nd Reliability, Stress Analysis, and Failure Prevention Conference; 25th Conference on Mechanical Vibration and Noise ◽

10.1115/detc2013-12788 ◽

2013 ◽

Cited By ~ 3

Author(s):

Huiyu Li ◽

Hua Li ◽

H. S. Tzou

Keyword(s):

Neural Network ◽

Cylindrical Shell ◽

Shape Memory Alloy ◽

Shape Memory ◽

Vibration Control ◽

Neutral Network ◽

Sma Actuators ◽

The Neural Network ◽

Network Method ◽

Shell Panel

This paper studies the vibration control of cylindrical shell panel. Shape memory alloy (SMA) wires are adopted as actuators and neural network training method is applied to enable SMA to output desired force. The proposed SMA actuators are fixed on the cylindrical shell panel and generate contracting forces while be heated electronically. The SMA actuators need to generate forces opposite to the external loading, and thus they can suppress the effect of external force. However, as SMAs present nonlinear relationship between temperature and force, it is difficult to output the desired stress profile. In this research, the hysteresis characteristics of SMAs are fitted by neural networks. The neural network model of SMA plant model is established based on the temperature input and force output; and the inverse model is generated with the force as input and temperature as output. The desired forces are obtained based on these neural network models. To validate the effectiveness of SMA actuators, the vibration response of cylindrical shell panel is analyzed with modal expansion method. The modal response under the controlling of SMA actuators is calculated based on the modal dynamic equation. The results show that SMA actuators controlled by the neural network method are effective to suppress the vibration of cylindrical panel shell. Primary experiments were performed to verify the proposed neutral network method. The results show that the SMA wire actuator generated desired force profile while heating using the neutral network method.

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