Prediction of OLED Temperature Distribution Based on Neural Network

2021 ◽  
Author(s):  
S. F. Lin ◽  
Paul C.-P. Chao
Keyword(s):  
Neural Network ◽  
Temperature Distribution ◽  
Power Consumption ◽  
Prediction Error ◽  
Prediction Method ◽  
Temperature Sensors ◽  
Temperature Prediction ◽  
Effects Of Temperature ◽  
Model Training ◽  
Error Method

Abstract In recent years, the market share of OLED screens has increased year by year. Compared with traditional LCD screens, the advantages of OLED screens with bright colors and lower power consumption are attributed to the fact that LCD needs a backlight LED as the light source, and the pixels of the OLED screen emit light by themselves, so the power consumption of OLED is reduced and the thickness becomes thinner. In addition to the above reasons, OLED has a special advantage, that is, OLED can be bent, so the use of OLED screens is becoming more and more popular, and has even exceeded LCD screens usage rate. Although OLED has many advantages, it still has some disadvantages that need to be improved. OLED has a short lifetime. After a period of use, the luminance of the OLED will degrade, and the luminance degradation experienced by the different region of OLED screen is different, called differential aging, so it will cause burn-in. There have been some studies on OLED luminance degradation, including research on OLED luminance degradation under different temperature conditions, but they have not discussed the effects of temperature in different areas of a panel. Therefore, this paper will discuss the temperature prediction for different regions of an OLED panel. The temperature prediction of the OLED panel is based on the four temperature sensors installed at the rear of the OLED panel and the picture displayed on the panel at the time. The prediction method is neural network that uses pre-collected data for model training. After the training is completed, the temperature distribution of the OLED panel is predicted based on the four temperature sensors and displayed pictures. The prediction error method uses mean square error, and the error value obtained is less than 0.2°C.

scholarly journals Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Xiliang Ma ◽  
Hua Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Prediction Error ◽  
Prediction Method ◽  
Gas Concentration ◽  
Quantum Genetic Algorithm ◽  
Rescue Robot ◽  
Concentration Prediction ◽  
Mine Rescue

The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway.

2124 Aluminum Alloy Aging Forming Springback Study Based on a Combination Forecasting Method

2014 ◽  
Vol 541-542 ◽  
pp. 277-282
Author(s):  
Zhong Gan ◽  
Zhi Wei Qian ◽  
Yu Shan Xia
Keyword(s):  
Neural Network ◽  
Aluminum Alloy ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Uniform Design ◽  
Prediction Method ◽  
Combination Method ◽  
Forecasting Method ◽  
Bending Radius ◽  
Springback Prediction

This paper proposes a more accurate springback prediction method of ageing forming for 2124 aluminum alloy. In age forming of panels, pre-bending radius, aging time and wall thickness of panels are selected as three parameters, make use of uniform design to arrange experiment and obtain springback radius using ABAQUS simulation. By means of regression analysis, the data is processed to get the influence caused by parameters on springback radius. Regression and BP neural network forecasting method are used respectively to predict springback radius and maximum prediction error is less than 31%. Combination method based on BP neural network is adopted and this method gets the satisfying prediction results that prediction error is within 5%. So conclusion can be drawn that prediction accuracy of combination method is much better than that of regression and BP neural network forecasting.

Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction

2022 ◽  
pp. 147509022110695
Author(s):  
Xiao-qi Zhang ◽  
Si-qi Jiang
Keyword(s):  
Neural Network ◽  
Storm Surge ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Optimization Algorithms ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Optimal Model ◽  
Swarm Optimization ◽  
Error Method

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.

scholarly journals Network security situation prediction based on feature separation and dual attention mechanism

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhijian Li ◽  
Dongmei Zhao ◽  
Xinghua Li ◽  
Hongbin Zhang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Smart Cities ◽  
Prediction Method ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Limited Attention ◽  
Network Attacks ◽  
Model Training ◽  
Overfitting Problem

AbstractWith the development of smart cities, network security has become more and more important. In order to improve the safety of smart cities, a situation prediction method based on feature separation and dual attention mechanism is presented in this paper. Firstly, according to the fact that the intrusion activity is a time series event, recurrent neural network (RNN) or RNN variant is used to stack the model. Then, we propose a feature separation method, which can alleviate the overfitting problem and reduce cost of model training by keeping the dimension unchanged. Finally, limited attention is proposed according to global attention. We sum the outputs of the two attention modules to form a dual attention mechanism, which can improve feature representation. Experiments have proved that compared with other existing prediction algorithms, the method has higher accuracy in network security situation prediction. In other words, the technology can help smart cities predict network attacks more accurately.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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