An analytical temperature prediction method for a chip power map

2005 ◽  
Author(s):  
K.K. Sikka
Keyword(s):  
Prediction Method ◽  
Temperature Prediction

scholarly journals A Top-oil Temperature Prediction Method for the Split Transformer Based on PSOGSA

2018 ◽  
Vol 366 ◽  
pp. 012060
Author(s):  
Zi Xin ◽  
Bengang Wei ◽  
Yongliang Liang ◽  
Yanshun Xu ◽  
Ruochen Guo ◽  
...  
Keyword(s):  
Prediction Method ◽  
Temperature Prediction

Rapid temperature prediction method for electronic equipment cabin

2018 ◽  
Vol 138 ◽  
pp. 83-93 ◽  
Author(s):  
Hongquan Qu ◽  
Shuo Fu ◽  
Liping Pang ◽  
Chen Ding ◽  
Helin Zhang
Keyword(s):  
Prediction Method ◽  
Electronic Equipment ◽  
Temperature Prediction ◽  
Rapid Temperature

Intensity Based Simulation of the Temperature Prediction in the Focal Region of Liver Using MRI-Guided High Intensity Focused Ultrasound (HIFU)

2016 ◽  
Vol 13 (10) ◽  
pp. 6728-6732
Author(s):  
P Revathy ◽  
V Sadasivam ◽  
T. Ajith Bosco Raj
Keyword(s):  
Focused Ultrasound ◽  
Treatment Time ◽  
Prediction Method ◽  
Treatment Temperature ◽  
High Intensity Focused Ultrasound ◽  
Bioheat Transfer ◽  
Transfer Model ◽  
Focal Region ◽  
Temperature Elevation ◽  
Temperature Prediction

In this research paper a new temperature prediction method is proposed to predict the temperature in liver during thermal ablation which also takes in to account the blood flow cooling. The proposed method suggest a modification of Pennes bioheat transfer equation (PBHTE) inorder to more accurately predict the treatment temperature. The temperature elevation by the proposed heat transfer model is compared with the PBHTE model and the other two heat continuum models by Wulff and Klinger. Appropriate temperature prediction is useful in treatment planning. This may reduce the recurrence level of cancer. Further the reduction in treatment time increases patient safety.

scholarly journals Seawater Temperature Prediction Method for Sustainable Marine Aquaculture

2017 ◽  
Author(s):  
Takanobu Otsuka ◽  
Yuji Kitazawa ◽  
Takayuki Ito
Keyword(s):  
Seawater Temperature ◽  
Prediction Method ◽  
Value Added ◽  
Temperature Data ◽  
Weather Data ◽  
Marine Resources ◽  
Temperature Prediction ◽  
Marine Aquaculture ◽  
Value Added Products

Aquaculture is growing ever more important due to the decrease in natural marine resources and increase inworldwide demand. To avoid losses due to aging and abnormalweather, it is important to predict seawater temperature in order to maintain a more stable supply, particularly for high value added products, such as pearls and scallops. The increase in species extinction is a prominent societal issue. Furthermore, in order to maintain a stable quality of farmed fishery, water temperature should be measured daily and farming methods altered according to seasonal stresses. In this paper, we propose an algorithm to estimate seawater temperature in marine aquaculture by combining seawater temperature data and actual weather data.

A Barn Temperature Prediction Method Based on MATLAB

2014 ◽  
Vol 543-547 ◽  
pp. 1206-1210
Author(s):  
De Hui Zhang ◽  
Xiao Qiang Wu ◽  
Chun You Zhang
Keyword(s):  
Prediction Method ◽  
Temperature Monitoring ◽  
Acquisition Time ◽  
Time Interval ◽  
Prediction Formula ◽  
Cattle Breeding ◽  
Temperature Prediction ◽  
Monitoring Point ◽  
Cattle Feeding ◽  
The One

In the Inner Mongolia beef cattle feeding, barn temperature is an important parameter. Barn temperature has an important impact on cattle breeding and beef production. In order to ensure that there is appropriate temperatures barn, data recorded in the barn a month temperature monitoring points, the acquisition time for each temperature monitoring point for the one-hour time interval. Using MATLAB software barn temperature data were analyzed, the data fit (least squares) and plotted, and finally get a barn temperature prediction formula. And use this formula to predict the temperature of the barn, forecasting results show that the design is reasonable, the error is small, can be applied in practice.

scholarly journals A Method for Greenhouse Temperature Prediction Based on XGBoost Algorithm and Linear Residual Model

CONVERTER ◽  
2021 ◽  
pp. 108-121
Author(s):  
Huijin Han, Et al.
Keyword(s):  
Mean Squared Error ◽  
Prediction Method ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Small Scale ◽  
Temperature Prediction ◽  
Precise Control ◽  
Squared Error ◽  
Vector Machines ◽  
Better Than

Temperature prediction is significant for precise control of the greenhouse environment. Traditional machine learning methods usually rely on a large amount of data. Therefore, it is difficult to make a stable and accurate prediction based on a small amount of data. This paper proposes a temperature prediction method for greenhouses. With the prediction target transformed to the logarithmic difference of temperature inside and outside the greenhouse,the method first uses XGBoost algorithm to make a preliminary prediction. Second, a linear model is used to predict the residuals of the predicted target. The predicted temperature is obtained combining the preliminary prediction and the residuals. Based on the 20-day greenhouse data, the results show that the target transformation applied in our method is better than the others presented in the paper. The MSE (Mean Squared Error) of our method is 0.0844, which is respectively 20.7%, 76.0%, 10.2%, and 95.3% of the MSE of LR (Logistic Regression), SGD (Stochastic Gradient Descent), SVM (Support Vector Machines), and XGBoost algorithm. The results indicate that our method significantly improves the accuracy of the prediction based on the small-scale data.

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