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

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

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.

Electronic Equipment Combination Fault Prediction Technology Research Based on LSSVM-HMM

2014 ◽  
Vol 687-691 ◽  
pp. 978-983
Author(s):  
Yan Ping Tian ◽  
Xiao Hui Ye ◽  
Ming Yin
Keyword(s):  
Failure Prediction ◽  
Prediction Method ◽  
Electronic Equipment ◽  
Condition Based Maintenance ◽  
Fault Prediction ◽  
Support Vector ◽  
Technology Research ◽  
State Prediction ◽  
Fault State ◽  
Fault Information

In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.

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.

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