TW200630834A - Method and device using intelligent theory to evaluate permeability of heat pipe - Google Patents
Method and device using intelligent theory to evaluate permeability of heat pipeInfo
- Publication number
- TW200630834A TW200630834A TW094105392A TW94105392A TW200630834A TW 200630834 A TW200630834 A TW 200630834A TW 094105392 A TW094105392 A TW 094105392A TW 94105392 A TW94105392 A TW 94105392A TW 200630834 A TW200630834 A TW 200630834A
- Authority
- TW
- Taiwan
- Prior art keywords
- learning
- neural network
- heat pipe
- vectors
- permeability
- Prior art date
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- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
In the invention, thirty-one attribute vectors for evaluating permeability of heat pipe are used as thirty input vectors of a back propagation neural network to respectively correspond to one output vector and then sequentially perform learning and order-descending process, detecting process and re-learning process corresponding to the eleven input vectors respectively. In the learning and order-descending process, K-L expansion method is employed to convert the attribute vectors of designed parameters of the heat pipe permeability onto the orthogonal main axes for preventing the attribute vectors from interfering with each other, and determine the minimum number of main axis vectors required for maintaining the estimation precision, thereby reducing the estimation complexity of the neural network. Further, the neural network uses the known input values and output values of the training samples (that is, the attribute vectors of the training samples and the corresponding design rules of the heat pipe permeability in the learning sample database) to adjust the weight of each node so as to obtain a minimum error between the output value of the neural network and the actual output value of the sample, which is used as a target function to optimize the bonding value of each node thereby increasing the estimation precision of neural network. When the learning and order-descending process is completed, the weight of each node is fixed to facilitate estimation in the detecting process. In the detecting process, the attribute vectors of the sample under detection are processed by K-L expansion method for performing main axis conversion and order descending, and the order-descended axes are used as input vectors to perform heat pipe permeability design and evaluation via the neural network. If there are erroneous samples in the evaluation process (wherein the erroneous sample represents a sample with an error actually evaluated by the neural network which is larger than a tolerance value), the erroneous samples are stored in the learning sample database to facilitate obtaining data for re-learning. In the re-learning process, with the erroneous samples added into the learning sample database, the K-L expansion method re-adjusts the orientations of the main axes and the neural network adjusts the weight of each node, thereby increasing the estimation precision for the method and device using intelligent theory to evaluate permeability of heat pipe.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW094105392A TW200630834A (en) | 2005-02-23 | 2005-02-23 | Method and device using intelligent theory to evaluate permeability of heat pipe |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW094105392A TW200630834A (en) | 2005-02-23 | 2005-02-23 | Method and device using intelligent theory to evaluate permeability of heat pipe |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| TW200630834A true TW200630834A (en) | 2006-09-01 |
| TWI294089B TWI294089B (en) | 2008-03-01 |
Family
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| TW094105392A TW200630834A (en) | 2005-02-23 | 2005-02-23 | Method and device using intelligent theory to evaluate permeability of heat pipe |
Country Status (1)
| Country | Link |
|---|---|
| TW (1) | TW200630834A (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10678244B2 (en) | 2017-03-23 | 2020-06-09 | Tesla, Inc. | Data synthesis for autonomous control systems |
| US11893393B2 (en) | 2017-07-24 | 2024-02-06 | Tesla, Inc. | Computational array microprocessor system with hardware arbiter managing memory requests |
| US11409692B2 (en) | 2017-07-24 | 2022-08-09 | Tesla, Inc. | Vector computational unit |
| US11157441B2 (en) | 2017-07-24 | 2021-10-26 | Tesla, Inc. | Computational array microprocessor system using non-consecutive data formatting |
| US10671349B2 (en) | 2017-07-24 | 2020-06-02 | Tesla, Inc. | Accelerated mathematical engine |
| US11561791B2 (en) | 2018-02-01 | 2023-01-24 | Tesla, Inc. | Vector computational unit receiving data elements in parallel from a last row of a computational array |
| US11215999B2 (en) | 2018-06-20 | 2022-01-04 | Tesla, Inc. | Data pipeline and deep learning system for autonomous driving |
| US11361457B2 (en) | 2018-07-20 | 2022-06-14 | Tesla, Inc. | Annotation cross-labeling for autonomous control systems |
| US11636333B2 (en) | 2018-07-26 | 2023-04-25 | Tesla, Inc. | Optimizing neural network structures for embedded systems |
| US11562231B2 (en) | 2018-09-03 | 2023-01-24 | Tesla, Inc. | Neural networks for embedded devices |
| CA3115784A1 (en) | 2018-10-11 | 2020-04-16 | Matthew John COOPER | Systems and methods for training machine models with augmented data |
| US11196678B2 (en) | 2018-10-25 | 2021-12-07 | Tesla, Inc. | QOS manager for system on a chip communications |
| US11816585B2 (en) | 2018-12-03 | 2023-11-14 | Tesla, Inc. | Machine learning models operating at different frequencies for autonomous vehicles |
| US11537811B2 (en) | 2018-12-04 | 2022-12-27 | Tesla, Inc. | Enhanced object detection for autonomous vehicles based on field view |
| US11610117B2 (en) | 2018-12-27 | 2023-03-21 | Tesla, Inc. | System and method for adapting a neural network model on a hardware platform |
| US10997461B2 (en) | 2019-02-01 | 2021-05-04 | Tesla, Inc. | Generating ground truth for machine learning from time series elements |
| US11567514B2 (en) | 2019-02-11 | 2023-01-31 | Tesla, Inc. | Autonomous and user controlled vehicle summon to a target |
| US10956755B2 (en) | 2019-02-19 | 2021-03-23 | Tesla, Inc. | Estimating object properties using visual image data |
-
2005
- 2005-02-23 TW TW094105392A patent/TW200630834A/en unknown
Also Published As
| Publication number | Publication date |
|---|---|
| TWI294089B (en) | 2008-03-01 |
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