TW200630833A - Method and device using intelligent theory to design heat dissipation opening of computer housing - Google Patents
Method and device using intelligent theory to design heat dissipation opening of computer housingInfo
- Publication number
- TW200630833A TW200630833A TW094105375A TW94105375A TW200630833A TW 200630833 A TW200630833 A TW 200630833A TW 094105375 A TW094105375 A TW 094105375A TW 94105375 A TW94105375 A TW 94105375A TW 200630833 A TW200630833 A TW 200630833A
- Authority
- TW
- Taiwan
- Prior art keywords
- learning
- neural network
- heat dissipation
- dissipation opening
- vectors
- Prior art date
Links
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- Feedback Control In General (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
In the invention, ten attribute vectors for designing heat dissipation opening of computer are used as ten input vectors of a back propagation neural network to respectively correspond to sixteen 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 dissipation opening 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 dissipation opening 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 dissipation opening 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 until not encountering erroneous determination for samples similar to the aforementioned erroneous samples in the subsequent detecting process, thereby increasing the estimation precision for the method and device using intelligent theory to design a heat dissipation opening of computer housing.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW094105375A TW200630833A (en) | 2005-02-23 | 2005-02-23 | Method and device using intelligent theory to design heat dissipation opening of computer housing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TW094105375A TW200630833A (en) | 2005-02-23 | 2005-02-23 | Method and device using intelligent theory to design heat dissipation opening of computer housing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| TW200630833A true TW200630833A (en) | 2006-09-01 |
| TWI306207B TWI306207B (en) | 2009-02-11 |
Family
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| TW094105375A TW200630833A (en) | 2005-02-23 | 2005-02-23 | Method and device using intelligent theory to design heat dissipation opening of computer housing |
Country Status (1)
| Country | Link |
|---|---|
| TW (1) | TW200630833A (en) |
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| US11157441B2 (en) | 2017-07-24 | 2021-10-26 | Tesla, Inc. | Computational array microprocessor system using non-consecutive data formatting |
| US11409692B2 (en) | 2017-07-24 | 2022-08-09 | Tesla, Inc. | Vector computational unit |
| 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 |
| SG11202103493QA (en) | 2018-10-11 | 2021-05-28 | Tesla Inc | 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 |
| US11150664B2 (en) | 2019-02-01 | 2021-10-19 | Tesla, Inc. | Predicting three-dimensional features for autonomous driving |
| 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 TW094105375A patent/TW200630833A/en not_active IP Right Cessation
Also Published As
| Publication number | Publication date |
|---|---|
| TWI306207B (en) | 2009-02-11 |
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