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
- 238000000034 method Methods 0.000 title abstract 14
- 230000017525 heat dissipation Effects 0.000 title abstract 6
- 239000013598 vector Substances 0.000 abstract 10
- 238000013528 artificial neural network Methods 0.000 abstract 8
- 238000006243 chemical reaction Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 abstract 1
- 238000011156 evaluation Methods 0.000 abstract 1
- 238000012854 evaluation process Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 abstract 1
- 230000002452 interceptive effect Effects 0.000 abstract 1
Landscapes
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
- Feedback Control In General (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
ID=45071326
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) |
Families Citing this family (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018125928A1 (en) | 2016-12-29 | 2018-07-05 | DeepScale, Inc. | Multi-channel sensor simulation for autonomous control systems |
| WO2018176000A1 (en) | 2017-03-23 | 2018-09-27 | DeepScale, 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 |
| 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 |
| US10671349B2 (en) | 2017-07-24 | 2020-06-02 | Tesla, Inc. | Accelerated mathematical engine |
| US12307350B2 (en) | 2018-01-04 | 2025-05-20 | Tesla, Inc. | Systems and methods for hardware-based pooling |
| 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 |
| KR20250078625A (en) | 2018-10-11 | 2025-06-02 | 테슬라, 인크. | 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 |
| US11150664B2 (en) | 2019-02-01 | 2021-10-19 | Tesla, Inc. | Predicting three-dimensional features for autonomous driving |
| 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 |
| WO2023023265A1 (en) | 2021-08-19 | 2023-02-23 | Tesla, Inc. | Vision-based system training with simulated content |
| US12462575B2 (en) | 2021-08-19 | 2025-11-04 | Tesla, Inc. | Vision-based machine learning model for autonomous driving with adjustable virtual camera |
-
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 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| TW200630833A (en) | Method and device using intelligent theory to design heat dissipation opening of computer housing | |
| TW200630819A (en) | Method of using intelligent theory to design heat dissipation module and device thereof | |
| TW200630834A (en) | Method and device using intelligent theory to evaluate permeability of heat pipe | |
| CN107977710B (en) | Electricity consumption abnormal data detection method and device | |
| CN114117945B (en) | A deep learning cloud service QoS prediction method based on user-service interaction graph | |
| CN108621159A (en) | A kind of Dynamic Modeling in Robotics method based on deep learning | |
| CN103237320B (en) | Wireless sensor network quantizes based on mixing the method for tracking target that Kalman is merged | |
| CN103955714A (en) | Navy detection model construction method and system and navy detection method | |
| CN115018162A (en) | Method and system for predicting machining quality in industrial finish machining process in real time | |
| CN119783478A (en) | An industrial design automation method based on artificial intelligence big model and intelligent agent | |
| Krithikaa et al. | Differential evolution with an ensemble of low-quality surrogates for expensive optimization problems | |
| CN103364703A (en) | Method for rapidly evaluating reliability of LED (light-emitting diode) product under multi-stress condition | |
| Most et al. | Robust Design Optimization in industrial virtual product development | |
| Singh et al. | Fpga implementation of a trained neural network | |
| CN114251075B (en) | Reservoir exploitation scheme determination method, device and equipment based on multi-target parameters | |
| US20100063946A1 (en) | Method of performing parallel search optimization | |
| Barrault et al. | A new analytical model for the NLMS algorithm | |
| Bader Eddin et al. | A sound insulation prediction model for floor structures in wooden buildings using neural networks approach | |
| CN113433893A (en) | A method for calibrating performance indexes of robot servo system based on backtracking Bayesian | |
| CN104572820B (en) | The generation method and device of model, importance acquisition methods and device | |
| Douglas | Exact expectation analysis of the LMS adaptive filter for correlated Gaussian input data | |
| CN117112449A (en) | Maturity assessment method, device, equipment and medium of data management tool | |
| Wang et al. | Identification of ball and plate system using multiple neural network models | |
| Huppmann et al. | Utilizing Differential Evolution for an Automated Compact Model Parameter Extraction | |
| Lund et al. | Methods for parameter ranking in nonlinear, mechanistic models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| MM4A | Annulment or lapse of patent due to non-payment of fees |