In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.
The H264/SVC codec allows for generation of hierarchical video streams. In the stream of this type video data belonging to different layers have different priority depending on their importance to the quality of the video and the decoding process. This creates new demands on the mechanisms of packet marking, and thus new challenges for the policy guaranteeing QoS parameters, such as those defined in the DiffServ architecture. Therefore, mechanisms of the traffic engineering used in the DiffServ network should, as far as possible, take into account internal distribution of priorities inside video streams. This may be achieved by implementing an appropriate method for packet pre-marking. The paper describes the Weighted Priority Pre-marking (WPP) algorithm for priority-aware SVC video streaming over a DiffServ network. Our solution takes into account the relative importance of the Network Abstraction Layer Units. It also does not require any changes in the implementation of the DiffServ marker algorithm. The results presented confirm that video transmission in the DiffServ domain, based on the WPP packet pre-marking, can provide better perceived video quality than the standard (best effort) streaming of multilayered SVC video. In addition, a comparison with the transmission of the same video content encoded with the H264/AVC codec also points to the superiority of our proposed method.
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