Air flow has significant effects on fuel consumption, performance, and comfort. Decreasing drag coefficient enhances fuel consumption and vehicle performance. Moreover, omitting or reducing the power of aerodynamic noise sources provides passengers comfort. In this paper, optimization of a hatchback rear end is conducted considering drag and aerodynamic noise objectives. To this end, five geometrical parameters of the hatchback rear end are chosen as design variables in two levels. Numerical simulation is applied to survey air flow features around the models in the wind tunnel. To reduce the number of runs, fraction factorial design algorithm is applied to generate layout of the simulations which decreased the number of case studies to half. Main and interaction effects of these factors on drag coefficient and acoustic power of the rear end source are derived using analysis of variance. Optimum level for each parameter is chosen considering simultaneous drag and noise goals. Finally, characteristics of air flow and acoustic power around optimum model are discussed.
Congestion is one of the most important challenges in optical networks. In a Passive Optical Network (PON), the Optical Line Terminal (OLT) is a bottleneck and congestion prone. In this paper, a framework is proposed with Forward Error Correction (FEC) at the IP layer combined with Weighted Round Robin (WRR) at the scheduling level to overcome packet-loss due to congestion in the OLT in order to achieve efficient video multicasting over PON. In the FEC scheme, Reed-Solomon (RS(n,k)) with erasure coding is used, where (n−k) erroneous symbols per n symbol blocks can be corrected. In our framework, an Internet Protocol TeleVision (IPTV) service provider uses the mentioned RS coding and generates redundant packets from regular IPTV packets in such a way that an Optical Network Unit (ONU) can recover lost packets from received packets, thus resulting in a better video quality. Simulation results show that using the proposed framework, an ONU can recover many lost packets and achieve better video quality under different traffic loads for its users. For instance, the proposed method can reduce packet loss rate by almost 55% and 10% under traffic load 0.9, respectively, compared with the Round Robin (RR) and WRR methods under symmetric traffic load. When High Receivers Queue (HRQ) traffic (i.e., traffic received by many users) is twice Low Receivers Queue (LRQ) traffic (i.e., traffic received by a small number of users), this reduction is almost 86% and 30% under traffic load 0.9. Finally, when LRQ traffic is twice HRQ traffic, the reduction in packet loss rate is almost 70% and 91% at traffic load 0.5. 相似文献
Journal of Inorganic and Organometallic Polymers and Materials - The original version of this article unfortunately contained mistakes. In line 9 of the abstract, 5% should read as 2%. The... 相似文献
The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition. 相似文献
This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm. 相似文献
The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall. 相似文献
This study proposes a novel combined primary and secondary control approach for direct current microgrids, specifically in islanded mode. In primary control, this approach establishes an appropriate load power sharing between the distributed energy resources based on their rated power. Simultaneously, it considers the load voltage deviation and provides satisfactory voltage regulation in the secondary control loop. The proposed primary control is based on an efficient droop mechanism that only deploys the local variable measurements, so as to overcome the side effects caused by communication delays. In the case of secondary control, two different methods are devised. In the first, low bandwidth communication links are used to establish the minimum required data transfer between the converters. The effect of communication delay is further explored. The second method excludes any communication link and only uses local variables. Accordingly, a self-sufficient control loop is devised without any communication requirement. The proposed control notions are investigated in MATLAB/Simulink platform to highlight system performance. The results demonstrate that both proposed approaches can effectively compensate for the voltage deviation due to the primary control task. Detailed comparisons of the two methods are also provided. 相似文献