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Modeling And Optimization Of Bidirectional Dual... __HOT__

The central part of the proposed energy storage system is the interface between the low-voltage grid and the battery (line-battery interface, abbreviated as LiBIF). This work focuses mostly on the design, modeling and optimization of this power electronic interface.

Modeling and Optimization of Bidirectional Dual...

An overview of a development process of a bidirectional low-voltage AC/DC converter for home energy storage has been presented. The AC/DC converter topology was selected, modeled and optimized with lowest overall losses in mind. The loss model of the Dual Active Bridge converter has been extended to include total converter losses. Also, the optimization procedure was preformed with the most general modulation Dual Active Bridge modulation strategy (TPS + FM). The results have been presented in Fig. 4.

COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.

Constructing efficiently expressed and well-balanced pathways is paramount for harnessing biology to its full industrial potential. Here, using the natural histone BDPs of P. pastoris as template, we combined multiple engineering strategies, including truncation and MDP bidirectionalization, to develop a library of sBDPs with a broad range of expression levels and ratios and with different regulation profiles. We found that this library not only covers diverse expression profiles but also is highly efficient in terms of the expression output. Even more, we demonstrated its utility for multi-gene pathway optimization, highlighted by simple optimization experiments for taxadiene and β-carotene production. By screening of our large 168 member library, we identified a subset of highly useful BDPs and compiled a minimal set of 12 BDPs (6 BDPs to be tested in both orientations, Table 1 and Supplementary Data 3 for annotated sequence files). These promoters have regulatory diversity, different strengths, and ratios. In addition, this subset offers extended diversity if cultivated with different carbon sources (glucose/glycerol, methanol). Screening with this initial set provides a foundation for subsequent fine-tuning.

Based on the abovementioned analysis, it can be found that no matter what the model predictive control method may be, it mainly focuses on path tracking, power system, and intelligent control, but it is rarely involved in the field of process industry production scheduling. Therefore, from the perspective of process production scheduling, this paper proposes for the first time a multiobjective lot sizing and scheduling integrated optimization model for multiproduct switching production in the process industry after comprehensively considering the bidirectional uncertainty of market demand and production ability. The neural network-based model predictive control method obtains the optimal decision to minimize the total completion time and switching cost. Among them, during the implementation of model predictive control, the neural network realizes the prediction of uncertain variables, and the heuristic algorithm can solve the constructed multiobjective optimization model.

Different from previous studies, the innovation of this paper is mainly reflected in the following aspects: (1) applying the neural network-based model predictive control method to the production scheduling direction of the process industry; (2) when designing the process industry lot sizing and scheduling integrated optimization model, it can comprehensively consider the bidirectional uncertainty of market demand and production ability; (3) in the process of process industry production, not only the intermediate inventory caused by the material flow in the material network is considered but also the lots in the manufacturing process are considered; and (4) the Elman neural network, the optimization model, and the IMOPSO algorithm are combined to complete the model predictive control so that the process industry can respond to uncertain situations in advance and can formulate a corresponding production scheduling scheme.

It can be seen from the abovementioned analysis that the prediction link of the model predictive control is realized by the Elman neural network. After the Elman neural network learns from historical orders, it can continuously train the network based on the minimum cumulative prediction error and effectively predict the bidirectional uncertain market demand and the production ability to output the product demand variables and the parameter variables related to the failure rate calculation. The optimization model is input, and the IMOPSO algorithm is used to solve the model, and the optimal decision-making scheme considering the foreseeable disturbance is obtained based on the minimum change and is sent back to the system. So far, the model predictive control for integrated optimization of lot and scheduling in the process industry with bidirectional uncertainties has been formed, and its specific implementation process is shown in Figure 2:

Through model predictive control, the production system can react in advance and make appropriate changes to offset foreseeable disturbances, resulting in lot sizing and scheduling schemes that consider bidirectional uncertainty, improving the overall robustness of the system. For the process industry, the realization of the model predictive control is beneficial for enterprises to deal with various environments of uncertainty and is conducive to the process industry for further optimization of the lot sizing and scheduling of multiproduct production. In future work, we also need to introduce a more effective heuristic algorithm and integrate it into the Elman neural network to obtain a more efficient neural network learning mechanism.

Normalizing flow models have been used successfully for generative image super-resolution (SR) by approximating complex distribution of natural images to simple tractable distribution in latent space through Invertible Neural Networks (INN). These models can generate multiple realistic SR images from one low-resolution (LR) input using randomly sampled points in the latent space, simulating the ill-posed nature of image upscaling where multiple high-resolution (HR) images correspond to the same LR. Lately, the invertible process in INN has also been used successfully by bidirectional image rescaling models like IRN and HCFlow for joint optimization of downscaling and inverse upscaling, resulting in significant improvements in upscaled image quality. While they are optimized for image downscaling too, the ill-posed nature of image downscaling, where one HR image could be downsized to multiple LR images depending on different interpolation kernels and resampling methods, is not considered. A new downscaling latent variable, in addition to the original one representing uncertainties in image upscaling, is introduced to model variations in the image downscaling process. This dual latent variable enhancement is applicable to different image rescaling models and it is shown in extensive experiments that it can improve image upscaling accuracy consistently without sacrificing image quality in downscaled LR images. It is also shown to be effective in enhancing other INN-based models for image restoration applications like image hiding.

Ghosh et al. presented a comparative analysis of one-way and bidirectional communication on RF-EH relay in CR networks [20]. The experiment results indicate that hybrid power time-switching relaying (HPTSR) performs better than power splitting relay (PSR) around \(35\%\). In [21], Sabuj and Hamamura illustrated the performance of RF energy harvesting in a random CR network where transmitter and receiver are deployed randomly. It is has been shown that outage probability is inversely proportional with transmission power, and harvested DC power improves with higher transmission power. They recommended RF-EH model as a good alternative for the longevity of wired battery devices. The authors in [22] studied particle swarm optimization algorithm for EH-based hybrid SWIPT CR network with bidirectional communication. Their primary objective was to increase the total system throughput and maximum energy efficiency of the network. Furthermore, in [23], authors studied the energy efficiency optimization model for bidirectional energy harvesting sensor networks. They introduced fractional programming and alternative search methods to achieve high transmission power and resolve the power constraint problem of relay and sensor nodes. 041b061a72

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