Discover the Secrets of Electrical Machine Design By Mittal Pdf Download

To obtain well-trained models that can still be employed productively, DL models have intensive memory and computational requirements due to their huge complexity and large numbers of parameters [193, 194]. One of the fields that is characterized as data-intensive is the field of healthcare and environmental science. These needs reduce the deployment of DL in limited computational-power machines, mainly in the healthcare field. The numerous methods of assessing human health and the data heterogeneity have become far more complicated and vastly larger in size [195]; thus, the issue requires additional computation [196]. Furthermore, novel hardware-based parallel processing solutions such as FPGAs and GPUs [197,198,199] have been developed to solve the computation issues associated with DL. Recently, numerous techniques for compressing the DL models, designed to decrease the computational issues of the models from the starting point, have also been introduced. These techniques can be classified into four classes. In the first class, the redundant parameters (which have no significant impact on model performance) are reduced. This class, which includes the famous deep compression method, is called parameter pruning [200]. In the second class, the larger model uses its distilled knowledge to train a more compact model; thus, it is called knowledge distillation [201, 202]. In the third class, compact convolution filters are used to reduce the number of parameters [203]. In the final class, the information parameters are estimated for preservation using low-rank factorization [204]. For model compression, these classes represent the most representative techniques. In [193], it has been provided a more comprehensive discussion about the topic.

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Electrical Machine Design By Mittal Pdf Download

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There are three cores of digital twin in intelligent design-manufacturing-maintenance, as shown in Figure 1, mainly including the design of digital models, the construction of physical models and the techniques for fusing digital and physical models, which should be compatible with each other. The construction of a physical model includes program design, program execution and data feedback. Design-manufacturing-maintenance involves cross-industry, cross-platform, human-machine interaction and collaboration, and machine-machine interaction and collaboration throughout the product lifecycle. As such, digital twin technology plays a key role in driving the development of industrial clustering.

For intelligent design, digital twin needs to be combined with next generation of information technology, such as machine learning, deep learning, cloud computing and big data [123]. Machine learning can be the result of a digital twin simulation with simple learning capabilities. Cloud computing can provide digital twin with multi-dimensional data computing technology and cloud data storage technology. Integrating cloud technology in digital twin can effectively reduce the computation time of complex systems and overcome the difficulties of storing large amounts of data [124], as shown in Figure 6. In this regard, manufacturing companies such as General Electric, Siemens and Tesla have already started to create application scenarios that enrich the digital twin through next-generation information technology [102].

Cells in an organism are exposed to many environmental cues,includingchemical and biomolecular species, mechanical stress, and bioelectricalsignals. The molecular machinery of biological cells allows them tosense these cues and dynamically modify their properties accordingto the environment. In the adaptive immune system, for example, antigenexposure leads to the activation of chemical signaling pathways thatdetermine T-cell and B-cell migration, differentiation, and proliferation.Additionally, epigenetic reprogramming prepares the cell to reactfaster and stronger to reinfection, while genetic recombination ofreceptor sequences encodes long-term antigen-specific memory in memorycells.6 Cells of the innate immune system,such as natural killer cells and macrophages, are also capable ofcarrying immunologic memory, which provides nonspecific immunity againsta range of pathogens.3,7,8 Detectionof pathogen- or damage-associated molecular patterns (PAMPs and DAMPs)stimulates epigenetic and metabolic reprogramming within the cells,altering gene transcription for innate immune responses. These changes,which persist from months to a few years, impart a memory to innateimmune cells that increases their sensitivity to nonspecific immunechallenges.

Nanomaterials play an important role inthe design of biomimetictraining systems. The benefits offered by nanomaterials in biointerfacemodulation, which have been discussed in previous reviews, can beextended to artificial trainable systems.35 First, scaling the building blocks to the nanometer scale, at theorder of which electrical, mechanical, and chemical energy amplitudesconverge, results in efficient energy transduction.36 Nanomaterials can serve as transducers that convert macroscalesignals into cues that are interpreted by cellular and material systemsto guide training. Second, nanomaterials are size-matched to biologicalcomponents, which enables them to mimic certain biological functions.37 For example, silicon nanowires that can be internalizedby cells can regulate cell migration, essentially mimicking the activityof the cytoskeleton.38,39 Functional nanomaterials thatemulate biological components can also be integrated into a syntheticmatrix and used to create biomimetic systems. A further advantageof nanomaterials is that they are capable of dynamic reconfigurationin response to external stimuli and can be fine-tuned to exhibit spatiotemporalheterogeneity, a property crucial for adaptive evolution when facedwith sudden changes. Finally, because nanostructures have a high surface-to-volumeratio, they can be functionalized through chemical grafting, addingyet another dimension to training. 2ff7e9595c