The paper investigates the applicability of convolutional neural network "U-Net" to a problem segmentation aircraft images. image method is based on "Carvana" implementation with architecture. For orientation recognition, built in Keras open library pretrained VGG16 used. approach considered allows be conducted. results experiments have shown possibility fairly accurate selection object interes...

the peak frequency [2]. When the textures are unstable, however, e.g, when peak frequencies have fluctuations, A novel neural network architecture for image simple comparison of the peak outputs may be ineffective. texture classification is introduced. The proposed Kernel The shortcomings of using the peak frequencies of each Modifying Neural Network (KM Net) which incorporates texture classes ...

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated whe...

The automobile is undergoing a revolution in the design of its electrical system. This is the result of increasingly sophisticated engine and body controls, as well as the introduction of new, electrically controlled functions. The main electrical bus of the future will be 42 V, and it will be buffered by a 36 V battery [1,2]. As many devices and electronic control units require voltages differ...

A long deep and wide artificial neural net (LDWNN) with multiple ensemble neural nets for individual frequency subbands is proposed for robust speech recognition in unknown noise. It is assumed that the effect of arbitrary additive noise on speech recognition can be approximated by white noise (or speech-shaped noise) of similar level across multiple frequency subbands. The ensemble neural nets...

A major challenge in performing pattern recognition with neural networks is large input data sets; for example, high-resolution static images. There is a direct relationship between the number of inputs and the number of neurons and links required to process those inputs. Specifically, as the number of inputs increases linearly, the complexity of the neural net increases exponentially. We prese...

The term Neural Architecture Search (NAS) refers to the automatic optimization of network architectures for a new, previously unknown task. Since testing an architecture is computationally very expensive, many optimizers need days or even weeks find suitable architectures. However, this search time can be significantly reduced if knowledge from previous searches on different tasks reused. In wo...