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Deployment of deep neural networks on embedded devices is becoming increasingly popular because it can reduce latency and energy consumption for data communication. This paper aims to give out a method for deployment the deep neural networks on a quad-rotor aircraft for further expanding its application scope.

In this paper, a design scheme is proposed to implement the flight mission of the quad-rotor aircraft based on multi-sensor fusion. It integrates attitude acquisition module, global positioning system position acquisition module, optical flow sensor, ultrasonic sensor and Bluetooth communication module, etc. A 32-bit microcontroller is adopted as the main controller for the quad-rotor aircraft. To make the quad-rotor aircraft be more intelligent, the study also proposes a method to deploy the pre-trained deep neural networks model on the microcontroller based on the software packages of the RT-Thread internet of things operating system.

This design provides a simple and efficient design scheme to further integrate artificial intelligence (AI) algorithm for the control system design of quad-rotor aircraft.

This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.

This paper aims to review the quad-porosity shale system from a production standpoint. Understanding the complex but coupled flow mechanisms in such reservoirs is essential to design appropriate completions and further, optimally produce them. Dual-porosity and dual permeability models are most commonly used to describe a typical shale gas reservoir.

Characterization of such reservoirs with extremely low permeability does not aptly capture the physics and complexities of gas storage and flow through their existing nanopores. This paper reviews the methods and experimental studies used to describe the flow mechanisms of gas through such systems, and critically recommends the direction in which this work could be extended. A quad-porosity shale system is defined not just as porosity in the matrix and fracture, but as a combination of multiple porosity values.

It has been observed from studies conducted that shale gas production modeled with conventional simulator/model is seen to be much lower than actually observed in field data. This paper reviews the various flow mechanisms in shale nanopores by capturing the physics behind the actual process. The contribution of Knudson diffusion and gas slippage, gas desorption and gas diffusion from Kerogen to total production is studied in detail.

The results observed from experimental studies and simulation runs indicate that the above effects should be considered while modeling and making production forecast for such reservoirs.