The potential application of infrared and Raman spectroscopies was explored as rapid and nondestructive tools for the identification of juvenile black seabream samples intoxicated by heavy metals (Zn, Cu, and Cd). Discrimination models were established on the basis of the infrared and Raman spectral data using three calibration methods, namely, partial least-squares discriminant analysis, least-squares support vector machines, and random forest. The combination of two spectroscopies was studied, in which three combination strategies were proposed and compared. Discrimination models achieved overall correct discriminations of 100% for identifying the fish intoxicated by one heavy metal or the heavy metal mixture. When samples intoxicated by different heavy metals were analyzed together, the discrimination accuracy remained >90%. Results confirmed the possibility of developing fast and reliable systems for the identification of juvenile black seabream intoxicated by heavy metals based on infrared and Raman spectroscopies.
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
This method provides an application example and a design reference for the implementation of AI algorithms on unmanned aerial vehicle or terminal robots.
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