Earticle

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

In this paper, we control attitude of an unstructured object with gyro actuator. It is well known that the attitude control of an object hanging with wire is not easy using usual actuators. Even though an actuator such as a fan can be used for control of the object, it is difficult to meet a desired control objectives. We, because of these reasons, make a gyro actuator with two motors. The first motor is responsible for spinning the wheel at high speed and the second motor is used to turn the inner gimbal. Applying the torque to the second motor, which results in the turn of the outer gimbal, torque about the vertical axis will be obtained while the wheel of the gyro is spinning constantly. This torque is used to control the attitude of the object attached. Gyro actuator utilize control unstructured object such as I-beam carrying by tower crane, and isolate construction workers from the dangerous environments. We derive a relationship of wheel and its motor, find a proper capacity of wheel motor in order to rotate a wheel. Through experiments of attitude control, we show to obtain desired control objectives.

Recently, the demand to interpret image data with artificial intelligence in various fields is rapidly increasing. Object recognition and detection techniques using deep learning are mainly used, and video integration analysis to determine unstructured object recognition is a particularly important problem. In the case of natural disasters or social disasters, there is a limit to the object recognition structure alone because it has an unstructured shape. In this paper, we propose intelligent video integration analysis system that can recognize unstructured objects based on video turning point and object detection. We also introduce a method to apply and evaluate object recognition using virtual augmented images from 2D to 3D through GAN.

An intention of this paper is design of a gyro actuator for the attitude control of an unstructured object. It is well known that the attitude control of an object hanging with wire is not easy using usual actuators. Even though an actuator such as a pan can be used for control of the object, it is difficult to meet a desired control objectives. We, for this reason, propose a gyro actuator for the attitude control of an unstructured object. The proposed gyro actuator consists of two motors. The first motor is responsible to spin the wheel and the second motor is used to turn the outer gimbal. Appling the torque to the second motor, which results in the turn of the outer gimbal, torque about the vertical axis will be obtained while a wheel of the gyro is spinning constantly. This torque is used to control the attitude of the object attached. The aim of this paper is of deriving the transfer function of the actuator and presenting the guideline of the design parameters such as the weight and the dimension of the wheel, motors, and the load capacity. Simulations to the mathematical model which has a state feedback control are conducted to show the validity of the proposed gyro actuator.