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Fire is one of the most commonly occurring disasters and is the main cause of catastrophic personal injury and devastating property damage. An early detection system is necessary to prevent fires from spreading out of control. In this paper, we propose a multistage fire detection method using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. In the first stage, fire candidates are detected by using their salient features, such as their color, flickering frequency, and brightness. In the second stage, a pretrained CNN model is used to extract the 2D features of flames that are the input for the LSTM network. In the last stage, a softmax classifier is utilized to determine whether the flames represent a true fire or a nonfire moving object. The experimental results show that our proposed method can achieve competitive performance compared with other state-of-the-art methods and is suitable for real-world applications.

This paper highlights a welding robot (WR) for its end effector to track a rectangular welding line (RWL). The WR includes five actuators which use a DC motor as a power source. Two controllers are proposed to control the WR's end effector: a main controller and a servo controller. Firstly, based on WR's kinematic equations and its feedback errors using backstepping method the main controller is proposed to design the reference-inputs for the WR's actuators in order that the WR's end effector tracks the RWL. Secondly, based on the dynamic equation of WR's actuator, the servo controller is designed using an active disturbance rejection control method. Finally, a control system incorporated with the main controller and the servo controllers make the WR's end effector robustly track a RWL in the presence of the modeling uncertainty and disturbances during the welding process. In experiment, the main controller which has a function as a master of the control system links to the five servo controllers which have a function as a slave via I2C communication. The effectiveness of the proposed control system is proven through the simulation and experimental results.

This paper highlights a welding robot (WR) for its end effector to track a rectangular welding line (RWL). The WR includes five actuators which use a DC motor as a power source. Two controllers are proposed to control the WR's end effector: a main controller and a servo controller. Firstly, based on WR's kinematic equations and its feedback errors using backstepping method the main controller is proposed to design the reference-inputs for the WR's actuators in order that the WR's end effector tracks the RWL. Secondly, based on the dynamic equation of WR's actuator, the servo controller is designed using an active disturbance rejection control method. Finally, a control system incorporated with the main controller and the servo controllers make the WR's end effector robustly track a RWL in the presence of the modeling uncertainty and disturbances during the welding process. In experiment, the main controller which has a function as a master of the control system links to the five servo controllers which have a function as a slave via I2C communication. The effectiveness of the proposed control system is proven through the simulation and experimental results.

BACKGROUND Streptococcus thoraltensis is a rare cause of human disease. This report describes a patient with infective endocarditis caused by S. thoraltensis and complicated by ischemic stroke. CASE REPORT A 68-year-old man was admitted for a 12-day duration of fever. He had a history of severe aortic valve stenosis, for which he underwent prosthetic valve replacement, and type 2 diabetes mellitus. Echocardiography revealed vegetation attached to the right coronary cusp of the prosthetic aortic valve and rupture of the sinus of Valsalva into the right ventricle. Blood cultures were positive for S. thoraltensis. He experienced an ischemic stroke involving the vegetation of the aortic valve and died of acute heart failure. CONCLUSIONS S. thoraltensis may be considered an emerging pathogen in patients with infective endocarditis.