Abstract: A bus bar assembly and current measuring device configured to reduce measurement errors through linearity compensation and temperature compensation for the difference in values of two or more measured currents, thereby achieving high-accuracy current measurement, is presented. The present invention discloses a bus bar assembly including: a first conductive plate and a second conductive plate each composed of a plurality of parts; and an insulator formed between the first conductive plate and the second conductive plate, wherein a common first terminal hole is formed at one end of the first conductive plate, one end of the second conductive plate, and one end of the insulator, a common second terminal hole is formed at respective opposite ends of the first conductive plate, the second conductive plate, and the insulator. According to the present invention, stable and highly reliable current measurement is possible through multiple shunt resistors based on a redundancy design.

Abstract: The present invention provides a battery monitoring method, which is performed by a battery monitoring device, including: measuring a first voltage drop across both ends of a first shunt resistor of a bus bar electrically connected to a battery and a second voltage drop across both ends of a second shunt resistor, which is in parallel or serial connection with the first shunt resistor; calculating a first current and a second current flowing, respectively, through the first shunt resistor and the second shunt resistor using a first voltage drop value and a second voltage drop value; and determining a state of the battery using a difference between a first current value and a second current value. According to the present invention, it is possible to increase the reliability of monitoring information related to the battery's state by utilizing current values that have undergone linearity compensation and temperature compensation.

Abstract: A computing resource management system using software modularization in a cluster computing environment in which computing devices are connected, including an application process running on each computing device and an algorithm processing process configured to run independently of the application process and perform task processing on the application process, the computing resource management system including: a task managing system configured to receive a task request message from an application process requiring a task from each computing device; a process managing system configured to confirm an algorithm processing process of computing devices connected to the cluster computing environment, and determine whether there is an algorithm processing process in an idle state to which the application process requested for a task will be assigned; and a performed managing system configured to confirm a result of an application process whose task is performed by the algorithm processing process.

Abstract: The inventive concept provides a teaching method for teaching a transfer position of a transfer robot. The teaching method includes: searching for an object on which a target object to be transferred by the transfer robot is placed, based on a 3D position information acquired by a first sensor; and acquiring coordinates of a second direction and coordinates of a third direction of the object based on a data acquired from a second sensor which is a different type from the first sensor.

Abstract: A super-resolution processing method of a moving image is provided. The super-resolution processing method of a moving image includes sequentially inputting a plurality of input frames included in the video to any one of a recurrent neural network (RNN) for super-resolution processing and a convolutional neural network (CNN) for super-resolution processing, sequentially inputting a frame sequentially output from the any one of the RNN and the CNN to an additional one of the RNN and the CNN, and upscaling a resolution of the output frame by carrying out deconvolution with respect to a frame sequentially output from the additional one of the RNN and the CNN.

Abstract: A super-resolution processing method of a moving image is provided. The super-resolution processing method of a moving image includes sequentially inputting a plurality of input frames included in the video to any one of a recurrent neural network (RNN) for super-resolution processing and a convolutional neural network (CNN) for super-resolution processing, sequentially inputting a frame sequentially output from the any one of the RNN and the CNN to an additional one of the RNN and the CNN, and upscaling a resolution of the output frame by carrying out deconvolution with respect to a frame sequentially output from the additional one of the RNN and the CNN.