Crowdworks Inc.

Mando Corporation

Electronics and Telecommunications Research Institute

Kyungpook National University Industry-academic Cooperation Foundation

Hyundai Motor Company

Kia Motors Corporation

University-industry Cooperation Group of Kyung Hee University

Korea Advanced Institute of Science and Technology

Intellectual Discovery Co., Ltd.

Bg T & a Co.

Min Woo Park

Jeong Sik Jang

Gwang Hoon Park

Kyu Heon Kim

Soon Ki Jung

Hyung Joon Seo

Kyung Hwan Park

Bon Tae Koo

Doug Young Suh

Jang Hong Choi

Doo Kyum Kim

Uiho Choi

Dongbeom Won

Ku Young Jung

Se Yoon Jeong

Jin Woo Hong

Do Yeon Kim

Won Seok Lee

Jun Ho Lee

Seok Bong Hyun

Young Hyun Kim

Tae Sang Park

Seong Su Park

Pil Jae Park

Jung Park

Jun Mo Kim

Jang Hyeon Lee

Jeong Hae Lee

Yun Ho Choi

Dong Hyun Shin

Yang Shin Kim

Seung Hyun Roh

Ju Yeong Kim

Kisu Kim

Young Ha Cho

Si Haeng Lee

Sang Jae Yoo

Jeong Myeong Shin

Ji Won Choe

Hae Chang Jung

Jung Pil Park

Geun Ho Jeong

Chae Seok Lim

Seong Seon Baek

Disclosed are a method and device for managing a project by using data filtering. A project is efficiently operated by dividing a project based on a minimum unit task and designing a plurality of child projects connected in sequential order such that a plurality of child projects proceed in order.

Method and device for managing project by using data filtering

Disclosed is a radar device capable of operating in a dual mode, which includes a transmitter that includes a first signal generator that generates a Doppler radar signal and a second signal generator that generates a Frequency Modulated Continuous Wave (FMCW) radar signal, a receiver that receives a reflected signal reflected from a target and converts the reflected signal to a digital signal, a signal processing circuit that processes the digital signal differently depending on the dual mode to output an output signal, a signal analysis circuit that analyzes the output signal, and a controller that controls operations of the transmitter, the receiver, the signal processing circuit, and the signal analysis circuit, and the dual mode includes a first mode in which the first signal generator is activated and a second mode in which the second signal generator is activated.

Radar device operating in dual mode and operation method thereof

Disclosed are a method and device for managing a project by using a data pointer. A project is efficiently operated by dividing a project based on a minimum unit task and designing a plurality of child projects connected in sequential order such that a plurality of child projects proceed in order.

Method and device for managing project by using data pointer

Disclosed is an automatic image classification and processing method based on the continuous processing structure of multiple artificial intelligence models. An automatic image classification and processing method based on a continuous processing structure of multiple artificial intelligence models includes receiving image data, generating a first feature extraction value by inputting the image data into a first feature extraction model among feature extraction models, generating a second feature extraction value by inputting the image data into a second feature extraction model among the feature extraction models, and determining a classification value of the image data by inputting the first and second feature extraction values into a classification model.

Automatic image classification and processing method based on continuous processing structure of multiple artificial intelligence model, and computer program stored in computer-readable recording medium to execute the same

Disclosed are a method and device for managing a project by using cost payment time point settings. A project is efficiently operated by dividing a project based on a minimum unit task and designing a plurality of child projects connected in sequential order such that a plurality of child projects proceed in order.

Method and device for managing project by using cost payment time point setting

Provided is a drive amplifier. A drive amplifier may include: a main circuit configured to receive an RF input signal and output a first RF output signal; and a selective bias adjustment circuit comprising a first common gate transistor to which a first common gate bias voltage is applied and a second common gate transistor to which a second common gate bias voltage is applied, and configured to output a second RF output signal using the first common gate transistor and the second common gate transistor.

Drive amplifier

Disclosed are a method and device for managing a project by using data merging. A project is efficiently operated by dividing a project based on a minimum unit task and designing a plurality of child projects connected in sequential order such that a plurality of child projects proceed in order.

Method and device for managing project by using data merging

A task multi-assignment method using a tier data structure of a crowdsourcing-based project, the method being a task assignment method using a tier data structure of a crowdsourcing-based project of a multi-assignment method of duplicately assigning each task to a plurality of n different workers, includes: configuring an assignment tier storage including a queue into which task data corresponding to a task that is not assigned to the worker is enqueued, and one or more jstacks into which task data corresponding to tasks that are each assigned j times (wherein j is a natural number less than n) are pushed; and assigning a task of the project to a worker (hereinafter, referred to as a target worker) who requests task assignment and requesting the target worker to perform the task, by using the assignment tier storage.

Task multi-assignment method, apparatus, and computer program using tier data structure of crowdsourcing-based project

A worker selection method according to characteristics of a crowdsourcing-based project is provided. The worker selection method according to characteristics of a crowdsourcing-based project comprises the steps of: identifying functional elements included in a project scheduled to open; extracting a worker's participation history in a previous project; extracting functional elements included in the previous project of the worker; extracting average working hours for each functional element with respect to the functional elements included in the project scheduled to open, among the functional elements included in the previous project; calculating a matching value of the worker for the project scheduled to open, on the basis of the extracted average working hours for each functional element and the number of tasks which have been performed using each functional element in the previous project by the worker; and selecting the worker on the basis of the calculated matching value of the worker for the project scheduled to open.

Method for selecting worker according to feature of project based on crowd sourcing

Provided is a method, device, and program for sampling a frame image of an object to be learned in a video for artificial intelligence video learning, and an image learning method thereof. The method includes receiving a raw video for the AI image learning, extracting a predetermined number of frame images from the received raw, detecting learning target objects in each of the frame images, removing a background other than the learning target objects from each of the frame images, measuring a movement amount of each of the detected learning target objects in a n-th frame image, from which the background is removed, and selecting the n-th frame image as the learning target frame image, by comparing a result of measuring the movement amount of each of the detected one or more learning target objects in the n-th frame image with a predetermined reference.

Method, device, and program for sampling frame image of object to be learned in video for artificial intelligence video learning, and image learning method thereof

An apparatus for recognizing an object using an image includes a depth map generator that generates a depth map using a feature map of the image based on a dilated convolutional neural network (DCNN) and an object recognition device that recognizes the object using the depth map generated by the depth map generator and the image.

Apparatus and method for recognizing object using image

Provided are a safety control system and method for an autonomous vehicle. The safety control system includes a sensor installed in a vehicle and including at least a camera and a light detection and ranging (LiDAR), a main domain control unit (DCU) configured to control autonomous driving from an origin to a destination on the basis of various kinds of information transferred through communication with the sensor, and a redundancy DCU configured to ensure safety of the vehicle by performing a safety function when an event occurs in the autonomous driving due to a fault of the main DCU. According to this configuration, the main DCU and the redundancy DCU are provided, and thus it is possible to simultaneously ensure a fully autonomous driving function and a system safety control function.

Safety control system and method for autonomous vehicle

A system for generating a disparity map includes: an image obtainer obtaining a left image and a right image; a matching cost calculator calculating a matching cost for each of a plurality of pixels of the left image and the right image; an accumulation and summation calculator calculating an accumulation value of one of the pixels based on the calculated matching cost, and calculating a relaxation accumulation value, which is an average of values obtained by multiplying each of relation coefficients between a disparity value of the one pixel and disparity values of surrounding pixels of the one pixel with the accumulation value of the one pixel; a disparity value deliver deriving a disparity value for each of the pixels based on the calculated relaxation accumulation value; and a disparity map generator generating the disparity map based on the derived disparity value.

System and method for generating disparity map by matching stereo images

Provided is an image processing device including: an area extraction unit extracting areas corresponding to size of a predetermined target object from an image; a mosaicking image creation unit connecting extracted areas to create a mosaicking image; and an object detection unit detecting an object from the mosaicking image.

Image processing apparatus and method for detecting object using equi-height mosaicking image, and vehicle operation assisting system employing same

Provided are an image processing apparatus and method, and a driving assistance system employing the same. The image processing apparatus includes a peripheral region extracting unit extracting, from an image, peripheral regions corresponding to a size of a target object determined in advance, a modifying unit modifying the peripheral regions to allow a viewpoint for the peripheral regions to be changed, a mosaicking image creating unit creating a mosaicking image by stitching the modified peripheral regions together, and an object detection unit detecting an object including a part of the target object from the mosaicking image.

Image processing apparatus and method for detecting partially visible object approaching from side using equi-height peripheral mosaicking image, and driving assistance system employing the same

Growing community of inventors

Gyeonggi-do, South Korea

Min Woo Park