본 연구는 콰욜라 작품에서 컴퓨터 알고리즘 기술이 주체로서, 인간이 재현한 이미지를 디지털 이미지로 재창조하는 데서 발생한 숭고적 의미와 특성을 고찰한다. 그리고 이를 통해 콰욜라 작품이 인간중심성을 비판적으로 성찰한다는 점을 살펴본다. 숭고에서 주체는 변형 되고 비결정화되어 이전과 다른 창조적 자아로 거듭난다. 숭고에서 주체의 이런 양상은 주체 로서 알고리즘 기술이 대상에 야기하는 현상과 유사하다. 알고리즘 기술은 대상을 데이터화 함으로써 대상에 창조적 잠재력을 준다. 데이터로서 비결정화 된 대상은 다른 존재들로 다시 변형되어 이전과 불일치할 가능성을 갖기 때문이다. ≪포로들 #B06 Captives #B06≫ (2014)과 ≪유적: 프로방스 Remains: Provance≫(2016)에서 알고리즘 기술은 대상을 변 형, 비결정화 하고 이전과 불일치하게 만들어 대상에 창조적 잠재력과 숭고적 특성을 준다. 그래서 인간 시각에 대한 차이를 발생시켜 인간 시각과 근본적으로 같지만 다른 형태를 지 니는 기계의 시각을 제시한다. 그리하여 알고리즘 기술은 인간이 기계의 시각을 통해 자신에 대한 메타적 보기를 가능케 한다.

The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

It is important to ensure worker’s safety from radiation hazard in decommissioning site. Real-time tracking of worker’s location is one of the factors necessary to detect radiation hazard in advance. In this study, the integrated algorithm for worker tracking has been developed to ensure the safety of workers. There are three essential techniques needed to track worker’s location, which are object detection, object tracking, and estimating location (stereo vision). Above all, object detection performance is most important factor in this study because the performance of tracking and estimating location is depended on worker detection level. YOLO (You Only Look Once version 5) model capable of real-time object detection was applied for worker detection. Among the various YOLO models, a model specialized for person detection was considered to maximize performance. This model showed good performance for distinguishing and detecting workers in various occlusion situations that are difficult to detect correctly. Deep SORT (Simple Online and Realtime Tracking) algorithm which uses deep learning technique has been considered for object tracking. Deep SORT is an algorithm that supplements the existing SORT method by utilizing the appearance information based on deep learning. It showed good tracking performance in the various occlusion situations. The last step is to estimate worker’s location (x-y-z coordinates). The stereo vision technique has been considered to estimate location. It predicts xyz location using two images obtained from stereo camera like human eyes. Two images are obtained from stereo camera and these images are rectified based on camera calibration information in the integrated algorithm. And then workers are detected from the two rectified images and the Deep SORT tracks workers based on worker’s position and appearance between previous frames and current frames. Two points of workers having same ID in two rectified images give xzy information by calculating depth estimation of stereo vision. The integrated algorithm developed in this study showed sufficient possibility to track workers in real time. It also showed fast speed to enable real-time application, showing about 0.08 sec per two frames to detect workers on a laptop with high-performance GPU (RTX 3080 laptop version). Therefore, it is expected that this algorithm can be sufficiently used to track workers in real decommissioning site by performing additional parameter optimization.