As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.

In this paper, a GAN-based data augmentation method is proposed for topology optimization. In machine learning techniques, a total amount of dataset determines the accuracy and robustness of the trained neural network architectures, especially, supervised learning networks. Because the insufficient data tends to lead to overfitting or underfitting of the architectures, a data augmentation method is need to increase the amount of data for reducing overfitting when training a machine learning model. In this study, the Ganerative Adversarial Network (GAN) is used to augment the topology optimization dataset. The produced dataset has been compared with the original dataset.

The fishery compensation by marine spatial planning such as routeing of ships and offshore wind farms is required objective data on whether fishing vessels are engaged in a target area. There has still been no research that calculated the number of fishing operation days scientifically. This study proposes a novel method for calculating the number of fishing operation days using the fishing trajectory data when investigating fishery compensation in marine spatial planning areas. It was calculated by multiplying the average reporting interval of trajectory data, the number of collected data, the status weighting factor, and the weighting factor for fishery compensation according to the location of each fishing vessel. In particular, the number of fishing operation days for the compensation of driftnet fishery was considered the daily average number of large vessels from the port and the fishery loss hours for avoiding collisions with them. The target area for applying the proposed method is the routeing area of ships of Jeju outer port. The yearly average fishing operation days were calculated from three years of data from 2017 to 2019. As a result of the study, the yearly average fishing operation days for the compensation of each fishing village fraternity varied from 0.0 to 39.0 days. The proposed method can be used for fishery compensation as an objective indicator in various marine spatial planning areas.

사회기반 시설물의 노후화에 대응해 이상 징후를 파악하고 유지보수를 위한 최적의 의사결정을 내리기 위해선 디지털 기반 SOC 시설물 유지관리 시스템의 개발이 필수적인데, 디지털 SOC 시스템은 장기간 구조물 계측을 위한 IoT 센서 시스템과 축적 데이터 처 리를 위한 클라우드 컴퓨팅 기술을 요구한다. 본 연구에서는 구조물의 다물리량을 장기간 측정할 수 있는 IoT센서와 클라우드 컴퓨팅 을 위한 서버 시스템을 개발하였다. 개발 IoT센서는 총 3축 가속도 및 3채널의 변형률 측정이 가능하고 24비트의 높은 해상도로 정밀 한 데이터 수집을 수행한다. 또한 저전력 LTE-CAT M1 통신을 통해 데이터를 실시간으로 서버에 전송하여 별도의 중계기가 필요 없 는 장점이 있다. 개발된 클라우드 서버는 센서로부터 다물리량 데이터를 수신하고 가속도, 변형률 기반 변위 융합 알고리즘을 내장하 여 센서에서의 연산 없이 고성능 연산을 수행한다. 제안 방법의 검증은 2개소의 실제 교량에서 변위계와의 계측 결과 비교, 장기간 운 영 테스트를 통해 이뤄졌다.

In this study, as part of the paradigm shift for manufacturing innovation, data from the multi-stage cold forging process was collected and based on this, a big data analysis technique was introduced to examine the possibility of quality prediction. In order for the analysis algorithm to be applied, the data collection infrastructure corresponding to the independent variable affecting the quality was built first. Similarly, an infrastructure for collecting data corresponding to the dependent variable was also built. In addition, a data set was created in the form of an independent variable-dependent variable, and the prediction accuracy of the quality prediction model according to the traditional statistical analysis and the tree-based regression model corresponding to the big data analysis technique was compared and analyzed. Lastly, the necessity of changing the manufacturing environment for the use of big data analysis in the manufacturing process was added.

The purpose of this paper is to understand the key factors for efficient maintenance of rapidly aging facilities. Therefore, the safety inspection/diagnosis reports accumulated in the unstructured data were collected and preprocessed. Then, the analysis was performed using a text mining analysis method. The derived vulnerabilities of tunnel facilities can be used as elements of inspections that take into account the characteristics of individual facilities during regular inspections and daily inspections in the short term. In addition, if detailed specification information and other inspection results(safety, durability, and ease of use) are used for analysis, it provides a stepping stone for supporting preemptive maintenance decision-making in the long term.