• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.449-458
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• 2023

Artificial Intelligence is providing convenience in various fields using big data and deep learning technologies. However, deep learning technology is highly vulnerable to adversarial examples, which can cause misclassification of classification models. This study proposes a method to detect and purification various adversarial attacks using StarGAN. The proposed method trains a StarGAN model with added Categorical Entropy loss using adversarial examples generated by various attack methods to enable the Discriminator to detect adversarial examples and the Generator to purification them. Experimental results using the CIFAR-10 dataset showed an average detection performance of approximately 68.77%, an average purification performance of approximately 72.20%, and an average defense performance of approximately 93.11% derived from restoration and detection performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.538-540
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• 2021

In this paper, we proposed a smart target detection system that detects and recognizes a designated target to provide relative motion information when performing a target detection mission of a drone. The proposed system focused on developing an algorithm that can secure adequate accuracy (i.e. mAP, IoU) and high real-time at the same time. The proposed system showed an accuracy of close to 1.0 after 100k learning of the Google Inception V2 deep learning model, and the inference speed was about 60-80[Hz] when using a high-performance laptop based on the real-time performance Nvidia GTX 2070 Max-Q. The proposed smart target detection system will be operated like a drone and will be helpful in successfully performing surveillance and reconnaissance missions by automatically recognizing the target using computer image processing and following the target.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.328-328
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• 2021

최근 전 지구적인 기후변화의 영향은 강우량의 집중을 야기하며 홍수피해의 규모를 증가시키는 영향을 끼친다. 특히, 아세안 국가들은 해수면 상승, 태풍 및 집중호우에 의한 침수피해 빈발로 최소 2,000만명이 영향을 받고 있다. 국내의 홍수예보모형을 수출하여 아세안 국가에 구축하고 있으나 통신 시설이 불안정하여 중앙제어 방식의 기존의 홍수예보시스템만으로는 긴급상황에 대한 대처가 부족할 수 있다. 따라서 본 연구에서는 하나의 관측소에서 수위, 강우의 관측과 홍수예측, 경보까지 한번에 가능한 관측소를 개발하기 위해 관측된 수위와 강우자료를 활용하여 인공지능기반의 하천수위예측 모형을 개발하였다. 목표 리드타임은 30분에서 6시간으로 설정하였으며 모형은 Tensorflow로 구축하였다. 시계열 자료의 예측에 적합한 LSTM 기법을 적용하였다. 연구의 대상지역은 건설연의 계측시험유역인 설마천유역으로 하였으며 학습에는 2009년부터 2020년까지의 10분 단위 수위 및 강우량자료를 활용하였다. 연구결과 설마천 유역은 규모가 작고 도달시간이 짧아 1시간 후 예측까지는 높은 정확도를 나타냈으나 3시간 이상의 예측결과는 다소 낮게 평가되었다. 다만, 비상상황에서 통신이 두절된 상황에서 위급하게 대피를 위해 홍수경보를 발령하는데는 활용이 가능 할 것으로 판단된다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.462-464
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• 2022

의료영상기반의 인공지능 연구는 질환의 조기진단 및 예측 분야에 눈부신 기술발전이 되어왔다. 장기 섬유증은 만성 염증성 질환의 질병 진행을 특징짓고 전 세계적으로 모든 원인으로 인한 사망률의 45%에 기여하며, 그중 간 섬유증은 주로 삶의 질과 예후를 결정한다. 해당 질환은 임상 현장에서 혈액데이터 분석 그리고 간생검을 통해 진단을 하고 있으나 최근 의료영상 분석을 통해 진단에 활용하고 있는 추세이다. 본 논문에서는 인공지능을 기반으로 하여, 간 섬유화를 진단하기 위해 MRI영상을 학습하여 질환에 대한 중증도 진단을 돕는 인공지능 모델을 제시하고자 한다. 이를 위해 인공지능 모델을 개발하는 과정과 그 결과를 보인다. 본 논문에서 제시한 모델을 통해 간 섬유화를 빠르게 진단할 수 있을 것으로 기대한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.327-329
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• 2023

순서 의존적 준비시간을 갖는 단일기계 생산라인에서 주어진 작업들을 효율적으로 수행하기 위해서는 최대한 동일하거나 유사한 유형의 작업물들을 연속적으로 처리하여 다음 번 작업물의 처리를 시작하기 전에 발생하는 준비시간을 최소화하여야 한다. 따라서, 대기 중인 것들 중 기계에 투입할 작업물을 적절히 선택하는 것이 중요하며, 이를 위해 작업 배정 규칙과 같은 휴리스틱을 사용할 수도 있지만, 이러한 해법들은 일반적으로 다양한 상황을 동적으로 고려하지 못하는 한계점을 갖는다. 따라서, 본 논문에서는 상용 3D 시뮬레이션 소프트웨어인 FlexSim을 사용하여 모형을 구성한 다음, 강화학습을 적용하여 대기 중인 작업물 중 최적의 후보를 선택하기 위한 작업 배정 모형을 개발하고자 한다. 세부적으로는 강화학습의 상태 및 보상을 달리 설정하면서 학습된 모형의 성능을 비교하고자 한다. 실험 결과를 통해 적절한 시뮬레이션 모형 구성과 강화학습의 파라미터 변수들을 적절히 조합하여 적절한 작업 배정 모형의 개발이 가능하다는 점을 알 수 있었다.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.200-214
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• 2024

Amid the flood of data, social network analysis is beneficial in searching for its hidden context and verifying several pieces of information. This can be used for detecting the spread model of infectious diseases, methods of preventing infectious diseases, mining of small groups and so forth. In addition, community detection is the most studied topic in social network analysis using graph analysis methods. The objective of this study is to examine signed attributed social networks and identify the maximal balanced cliques that are both absolute and fair. In the same vein, the purpose is to ensure fairness in complex networks, overcome the "information cocoon" bottleneck, and reduce the occurrence of "group polarization" in social networks. Meanwhile, an empirical study is presented in the experimental section, which uses the personal information of 77 employees of a research company and the trust relationships at the professional level between employees to mine some small groups with the possibility of "group polarization." Finally, the study provides suggestions for managers of the company to align and group new work teams in an organization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.670-684
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• 2024

3D Cross-Modal Retrieval (3DCMR) is a task that retrieves 3D objects regardless of modalities, such as images, meshes, and point clouds. One of the most prominent methods used for 3DCMR is the Cross-Modal Center Loss Function (CLF) which applies the conventional center loss strategy for 3D cross-modal search and retrieval. Since CLF is based on center loss, the center features in CLF are also susceptible to subtle changes in hyperparameters and external inferences. For instance, performance degradation is observed when the batch size is too small. Furthermore, the Mean Squared Error (MSE) used in CLF is unable to adapt to changes in batch size and is vulnerable to data variations that occur during actual inference due to the use of simple Euclidean distance between multi-modal features. To address the problems that arise from small batch training, we propose a Noisy Center Loss (NCL) method to estimate the optimal center features. In addition, we apply the simple Siamese representation learning method (SimSiam) during optimal center feature estimation to compare projected features, making the proposed method robust to changes in batch size and variations in data. As a result, the proposed approach demonstrates improved performance in ModelNet40 dataset compared to the conventional methods.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.63-81
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• 2024

With the advancement of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), the metaverse has emerged as a transformative model across various sectors, offering a three-dimensional virtual world where activities mirroring the real world occur. This study delves into the significant factors influencing fashion brand companies' investments in the metaverse, an evolved concept from Virtual Reality (VR) that extends beyond gaming to include real-life activities through avatars. This study highlights the surge in virtual fashion engagements, as evidenced by increased avatar updates and purchases of digital fashion items on platforms like Roblox. Luxury brands are steadily entering the metaverse indicating a new revenue stream within the fashion industry. This study employs a mixed-methods approach, integrating text mining and interviews to identify key factors for fashion companies considering metaverse investments. By proposing strategies based on these findings, this study not only enriches academic discourse in fashion, e-commerce, and information systems but also serves as a guideline for fashion companies aiming to navigate the burgeoning digital market, contributing to the generation of new revenue streams in the fashion sector.

• Science of Emotion and Sensibility
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• v.27 no.2
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• pp.91-104
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• 2024

The increase in the use of artificial intelligence (AI) in the workplace has introduced changes to traditional working environments. However, these are changes not only to employee productivity but also to how employees feel and think about their work. Based on prior research that has suggested connections between employees' perceptions of AI and their emotions and thoughts at work, the present study tested a moderated mediation model in which the perception of AI opportunity is indirectly related to job insecurity via employee hope, with tenure as a moderator. Data obtained from 290 Korean full-time employees illustrated that the perception of AI opportunity was negatively related to job insecurity through hope acting as a mediator. In addition, this indirect relationship was found to be dependent on the moderating role of tenure. Specifically, at lower levels of tenure, the aforementioned indirect relationship was statistically significant, but at higher levels of tenure, this indirect relationship was no longer found to be statistically significant. The implications, limitations, and future research directions of this study are discussed.