빅데이터를 이용한 스마트시티의 공공안전

날짜 : 2024. 9. 5.

장소 : 경기대학교 육영관 5층 세미나실

내용 : 2024년도 우수 해외학자 및 관련 기관 실무자 초청 워크숍 및 자문 회의

초청연사 : 코메타니 유스케 / 카가와 대학 조교수

초청강연 : 일본의 스마트시티 프로젝트: 다카마쓰 적용 사례를 중심으로


05
Sep
빅데이터를 이용한 스마트시티의 공공안전

날짜 : 2024. 9. 5.

장소 : 경기대학교 육영관 5층 세미나실

내용 : 2024년도 우수 해외학자 및 관련 기관 실무자 초청 워크숍 및 자문 회의

초청연사 : 코메타니 유스케 / 카가와 대학 조교수

초청강연 : 일본의 스마트시티 프로젝트: 다카마쓰 적용 사례를 중심으로


The 19th Asia Pacific International Conference on Information Science and Technology 2024

일시 : 2024. 6. 23. ~ 2024. 6. 26

장소 :  Japan, Kagawa, Takamatsu

내용 : 국제학술대회 참석 및 논문발

23
Jun
The 19th Asia Pacific International Conference on Information Science and Technology 2024

일시 : 2024. 6. 23. ~ 2024. 6. 26

장소 :  Japan, Kagawa, Takamatsu

내용 : 국제학술대회 참석 및 논문발

Dong Nai Technology University 연구협력 도모 및 기술 교류

일시 : 2024. 5. 22. ~ 2024. 5. 26.

장소 : 베트남, DNTU(동나이 기술 대학교)

내용 : Dong Nai Technology University 연구협력 도모 및 기술 교류를 위한 초청 세미나

22
May
Dong Nai Technology University 연구협력 도모 및 기술 교류

일시 : 2024. 5. 22. ~ 2024. 5. 26.

장소 : 베트남, DNTU(동나이 기술 대학교)

내용 : Dong Nai Technology University 연구협력 도모 및 기술 교류를 위한 초청 세미나

한국인터넷정보학회 2024년도 춘계학술발표대회

일시 : 2024. 4. 25. ~ 2024. 4. 27. 

장소 : 부산 해운대센텀호텔

내용 : 한국인터넷정보학회 2024년도 춘계학술발표대회


25
Apr
한국인터넷정보학회 2024년도 춘계학술발표대회

일시 : 2024. 4. 25. ~ 2024. 4. 27. 

장소 : 부산 해운대센텀호텔

내용 : 한국인터넷정보학회 2024년도 춘계학술발표대회


연구소 체육의 날

일시 : 2024. 3. 28. 

장소 : 광교 저수지

내용 : 연구소 체육의 날


28
Mar
연구소 체육의 날

일시 : 2024. 3. 28. 

장소 : 광교 저수지

내용 : 연구소 체육의 날


Winter in Kyonggi University


22
Feb
Winter in Kyonggi University


High Performance Computing (HPC) Asia 2024

일시 : 2024. 01. 25. ~ 2024. 01. 27. 

장소 : Aichi Industry & Labor Center (WINC AICHI), Nagoya

내용 : 학술대회 참석 및 논문발표





25
Jan
High Performance Computing (HPC) Asia 2024

일시 : 2024. 01. 25. ~ 2024. 01. 27. 

장소 : Aichi Industry & Labor Center (WINC AICHI), Nagoya

내용 : 학술대회 참석 및 논문발표





콘텐츠융합소프트웨어연구소 워크숍

일시 : 2024. 01. 11.

장소 : 경기대학교 6405호 세미나실

내용 : 참여연구진 연구현황 및 계획 발표, 외부인사 초청 강연


11
Jan
콘텐츠융합소프트웨어연구소 워크숍

일시 : 2024. 01. 11.

장소 : 경기대학교 6405호 세미나실

내용 : 참여연구진 연구현황 및 계획 발표, 외부인사 초청 강연


Research Papers
Traffic classification using distributions of latent space in software-defined networks: An experimental evaluation
Jang, Y., Kim, N., & Lee, B. D. (2023.)
Engineering Applications of Artificial Intelligence, vol. 119, 105736.
With the emergence of new Internet services and the drastic increase in Internet traffic, traffic classification has become increasingly important to effectively satisfy the quality of service to users. The traffic classification system should be resilient and operate smoothly regardless of network conditions or performance and should be capable of handling various classes of Internet services. This paper proposes a traffic classification method in a software-defined network environment that employs a variational autoencoder (VAE) to accomplish this. The proposed method trains the VAE using six statistical features and extracts the distributions of latent features for the flows in each service class. Furthermore, it classifies the query traffic by comparing the distributions of latent features for the query traffic with the learned distributions of the service classes. For the experiment, the statistical features of network flows were collected from real-world domestic and overseas Internet services for training and testing. According to the experimental results, the proposed method has an average accuracy of 89%. This accuracy was 52%, 47%, 39%, 59%, and 26% higher than conventional statistics-based classification methods, MLP, AE+MLP, VAE+MLP, and SVM, respectively. This result clearly suggests that probability distributions of latent features, rather than specific values for latent features, can be used as more stable features.

Explainable Anomaly Detection Using Vision Transformer Based SVDD
Baek, Ji-Won & Chung, Kyungyong. (2023.)
Computers, Materials & Continua. vol. 74, no. 6573-6586.
Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships. It is possible to offer the explainable basis of decision-making for inference results. Through the causality of risk factors that have an ambiguous association in big medical data, it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status. In addition, the technique makes it possible to accurately predict disease risk for anomaly detection. Vision transformer for anomaly detection from image data makes classification through MLP. Unfortunately, in MLP, a vector value depends on patch sequence information, and thus a weight changes. This should solve the problem that there is a difference in the result value according to the change in the weight. In addition, since the deep learning model is a black box model, there is a problem that it is difficult to interpret the results determined by the model. Therefore, there is a need for an explainable method for the part where the disease exists. To solve the problem, this study proposes explainable anomaly detection using vision transformer-based Deep Support Vector Data Description (SVDD). The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision transformer. In order to draw the explain-ability of model results, it visualizes normal parts through Grad-CAM. In health data, both medical staff and patients are able to identify abnormal parts easily. In addition, it is possible to improve the reliability of models and medical staff. For performance evaluation normal/abnormal classification accuracy and f-measure are evaluated, according to whether to apply SVDD. Evaluation Results The results of classification by applying the proposed SVDD are evaluated excellently. Therefore, through the proposed method, it is possible to improve the reliability of decision-making by identifying the location of the disease and deriving consistent results.

STAug: Copy-Paste Based Image Augmentation Technique Using Salient Target
J. -S. Kang and K. Chung, (2022.)
IEEE Access, vol. 10, pp. 123605-123613.
High-quality, large-capacity data are essential for training a deep learning vision model. However, to construct crop image data, absolute growth time is required for crop growth. In addition, it is characterized by unbalanced data, with fewer abnormal data than normal data. Therefore, building high-quality, large-scale datasets is challenging. Many studies have used data augmentation of plant images to solve this problem. However, plants require data augmentation that does not compromise their color, texture, or shape. This study proposes the use of salient target augmentation (STAug) as a data augmentation technique to protect the colors and shapes of plant images. The proposed method pastes one image’s salient target into a different image to mix the two images. It uses a salient object detection model to generate a salient object mask of the plant. Using the generated mask, a salient target was identified and cropped in a plant image, and the cropped image data were pasted to different background data for augmentation. Concat mask, a combination of each image’s salient object mask, was designed to create the label of the generated image. It is possible to create a rigid classification model by augmenting the data without damaging the plant features. To verify the performance of the proposed STAug, we compared its performance with that of other data-augmentation policies. When STAug and other augmentation techniques were applied in combination, an accuracy of 0.9733 was achieved. We demonstrated a better classification performance than when it was not applied.

Intellectual Property Rights
객체 빈도-역장면 빈도를 이용한 비디오 검색 장치 및 방법
[Domestic Patents]

객체간 거리를 이용한 무질서 기반의 위험 예측 방법 및 시스템
[Domestic Patents]

등록 멀티 클라이언트 태깅 정보 수집 시스템
[Software Property-Right]