• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.52.3-52
• /
• 2001

There are some problems to implement a realtime network system with Ethernet because the MAC(Medium Access Control) of Ethernet uses a CSMA/CD protocol, which introduces unexpected delays. In this paper, we designed a method to solve problems due to the Ethernet MAC. This method introduces a pseudo-MAC in application layer to support the real-time feature. So the presented method doesn´t need any modifications of protocols such as UDP/IP/MAC. The presented pseudo-MAC is based on both a token passing protocol and a publisher-subscriber protocol. The suggested realtime protocol is implemented and tested practically in a local area network. The proposed real-time network consists of a token controller node and general nodes.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2019.06a
• /
• pp.32-34
• /
• 2019

최근 딥러닝 기반의 다양한 물체 검출 알고리즘이 제안되어 높은 성능을 보이고 있다. 본 논문은 이러한 딥러닝 기반 물체 검출의 성능을 향상시키기 위해 입력영상에서 추출된 특징 지도를 강화하는 비지역적 특징 융합과, 이를 이용한 물체 검출 기법을 제안한다. 제안 기법은 입력영상에서 CNN 을 통해 추출한 특징 지도를 비지역적 특징 강화 블록을 통해 강화한다. 해당 블록 내에서 입력된 특징 지도는 먼저 여러 리셉티브 필드를 갖는 특징 지도로 분기된다. 그리고 분기된 특징 지도들은 비지역적 특징 융합 모듈에 의해 융합되어 강화된다. 이러한 과정을 통해 강화된 특징 지도는 비지역적 문맥 정보가 강화된 특성을 가지며, 해당 특징 지도를 이용하여 최종적으로 물체 검출을 수행한다. Pascal VOC 공인 데이터세트를 통한 실험 결과, 제안 기법은 기존 비교 기법 대비 향상된 검출 성능을 보인다.

• Journal of the Korean Magnetic Resonance Society
• /
• v.24 no.4
• /
• pp.96-103
• /
• 2020

A new NMR method to monitor solvent-localized NMR signals in the two-phase liquid system is suggested. This method based on intermolecular single-quantum coherence (iSQC). Here, we exploited the feature of the local action of distant dipolar field (DDF) effect in order to filter out specific NMR signals dissolved in different solvents. This solvent specific iSQC spectroscopy was carried out on a model two-phase liquid system (D-glucose in water/palmitic acid in chloroform), and showed solvent-localized NMR signals. We believe our approaches might be useful in metabolic analysis such as two-phase liquid extraction scheme for labile chemical species.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.131-139
• /
• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• The Journal of Korea Robotics Society
• /
• v.16 no.3
• /
• pp.238-244
• /
• 2021

Image mosaicking is a common and useful technique to visualize a global map by stitching a large number of local images obtained from visual surveys in underwater environments. In particular, visual inspection of underwater structures using underwater robots can be a potential application for image mosaicking. Feature-based pairwise image registration is a commonly employed process in most image mosaicking algorithms to estimate visual odometry information between compared images. However, visual features are not always uniformly distributed on the surface of underwater structures, and thus the performance of image registration can vary significantly, which results in unnecessary computations in image matching for poor-conditioned image pairs. This study proposes a pairwise registrability measure to select informative image pairs and to improve the overall computational efficiency of underwater image mosaicking algorithms. The validity and effectiveness of the image mosaicking algorithm considering the pairwise registrability are demonstrated using an experimental dataset obtained with a full-scale ship in a real sea environment.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.758-761
• /
• 2010

본 논문은 스테레오 시각에서 3차원 정보를 얻기 위해 지역적 특징 정보를 이용한 유전 알고리즘 기반의 다해상도 스테레오 영상 정합 방법을 제안하고자 한다. 스테레오 영상에서 대응점을 찾아 변위를 계산하는 문제는 최적화 기법으로 해결할 수 있다. 최적화 문제 해결에 우수한 유전 알고리즘을 이용해 조밀한 변위도를 구하고 정합의 효율성을 위해 계층적 다해상도 구조를 적용하여 영상 피라미드를 만든다. 그리고 변위도의 정확도를 높이기 위해 변위 전파 과정에서 지역적 특징 정보를 추출하여 이용한다. 실험을 통해 제안한 방법이 변위 탐색 시간을 감소시킬 뿐만 아니라 정합의 타당성이 보장됨을 확인하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.04a
• /
• pp.502-504
• /
• 2010

We propose novel transition rule of cellular automata for image enhancement and sharpening algorithm using it. Transition rule present sequential and parallel behavior. it also satisfy Lyapunov function. This image sharpening was developed and experimented by using a dynamic feature of convergence to fixed points. We can obtain efficiently sharpened image by performing arithmetic operation at the gradual parts of difference of brightness without image information.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.573-576
• /
• 2022

본 논문에서는 삼각형 구조로 구성된 3차원 메쉬(Mesh)에서 합성곱 신경망(Convolutional Neural Network, CNN)의 정확도를 개선시킬 수 있는 새로운 학습 표현 기법을 제시한다. 우리는 메쉬를 구성하고 있는 삼각형의 넓이와 그 로컬 특징을 기반으로 학습을 진행한다. 일반적으로 딥러닝은 인공신경망을 수많은 계층 형태로 연결한 기법을 말하며, 주요 처리 대상은 오디오 파일과 이미지이었다. 인공지능에 대한 연구가 지속되면서 3차원 딥러닝이 도입되었지만, 기존의 학습과는 달리 3차원 학습은 데이터의 확보가 쉽지 않다. 혼합현실과 메타버스 시장으로 인해 3차원 모델링 시장이 증가가 하면서 기술의 발전으로 데이터를 획득할 수 있는 방법이 생겼지만, 3차원 데이터를 직접적으로 학습 표현하는 방식으로 적용하는 것은 쉽지 않다. 그렇기 때문에 본 논문에서는 산업 현장에서 사용되는 데이터인 삼각형 메쉬 구조를 바탕으로 기존 방법보다 정확도가 높은 학습 기법을 제안한다.

• ETRI Journal
• /
• v.46 no.3
• /
• pp.513-525
• /
• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Journal of Astronomy and Space Sciences
• /
• v.23 no.2
• /
• pp.117-126
• /
• 2006

The Diurnal variation of galactic cosmic ray (GCR) flux intensity observed by the ground Neutron Monitor (NM) shows a sinusoidal pattern with the amplitude of $1{\sim}2%$ of daily mean. We carried out a statistical study on tendencies of the local times of GCR intensity daily maximum aad minimum. To test the influences of the solar activity and the location (cut-off rigidity) on the distribution in the local times of maximum and minimum GCR intensity, we have examined the data of 1996 (solar minimum) and 2000 (solar maximum) at the low-latitude Haleakala (latitude: 20.72 N, cut-off rigidity: 12.91 GeV) and the high-latitude Oulu (latitude: 65.05 N, cut-off rigidity: 0.81 GeV) NM stations. The most frequent local times of the GCR intensity daily maximum and minimum come later about $2{\sim}3$ hours in the solar activity maximum year 2000 than in the solar activity minimum you 1996. Oulu NM station whose cut-off rigidity is smaller has the most frequent local times of the GCR intensity maximum and minimum later by $2{\sim}3$ hours from those of Haleakala station. This feature is more evident at the solar maximum. The phase of the daily variation in GCR is dependent upon the interplanetary magnetic field varying with the solar activity and the cut-off rigidity varying with the geographic latitude.