• 한국환경과학회지
• /
• 제8권3호
• /
• pp.299-307
• /
• 1999

In order to fast predict the wind-driven current in a small bay, a convolution method in which the wind-driven current can be generated only with the local wind is developed and applied in the idealized bay and the idealized Sachon Bay. The accuracy of the convlution method is assessed through a series of the numerical experiements carried out in the jidealized bay and the idealized Sachon Bay. The optimum response function for the convolution method is obtained by minimizing the root man square (rms) difference between the current given by the numerical model and the current given by the convolution method. The north-south component of the response function shows simultaneous fluctuations in the wind and wind-driven current at marginal region while it shows "sea-saw" fluctuations (in which the wind and wind-driven current have opposite direction) at the central region in the idealized Sachon Bay. The present wind is strong enough to influence on the wind-driven current especially in the idealized Sachon Bay. The spatial average of the rms ratio defined as the ratio of the rms error to the rms speed is 0.05 in the idealized bay and 0.26 in the idealized Sachon Bay. The recover rate of kinetic energy(rrke) is 99% in the idealized bay and 94% in the idealized Sachon Bay. Thus, the predicted wind-driven current by the convolution model is in a good agreement with the computed one by the numerical model in the idealized bay and the idealized Sachon Bay.achon Bay.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권11호
• /
• pp.2903-2923
• /
• 2023

Knowledge recommendation is a type of recommendation system that recommends knowledge content to users in order to satisfy their needs. Although using graph neural networks to extract data features is an effective method for solving the recommendation problem, there is information loss when modeling real-world problems because an edge in a graph structure can only be associated with two nodes. Because one super-edge in the hypergraph structure can be connected with several nodes and the effectiveness of knowledge graph for knowledge expression, a dual-channel hypergraph convolutional neural network model (DCHC) based on hypergraph structure and knowledge graph is proposed. The model divides user data and knowledge data into user subhypergraph and knowledge subhypergraph, respectively, and extracts user data features by dual-channel hypergraph convolution and knowledge data features by combining with knowledge graph technology, and finally generates recommendation results based on the obtained user embedding and knowledge embedding. The performance of DCHC model is higher than the comparative model under AUC and F1 evaluation indicators, comparative experiments with the baseline also demonstrate the validity of DCHC model.

• 한국산업융합학회 논문집
• /
• 제26권1호
• /
• pp.113-119
• /
• 2023

In this paper, we propose an optimization of a pose estimation deep learning model for recognition of abnormal behavior in unmanned stores using radio frequencies. The radio frequency use millimeter wave in the 30 GHz to 300 GHz band. Due to the short wavelength and strong straightness, it is a frequency with less grayness and less interference due to radio absorption on the object. A millimeter wave radar is used to solve the problem of personal information infringement that may occur in conventional CCTV image-based pose estimation. Deep learning-based pose estimation models generally use convolution neural networks. The convolution neural network is a combination of convolution layers and pooling layers of different types, and there are many cases of convolution filter size, number, and convolution operations, and more cases of combining components. Therefore, it is difficult to find the structure and components of the optimal posture estimation model for input data. Compared with conventional millimeter wave-based posture estimation studies, it is possible to explore the structure and components of the optimal posture estimation model for input data using genetic algorithms, and the performance of optimizing the proposed posture estimation model is excellent. Data are collected for actual unmanned stores, and point cloud data and three-dimensional keypoint information of Kinect Azure are collected using millimeter wave radar for collapse and property damage occurring in unmanned stores. As a result of the experiment, it was confirmed that the error was moored compared to the conventional posture estimation model.

• 한국정보통신학회논문지
• /
• 제5권4호
• /
• pp.645-652
• /
• 2001

본 논문은 근거리 차량간 통신(IVC)에서 첨단차량도로시스템(AVHS)의 플래툰 주행에 적합한 One-Ray Rician 채널 모델을 적용하여 간섭신호에 강한 장점을 지닌 60GHz 밑리미터파에서 패킷 오율 특성을 분석하였다. 차후에 사용자의 욕구 증대에 따른 멀티미디어 서비스를 만족시키기 위해서 Convolution 부호화 기법과 MRC 다이버시티 수신 기법을 동시에 적용하였으며 그에 따른 DS-CDMA/QPSK System의 패킷 오율 특성을 분석하였다.

• ETRI Journal
• /
• 제44권2호
• /
• pp.208-219
• /
• 2022

Crime amount prediction is crucial for optimizing the police patrols' arrangement in each region of a city. First, we analyzed spatiotemporal correlations of the crime data and the relationships between crime and related auxiliary data, including points-of-interest (POI), public service complaints, and demographics. Then, we proposed a crime amount prediction model based on 2D convolution and long short-term memory neural network (2DCONV-LSTM). The proposed model captures the spatiotemporal correlations in the crime data, and the crime-related auxiliary data are used to enhance the regional spatial features. Extensive experiments on real-world datasets are conducted. Results demonstrated that capturing both temporal and spatial correlations in crime data and using auxiliary data to extract regional spatial features improve the prediction performance. In the best case scenario, the proposed model reduces the prediction error by at least 17.8% and 8.2% compared with support vector regression (SVR) and LSTM, respectively. Moreover, excessive auxiliary data reduce model performance because of the presence of redundant information.

• 방송공학회논문지
• /
• 제23권5호
• /
• pp.669-681
• /
• 2018

본 논문에서는 고해상도 자궁경부암 세포사진을 CNN(Convolution Neural Network)을 통해 효과적으로 인식 및 분류하는 방법을 소개한다. 이때 고려되는 세포의 종류는 Ascus, Inflammation, RCC, Normal 로 네 가지가 있다. 본 논문에서는 먼저 기존의 고해상도 이미지를 분류하는 알고리즘을 소개하고, 이 방법을 이용하여 고해상도 세포사진을 분류하는 과정에서 어떤 정보의 손실이 발생하는지 분석한 후, 이를 해결하기 위한 방법을 제시한다. 이를 위해서 본 논문에서 제안하는 학습 모델에서는 dilated convolution을 이용하여 고해상도 사진의 정보의 손실을 최소한으로 줄임과 동시에 학습속도 빠르게 하는 알고리즘을 제시한다. 또한 이미지 전처리 과정으로 임계치를 사용함으로써 암세포를 판단하는데 혼란을 줄 수 있는 부분을 제거함으로써 인식률을 향상시킨다. 본 논문에서 제시되는 실험 결과를 통해, 제안한 알고리즘이 기존 기술보다 높은 인식률을 제공하는 것을 확인할 수 있었다.

• 한국방재안전학회논문집
• /
• 제8권1호
• /
• pp.15-19
• /
• 2015

수문학에서 사용되는 강우-유출 모델의 경우 선형적인 시스템을 기반으로 유효강수량으로부터 시간적 지연을 통해서 유출량이 결정되는데 그 양은 강우량의 선형적인 비로 표현되어서 결국 합성곱을 통해 해석되게 된다. 또한 자료에 대한 확률론적 분석에 많이 이용되는 비매개변수 핵밀도함수의 경우, 핵(Kernel)의 의미자체가 합성곱에서 나온 것으로서 개개의 자료를 바탕으로 핵을 통해 매끄러운 확률밀도함수를 구하게 된다. 본 연구에서는 합성곱을 바탕으로 강우-유출 모델과 비매개변수 확률밀도함수를 해석하는 방법에 대해서 되짚어 보고 그 공통적인 특성과 다른 점을 수학적으로 나타내 줌으로써 사용되는 합성곱 함수의 유용성에 대해서 논하였다.

• Journal of Multimedia Information System
• /
• 제8권4호
• /
• pp.203-210
• /
• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• 한국정보전자통신기술학회논문지
• /
• 제14권4호
• /
• pp.314-322
• /
• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• 한국농공학회논문집
• /
• 제56권1호
• /
• pp.11-20
• /
• 2014

Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detecting the communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of the probabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For the probabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communities of nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparing with Monte-carlo Simulation and other community-detecting methods.