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| 1 | Application-layer protocol for collaborative multimedia presentations 显示文摘 | Eenjun Hwang Prabhakaran B | 2003 | Multimedia Tools Appl2003，22，2： | 1 |
| 2 | FMF: Query adaptive melody retrieval system显示文摘 | Rho Seungmin Hwang Eenjun | 2006 | The Journal of Systems and Software2006，79，： | 1 |
| 3 | MUSEMBLE:A novel music retrieval system with automatic voice querytranscription and reformulation显示文摘 | Rho Seungmin Han Byeong-jun Hwang Eenjun | 2008 | The Journal of Systems and Software2008，81，： | 1 |
| 4 | Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting显示文摘One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time.Usually,vaccine production preparation must be done at least six months in advance,and accurate long-term influenza forecasting is essential for this.Although diverse machine learning models have been proposed for influenza forecasting,they focus on short-term forecasting,and their performance is too dependent on input variables.For a country’s longterm influenza forecasting,typical surveillance data are known to be more effective than diverse external data on the Internet.We propose a two-stage data selection scheme for worldwide surveillance data to construct a longterm forecasting model for influenza in the target country.In the first stage,using a simple forecasting model based on the country’s surveillance data,we measured the change in performance by adding surveillance data from other countries,shifted by up to 52 weeks.In the second stage,for each set of surveillance data sorted by accuracy,we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage.Using the selected surveillance data,we trained a new longterm forecasting model for influenza and perform influenza forecasting for the target country.We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method.We report some of the results. | Jaeuk Moon Seungwon Jung Sungwoo Park Eenjun Hwang | 2021 | Computers, Materials & Continua2021，，9： | 1 |
| 5 | Spatial Similarity and Annotation-based Image Retrieval System 显示文摘 | SooCheol Lee EenJun Hwang | 2002 | Multimedia Software Engineering2002，，12： | 1 |
| 6 | Query adaptive melody retrieval system显示文摘 | Seungmin Rho Eenjun Hwang FMF | 2006 | The Journal of Systems and Software2006，79，： | 1 |
| 7 | UFC-Net with Fully-Connected Layers and Hadamard Identity Skip Connection for Image Inpainting显示文摘Image inpainting is an interesting technique in computer vision and artificial intelligence for plausibly filling in blank areas of an image by referring to their surrounding areas.Although its performance has been improved significantly using diverse convolutional neural network(CNN)-based models,these models have difficulty filling in some erased areas due to the kernel size of the CNN.If the kernel size is too narrow for the blank area,the models cannot consider the entire surrounding area,only partial areas or none at all.This issue leads to typical problems of inpainting,such as pixel reconstruction failure and unintended filling.To alleviate this,in this paper,we propose a novel inpainting model called UFC-net that reinforces two components in U-net.The first component is the latent networks in the middle of U-net to consider the entire surrounding area.The second component is the Hadamard identity skip connection to improve the attention of the inpainting model on the blank areas and reduce computational cost.We performed extensive comparisons with other inpainting models using the Places2 dataset to evaluate the effectiveness of the proposed scheme.We report some of the results. | Chung-Il Kim Jehyeok Rew Yongjang Cho Eenjun Hwang | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 8 | Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting显示文摘Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model. | Wonyong Chung Jaeuk Moon Dongjun Kim Eenjun Hwang | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 9 | Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images显示文摘Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration.Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively,but they have unavoidable disadvantages when used to analyze skin features accurately.This study proposes a hybrid segmentation scheme consisting of Deeplab v3+with an Inception-ResNet-v2 backbone,LightGBM,and morphological processing(MP)to overcome the shortcomings of handcraft-based approaches.First,we apply Deeplab v3+with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells.Then,LightGBM and MP are used to enhance the pixel segmentation quality.Finally,we determine several skin features based on the results of wrinkle and cell segmentation.Our proposed segmentation scheme achieved a mean accuracy of 0.854,mean of intersection over union of 0.749,and mean boundary F1 score of 0.852,which achieved 1.1%,6.7%,and 14.8%improvement over the panoptic-based semantic segmentation method,respectively. | Jehyeok Rew Hyungjoon Kim Eenjun Hwang | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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