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Enhanced Image Harmonization Scheme Using LAB Color Space-based Loss Function and Data Preprocessing
Doyeon Kim, Eunbeen Kim, Hyeonwoo Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2024.51.8.729
Image composition, which involves combining the background and foreground from different images to create a new image, is a useful technique in image editing. However, it often results in awkward images due to differences in brightness and color tones between the background and foreground. Image harmonization techniques aim to reduce this incongruity and have gained significant attention in the field of image editing. These techniques allow for realistic matching of color tones between the foreground and background. Existing deep learning models for image harmonization have shown promise in achieving harmonization performance through the use of large-scale training datasets. However, these models tend to exhibit poor generalization performance when the loss function does not effectively consider brightness or when the dataset has a biased brightness distribution. To address these issues, we propose an image harmonization scheme that is robust to variations in brightness. This scheme incorporates an LAB color space-based loss function, which explicitly calculates the brightness of a given image, and an LAB color space-based preprocessing scheme to create a dataset with a balanced brightness distribution. Experimental results on public image datasets demonstrate that the proposed scheme exhibits robust harmonization performance under various brightness conditions.
Photovoltaic Power Forecasting Scheme Based on Graph Neural Networks through Long- and Short-Term Time Pattern Learning
Jaeseung Lee, Sungwoo Park, Jaeuk Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2024.51.8.690
As the use of solar energy has become increasingly common in recent years, there has been active research in predicting the amount of photovoltaic power generation to improve the efficiency of solar energy. In this context, photovoltaic power forecasting models based on graph neural networks have been presented, going beyond existing deep learning models. These models enhance prediction accuracy by learning the interactions between regions. Specifically, they consider how the amount of photovoltaic power in a specific region is affected by the climate conditions of adjacent regions and the time pattern of photovoltaic power generation. However, existing models mainly rely on a fixed graph structure, making it difficult to capture temporal and spatial interactions. In this paper, we propose a graph neural networks-based photovoltaic power forecasting scheme that takes into account both long-term and short-term time patterns of regional photovoltaic power generation data. We then incorporate these patterns into the learning process to establish correlations between regions. Compared to other graph neural networks-based prediction models, our proposed scheme achieved a performance improvement of up to 7.49% based on the RRSE, demonstrating its superiority.
An Image Harmonization Method with Improved Visual Uniformity of Composite Images in Various Lighting Colors
Doyeon Kim, Jonghwa Shim, Hyeonwoo Kim, Changsu Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2024.51.4.345
Image composition is a technique that creates a composite image by arranging foreground objects extracted from other images onto a background image. To improve the visual uniformity of the composite images, deep learning-based image harmonization techniques that adjust the lighting and color of foreground objects to match the background image have been actively proposed recently. However, existing techniques have limited performance in visual uniformity because they adjust colors only for the lighting color distribution of the dataset used for training. To address this problem, we propose a novel image harmonization scheme that has robust performance for various lighting colors. First, iHColor, a new dataset composed of various lighting color distributions, is built through data preprocessing. Then, a pre-trained GAN-based Harmonization model is fine-tuned using the iHColor dataset. Through experiments, we demonstrate that the proposed scheme can generate harmonized images with better visual uniformity than existing models for various lighting colors.
Zero-Shot Solar Power Efficiency Prediction Method Considering PCC-Based Climate Similarity
Dongjun Kim, Sungwoo Park, Jaeuk Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2023.50.7.581
Thermal power generation is a power generation method that occupies a large proportion in Korea and abroad due to its low unit price. However, due to its disadvantage of emitting large amounts of harmful substances that can cause health and environmental problems, renewable energy is in the spotlight as an alternative power source. Among various renewable energy generation methods, solar power generation is receiving the most attention because of its advantages such as ease in maintenance. Various solar power generation forecasting studies are being conducted to improve the uncertainty of volatile solar power generation and ensure stability in power supply. However, existing studies have limitations in that they are only applicable when there is a sufficient amount of historical power generation data. Therefore, this paper proposes a solar power generation efficiency prediction method based on zero-shot learning that utilizes historical data of similar regions by concerning weather similarity to solve the cold-start problem, a problem that occurs in prediction when historical data in the target region are lacking. Comparison results revealed that the proposed method had better performance overall in the target area, with a one-hour-based method showing the best prediction performance among other criteria.
Super Resolution-based Robust Image Inpainting for Large-scale Missing Regions
Jieun Lee, SeungWon Jung, Jonghwa Shim, Eenjun Hwang
http://doi.org/10.5626/JOK.2022.49.9.708
Image inpainting is a method of filling missing regions of an image with plausible imagery. Even though the performance of recent inpainting methods has been significantly improved owing to the introduction of deep learning, unnatural results can be obtained when an input image has a large-scale missing region, contains a complex scene, or is a high-resolution image. In this study, we propose a super resolution-based two-stage image inpainting method, motivated by the point that inpainting performance in low-resolution images is better than in high-resolution images. In the first step, we convert a high-resolution image into a low-resolution image and then perform image inpainting, which results in the initial output image. In the next step, the initial output image becomes the final output image, with the same resolution as the original input image using the super resolution model. To verify the effectiveness of the proposed method, we conducted quantitative and qualitative evaluations using the high-resolution Urban100 dataset. Furthermore, we analyzed the inpainting performance depending on the size of the missing region and demonstrated that the proposed method could generate satisfactory results in a free-form mask.
Detecting Mode Drop and Collapse in GANs Using Simplified Frèchet Distance
Chung-Il Kim, Seungwon Jung, Jihoon Moon, Eenjun Hwang
http://doi.org/10.5626/JOK.2019.46.10.1012
Even though generative adversarial network (GAN) is an excellent model for generating data based on the estimation of real data distribution by of two adversarial learning network, this model often suffers from mode drop that does not learn distribution during learning, or mode collapse that generates only one or very few distribution samples. Most studies to detect these problems have used well-balanced data or additional neural network models. In this paper, we propose a method to detect mode drop and collapse by using a simplified Frèchet distance, which does not require any additional model or well-balanced data. Through various experiments, we showed that our proposed distance metric detected mode drop and collapse more accurately than any other metrics used in GANs.
A Twitter News-Classification Scheme Using Semantic Enrichment of Word Features
Seonmi Ji, Jihoon Moon, Hyeonwoo Kim, Eenjun Hwang
http://doi.org/10.5626/JOK.2018.45.10.1045
Recently, with the popularity of Twitter as a news platform, many news articles are generated, and various kinds of information and opinions about them spread out very fast. But since an enormous amount of Twitter news is posted simultaneously, users have difficulty in selectively browsing for news related to their interests. So far, many works have been conducted on how to classify Twitter news using machine learning and deep learning. In general, conventional machine learning schemes show data sparsity and semantic gap problems, and deep learning schemes require a large amount of data. To solve these problems, in this paper, we propose a Twitter news-classification scheme using semantic enrichment of word features. Specifically, we first extract the features of Twitter news data using the Vector Space Model. Second, we enhance those features using DBpedia Spotlight. Finally, we construct a topic-classification model based on various machine learning techniques and demonstrate by experiments that our proposed model is more effective than other traditional methods.
Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data
Jihoon Moon, Jinwoong Park, Sanghoon Han, Eenjun Hwang
http://doi.org/10.5626/JOK.2017.44.9.954
A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.
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