• Design Convergence Study
• /
• v.19 no.2
• /
• pp.107-122
• /
• 2020

"lux et sonitus" is an audiovisual art exhibition series with an artistic combination of music and video at its center. Since its first introduction in 2013, the series have been held five times under the theme of "exhibition of music", presenting works featuring both audio and visual media in an effort to explore the key issues in the field of audiovisual art. In addition to the previous achievement of the exhibition, recent works from the series feature new concepts that explore the possibility of expanding the realm of synesthesia. In this paper, details of the entire series are summarized. In addition, theoretical background behind creative results of the series is analyzed in the context of music, synesthesia and space found in Nam June Paik's audiovisual artwork as a source of inspiration. This will contribute to establishing a vision for the creation/analysis of audiovisual art in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1814-1828
• /
• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.3
• /
• pp.59-67
• /
• 2021

Blockchain technology began in 2008 when an unidentified person named Satoshi Nakamoto proposed a cryptocurrency called Bitcoin. Satoshi Nakamoto had distrust of the existing financial system and wanted to implement a financial system that is robust against hacking or mannipulation without a middleman such as a bank through blockchain technology. Satoshi proposed a blockchain as a technology to prevent the creation of the bitcoin and forging of transactions, and through this, the functions of issuance, transaction, and verification of currency were implemented. Since then, Ethereum, a cryptocurrency that can implement the smart contract on the blockchain, has been developed, allowing financial products that require complex contracts such as deposits, loans, insurance, and derivatives to be brought into the area of cryptocurrency. In addition, it is expanding the possibility of substituting products provided by financial institutions through combination with real assets. These applications are defined as Decentralized Finance (DeFi). This paper was prepared to understand the overall technical understanding of DeFi and to introduce the services currently in operation. First, the technologies and ecosystems that implement the overall DeFi are explained, and then the representative DeFi services are categorized by feature and described.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.294-302
• /
• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.5
• /
• pp.73-82
• /
• 2022

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.

• Journal of Biomedical Engineering Research
• /
• v.44 no.1
• /
• pp.11-18
• /
• 2023

Currently, studies to predict the risk of rectal cancer surgery select MRI image slices based on the clinical experience of surgeons. The purpose of this study is to semi-automatically select and classify 2D MRI image slides to predict the risk of rectal cancer surgery using biomarkers. The data used were retrospectively collected MRI imaging data of 50 patients who underwent laparoscopic surgery for rectal cancer at Gachon University Gil Medical Center. Expert-selected MRI image slices and non-selected slices were screened and radiomics was used to extract a total of 102 features. A total of 16 approaches were used, combining 4 classifiers and 4 feature selection methods. The combination of Random Forest and Ridge performed with a sensitivity of 0.83, a specificity of 0.88, an accuracy of 0.85, and an AUC of 0.89±0.09. Differences between expert-selected MRI image slices and non-selected slices were analyzed by extracting the top five significant features. Selected quantitative features help expedite decision making and improve efficiency in studies to predict risk of rectal cancer surgery.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.93-103
• /
• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.6
• /
• pp.992-1001
• /
• 2022

The maritime traffic analysis has been conducted in various ways to solve problems arising from the complex marine environment. However, recent trends in the maritime industry, such as the development of the maritime autonomous surface ships (MASS), suggest that maritime traf ic analysis needs change. Accordingly, based on the studies conducted over the past decade for improvements, automatic identification system (AIS) data is mainly used for maritime traffic analysis. Moreover, the use of geographic information that directly af ects ship operation is relatively insufficient. Therefore, this study presented a method of using a combination of shape format data and AIS data to enhance maritime traffic analysis in preparation for the commercialization of autonomous ships. Consequently, extractable marine traffic characteristics were presented when shape format data were used for marine traffic analysis. This is expected to be used for marine traffic analysis for the introduction of autonomous ships in the future.

• Journal of Advanced Technology Convergence
• /
• v.2 no.3
• /
• pp.17-24
• /
• 2023

I am designing a research paper with the aim of studying hybrid vehicles. Hybrid vehicles, as the next-generation automobiles, feature a combination of internal combustion engines and battery engines, resulting in a revolutionary reduction in fuel consumption and harmful gas emissions compared to conventional vehicles. The electric motor in hybrid cars derives power from a high-voltage battery installed within the vehicle, which is recharged during vehicle motion. In contrast to traditional cars, which often experience energy losses due to idling caused by traffic congestion, hybrid systems optimize efficiency by skillfully managing the interplay between the internal combustion engine and the electric motor. This approach effectively addresses the inherent drawbacks of gasoline or diesel engines.Hybrid cars offer an array of benefits, including improved fuel efficiency, environmental friendliness, cost-effectiveness, and reduced noise emission. Consequently, they are progressively becoming a favored alternative among a growing number of individuals. This research endeavor has the potential to contribute towards curbing environmental pollution and dedicating efforts to future automotive research.

• Journal of Biosystems Engineering
• /
• v.22 no.2
• /
• pp.237-246
• /
• 1997

Shucking(removing the meat from the shell) an oyster requires that the muscle attachments to the two shell valves and the hinge be severed. Described here is the computer vision software needed to locate the oyster hinge line so it can be automatically severed, one step in development of an automated oyster shucker. Oysters are first prepared by washing and trimming off a small shell piece on the oyster hinge end to provide access to the outer hinge surface. A computer vision system employing a color video comera then gabs an image of the hinge end of the oyster shell. This image is Processed by the computer using software. The software is a combination of commercially available and custom written routines that locate the oyster hinge. The software uses four feature variables, circularity, rectangularity, aspect-ration, and Euclidian distance, to distinguish the hinge object from other dark colored objects on the hinge end of the oyster. Several techniques, including shrink-expand, thresholding, and others, were used to secure an image that could be reliably and efficiently processed to locate the oyster hinge line.