• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.9-14
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• 2024

Hue, value, and chroma are the fundamental colour components used in colour property research to identify colour in the digital media environment. In the Munsell colour system, which is based on the characteristics of visual perception, the basic properties are classified into hue, value, and chroma. The methods for recording these three properties can be divided into the colour appearance system and the colour mixing system: in the former, they are documented based on a colour chart that focuses on visual perception, and in the latter, accurate numerical records are kept without concern for discolouration. Colour terminology is crucial for conveying and expressing colours, and colours can be classified and defined according to the combination of hue, value, and chroma. With the development of various media, it has become possible to represent a range of colours previously unachievable, necessitating basic research into the characteristics of colour perception by further subdividing digital-oriented colour studies. In this study, we conducted psychophysical experiments to identify and analyse the categories of value and chroma needed to recognise each colour among the ten representative colours of the Munsell colour system, based on visual perception on a display. This study analyses the results of these experiments, defines their significance as foundational research data on colour perception characteristics, and suggests directions for future research.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.78-88
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• 2024

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.206-212
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• 2024

Purpose: This study conducted research on the sharing of information to enhance the survival rate of emergency patients by swiftly transferring them to appropriate hospitals through sharing the patients' conditions, treatment histories, and transportation information with the Maritime Police Agency and relevant agencies when emergencies occur in the marine environment. Methods: In this study, emergency patient information classified in a smartphone app was received, stored, and transmitted using the LoRa communication method by electronic triage tags, and the transmitted emergency patient information was implemented to be collected in real-time through a hybrid triage system along with LoRa receivers. Results: Through the hybrid triage system, it was possible to receive emergency patient information according to the distance or confirm delayed reception. It was observed that most data were received when the distance was short, while data reception was unsuccessful in relatively longer distances. Conclusion: It was confirmed that in mass disaster environments where internet communication is impossible, rapid and accurate understanding of casualty information at disaster sites and appropriate disaster responses can be achieved using self-networking methods such as LoRa communication. However, limitations inherent in communication methods were also recognized. Further research on various communication methods is required to collect emergency patient information and transfer them to appropriate hospitals in situations where internet communication is unavailable.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.3
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• pp.281-301
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• 2024

The power cable tunnels which are part of the underground transmission line project, are constructed using the shield TBM method. The disc cutter among the shield TBM components plays an important role in breaking rock mass. Efficient tunnel construction is possible only when appropriate replacement occurs as the wear limit is reached or damage such as uneven wear occurs. A study was conducted to determine the wear conditions of disc cutter using a deep learning algorithm based on real-time measurement data of wear and rotation speed. Based on the results of full-scaled tunnelling tests, it was confirmed that measurement data was obtained differently depending on the wear conditions of disc cutter. Using real-time measurement data, an algorithm was developed to determine disc cutter wear characteristics based on a convolutional neural network model. Distributional patterns of data can be learned through CNN filters, and the performance of the model that can classify uniform wear and uneven wear through these pattern features.

• Journal of Platform Technology
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• v.11 no.5
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• pp.59-71
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• 2023

This paper proposes a method of estimating the illumination chromaticity of a scene in which an image is taken. Storring and Bianco proposed a method of estimating illuminant chromaticity using skin color. Storring et al. used skin color distribution characteristics and black body locus, but there is a problem that the link between the locus and CIE-xy data is reduced. Bianco et al. estimated the illuminant chromaticity by comparing the skin color distribution in standard lighting with the skin color distribution in the input image. This method is difficult to measure and secure as much skin color as possible in various illumination. The proposed method can estimate the illuminant chromaticity for any input image by analyzing the relationship between the skin color information and the illuminant chromaticity. The estimation method is divided into an analysis stage and a test stage, and the data set was classified into an analysis group and a test group and used. Skin chromaticity is calculated by obtaining skin color areas from all input images of the analysis group, respectively. A mapping is obtained by analyzing the correlation between the average set of skin chromaticity and the reference illuminant chromaticity set. The calculated mapping is applied to all input images of the analysis group to estimate the illuminant chromaticity, calculate the error with the reference illuminant chromaticity, and repeat the above process until there is no change in the error to obtain a stable mapping. The obtained mapping is applied to the test group images similar to the analysis stage to estimate the illuminant chromaticity. Since there is no independent data set containing skin area and illuminant reference information, the experimental data set was made using some of the images of the Intel TAU data set. Compared to Finlayson, a similar theory-based existing method, it showed performance improvement of more than 40%, Zhang 11%, and Kim 16%.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.

• Journal of the Korean Society of Radiology
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• v.85 no.4
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• pp.714-726
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• 2024

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.47-56
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• 2024

This paper proposes a method for fine-tuning BERT-based natural language processing models to perform sentiment analysis on Korean review data. By varying the input sequence length during this process and comparing the performance, we aim to explore the optimal performance according to the input sequence length. For this purpose, text review data collected from the clothing shopping platform M was utilized. Through web scraping, review data was collected. During the data preprocessing stage, positive and negative satisfaction scores were recalibrated to improve the accuracy of the analysis. Specifically, the GPT-4 API was used to reset the labels to reflect the actual sentiment of the review texts, and data imbalance issues were addressed by adjusting the data to 6:4 ratio. The reviews on the clothing shopping platform averaged about 12 tokens in length, and to provide the optimal model suitable for this, five BERT-based pre-trained models were used in the modeling stage, focusing on input sequence length and memory usage for performance comparison. The experimental results indicated that an input sequence length of 64 generally exhibited the most appropriate performance and memory usage. In particular, the KcELECTRA model showed optimal performance and memory usage at an input sequence length of 64, achieving higher than 92% accuracy and reliability in sentiment analysis of Korean review data. Furthermore, by utilizing BERTopic, we provide a Korean review sentiment analysis process that classifies new incoming review data by category and extracts sentiment scores for each category using the final constructed model.

• The Transactions of the Korea Information Processing Society
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• v.13 no.8
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• pp.374-381
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• 2024

Documentation repositories on GitHub are used to share information that is helpful in performing various tasks. Popular documentation repositories have an advantage in attracting contributors who can help manage and extend documentation repository. Therefore, it is important to understand the characteristic of documentation repositories helpful to obtain popularity for developing strategies attracting attention of users. This paper presents a study on GitHub documentation repositories. To conduct the study, we collected 566 documentation repositories from GitHub and manually categorized their topic into 30 topics. Based on the stargazer score of the collected documentation repositories, we divided the collected documentation repositories into popular and unpopular documentation repository groups and investigated the topics in the popular documentation group. Then we statistically examined the differences in README characteristics of the popular and unpopular documentation repository groups. As a result, we found that the studied documentation repositories have 23 popular topics. We also found that the popular and unpopular documentation repository groups have differences in 5 README characteristics. The result of our study indicates that what documentation repository become popular in GitHub.

• Radiation Oncology Journal
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• v.23 no.2
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• pp.61-70
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• 2005

Purpose: Uterine cervix cancer is one of the most prevalent women cancer in Korea. We analysed published papers in Korea with comparing Patterns of Care Study (PCS) articles of United States and Japan for the purpose of developing and processing Korean PCS. Materials and Methods: We searched PCS related foreign-produced papers in the PCS homepage (212 articles and abstracts) and from the Pub Med to find Structure and Process of the PCS. To compare their study with Korean papers, we used the internet site 'Korean Pub Med' to search 99 articles regarding uterine cervix cancer and radiation therapy. We analysed Korean paper by comparing them with selected PCS papers regarding Structure, Process and Outcome and compared their items between the period of before 1980's and 1990's. Results: Evaluable papers were 28 from United States, 10 from the Japan and 73 from the Korea which treated cervix PCS items. PCS papers for United States and Japan commonly stratified into $3\~4$ categories on the bases of the scales characteristics of the facilities, numbers of the patients, doctors, Researchers restricted eligible patients strictly. For the process of the study, they analysed factors regarding pretreatment staging in chronological order, treatment related factors, factors in addition to FIGO staging and treatment machine. Papers in United States dealt with racial characteristics, socioeconomic characteristics of the patients, tumor size (6), and bilaterality of parametrial or pelvic side wail invasion (5), whereas papers from Japan treated of the tumor markers. The common trend in the process of staging work-up was decreased use of lymphangiogram, barium enema and increased use of CT and MRI over the times. The recent subject from the Korean papers dealt with concurrent chemoradiotherapy (9 papers), treatment duration (4), tumor markers (B) and unconventional fractionation. Conclusion: By comparing papers among 3 nations, we collected items for Korean uterine cervix cancer PCS. By consensus meeting and close communication, survey items for cervix cancer PCS were developed to measure structure, process and outcome of the radiation treatment of the cervix cancer. Subsequent future research will focus on the use of brachytherapy and its impact on outcome including complications. These finding and future PCS studies will direct the development of educational programs aimed at correcting identified deficits in care.