• Korean Journal of Radiology
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• v.22 no.12
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• pp.2017-2025
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• 2021

Objective: To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods: A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results: The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33-0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance (p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion: The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.3
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• pp.151-158
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• 2024

Although on-device artificial intelligence (AI) has gained attention to diagnosing machine faults in real time, most previous studies did not consider the model retraining and redeployment processes that must be performed in real-world industrial environments. Our study addresses this challenge by proposing an on-device AI-based real-time machine fault diagnosis system that utilizes continual learning. Our proposed system includes a lightweight convolutional neural network (CNN) model, a continual learning algorithm, and a real-time monitoring service. First, we developed a lightweight 1D CNN model to reduce the cost of model deployment and enable real-time inference on the target edge device with limited computing resources. We then compared the performance of five continual learning algorithms with three public bearing fault datasets and selected the most effective algorithm for our system. Finally, we implemented a real-time monitoring service using an open-source data visualization framework. In the performance comparison results between continual learning algorithms, we found that the replay-based algorithms outperformed the regularization-based algorithms, and the experience replay (ER) algorithm had the best diagnostic accuracy. We further tuned the number and length of data samples used for a memory buffer of the ER algorithm to maximize its performance. We confirmed that the performance of the ER algorithm becomes higher when a longer data length is used. Consequently, the proposed system showed an accuracy of 98.7%, while only 16.5% of the previous data was stored in memory buffer. Our lightweight CNN model was also able to diagnose a fault type of one data sample within 3.76 ms on the Raspberry Pi 4B device.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.993-1001
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• 2024

The TANDEM project is a European initiative funded under the EURATOM program. The project started on September 2022 and has a duration of 36 months. TANDEM stands for Small Modular ReacTor for a European sAfe aNd Decarbonized Energy Mix. Small Modular Reactors (SMRs) can be hybridized with other energy sources, storage systems and energy conversion applications to provide electricity, heat and hydrogen. Hybrid energy systems have the potential to strongly contribute to the energy decarbonization targeting carbon-neutrality in Europe by 2050. However, the integration of nuclear reactors, particularly SMRs, in hybrid energy systems, is a new R&D topic to be investigated. In this context, the TANDEM project aims to develop assessments and tools to facilitate the safe and efficient integration of SMRs into low-carbon hybrid energy systems. An open-source "TANDEM" model library of hybrid system components will be developed in Modelica language which, by coupling, will extend the capabilities of existing tools implemented in the project. The project proposes to specifically address the safety issues of SMRs related to their integration into hybrid energy systems, involving specific interactions between SMRs and the rest of the hybrid systems; new initiating events may have to be considered in the safety approach. TANDEM will study two hybrid systems covering the main trends of the European energy policy and market evolution at 2035's horizon: a district heating network and power supply in a large urban area, and an energy hub serving energy conversion systems, including hydrogen production; the energy hub is inspired from a harbor-like infrastructure. TANDEM will provide assessments on SMR safety, hybrid system operationality and techno-economics. Societal considerations will also be encased by analyzing European citizen engagement in SMR technology safety.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.27-39
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• 2024

Soil texture is determined by the proportions of sand, silt, and clay within the soil, which influence characteristics such as porosity, water retention capacity, electrical conductivity (EC), and pH. Traditional classification of soil texture requires significant sample preparation including oven drying to remove organic matter and moisture, a process that is both time-consuming and costly. This study aims to explore an alternative method by developing an AI model capable of predicting soil texture from images of pre-sorted soil samples using computer vision and deep learning technologies. Soil samples collected from agricultural fields were pre-processed using sieve analysis and the images of each sample were acquired in a controlled studio environment using a smartphone camera. Color distribution ratios based on RGB values of the images were analyzed using the OpenCV library in Python. A convolutional neural network (CNN) model, built on PyTorch, was enhanced using Digital Image Processing (DIP) techniques and then trained across nine distinct conditions to evaluate its robustness and accuracy. The model has achieved an accuracy of over 80% in classifying the images of pre-sorted soil samples, as validated by the components of the confusion matrix and measurements of the F1 score, demonstrating its potential to replace traditional experimental methods for soil texture classification. By utilizing an easily accessible tool, significant time and cost savings can be expected compared to traditional methods.

• The Journal of Bigdata
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• v.4 no.2
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• pp.1-12
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• 2019

As Artificial Intelligence (AI) technology develops, it is applied to various fields such as image, voice, and text. AI has shown fine results in certain areas. Researchers have tried to predict the stock market by utilizing artificial intelligence as well. Predicting the stock market is known as one of the difficult problems since the stock market is affected by various factors such as economy and politics. In the field of AI, there are attempts to predict the ups and downs of stock price by studying stock price patterns using various machine learning techniques. This study suggest a way of predicting stock price patterns based on the Convolutional Neural Network(CNN) among machine learning techniques. CNN uses neural networks to classify images by extracting features from images through convolutional layers. Therefore, this study tries to classify candlestick images made by stock data in order to predict patterns. This study has two objectives. The first one referred as Case 1 is to predict the patterns with the images made by the same-day stock price data. The second one referred as Case 2 is to predict the next day stock price patterns with the images produced by the daily stock price data. In Case 1, data augmentation methods - random modification and Gaussian noise - are applied to generate more training data, and the generated images are put into the model to fit. Given that deep learning requires a large amount of data, this study suggests a method of data augmentation for candlestick images. Also, this study compares the accuracies of the images with Gaussian noise and different classification problems. All data in this study is collected through OpenAPI provided by DaiShin Securities. Case 1 has five different labels depending on patterns. The patterns are up with up closing, up with down closing, down with up closing, down with down closing, and staying. The images in Case 1 are created by removing the last candle(-1candle), the last two candles(-2candles), and the last three candles(-3candles) from 60 minutes, 30 minutes, 10 minutes, and 5 minutes candle charts. 60 minutes candle chart means one candle in the image has 60 minutes of information containing an open price, high price, low price, close price. Case 2 has two labels that are up and down. This study for Case 2 has generated for 60 minutes, 30 minutes, 10 minutes, and 5minutes candle charts without removing any candle. Considering the stock data, moving the candles in the images is suggested, instead of existing data augmentation techniques. How much the candles are moved is defined as the modified value. The average difference of closing prices between candles was 0.0029. Therefore, in this study, 0.003, 0.002, 0.001, 0.00025 are used for the modified value. The number of images was doubled after data augmentation. When it comes to Gaussian Noise, the mean value was 0, and the value of variance was 0.01. For both Case 1 and Case 2, the model is based on VGG-Net16 that has 16 layers. As a result, 10 minutes -1candle showed the best accuracy among 60 minutes, 30 minutes, 10 minutes, 5minutes candle charts. Thus, 10 minutes images were utilized for the rest of the experiment in Case 1. The three candles removed from the images were selected for data augmentation and application of Gaussian noise. 10 minutes -3candle resulted in 79.72% accuracy. The accuracy of the images with 0.00025 modified value and 100% changed candles was 79.92%. Applying Gaussian noise helped the accuracy to be 80.98%. According to the outcomes of Case 2, 60minutes candle charts could predict patterns of tomorrow by 82.60%. To sum up, this study is expected to contribute to further studies on the prediction of stock price patterns using images. This research provides a possible method for data augmentation of stock data.

• Journal of Astronomy and Space Sciences
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• v.22 no.1
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• pp.1-12
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• 2005

The Korean VLBI Network (KVN) will open a new field of research in astronomy, geodesy and earth science using the newest three Elm radio telescopes. This will expand our ability to look at the Universe in the millimeter regime. Imaging capability of radio interferometry is highly dependent upon the antenna configuration, source size, declination and the shape of target. In this paper, imaging simulations are carried out with the KVN system configuration. Five test images were used which were a point source, multi-point sources, a uniform sphere with two different sizes compared to the synthesis beam of the KVN and a Very Large Array (VLA) image of Cygnus A. The declination for the full time simulation was set as +60 degrees and the observation time range was -6 to +6 hours around transit. Simulations have been done at 22GHz, one of the KVN observation frequency. All these simulations and data reductions have been run with the Astronomical Image Processing System (AIPS) software package. As the KVN array has a resolution of about 6 mas (milli arcsecond) at 220Hz, in case of model source being approximately the beam size or smaller, the ratio of peak intensity over RMS shows about 10000:1 and 5000:1. The other case in which model source is larger than the beam size, this ratio shows very low range of about 115:1 and 34:1. This is due to the lack of short baselines and the small number of antenna. We compare the coordinates of the model images with those of the cleaned images. The result shows mostly perfect correspondence except in the case of the 12mas uniform sphere. Therefore, the main astronomical targets for the KVN will be the compact sources and the KVN will have an excellent performance in the astrometry for these sources.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.241-263
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• 2010

KoFlux is a Korean network of micrometeorological tower sites that use eddy covariance methods to monitor the cycles of energy, water, and carbon dioxide between the atmosphere and the key terrestrial ecosystems in Korea. KoFlux embraces the mission of AsiaFlux, i.e. to bring Asia's key ecosystems under observation to ensure quality and sustainability of life on earth. The main purposes of KoFlux are to provide (1) an infrastructure to monitor, compile, archive and distribute data for the science community and (2) a forum and short courses for the application and distribution of knowledge and data between scientists including practitioners. The KoFlux community pursues the vision of AsiaFlux, i.e., "thinking community, learning frontiers" by creating information and knowledge of ecosystem science on carbon, water and energy exchanges in key terrestrial ecosystems in Asia, by promoting multidisciplinary cooperations and integration of scientific researches and practices, and by providing the local communities with sustainable ecosystem services. Currently, KoFlux has seven sites in key terrestrial ecosystems (i.e., five sites in Korea and two sites in the Arctic and Antarctic). KoFlux has systemized a standardized data processing based on scrutiny of the data observed from these ecosystems and synthesized the processed data for constructing database for further uses with open access. Through publications, workshops, and training courses on a regular basis, KoFlux has provided an agora for building networks, exchanging information among flux measurement and modelling experts, and educating scientists in flux measurement and data analysis. Despite such persistent initiatives, the collaborative networking is still limited within the KoFlux community. In order to break the walls between different disciplines and boost up partnership and ownership of the network, KoFlux will be housed in the National Center for Agro-Meteorology (NCAM) at Seoul National University in 2011 and provide several core services of NCAM. Such concerted efforts will facilitate the augmentation of the current monitoring network, the education of the next-generation scientists, and the provision of sustainable ecosystem services to our society.

• Spatial Information Research
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• v.22 no.3
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• pp.23-24
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• 2014

NGII (National Geographic Information Institute) has been providing VRS (Virtual Reference Station) service so that could determine precise positioning in real time since 2007. However, since the VRS service has to maintain the connected status with VRS server, the number of users who can use VRS service are limited by capacity of VRS server. To solve this problem, NGII has been providing FKP (Virtual Reference Station) service using one way telecommunication from November 1, 2012. Therefore, it is predicted that the usage of FKP service will increase in public surveying and cadastral resurveying in the future. However, the studies with respect to analysis of FKP precision for applying to public surveying and cadastral resurveying is not conducted enough. In this study, to analyse the application possibility of FKP on the public surveying and cadastral resurveying, the two kind analysis were performed. First is the analysis of accuracy according to the configuration of reference station of FKP and VRS. One is consisted of same reference stations, another is consisted of different reference stations. Second is the accuracy anlalysis of horizontal and vertical positioning acquiring VRS and FKP data in various measurement environment based on VRS regulation. Result of first study, Positioning accuracy according to the configuration of the reference stations satisfies related regulation. However, accuracy of FKP in case of different reference stations is worse than in case of same reference stations.. The result of second test shows that the horizontal precision of FKP and VRS in good measurement environment satisfy the allowed precision. However, in some case, horizontal precision of FKP and VRS in poor measurement environment exceed the allowed precision. In addition, the number of exceeding the allowed precision in the FKP is more than the VRS. The vertical precision of the VRS satisfy related work provision. In conclusion, the result of this study shows that the FKP only in open area should be used for public survey and cadastral resurvey. Therefore the additional studies with respect to the improvement of FKP precision should be conducted.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.6
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• pp.16-24
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• 2020

The purpose of this study is to analyze the usage behavior of Seoullo 7017, the first public garden in Korea, to understand the usage status by analyzing blogs, and to present usage behavior and improvement plans for Seoullo 7017. From June 2017 to May 2020, after Seoullo 7017 was open to citizens, character data containing 'Seoullo 7017' in the title and contents of NAVER and·DAUM blogs were converted to text mining and socialization, a Big Data technique. The analysis was conducted using social network analysis. The summary of the research results is as follows. First of all, the ratio of men and women searching for Seoullo 7017 online is similar, and the regions that searched most are in the order of Seoul and Gyeonggi, and those in their 40s and 50s were the most interested. In other words, it can be seen that there is a lack of interest in regions other than Seoul and Gyeonggi and among those in their 10s, 20s, and 30s. The main behaviors of Seoullo 7017 are' night view' and 'walking', and the factors that affect culture and art are elements related to culture and art. If various programs and festivals are opened and actively promoted, the main behavior will be more varied. On the other hand, the main behavior that the users of Seoullo 7017 want is 'sit', which is a static behavior, but the physical conditions are not sufficient for the behavior to occur. Therefore, facilities that can cause sitting behavior, such as shades and benches must be improved to meet the needs of visitors. The peculiarity of the change in the behavior of Seoullo 7017 is that it is recognized as a good place to travel alone and a good place to walk alone as a public multi-use facility and group activities are restricted due to COVID-19. Accordingly, in a situation like the COVD-19 pandemic, more diverse behaviors can be derived in facilities where people can take a walk, etc., and the increase of various attractions and the satisfaction of users can be increased. Seoullo 7017, as Korea's first public pedestrian area, was created for urban regeneration and the efficient use of urban resources in areas beyond the meaning of public spaces and is a place with various values such as history, nature, welfare, culture, and tourism. However, as a result of the use behavior analysis, various behaviors did not occur in Seoullo 7017 as expected, and elements that hinder those major behaviors were derived. Based on these research results, it is necessary to understand the usage behavior of Seoullo 7017 and to establish a plan for spatial system and facility improvement, so that Seoullo 7017 can be an important place for urban residents and a driving force to revitalize the city.

• Journal of the Korean Institute of Landscape Architecture
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• v.44 no.2
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• pp.83-95
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• 2016

Strategies for parks and open spaces in the 21st century have moved from focusing on specific elements, such as quantitative growth and ecological and recreational aspects, to green infrastructure, which refers to a multi-functional network of open and green spaces offering a range of benefits. In the case of London, green infrastructure is realised as an integral part of urban infrastructure, involving physical and social infrastructure as well as practical spatial planning at the local level within statutory urban planning as part of a continuously developing green infrastructure framework with a theoretical basis. Taking this perspective, the present study looks at alterations to and developments in green infrastructure policies in the London Plan, the green grid framework as detailed in the city's strategic implementation of green infrastructure. Various trends and characteristics of the policies adopted in the London Plan and some implications are deduced, with three main results being identified. The first is a clear division of roles among the national government, Greater London Authority and borough councils, with local plans established under the guidance of the National Planning Policy Framework (NPPF) and the London Plan. Green infrastructure policies in the London Plan have been applied at a high rate in the boroughs' local plans, which leads to another, linked point. Secondly, green infrastructure policies and the green grid as an implementation framework have been consistently extended and developed through consolidating the London Plan, despite the change of government. Finally, in order to achieve the London Plan, the Mayor of London implemented policies by partnership and supporting programmes for London boroughs. Recently, the Seoul Metropolitan Authority introduced a parks and green spaces development policy, but the London case remains a good example; this is because green infrastructure policies in London were not a manifesto pledge but rather have been continuously and consistently advanced regardless of party politics and thus realised as long-term planning.