• Journal of radiological science and technology
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• v.28 no.4
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• pp.317-325
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• 2005

The recent medical treatment guidelines and the development of information technology make hospitals reduce the expense in surrounding environment and it requires improving the quality of medical treatment of the hospital. That is, with the new guidelines and technology, hospital business escapes simple fee calculation and insurance claim center. Moreover, MIS(Medical Information System), PACS(Picture Archiving and Communications System), OCS(Order Communicating System), EMR(Electronic Medical Record), DSS(Decision Support System) are also developing. Medical Information System is evolved toward integration of medical IT and situation si changing with increasing high speed in the ICT convergence. These changes and development of ubiquitous environment require fundamental change of medical information system. Mobile medical information system refers to construct wireless system of hospital which has constructed in existing environment. Through RFID development in existing system, anyone can log on easily to Internet whenever and wherever. RFID is one of the technologies for Automatic Identification and Data Capture(AIDC). It is the core technology to implement Automatic processing system. This paper provides a comprehensive basic review of RFID model in Korea and suggests the evolution direction for further advanced RFID application services. In addition, designed and implemented DB server's agent program and Client program of Mobile application that recognized RFID tag and patient data in the ubiquitous environments. This system implemented medical information system that performed patient data based EMR, HIS, PACS DB environments, and so reduced delay time of requisition, medical treatment, lab.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.119-135
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• 2022

This paper used dynamic centrifuge tests to examine the seismic response for a deep temporary retaining wall with four input motions of 100, 1,000, and 2,400 years of return periods. The centrifuge model was designed based on an actual deep excavation design with a 50 m maximum excavation depth. The model backfill was prepared with dry silica sand at a relative density of 55%, and the retaining wall was modeled as a 24.8 m height diaphragm wall supported by struts. Acceleration response was amplified at the backfill surface, top of the wall, and near bedrock. However, in the middle of the model, input motion was de-amplified. The member forces of the wall and strut induced by the seismic load, which excited, were compared with the member force at rest condition. The wall's maximum negative and positive moments were increased to 36% and 10% compared to the maximum moment at rest. The maximum axial force increases to 70% of the at rest axial force on the bottom strut. The equivalent static analysis using Mononobe-Okabe (M-O) and Seed-Whitman (S-W) seismic earth pressures were compared to the centrifuge results. Considering the bending moment, the analysis results with the M-O theory underestimates but that with the S-W theory overestimates.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.129-141
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• 2024

Amidst the acceleration of digital transformation across various sectors, the financial market is increasingly focusing on the development of digital and electronic payment methods, including currency. Among these, Central Bank Digital Currencies (CBDC) are emerging as future digital currencies that could replace physical cash. They are stable, not subject to value fluctuation, and can be exchanged one-to-one with existing physical currencies. Recently, both domestic and international efforts are underway in researching and developing CBDCs. However, current CBDC systems face scalability issues such as delays in processing large transactions, response times, and network congestion. To build a universal CBDC system, it is crucial to resolve these scalability issues, including the low throughput and network overload problems inherent in existing blockchain technologies. Therefore, this study proposes a solution based on reinforcement learning for handling large-scale data in a CBDC environment, aiming to improve throughput and reduce network congestion. The proposed technology can increase throughput by more than 64 times and reduce network congestion by over 20% compared to existing systems.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.1-21
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• 2012

In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of radiological science and technology
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• v.35 no.3
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• pp.265-273
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• 2012

Aquisition of accurate beam data is very important to calculate a reliable dose distribution of the treatment planning system for small radiation fields in intensity-modulated radiation therapy(IMRT) and stereotactic radiosurgery(SRS). For the measurement of small fields, the choice of a suitable detector is important due to the shape gradient in profile penumbra, the lack of lateral electronic equilibrium, and the effect of effective detector volume. Therefore, this study was to analyze the dosimetric characteristics of various detectors in measurement of beam data for small fields of linear accelerator. 0.01cc and 0.13cc ion chambers (CC01 and CC13) and a stereotactic diode detector(SFD) were used for measurement of small fields. The beam data, including the percent depth dose, output factor, and beam profile were acquired under 6 MV and 15 MV photon beams. Measurements were performed with the field size ranging from $2{\times}2cm^2$ to $5{\times}5cm^2$. For $2{\times}2cm^2$ field size, the differences of the ratios of $PDD_{20}$ and $PDD_{10}$ measured by CC01 and SFD detectors were 1.02% and 0.12% for 6 MV and 15 MV photon beams, respectively. For field sizes larger than $3{\times}3cm^2$, the differences of values of $PDD_{20}/PDD_{10}$ obtained from each detector were 1.15% and 0.71% for 6 MV and 15 MV photon beams, respectively. The output factors obtained from CC01 and SFD for $2{\times}2cm^2$ field size were within 0.5% and 1.5% for 6 MV and 15 MV, respectively. The differences in output factor of three detectors for $3{\times}3cm^2$ to $5{\times}5cm^2$ field sizes were within 0.5%. Profile penumbras measured by the SFD, CC01, and CC13 detectors at three depths were average 2.7 mm and 3.5 mm, 3.4 mm and 4.3 mm, and 5.2 mm and 6.1 mm for 6 MV and 15 MV photon beams, respectively. In conclusion, it could be possible to use of the CC01 and SFD detectors for the measurement of percent depth dose and output factor for $2{\times}2cm^2$ field size, and to use of three detectors for $3{\times}3cm^2$ to $5{\times}5cm^2$ field sizes. CC01 and SFD detectors, consider ably smaller than the radiation field, should be used in order to accurately measure the profile penumbra for small field sizes.

• Journal of the Korean GEO-environmental Society
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• v.19 no.10
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• pp.13-19
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• 2018

Following the 2016 Gyeongju earthquake, the Pohang Earthquake occurred in 2017, and the south-east region in Korea is under the threat of an earthquake. Especially, in the Pohang Earthquake, the liquefaction phenomenon occurred in the sedimentation area of the coast, and preparation of countermeasures is very important. The soil liquefaction can affect the underground facilities directly as well as various structures on the ground. Therefore, it is necessary to identify the liquefaction risk of facilities and the structures against the possible earthquakes and to prepare countermeasures to minimize them. In this study, we investigated the seismic liquefaction risk about the electric power utility tunnels in the southeast area where the earthquake occurred in Korea recently. In the analysis of seismic liquefaction risk, the earthquake with return period 1000 years and liquefaction potential index are used. The liquefaction risk analysis was conducted in two stages. In the first stage, the liquefaction risk was analyzed by calculating the liquefaction potential index using the ground survey data of the location of electric power utility tunnels in the southeast region. At that time, the seismic amplification in soil layer was considered by soil amplification factor according to the soil classification. In the second stage, the liquefaction risk analysis based on the site response analyses inputted 3 earthquake records were performed for the locations determined to be dangerous from the first step analysis, and the final liquefaction potential index was recalculated. In the analysis, the site investigation data were used from the National Geotechnical Information DB Center. Finally, it can be found that the proposed two stage assessments for liquefaction risk that the macro assessment of liquefaction risk for the underground facilities including the electric power utility tunnel in Korea is carried out at the first stage, and the second risk assessment is performed again with site response analysis for the dangerous regions of the first stage assessment is reasonable and effective.

• Journal of the Korea Concrete Institute
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• v.25 no.5
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• pp.485-496
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• 2013

In recent years, frequent terror or military attacks by explosion or impact accidents have occurred. Examplary case of these attacks were World Trade Center collapse and US Department of Defense Pentagon attack on Sept. 11 of 2001. These attacks of the civil infrastructure have induced numerous casualties and property damage, which raised public concerns and anxiety of potential terrorist attacks. However, a existing design procedure for civil infrastructures do not consider a protective design for extreme loading scenario. Also, the extreme loading researches of prestressed concrete (PSC) member, which widely used for nuclear containment vessel, gas tank, bridges, and tunnel, are insufficient due to experimental limitations of loading characteristics. To protect concrete structures against extreme loading such as explosion and impact with high strain rate, understanding of the effect, characteristic, and propagation mechanism of extreme loadings on structures is needed. Therefore, in this paper, to evaluate the impact resistance capacity and its protective performance of bi-directional unbonded prestressed concrete member, impact tests were carried out on $1400mm{\times}1000mm{\times}300mm$ for reinforced concrete (RC), prestressed concrete without rebar (PS), prestressed concrete with rebar (PSR, general PSC) specimens. According to test site conditions, impact tests were performed with 14 kN impactor with drop height of 10 m, 5 m, 4 m for preliminary tests and 3.5 m for main tests. Also, in this study, the procedure, layout, and measurement system of impact tests were established. The impact resistance capacity was measured using crack patterns, damage rates, measuring value such as displacement, acceleration, and residual structural strength. The results can be used as basic research references for related research areas, which include protective design and impact numerical simulation under impact loading.