• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Radiation Oncology Journal
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• v.29 no.2
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• pp.91-98
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• 2011

Purpose: To assess the usefulness of implanted fiducial markers in the setup of hypofractionated radiotherapy for prostate cancer patients by comparing a fiducial marker matched setup with a pelvic bone match. Materials and Methods: Four prostate cancer patients treated with definitive hypofractionated radiotherapy between September 2009 and August 2010 were enrolled in this study. Three gold fiducial markers were implanted into the prostate and through the rectum under ultrasound guidance around a week before radiotherapy. Glycerin enemas were given prior to each radiotherapy planning CT and every radiotherapy session. Hypofractionated radiotherapy was planned for a total dose of 59.5 Gy in daily 3.5 Gy with using the Novalis system. Orthogonal kV X-rays were taken before radiotherapy. Treatment positions were adjusted according to the results from the fusion of the fiducial markers on digitally reconstructed radiographs of a radiotherapy plan with those on orthogonal kV X-rays. When the difference in the coordinates from the fiducial marker fusion was less than 1 mm, the patient position was approved for radiotherapy. A virtual bone matching was carried out at the fiducial marker matched position, and then a setup difference between the fiducial marker matching and bone matching was evaluated. Results: Three patients received a planned 17-fractionated radiotherapy and the rest underwent 16 fractionations. The setup error of the fiducial marker matching was $0.94{\pm}0.62$ mm (range, 0.09 to 3.01 mm; median, 0.81 mm), and the means of the lateral, craniocaudal, and anteroposterior errors were $0.39{\pm}0.34$ mm, $0.46{\pm}0.34$ mm, and $0.57{\pm}0.59$ mm, respectively. The setup error of the pelvic bony matching was $3.15{\pm}2.03$ mm (range, 0.25 to 8.23 mm; median, 2.95 mm), and the error of craniocaudal direction ($2.29{\pm}1.95$ mm) was significantly larger than those of anteroposterior ($1.73{\pm}1.31$ mm) and lateral directions ($0.45{\pm}0.37$ mm), respectively (p<0.05). Incidences of over 3 mm and 5 mm in setup difference among the fractionations were 1.5% and 0% in the fiducial marker matching, respectively, and 49.3% and 17.9% in the pelvic bone matching, respectively. Conclusion: The more precise setup of hypofractionated radiotherapy for prostate cancer patients is feasible with the implanted fiducial marker matching compared with the pelvic bony matching. Therefore, a less marginal expansion of planning target volume produces less radiation exposure to adjacent normal tissues, which could ultimately make hypofractionated radiotherapy safer.

• Science of Emotion and Sensibility
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• v.22 no.1
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• pp.23-34
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• 2019

The U-Healthcare era has evolved with the development of the Internet of things (IoT) in the early stages of being connected as a society. Already, many changes such as increased well-being and the extension of human life are becoming evident across cultures. Korea entered the growing group of aging societies in 2017, and its silver industry is expected to grow rapidly by adopting the IoT of a super-connected society. In particular, the senior shift phenomenon has resulted in increased interest in the promotion of the health and well-being of the emergent silver generation which, unlike the existing silver generation, is highly active and wields great economic power. This study conducted in-depth interviews to investigate the characteristics of the new silver generation, and to develop the design for a wearable serious game that intends to boost the interest of the elderly in exercise and fitness activities according to their personalized physical training regimes as prescribed by the U-Hospital service. The usage scenario of this wearable serious game for the 'U-silver generation' is derived from social necessity. Medical professionals can utilize this technology to conduct health examinations and to monitor the rehabilitation of senior patients. The elderly can also use this tool to request checkups or to interface with their healthcare providers. The wearable serious game is further aimed at mitigating concerns about the deterioration of the physical functions of the silver generation by applying personalized exercise prescriptions. The present investigation revealed that it is necessary to merge the on / off line community activities to meet the silver generation's daily needs for connection and friendship. Further, the sustainability of the serious game must be enhanced through the inculcation of a sense of accomplishment as a player rises through the levels of the game. The proposed wearable serious game is designed specifically for the silver generation that is inexperienced in using digital devices: simple game rules are applied to a familiar interface grounded on the gourmet travels preferred by the target players to increase usability.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.63-76
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• 2018

The purpose of this study was to investigate the effect of contact type textile electrode structure on heart activity signal acquisition for smart healthcare. In this study, we devised six contact type textile electrodes whose electrode size and configuration were manipulated for measuring heart activity signals using computerized embroidery. We detected heart activity signals using a modified lead II and by attaching each textile electrode to the chest band in four healthy male subjects in a standing static posture. We measured the signals four times repeatedly for all types of electrodes. The heart activity signals were sampled at 1 kHz using a BIOPAC ECG100, and the detected original signals were filtered through a band-pass filter. To compare the performance of heart activity signal acquisition among the different structures of the textile electrodes, we conducted a qualitative analysis using signal waveform and size as parameters. In addition, we performed a quantitative analysis by calculating signal power ratio (SPR) of the heart activity signals obtained through each electrode. We analyzed differences in the performance of heart activity signal acquisition of the six electrodes by performing difference and post-hoc tests using nonparametric statistic methods on the calculated SPR. The results showed a significant difference both in terms of qualitative and quantitative aspects of heart activity signals among the tested contact type textile electrodes. Regarding the configurations of the contact type textile electrodes, the three-dimensionally inflated electrode (3DIE) was found to obtain better quality signals than the flat electrode. However, regarding the electrode size, no significant difference was found in performance of heart signal acquisition for the three electrode sizes. These results suggest that the configuration method (flat/3DIE), which is one of the two requirements of a contact type textile electrode structure for heart activity signal acquisition, has a critical effect on the performance of heart activity signal acquisition for wearable healthcare. Based on the results of this study, we plan to develop a smart clothing technology that can monitor high-quality heart activity without time and space constraints by implementing a clothing platform integrated with the textile electrode and developing a performance improvement plan.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.2
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• pp.38-42
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• 2019

Purpose Although computed tomography (CT) is used for coronary artery calcification, it is difficult to differentiate between high risk microcalcifications. Studies have shown that $^{18}F$-sodium fluoride ($^{18}F-NaF$) is very useful for the diagnosis of microcalcifications. In this study, we aimed to evaluate the usefulness of $^{18}F-NaF$ PET imaging in quantitative evaluation of calcification. Materials and Methods A total of 45 patients ($67.1{\pm}6.9years\;old$) were injected with 250 MBq of $^{18}F-NaF$ for 1 hour and images were acquired for 30 minutes. All patients underwent CT angiography (CTAngiography, CTA) before the PET scan. The SUVmax of calcification was measured and the background radioactivity of the left atrium was measured to determine Target to Background (TBR) and quantitatively analyzed. High risk group was classified through ROC curve (Receiver Operating Characteristic Curve). Results There were 226 coronary artery calcifications in the cohort and SUVmax was $1.15{\pm}0.39$. Of the 28 patients (62%), 58 were classified as high risk (TBR > 1.25). The remaining 168 were $TBR{\leq}1.25$. Conclusion $^{18}F-NaF$ PET images were available for quantitative assessment of microcalcifications and could be classified into high-risk groups. The combination of angiographic CT and $^{18}F-NaF$ PET may be a new method for early diagnosis of high-risk microcalcifications.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.85-93
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• 2021

In hydroponic cultivation, in order to investigate the change of lettuce growth and physiologically active substances through CO2 tablet treatment in nutrient solution, we used a solid carbonated tablets commercially available in the Netherlands. The experiment consisted of 0.5-fold, 1-fold, and 2-fold treatment groups with no treatment as a control. As a result, the atmospheric CO2 concentration in the chamber after CO2 tablet treatment showed the highest value at 472.2 µL·L-1 in the 2-fold treatment zone immediately after treatment, and the pH in the nutrient solution decreased the most to pH 6.03 in the 2-fold treatment zone. After that, over time, the CO2 concentration and pH recovered to the level before treatment. Leaf width and leaf area of lettuce showed the highest values of 17.1cm and 1067.14 ㎠ when treated 2-fold with CO2 tablet, while fresh weight and dry weight of the above-ground part were highest at 63.87 g and 3.08 g in 0.5-fold treatment. The root length of lettuce was the longest (28.4 cm) in the control, but there was no significant difference in the fresh weight and the dry weight among the treatments. Apparently, it was observed that the root length of the lettuce was shortened by CO2 tablet treatment and a lot of side roots occurred. In addition, there was a growth disorder in which the roots turned black, but it was found that there was no negative effect on the growth of the above-ground part. As a result of analyzing the bioactive compounds of lettuce by CO2 tablet treatment, chlorogenic acid and quercetin were detected. As a result of quantitative analysis, chlorogenic acid increased by 249% compared to the control in 1-fold treatment, but quercetin decreased by 37%. As a result of comparing the DPPH radical scavenging ability showing antioxidant activity, the control and 0.5-fold treatment showed significantly higher values than the 1-fold and 2-fold treatments. This suggests that carbonated water treatment is effective in increasing the growth and bioactive compounds of hydroponic lettuce.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.