• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of agriculture & life science
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• v.51 no.2
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• pp.117-126
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• 2017

Hanwoo young bulls are selected based on performance test using the weight at 12 months and pedigree index comprising marbling score. Pedigree index was not based on the progeny tested data but the breeding value of the proven bulls; resulting a lower accuracy. The progeny testing of the young bulls was categorized into testing at farm and at the test station. The farm tested data was difficult to compare with those from test station data. Farm tested bulls had different slaughter ages than those for test station bulls. Therefore, this study had considered a different age at slaughter for respective records on ultrasound traits. Records on body weight at 12 months, ultrasound measures at 12 and 24 months(uIMF, uEMA, uBFT, and uRFT), and carcass traits(CWT, EMA, BFT, and MS) were collected from steers and bulls of Hanwoo national improvement scheme between 2008 and 2013. Fixed effects of batch, test date, test station, personnel for measurement, personnel for judging, and a linear covariate of weight at measurement were fitted in the animal models for ultrasound traits. The ranges of heritability estimates of the ultrasound traits at 12 and 24 months were 0.21-0.43 and 0.32-0.47, respectively. Ultrasound traits at 12 and 24 months between similar carcass traits was genetically correlated at 0.52-0.75 and 0.86-0.89, respectively.

• The Journal of Korean Society for Radiation Therapy
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• v.29 no.2
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• pp.43-51
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• 2017

Purpose: The purpose of this study is to evaluate the dosimetric effects of couch attenuation and air gaps using 3D phantom for prone breast radiation therapy. Materials and method: A 3D printer(Builder Extreme 1000) and computed tomography (CT) images of a breast cancer patient were used to manufacture the customized breast phantom. Eclipse External Beam Planning 13.6 (Varian Medical Systems Palo Alto, CA, USA) was used to create the treatment plan with a dose of 200 cGy per fraction with 6 MV energy. The Optically Stimulated Luminescence Detector(OSLD) was used to measure the skin dose at four points (Med 1, Med 2, Lat 1, Lat 2) on the 3D phantom and ion-chamber (FC65-G) were used to perform the in-vivo dosimetry at the two points (Anterior, Posterior). The Skin dose and in-vivo dosimetry were measured with reference air gap (3 cm) and increased air gaps (1, 2, 3, 4, 5, 6 cm) from reference distance between the couch and 3D phantom. Results: As a result, measurement for the skin dose at lateral point showed a similar value within ${\pm}4%$ compared to the plan. While the air gap increased, skin dose at medial 1 was reduced. And it was also reduced over 7 % when the air gap was more than 3 cm compared to radiation therapy plan. At medial 2 it was reduced over 4 % as well. The changes of dose from variety of the air gap showed similar value within ${\pm}1%$ at posterior. As the air gap was increased, the dose at anterior was also increased and it was increased by 1 % from the air gap distance more than 3 cm. Conclusion: Dosimetrical measurement using 3D phantom is very useful to evaluate the dosimetric effects of couch attenuation and air gaps for prone breast radiation therapy. And it is possible to reduce the skin dose and increase the accuracy of the radiation dose delivery by appling the optimized air gap.

• Korean Journal of Acupuncture
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• v.30 no.4
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• pp.252-263
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• 2013

Objectives : Skin color deformation by cupping has been widely used as a diagnostic parameter in Traditional Korean Medicine(TKM). Skin color deformation such as ecchymoses and purpura is induced by local vacuum in a suction cup. Since existing studies have relied on a visual diagnostic method, there is a need to use the quantitative measurement method. Methods : We conducted an analysis of cross-polarization photographic images to assess the changes in skin color deformation. The skin color variation was analyzed using $L^*a^*b^*$ space and the skin erythema index(E.I.). The meridian theory in TKM indicates that the condition of primary internal organs is closely related to the skin color deformation at special acupoints. Before conducting these studies, it is necessary to evaluate whether or not skin color deformation is influenced by muscle condition. Hence, we applied cupping at BL13, BL15, BL18, BL20 and BL23 at Bladder Meridian(BL) and measured blood lactate at every acupoint. Results : We confirmed the high system measurement accuracy, and observed the diverse skin color deformations. Moreover, we confirmed that the $L^*$, $a^*$ and E.I. had not changed after 40 minutes(p>0.05). The distribution of blood lactate levels at each part was observed differently. Blood lactate level and skin color deformation at each part was independent of each other. Conclusions : The negative pressure produced by the suction cup induces a reduction in the volumetric fraction of melanosomes and subsequent reduction in epidermal thickness. The relationship between variations of tissue and skin properties and skin color deformation degree must be investigated prior to considering the relationship between internal organ dysfunction and skin color deformation.

• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.99-120
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• 1997

Solar radiation is scattered and absorbed atmospheric compositions in the atmosphere before it reaches the surface and, then after reflected at the surface, until it reaches the satellite sensor. Therefore, consideration of the radiative transfer through the atmosphere is essential for the quantitave analysis of the satellite sensed data, specially at shortwave region. This study examined a feasibility of using radiative transfer code for estimating the atmospheric effects on satellite remote sensing data. To do this, the flux simulated by LOWTRAN7 is compared with CAGEX data in shortwave region. The CAGEX (CERES/ARM/GEWEX Experiment) data provides a dataset of (1) atmospheric soundings, aerosol optical depth and albedo, (2) ARM(Aerosol Radiation Measurement) radiation flux measured by pyrgeometers, pyrheliometer and shadow pyranometer and (3) broadband shortwave flux simulated by Fu-Liou's radiative transfer code. To simulate aerosol effect using the radiative transfer model, the aerosol optical characteristics were extracted from observed aerosol column optical depth, Spinhirne's experimental vertical distribution of scattering coefficient and D'Almeida's statistical atmospheric aerosols radiative characteristics. Simulation of LOWTRAN7 are performed on 31 sample of completely clear days. LOWTRAN's result and CAGEX data are compared on upward, downward direct, downward diffuse solar flux at the surface and upward solar flux at the top of the atmosphere(TOA). The standard errors in LOWTRAN7 simulation of the above components are within 5% except for the downward diffuse solar flux at the surface(6.9%). The results show that a large part of error in LOWTRAN7 flux simulation appeared in the diffuse component due to scattering mainly by atmispheric aerosol. For improving the accuracy of radiative transfer simulation by model, there is a need to provide better information about the radiative charateristrics of atmospheric aerosols.

• Journal of radiological science and technology
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• v.39 no.2
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• pp.149-156
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• 2016

Measurement of cardiac blood flow using the magnetic resonance imaging has been limited due to breathing and involuntary movements of the heart. The present study attempted to improve the accuracy of cardiac blood flow testing through phase contrast magnetic resonance imaging by presenting the adequate breathing method and imaging variables by comparing the measurement values of cardiac blood flow. Each was evaluated by comparing the breath hold retrospective 1NEX and non breath hold retrospective 1-3NEX in the ascending aorta and descending aorta. As a result, the average blood flow amount/velocity of the breath hold retrosepctive 1NEX method in the ascending aorta were $96.17{\pm}19.12ml/sec$, $17.04{\pm}4.12cm/sec$ respectively, which demonstrates a statistically significant difference(p<0.05) with the non-breath hold retrospective method 1NEX of $72.31{\pm}13.27ml$ and $12.32{\pm}3.85$. On the other hand, the average 2NEX blood flow and mean flow velocity is $101.90{\pm}24.09$, $16.84{\pm}4.32$, 3NEX $103.06{\pm}25.49$, $16.88{\pm}4.19$ did not show statistically significant differences(p>0.05).The average blood flow amount/ velocity of the breath hold retrospective 1NEX method in the descending aorta were $76.68{\pm}19.72ml/s$, and $22.23{\pm}4.8$, which did not demonstrate a significant difference in comparison to non-breath hold retrospective method 1-3 NEX. Therefore, the non breath hold retrospective method does not significantly differ in terms of cardiac blood flow in comparison with the breath hold retrospective method in accordance with the increase of NEX, so pediatric patients or patients who are not able to breathe well must have the diagnostic value of their cardiac blood flow tests improved.

• Investigative Magnetic Resonance Imaging
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• v.18 no.1
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• pp.52-58
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• 2014

Purpose : To establish a pH measurement system for a mouse tumor study using a clinical scanner, to develop the $^1H$ and 31P radio frequency (RF) coil system and to test pH accuracy with phantoms. Materials and Methods: The $^1H$ and the $^{31}P$ surface coils were designed to acquire signals from mouse tumors. Two coils were positioned orthogonally for geometric decoupling. The pH values of various pH phantoms were calculated using the $^1H$ decoupled $^{31}P$ MR spectrum with the Henderson-Hasselbalch equation. The calculated pH value was compared to that of a pH meter. Results: The mutual coil coupling was shown in a standard $S_{12}$. Coil coupling ($S_{12}$) were -73.0 and -62.3 dB respectively. The signal-to-noise ratio (SNR) obtained from the homogeneous phantom $^1H$ image was greater than 300. The high resolution in vivo mice images were acquired using a $^{31}P$-decoupled $^1H$ coil. The pH values calculated from the $^1H$-decoupled $^{31}P$ spectrum correlated well with the values measured by pH meter ($R^2$=0.97). Conclusion: Accurate pH values can be acquired using a $^1H$-decoupled $^{31}P$ RF coil with a clinical scanner. This two-surface coil system could be applied to other nuclear MRS or MRI.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.2
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• pp.115-121
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• 2013

Purpose: Latest linear accelerator and the introduction of new measurement equipment to the agency that the introduction of this equipment in the future, by analyzing the process of confirming the usefulness of the preparation process for applying it in the clinical causes some problems, should be helpful. Materials and Methods: All measurements TrueBEAM STX (Varian, USA) was used, and a file specific to each energy, irradiation conditions, the dose distribution was calculated using a computerized treatment planning equipment (Eclipse ver 10.0.39, Varian, USA). Measuring performance and cause errors in MapCHECK 2 were analyzed and measured against. In order to verify the performance of the MapCHECK 2, 6X, 6X-FFF, 10X, 10X-FFF, 15X field size $10{\times}10$ cm, gantry $0^{\circ}$, $180^{\circ}$ direction was measured by the energy. IGRT couch of the CT values affect the measurements in order to confirm, CT number values : -800 (Carbon) & -950 (COUCH in the air), -100 & 6X-950 in the state for FFF, 15X of the energy field sizes $10{\times}10$, gantry $180^{\circ}$, $135^{\circ}$, $275^{\circ}$ directionwas measured at, MapPHAN allocated to confirm the value of HU were compared, using the treatment planning computer for, Measurement error problem by the sharp edges MapPHAN Learn gantry direction MapPHAN of dependence was measured in three ways. GANTRY $90^{\circ}$, $270^{\circ}$ in the direction of the vertically erected settings 6X-FFF, 15X respectively, and Setting the state established as a horizontal field sizes $10{\times}10$, $90^{\circ}$, $45^{\circ}$, $315^{\circ}$, $270^{\circ}$ of in the direction of the energy-6X-FFF, 15X, respectively, were measured. Without intensity modulated beam of the third open arc were investigated. Results: Of basic performance MapCHECK confirm the attenuation measured by Couch, measured from the measured HU values that are assigned to the MAP-PHAN, check for calculation accuracy for the angled edge of the MapPHAN all come in a range of valid measurement errors do not affect the could see. three ways for the Gantry direction dependence, the first of the meter built into the value of the Gantry $270^{\circ}$ (relative $0^{\circ}$), $90^{\circ}$ (relative $180^{\circ}$), 6X-FFF, 15X from each -1.51, 0.83% and -0.63, -0.22% was not affected by the AP/PA direction represented. Setting the meter horizontally Gantry $90^{\circ}$, $270^{\circ}$ from the couch, Energy 6X-FFF 4.37, 2.84%, 15X, -9.63, -13.32% the difference. By-side direction measurements MapPHAN in value is not within the valid range can not, because that could be confirmed as gamma pass rate 3% of the value is greater than the value shown. You can check the Open Arc 6X-FFF, 15X energy, field size $10{\times}10$ cm $360^{\circ}$ rotation of the dose distribution in the state to look at nearly 90% pass rate to emerge. Conclusion: Based on the above results, the MapPHAN gantry direction dependence by side in the direction of the beam relative dose distribution suitable for measuring the gamma value, but accurate measurement of the absolute dose can not be considered is. this paper, a more accurate treatment plan in order to confirm, Reduce the tolerance for VMAT, such as lateral rotation investigation in order to measure accurate absolute isodose using a combination of IMF (Isocentric Mounting Fixture) MapCHEK 2, will be able to minimize the impact due to the angular dependence.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.