• The Journal of Dong Guk Oriental Medicine
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• v.4
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• pp.109-140
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• 1995

As we considered in the main subjects, investigations on the theory of 'Doctrine on five elements' motion and six kinds of natural factors(運氣學說)' through 'Wang Bing's Commentary(王氷 注本)' of 'The seven great chapters in The Yellow Emperor's Internal Classic Su Wen' ("黃帝內經素問 七篇大論") are as follows. (1) In The seven great chapters("七篇大論")' Wang Bing supplement theory and in the academic aspects as a interpreter, judging from 'forget(亡)' character. expressed in the 'The missing chapters("素問遺篇")', 'Bonbyung-ron("本病論")' and 'Jabeob-ron(刺法論)', 'The seven great chapters("七篇大論")' must be supplementary work by Wang Bing. Besides, he quoted such forty books as medical books, taoist books, confucianist books, miscellaneous books, etc in the commentary and the contents quoted in the 'Su Wen(素問)' and 'Ling Shu("靈樞")' scripture nearly occupy in the book. As a method of interpreting scripiure as scripture, he edited the order of 'Internal Classic("內經")' ascended from the ancient time and when he compensated for commentary, with exhaustive scholarly mind and by observing the natural phenomena practically and writing the pathology and the methods of treatment. We knew that the book is combined with the study of 'Doctrine on five elements motion and six kinds of natural factors(運氣學說)' (2) When we compare, analyze the similar phrase of 'The seven great chapters in The Yellow Emperor's Internal Classic Su Wen'("黃帝內經素問ㆍ七篇大論") through 'Wang Bing's Commentary(王氷 注本)', he tells abouts organized 'five elements(五行)' and 'heaven's regularly movement(天道運行)' rather than 'Emyangengsangdae-ron("陰陽應象大論")' in 'The seven great chapters("七篇大論")'. Also the 'Ohanunhangdae-ron("五運行大論")' because the repeated sentences with 'Emyangengsangdae-ron("陰陽應象大論")' is long they are omitted. And in the 'Youkmijidae-ron("六微旨大論")', 'Cheonjin ideology(天眞四象)' based on the 'Sanggocheonjin- ron("上古天眞論")', 'Sagijosindae-ron("四氣調神大論")' is written and in the 'Gigoupyondae-ron("氣交變大論")', the syndrome and symptom are explained in detail rather than 'Janggibeobsi-ron("藏氣法時論")', 'Okgijinjang-ron ("玉機眞藏論")' and in the 'Osangieongdae-ron("五常政大論")', the concept of 'five element(五行)' of the 'Gemgwejineon-ron("金櫃眞言論")' is expanded to 'the five elements' motion concept(五運槪念)' and in the 'Youkwonjeonggidae-ron("六元正紀大論")', explanations of 'The five elements' motion and six kinds of natural factors(運氣)' function are mentioned mainly and instead systematic pathology is not revealed rather than 'Emyangengsangdae-ron("陰陽應象大論")'. And in the 'Jijinyodae-ron("至眞要大論")', explanations of the change of atmosphere which correspond to treatment principle by 'The three Yin and Yang(三陰三陽)' as a progressed concepts are revealed. Therefore there are much similarity between the phrase of 'Emyangengsangdae-ron("陰陽應象大論")' and 'chapters of addition(補缺之篇)'. Generally, the doctrine which 'The seven great chapters("七篇大論")' are added by Wang Bing(王氷) is supported because there are more profound concepts rather than the other chapter in 'The seven great chapters("七篇大論")'. (3) When we study Wang Bing's(王氷) 'Pattern on five elements motion and six kinds of natural factors(運氣格局)' in 'The seven great chapter("七篇大論")', in the 'Cheonwongi-dae-ron("天元紀大論")', With 'Cheonjin ideology(天眞思想)' and the concepts of 'Owang(旺)'${\cdot}$'Sang(相)'${\cdot}$'Sa(死)'${\cdot}$'Su(囚)'${\cdot}$'Hu(休)' and 'Cheonbu(天符)'${\cdot}$'Sehwoi(歲會)' are measured time-spacially to the concept of 'Three Sum(三合)' the concept of 'Taeulcheonbu(太乙天符)' is explained. In the 'Ounhangdae-ron("五運行大論")', 'The calender Signs five Sum(天干五合)' is compared to the concepts of 'couples(夫婦)', 'weak-strong(柔强)' and in the 'Youkmijidae-ron("六微旨大論")', 'the relationship of obedience and disobedience(順逆關係)' which conform to the 'energy status(氣位)' change and 'monarch-minister(君相)' position is mentioned. In the 'Gikyobyeondae-ron("氣交變大論")', the concept of 'Sang-duk(相得)', 'Pyungsang(平常)' is emphasized but concrete measurement is mentioned. In the 'Osangieongdae-ron("五常政大論")', the detailed explanation with twenty three 'systemic of the five elements' motion(五運體系)' form and 'rountine-contrary treatment(正治. 反治)' with 'chill-fever-warm-cold(寒${\cdot}$熱${\cdot}$溫${\cdot}$凉)' are mentioned according to the 'analyse and differentiate pathological conditions in accordance with the eight principal syndromes(八綱辨證)'. In the 'Youkwonjeonggidae-ron("六元正紀大論")', Wang Bing of doesn't mention the concepts of 'Jungwun(中運)' that is seen in the original classic. In the new corrective edition, as the concepts of 'Jungwun, Dongcheonbu, Dongsehae and Taeulcheonbu(中運, 同天符, 同歲會, 太乙天符)' is appeared, Wang Bing seems to only use the concepts of 'Daewun, Juwun, and Gaekwun(大運, 主運, 客運)'. In the 'Jijinyodaeron("至眞要大論")', Wang Bing added detailed commentary to pathology and treatment doctrine by explaining the numerous appearances of 'Sebo, sufficiency, deficiency(歲步, 有餘, 不足)' and in the relation of 'victory-defeat(勝復)', he argued clearly that it is not mechanical estimation. (4) When we observe the Wang Bing's originality on the study of 'the theory of Doctrine on five elements' motion and six kinds of natural factors(運氣學說)', he emphasized 'The idea of Jeongindogi and Health preserving(全眞導氣${\cdot}$養生思想)' by adding 'Wang Bing's Commentary(王氷 注本)' of 'The seven great chapters("七篇大論")' and explained clearly 'The theory of Doctrine on five elements' motion and six kinds of natural factors(運氣學說)' and simpled and expanded the meaning of 'man, as a microcosm, is connected with the macrocosm(天人相應)' and with 'Atmosphere theory(大氣論)' also explained the meaning of 'rising and falling mechanism(升降氣機)'. In the sentence of 'By examining the pathology, take care of your health(審察病機 無失氣宜)'. he explained the meaning of pathology of 'heart-kidney-water-fire(心腎水火)' and suggested the doctrine and management of prescription. In the estimation and treatment, by suggesting 'asthenia and sthenia(虛實)' two method's estimation, 'contrary treatment(反治)' and treatment principals of 'falling heart fire tonifyng kidney water(降心火益腎水)', 'two class of chill and fever(寒熱二綱)' were demonstrated. There are 'inside and outside in the illness and so inner and outer in the treatment(病有中外 治有表囊)'. This sentence suggests concertedly. 'two class of superfies and interior(表囊二綱)' conforming to the position of disease. Therefore Wang Bing as an excellent theorist and introduced 'Cheoniin ideology(天眞思想)' as a clinician and realized the medical science. With these accomplishes mainly written in 'The theory of Doctrine on five elements' motion and six kinds of natural factors(運氣學說)' of 'The seven great chapters("七篇大論")', he interpreted the ancient medical scriptures and expanded the meaning of scriptures and conclusively contributed to the development of the study 'Korean Oriental Medicine(韓醫學)'.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.1203-1210
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• 2012

As the area of upland crops increase, it is become more important for farmers to understand status of soil water at their own fields due to key role of proper irrigation. In order to estimate daily water balance and soil water content with simple weather data and irrigation records, we have developed the model for estimating water balance and soil water content, called AFKAE0.5, and verified its simulated results comparing with daily change of soil water content observed by soil profile moisture sensors. AFKAE0.5 has two hypothesis before establishing its system. The first is the soil in the model has 300 mm in depth with soil texture. And the second is to simplify water movement between the subjected soil and beneath soil dividing 3 categories which is defined by soil water potential. AFKAE0.5 characterized with determining the amount of upward and downward water between the subjected soil and beneath soil. As a result of simulation of AFKAE0.5 at Gongju region with red pepper cultivation in 2005, the water balance with input minus output is recorded as - 88 mm. the amount of input water as precipitation, irrigation, and upward water is annually 1,043, 0, and 207 mm, on the other, output as evapotranspiration, run-off, and percolation is 831, 309, and 161 mm, respectively.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.19-29
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• 2006

The first Japanese pilot-scale $CO_2$ sequestration project has been undertaken in an onshore saline aquifer, near Nagaoka in Niigata prefecture, and time-lapse well logs were carried out in observation wells to detect the arrival of injected $CO_2$ and to evaluate $CO_2$ saturation in the reservoir. $CO_2$ was injected into a thin permeable zone at the depth of 1110m at a rate of 20-40 tonnes per day. The total amount of injected $CO_2$ was 10400 tonnes, during the injection period from July 2003 to January 2005. The pilot-scale demonstration allowed an improved understanding of the $CO_2$ movement in a porous sandstone reservoir, by conducting time-lapse geophysical well logs at three observation wells. Comparison between neutron well logging before and after the insertion of fibreglass casing in observation well OB-2 showed good agreement within the target formation, and the higher concentration of shale volume in the reservoir results in a bigger difference between the two well logging results. $CO_2$ breakthrough was identified by induction, sonic, and neutron logs. By sonic logging, we confirmed P-wave velocity reduction that agreed fairly well with a laboratory measurement on drilled core samples from the Nagaoka site. We successfully matched the history changes of sonic P-wave velocity and estimated $CO_2$ saturation a(ter breakthrough in two observation wells out of three. The sonic-velocity history matching result suggested that the sweep efficiency was about 40%. Small effects of $CO_2$ saturation on resistivity resulted in small changes in induction logs when the reservoir was partially saturated. We also found that $CO_2$ saturation in the $CO_2$-bearing zone responded to suspension of $CO_2$ injection.

• Progress in Medical Physics
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• v.24 no.1
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• pp.1-24
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• 2013

As image-guided radiation therapy (IGRT) has been commonly used for more accurate patient setup and monitoring tumor movement during radiation therapy, the necessity for management of imaging dose is increased. However, it has not been an interest issue to radiation therapy communities because the imaging dose is much lower than the therapeutic dose. However, since the cumulative dose from 4DCT and repeated imaging for daily setup verificationin would not be ignorable, appropriate dose management based on ALARA (As Low As Reasonably Achievable) principle is required. In this study, we aimed that (1) survey on imaging equipments and modalities used for IGRT, (2) estimation of IGRT imaging dose depending on treatment types and equipments, (3) collecting data of effective dose on treatment sites from each equipment and imaging protocol, and thus finally provide guideline for imaging dose reduction and optimization.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.848-857
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• 2018

Recently, around the world, active development of new renewable energy sources including solar power, waves, and fuel cells, etc. has taken place. Particularly, floating offshore wind farms have been developed for saving costs through large scale production, using high-quality wind power and minimizing noise damage in the ocean area. The development of floating wind farms requires an evaluation of the Maritime Safety Audit Scheme under the Maritime Safety Act in Korea. Floating wind farms shall be assessed by applying the line and area concept for systematic development, management and utilization of specified sea water. The development of appropriate evaluation methods and standards is also required. In this study, proper standards for marine traffic surveys and assessments were established and a systemic treatment was studied for assessing marine spatial area. First, a marine traffic data collector using AIS or radar was designed to conduct marine traffic surveys. In addition, assessment methods were proposed such as historical tracks, traffic density and marine traffic pattern analysis applying the line and area concept. Marine traffic density can be evaluated by spatial and temporal means, with an adjusted grid-cell scale. Marine traffic pattern analysis was proposed for assessing ship movement patterns for transit or work in sea areas. Finally, conceptual design of a Marine Traffic and Safety Assessment Solution (MaTSAS) was competed that can be analyzed automatically to collect and assess the marine traffic data. It could be possible to minimize inaccurate estimation due to human errors such as data omission or misprints through automated and systematic collection, analysis and retrieval of marine traffic data. This study could provides reliable assessment results, reflecting the line and area concept, according to sea area usage.

• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.676-683
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• 2022

Due to the effects of rapid changes in the living environment since 2000 and the recent unforeseen pandemic, people are refraining from domestic and international traveling and movement, and outdoor activities for health and the public value of forest trails, called Dullegil Trail in Korea, have become more important. This study estimated the environmental value of the tentatively named "East-West Trail," which connects the forest trails crossing Chungcheong and Gyeongsang Provinces using CVM (Contingent Valuation Method). It surveyed visitors to the East-West Trail, and 725 questionnaires were used for analysis. The average characteristics of respondents were those who exercised 2-3 times per week, visited a forest trail not far from their residence with friends or family, and showed a tendency to spend 50 thousand Korean won or more per visit. Visitors to the Dullegil Trail felt that there was a shortage of information boards on the forest trail, and they preferred a shelter in appropriate locations. We used a double-bounded dichotomous choice (BDDC) logit model proposed by Hanemann to measure the conservation value of the East-West Trail. It was estimated that the environmental value that a visitor to the East-West Trail could obtain was 30,087 won per trip. The estimated environmental value of the East-West Trail can be converted to about 94 billion won total visitors annually based on the population belonging to the direct-use zone near the East-West Trail. As there has been no study on the environmental value of forest trails using CVM, the results of this study will be able to suggest the feasibility of the government policies on forest trails.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• 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.