Geology of Athabasca Oil Sands in Canada (캐나다 아사바스카 오일샌드 지질특성)
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- The Korean Journal of Petroleum Geology
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- v.14 no.1
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- pp.1-11
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- 2008
As conventional oil and gas reservoirs become depleted, interests for oil sands has rapidly increased in the last decade. Oil sands are mixture of bitumen, water, and host sediments of sand and clay. Most oil sand is unconsolidated sand that is held together by bitumen. Bitumen has hydrocarbon in situ viscosity of >10,000 centipoises (cP) at reservoir condition and has API gravity between
The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.
Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.
The expansion of the Changjiang Diluted Water (CDW) plume during summer is known to be a major factor influencing phytoplankton diversity, community structure, and the regional marine environment of the northern East China Sea (ECS). The discharge of the CDW plume was very high in the summer of 2020, and cruise surveys and stationary monitoring were conducted to understand the dynamics of changes in environmental characteristics and the impact on phytoplankton diversity and community structure. A cruise survey was conducted from August 16 to 17, 2020, using R/V Eardo, and a stay survey at the Ieodo Ocean Research Station (IORS) from August 15 to 21, 2020, to analyze phytoplankton diversity and community structure. The southwestern part of the survey area exhibited low salinity and high chlorophyll a fluorescence under the influence of the CDW plume, whereas the southeastern part of the survey area presented high salinity and low chlorophyll a fluorescence under the influence of the Tsushima Warm Current (TWC). The total chlorophyll a concentrations of surface water samples from 12 sampling stations indicated that nano-phytoplankton (20-3 ㎛) and micro-phytoplankton (> 20 ㎛) were the dominant groups during the survey period. Only stations strongly influenced by the TWC presented approximately 50% of the biomass contributed by pico-phytoplankton (< 3 ㎛). The size distribution of phytoplankton in the surface water samples is related to nutrient supplies, and areas where high nutrient (nitrate) supplies were provided by the CDW plume displayed higher biomass contribution by micro-phytoplankton groups. A total of 45 genera of nano- and micro-phytoplankton groups were classified using morphological analysis. Among them, the dominant taxa were the diatoms Guinardia flaccida and Nitzschia spp. and the dinoflagellates Gonyaulax monacantha, Noctiluca scintillans, Gymnodinium spirale, Heterocapsa spp., Prorocentrum micans, and Tripos furca. The sampling stations affected by the TWC and low in nitrate concentrations presented high concentrations of photosynthetic pico-eukaryotes (PPE) and photosynthetic pico-prokaryotes (PPP). Most sampling stations had phosphate-limited conditions. Higher Synechococcus concentrations were enumerated for the sampling stations influenced by low-nutrient water of the TWC using flow cytometry. The NGS analysis revealed 29 clades of Synechococcus among PPP, and 11 clades displayed a dominance rate of 1% or more at least once in one sample. Clade II was the dominant group in the surface water, whereas various clades (Clades I, IV, etc.) were found to be the next dominant groups in the SCM layers. The Prochlorococcus group, belonging to the PPP, observed in the warm water region, presented a high-light-adapted ecotype and did not appear in the northern part of the survey region. PPE analysis resulted in 163 operational taxonomic units (OTUs), indicating very high diversity. Among them, 11 major taxa showed dominant OTUs with more than 5% in at least one sample, while Amphidinium testudo was the dominant taxon in the surface water in the low-salinity region affected by the CDW plume, and the chlorophyta was dominant in the SCM layer. In the warm water region affected by the TWC, various groups of haptophytes were dominant. Observations from the IORS also presented similar results to the cruise survey results for biomass, size distribution, and diversity of phytoplankton. The results revealed the various dynamic responses of phytoplankton influenced by the CDW plume. By comparing the results from the IORS and research cruise studies, the study confirmed that the IORS is an important observational station to monitor the dynamic impact of the CDW plume. In future research, it is necessary to establish an effective use of IORS in preparation for changes in the ECS summer environment and ecosystem due to climate change.
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.
The Tomb of Jang Mui located in Bongsan-gun, Hwanghae-do Province has attracted much attention since its first investigation due to the inscribed bricks found there that have allowed the guessing of the name and official title of its occupant and construction date. Inscriptions on these bricks, such as the "Prefect of Daebang Commandery Jang Mui" and the "Mu" (戊, the fifth sign of the Chinese calendar), and "Sin" (申, the ninth sign of the Chinese zodiac), have become the basis for believing the location of the government office of Daebang Commandery to be in Bongsangun, Hwanghae-do Province rather than somewhere in the Hangang River region. From the early days of its investigation, the tomb was suggested as historic remains of the Daebang Commandery along with the Earthen Fortress in Jitap-ri. Inscribed bricks excavated from the Tomb of Jang Mui were featured in several books and articles in the form of photographs and rubbings, leading to a vast body of studies on its construction period and the characteristics of its occupant that drew upon interpretations of the inscriptions. However, the inscribed bricks themselves were not publicly available outside those held in the collection of the University of Tokyo, making it difficult to expect consistent research findings on the types of inscribed bricks and their contents. Following previous studies re-examining the structure of the tomb and the materials used for its construction, most scholars dated the Tomb of Jang Mui to 348, a period after the collapse of Daebang Commandery. However, there is still a lack of adequate examination of the bricks, which account for the majority of the artifacts excavated from the tomb. Among the bricks excavated from most brick chamber tombs, including the Tomb of Jang Mui, only those with inscriptions or designs have been collected. Moreover, among these, only those with inscriptions or designs on the stretcher faces have been documented. Accordingly, the bricks themselves have been notably understudied. This paper intends to reorganize the contents of the inscriptions on eleven types (out of sixty-one pieces) of bricks in the collection of the National Museum of Korea, which make up the majority of the bricks excavated from the Tomb of Jang Mui. It also classified them according to their shapes. Furthermore, it examined the bricks from the Tomb of Jang Mui as architectural materials by focusing on their production techniques, including their forming, drying, and firing. Taking a more specific approach, it then compared the results to other bricks from the second century through the fourth century: those from the brick chamber tombs of the Nangnang and Daebang Commanderies and those from the brick chamber tombs built after Nangnang and Daebang Commanderies were ousted. The examination of bricks from the Tomb of Jang Mui has revealed that these bricks were basically produced using the brick manufacturing techniques of Nangnang, but they incorporated new elements found in bricks from brick chamber tombs or brick-and-stone chamber tombs constructed around the mid-fourth century in terms of their size, the use of lime, and the number of inscribed bricks. This supports the prevailing view that the date of the construction of the Tomb of Jang Mui is 348. The Tomb of Jang Mui sustained the existing brick chamber tomb burial tradition, but its ceiling was finished with stone. It demonstrates a blending of the brick chamber tomb practice of the Nangnang and Daebang Commanderies by using bricks produced based on related techniques, but with new elements such as the addition of a lime layer to the bricks. This fusion reflects the political circumstances of its time, such as the expulsion of the Daebang Commandery and the advance of the Goguryeo Kingdom, leading to diverse interpretations. Given archaeological evidence such as the structure, materials, and location of the tomb, the Tomb of Jang Mui appears to be highly related to the Goguryeo Kingdom. However, the forms of the inscribed bricks and the contents of the inscriptions share similarities with brick chamber tombs constructed during the third and fourth centuries in the Jiangsu and Zhejiang regions in China. Further studies on whether the use of lime was an influence from Goguryeo or a continuation of the Daebang tradition and a comparative examination with contemporaneous stone ceiling tombs will provide a more refined understanding of the Tomb of Jang Mui.
This study, designed to establish a classification system of paddy soils and suitability groups on productivity and management of paddy land based on soil characteristics, has been made for the paddy soils on the Gimje-Mangyeong plains. The morphological, physical and chemical properties of the 15 paddy soil series found on these plains are briefly as follows: Ten soil series (Baeggu, Bongnam, Buyong, Gimje, Gongdeog, Honam, Jeonbug, Jisan, Mangyeong and Suam) have a B horizon (cambic B), two soil series (Geugrag and Hwadong) have a Bt horizon (argillic B), and three soil series (Gwanghwal, Hwagye and Sindab) have no B or Bt horizons. Uniquely, both the Bongnam and Gongdeog series contain a muck layer in the lower part of subsoil. Four soil series (Baeggu, Gongdeog, Gwanghwal and Sindab) generally are bluish gray and dark gray, and eight soil series (Bongnam, Buyong, Gimje, Honam, Jeonbug, Jisan, Mangyeong and Suam) are either gray or grayish brown. Three soil series (Geugrag, Hwadong and Hwagye), however, are partially gleyed in the surface and subsurface, but have a yellowish brown to brown subsoil or substrata. Seven soil series (Bongnam, Buyong, Geugrag, Gimje, Gongdeog, Honam and Hwadong) are of fine clayey texture, three soil series (Baeggu, Jeonbug and Jisan) belong to fine loamy and fine silty, three soil series (Gwanghwal, Mangyeong and Suam) to coarse loamy and coarse silty, and two soil series (Hwagye and Sindab) to sandy and sandy skeletal texture classes. The carbon content of the surface soil ranges from 0.29 to 2.18 percent, mostly 1.0 to 2.0 percent. The total nitrogen content of the surface soil ranges from 0.03 to 0.25 percent, showing a tendency to decrease irregularly with depth. The C/N ratio in the surface soil ranges from 4.6 to 15.5, dominantly from 8 to 10. The C/N ratio in the subsoil and substrata, however, has a wide range from 3.0 to 20.25. The soil reaction ranges from 4.5 to 8.0. All soil series except the Gwanghwal and Mangyeong series belong to the acid reaction class. The cation exchange cpacity in the surface soil ranges from 5 to 13 milliequivalents per 100 grams of soil, and in all the subsoil and substrata except those of a sandy texture, from 10 to 20 milliequivalents per 100 grams of soil. The base saturation of the soil series except Baeggu and Gongdeog is more than 60 percent. The active iron content of the surface soil ranges from 0.45 to 1.81 ppm, easily-reduceable manganese from 15 to 148 ppm, and available silica from 36 to 366 ppm. The iron and manganese are generally accumulated in a similar position (10 to 70cm. depth), and silica occurs in the same horizon with that of iron and manganese, or in the deeper horizons in the soil profile. The properties of each soil series extending from the sea shore towards the continental plains change with distance and they are related with distance (x) as follows: y(surface soil, clay content) =
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
According to Soil Taxonomy which has been developed over the past 20 years in the soil conservation service of the U. S. D. A, Soils in Korea are classified. This system is well suited for the classification of the most of soils. But paddy field soils have some difficulties in classification because Soil Taxonomy states no proposals have yet been developed for classifying artificially irrigated soils. This paper discusses some problems in the application of Taxonomy and suggestes the classification of paddy field soils in Korea. Following is the summary of the paper. 1. Anthro aquic, Aquic Udipsamments : The top soils of these soils are saturated with irrigated water at some time of year and have mottles of low chroma(2 or less) more than 50cm of the soil surface. (Ex. Sadu, Geumcheon series) 2. Anthroaquic Udipsamments : These sails are like Anthroaquic, Aquic Udipsamments except for the mottles of low chroma within 50cm of the soil surface. (Ex. Baegsu series) 3. Halic Psammaquents : These soils contain enough salts as distributed in the profile that they interfere with the growth of most crop plants and located on the coastal dunes. The water table fluctuates with the tides. (Ex. Nagcheon series) 4. Anthroaquic, Aquic Udifluvents : They have some mottles that have chroma of 2 or less in more than 50cm of the surface. The upper horizon is saturated with irrigated water at sometime. (Ex. Maryeong series) 5. Anthro aquic Udifluvents : These soils are saturated with irrigated water at some time of year and have mottles of low chroma(2 or less) within 50cm of the surface soils. (Ex. Haenggog series) 6. Fluventic Haplaquepts : These soils have a content of organic carbon that decreases irregularly with depth and do not have an argillic horizon in any part of the pedon. Since ground water occur on the surface or near the surface, they are dominantly gray soils in a thick mineral regolith. (Ex Baeggu, Hagseong series) 7. Fluventic Thapto-Histic Haplaquepts : These soils have a buried organic matter layer and the upper boundary is within 1m of the surface. Other properties are same as Fluventic Haplaquepts. (Ex. Gongdeog, Seotan series) 8. Fluventic Aeric Haplaquepts : These soils have a horizon that has chroma too high for Fluventic Haplaquepts. The higher chroma is thought to indicate either a shorter period of saturation of the whole soils with water or some what deeper ground water than in the Fluventic Haplaquepts. The correlation of color with soil drainage classes is imperfect. (Ex. Mangyeong, Jeonbug series) 9. Fluventic Thapto-Histic Aeric Haplaquepts : These soils are similar to Fluventic Thapto Histic Haplaquepts except for the deeper ground water. (Ex. Bongnam series) 10. Fluventic Aeric Sulfic Haplaquepts : These soils are similar to Fluventic Aeric Haplaquepts except for the yellow mottles and low pH (<4.0) in some part between 50 and 150cm of the surface. (Ex. Deunggu series) 11. Fluventic Sulfaquepts : These soils are extremely acid and toxic to most plant. Their horizons are mostly dark gray and have yellow mottles of iron sulfate with in 50cm of the soil surface. They occur mainly in coastal marshes near the mouth of rivers. (Ex. Bongrim, Haecheog series) 12. Fluventic Aeric Sulfaquepts : They have a horizon that has chroma too high for Fluventic Sulfaquepts. Other properties are same as Fluventic Sulfaquepts. (Ex. Gimhae series) 13. Anthroaquic Fluvaquentic Eutrochrepts : These soils have mottles of low chroma in more than 50cm of the surface due to irrigated water. The base saturation is 60 percent or more in some subhroizon that is between depth of 25 and 75cm below the surface. (Ex. Jangyu, Chilgog series) 14. Anthroaquic Dystric Fluventic Eutrochrepts : These soils are similar to Anthroaquic Fluvaquentic Eutrochrepts except for the low chroma within 50cm of the surface. (Ex. Weolgog, Gyeongsan series) 15. Anthroaquic Fluventic Dystrochrepts : These soils have mottles that have chroma of 2 or less within 50cm of the soil surface due to artificial irrigation. They have lower base saturation (<60 percert) in all subhorizons between depths of 25 and 75cm below the soil surface. (Ex. Gocheon, Bigog series) 16. Anthro aquic Eutrandepts : These soils are similar to Anthroaquic Dystric Fluventic Eutrochrepts except for lower bulk density in the horizon. (Ex. Daejeong series) 17. Anthroaquic Hapludalfs : These soils' have a surface that is saturated with irrigated water at some time and have chroma of 2 or less in the matrix and higher chroma of mottles within 50cm of the surface. (Ex. Hwadong, Yongsu series) 18. Anthro aquic, Aquic Hapludalfs : These soils are similar to Anthro aquic Hapludalfs except for the matrix that has chroma 2 or less and higher chroma of mottles in more than 50cm of the surface. (Ex. Geugrag, Deogpyeong se ries)