• Title/Summary/Keyword: daily time series

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Retrieval of Oceanic Skin Sea Surface Temperature using Infrared Sea Surface Temperature Autonomous Radiometer (ISAR) Radiance Measurements (적외선 라디오미터 관측 자료를 활용한 해양 피층 수온 산출)

  • Kim, Hee-Young;Park, Kyung-Ae
    • Journal of the Korean earth science society
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    • v.41 no.6
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    • pp.617-629
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    • 2020
  • Sea surface temperature (SST), which plays an important role in climate change and global environmental change, can be divided into skin sea surface temperature (SSST) observed by satellite infrared sensors and the bulk temperature of sea water (BSST) measured by instruments. As sea surface temperature products distributed by many overseas institutions represent temperatures at different depths, it is essential to understand the relationship between the SSST and the BSST. In this study, we constructed an observation system of infrared radiometer onboard a marine research vessel for the first time in Korea to measure the SSST. The calibration coefficients were prepared by performing the calibration procedure of the radiometer device in the laboratory prior to the shipborne observation. A series of processes were applied to calculate the temperature of the layer of radiance emitted from the sea surface as well as that from the sky. The differences in skin-bulk temperatures were investigated quantitatively and the characteristics of the vertical structure of temperatures in the upper ocean were understood through comparison with Himawari-8 geostationary satellite SSTs. Comparison of the skin-bulk temperature differences illustrated overall differences of about 0.76℃ at Jangmok port in the southern coast and the offshore region of the eastern coast of the Korean Peninsula from 21 April to May 6, 2020. In addition, the root-mean-square error of the skin-bulk temperature differences showed daily variation from 0.6℃ to 0.9℃, with the largest difference of 0.83-0.89℃ at 1-3 KST during the daytime and the smallest difference of 0.59℃ at 15 KST. The bias also revealed clear diurnal variation at a range of 0.47-0.75℃. The difference between the observed skin sea surface temperature and the satellite sea surface temperature showed a mean square error of approximately 0.74℃ and a bias of 0.37℃. The analysis of this study confirmed the difference in the skin-bulk temperatures according to the observation depth. This suggests that further ocean shipborne infrared radiometer observations should be carried out continuously in the offshore regions to understand diurnal variation as well as seasonal variations of the skin-bulk SSTs and their relations to potential causes.

LSTM Based Prediction of Ocean Mixed Layer Temperature Using Meteorological Data (기상 데이터를 활용한 LSTM 기반의 해양 혼합층 수온 예측)

  • Ko, Kwan-Seob;Kim, Young-Won;Byeon, Seong-Hyeon;Lee, Soo-Jin
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.603-614
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    • 2021
  • Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.

Dynamic Equilibrium Position Prediction Model for the Confluence Area of Nakdong River (낙동강 합류부 삼각주의 동적 평형 위치 예측 모델: 감천-낙동강 합류점 중심 분석 연구)

  • Minsik Kim;Haein Shin;Wook-Hyun Nahm;Wonsuck Kim
    • Economic and Environmental Geology
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    • v.56 no.4
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    • pp.435-445
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    • 2023
  • A delta is a depositional landform that is formed when sediment transported by a river is deposited in a relatively low-energy environment, such as a lake, sea, or a main channel. Among these, a delta formed at the confluence of rivers has a great importance in river management and research because it has a significant impact on the hydraulic and sedimentological characteristics of the river. Recently, the equilibrium state of the confluence area has been disrupted by large-scale dredging and construction of levees in the Nakdong River. However, due to the natural recovery of the river, the confluence area is returning to its pre-dredging natural state through ongoing sedimentation. The time-series data show that the confluence delta has been steadily growing since the dredging, but once it reaches a certain size, it repeats growth and retreat, and the overall size does not change significantly. In this study, we developed a model to explain the sedimentation-erosion processes in the confluence area based on the assumption that the confluence delta reaches a dynamic equilibrium. The model is based on two fundamental principles: sedimentation due to supply from the tributary and erosion due to the main channel. The erosion coefficient that represents the Nakdong River confluence areas, was obtained using data from the tributaries of the Nakdong River. Sensitivity analyses were conducted using the developed model to understand how the confluence delta responds to changes in the sediment and water discharges of the tributary and the main channel, respectively. We then used annual average discharge of the Nakdong River's tributaries to predict the dynamic equilibrium positions of the confluence deltas. Finally, we conducted a simulation experiment on the development of the Gamcheon-Nakdong River delta using recorded daily discharge. The results showed that even though it is a simple model, it accurately predicted the dynamic equilibrium positions of the confluence deltas in the Nakdong River, including the areas where the delta had not formed, and those where the delta had already formed and predicted the trend of the response of the Gamcheon-Nakdong River delta. However, the actual retreat in the Gamcheon-Nakdong River delta was not captured fully due to errors and limitations in the simplification process. The insights through this study provide basic information on the sediment supply of the Nakdong River through the confluence areas, which can be implemented as a basic model for river maintenance and management.

A Study on the Application of Outlier Analysis for Fraud Detection: Focused on Transactions of Auction Exception Agricultural Products (부정 탐지를 위한 이상치 분석 활용방안 연구 : 농수산 상장예외품목 거래를 대상으로)

  • Kim, Dongsung;Kim, Kitae;Kim, Jongwoo;Park, Steve
    • Journal of Intelligence and Information Systems
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    • v.20 no.3
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    • pp.93-108
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    • 2014
  • To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

REPORT OF EXPERIENCE WITH KIMURA'S DISEASE (기무라씨 질환, 5 예 보고)

  • Seel David J.;Park Yoon-Kyu;Lee Kwang-Min
    • Korean Journal of Head & Neck Oncology
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    • v.5 no.1
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    • pp.39-46
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    • 1989
  • Kimura's Disease is a chronic inflammatory and proliferative condition producing subcutaneous masses especially in the head and neck area. This report of our experience with 5 patients with this disease is the first in the Korean surgical literature. Kimura's Disease is thought to be part of the larger spectrum of the entity known as angiolymphoid hyperplasia with eosinophilia (ALHE). It is characterized pathologically by hyperplastic lymphoid follicles, eosinophilic infiltration, and vase 비 ar proliferation. It produces masses which are most common in the area of the parotid, submandibular gland and upper neck. These masses occupy the subcutaneous tissues but also extend into salivary tissue and into upper neck nodes. One of our patients had masses in the groin. The tumors are extremely vascular due to the presence of new proliferative vessels and sinusoids. The average age of our 5 patients was 35, but all but one case were younger than 38 years of age. The male: female ratio was 3 : 2, and the average duration of symptoms was 5,2years. All patients had peripheral blood eosinophilia. All had multiple masses, sometimes symmetrical. The management was surgery alone in one case, surgery and steroids in one case, surgery and radiotherapy in two cases, and all three modalities in one case. The relationship of this entity to ALHE and our experience in the management of this disease are presented. A clinicopathological discrepancy alerted us to the existence of Kimura's Disease. A nineteen-year old male presented with subcutaneous masses over both mastoid areas present for 3 years (Case III). When biopsy on each side was reported as 'eosinophilic granuloma' we submitted the slides to an internationally expert pathologist. Symmetrically occurring tumors in the peri-parotid subcutaneous areas did not fit any category of neoplasm or granuloma known to us. The diagnosis, made by Dr. Gist Fan at the Ochsner Clinic, was Kimura's Disease. We found two additional cases in a review of soft tissue eosinophilic granuloma previously reported at Presbyterian Medical Center, and since then have diagnosed two new cases. These five cases constitute the basis for this, the largest series to be reported in Korea. These vascular, tumor-like lesions of the skin, subcutaneous areas and subjacent structures of the head and neck have been a variety of names, such as angiolymphoid hyperplasia with eosinophilia, eosinophilic hyperplastic lymphogranuloma, angioblastic lymphoid hyperplasia with eosinophilia, histioid hemangioma, and epithelioid hemangioma. The history of this disease spectrum dates back to 1937 when Kimm and Szeto (1) reported 7 cases of 'eosinophilic hyperplastic lymphogranuloma' in the Proceedings of the Chinese Medical Journal. In 1948 Kimura and his associates(2) reported additional cases in Japan under the title 'On the unusual granulation combined with hyperplastic changes of lymphatic tissue.' From then until 1966 several hundred cases were reported in China and Japan. The first report from the West was by Wells and Whimster(3) in the British Journal of Dermatology, in 1969. These authors coined the term, angiolymphoid hyperplasia with eosinophilia (ALHE). Since that time a debate has ensued as to whether Kimura's Disease and ALHE are distinct entities, or whether Kimura's is part of the larger spectrum of ALHE, perhaps a later or advanced phase. From the clinical perspective, surgeons should be aware of the diagnosis of Kimura's Disease not only as part of the differential diagnosis of head and neck tumors but also because these lesions are indolent, and generally require conservative surgical removal as part of the management program. CASE I. A 37-year-old female company employee presented in August 1982 with submental swelling of 12 years' duration and with inguinal swelling of 7 years' duration. The submental mass measured 5x5cm. and the inguinal mass was 8x4cm. in size. Peripheral eosinophilia varying from 14% to 40% was found. On August 20, 1982, the submental mass was removed and a superficial groin dissection was done. In May 1983 an intraoral lesion of the palate was removed. The patient is free of disease. CASE II. A 23-year-old unemployed man visited this hospital for the first time in July, 1984, with swelling of the right cheek present for 6 years. The mass was soft and ill-defined but measured 10x20cm. and extended from the submandibular upper neck to the zygomatic arch, and from the mastoid to the cheek, over the parotid gland. Eosinophilia varying from 27% to 29% was noted in the peripheral blood. On March 21, 1986, the lesion was resected. The procedure comprised an extended superficial parotidectomy from the temporalis fascia to the upper neck. Post-operatively radiotherapy 3000 rad tissue dose was administered using the 6 MeV linear accelerator. The patient remains free of disease. CASE III. A 19-year-old student came to the clinic with masses over both mastoid areas, present 3 years. On the right there were two adjacent lesions, one over the mastoid, the other in the upper jugular level of the neck. On the left it was a single mass over the mastoid. Eosinophilia varied from 13 to 32% in the peripheral blood, and 11.6% in the bone marrow. Incisional biopsy revealed 'eosinophilic granuloma' and a trial of predisolone was employed. The mass increased in size so a small dose of radiation (600 rads) was used, with substantial regression,. The lesion on the left was excised and follwed by 1000 rads radiotherapy. Finally recurrent tumor on the right side was removed on November 5, 1985. The patient remains free of disease. CASE N. A 29-year-old local merchant had had swelling of both upper necks since childhood. At the time of his first visit on March 17, 1986, the right submandibular mass measured 5x3.5cm. and the ,right upper neck and parotid tail mass measured 2.5cm. On the left there were masses in the upper neck, the largest of which measured 2.5cm, and of the parotid tail, 2.0cm. in size.(See Fig. 1) Peripheral eosinophilia of 39% was recorded. Left side partial parotidectomy and resection of the upper neck and subdigstric mases was done on May 2, 1986. The mass involving the right parotid tail and upper neck nodes was removed on Angust 7,1986. Postoperatively the patient was placed on prednisolone 30 mg. per day. No definite masses are palpable. CASE V. A 66-year-old housewife informed us, at the time of her first visit in May, 1986, that she had had multiple neck masses since 10 years ago. On the right side there was a 2.5cm. subcutaneous mass of the upper neck, over the upper jugular chain. On the left there was a 9x4.5cm. mass involving the entire parotid, the post-auricular area and the upper neck. A third mass presented in the submental area and measured 3.5cm. (See Fig. 2) Eosinophilia of 51% was noted in the peripheral blood. partial excision of the left upper neck lesion and complete excision of the submental mass were performed on june 6, 1986. post-operatively she was placed on 20 mg. of prednisolone daily, but when the mass re-grew after two months she was referred to Radiation Therapy for a 2500 rad course of treatment. A barely palpable thickening remains.

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A Statistical model to Predict soil Temperature by Combining the Yearly Oscillation Fourier Expansion and Meteorological Factors (연주기(年週期) Fourier 함수(函數)와 기상요소(氣象要素)에 의(依)한 지온예측(地溫豫測) 통계(統計) 모형(模型))

  • Jung, Yeong-Sang;Lee, Byun-Woo;Kim, Byung-Chang;Lee, Yang-Soo;Um, Ki-Tae
    • Korean Journal of Soil Science and Fertilizer
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    • v.23 no.2
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    • pp.87-93
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    • 1990
  • A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.

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A Study on Commodity Asset Investment Model Based on Machine Learning Technique (기계학습을 활용한 상품자산 투자모델에 관한 연구)

  • Song, Jin Ho;Choi, Heung Sik;Kim, Sun Woong
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.127-146
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    • 2017
  • Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.