Purpose: Tissue inhomogeneity such as lung affects tumor dose as well as transmission dose in new concept of on-line dosimetry which estimates tumor dose from transmission dose using the new algorithm. This study was carried out to confirm accuracy of correction by tissue density in tumor dose estimation utilizing transmission dose. Methods: Cork phantom (CP, density $0.202\;gm/cm^3$) having similar density with lung parenchyme and polystyrene phantom (PP, density $1.040\;gm/cm^3$) having similar density with soft tissue were used. Dose measurement was carried out under condition simulating human chest. On simulating AP-PA irradiation, PPs with 3 cm thickness were placed above and below CP, which had thickness of 5, 10, and 20 cm. On simulating lateral irradiation, 6 cm thickness of PP was placed between two 10 cm thickness CPs additional 3 cm thick PP was placed to both lateral sides. 4, 6, and 10 MV x-ray were used. Field size was in the range of $3{\times}3$ cm through $20{\times}20$ cm, and phantom-chamber distance (PCD) was 10 to 50 cm. Above result was compared with another sets of data with equivalent thickness of PP which was corrected by density. Result: When transmission dose of PP was compared with equivalent thickness of CP which was corrected with density, the average error was 0.18 (${\pm}0.27$) % for 4 MV, 0.10 (${\pm}0.43$) % for 6 MV, and 0.33 (${\pm}0.30$) % for 10 MV with CP having thickness of 5 cm. When CP was 10 cm thick, the error was 0.23 (${\pm}0.73$) %, 0.05 (${\pm}0.57$) %, and 0.04 (${\pm}0.40$) %, while for 20 cm, error was 0.55 (${\pm}0.36$) %, 0.34 (${\pm}0.27$) %, and 0.34 (${\pm}0.18$) % for corresponding energy. With lateral irradiation model, difference was 1.15 (${\pm}1.86$) %, 0.90 (${\pm}1.43$) %, and 0.86 (${\pm}1.01$) % for corresponding energy. Relatively large difference was found in case of PCD having value of 10 cm. Omitting PCD with 10 cm, the difference was reduced to 0.47 (${\pm}$1.17) %, 0.42 (${\pm}$0.96) %, and 0.55 (${\pm}$0.77) % for corresponding energy. Conclusion When tissue inhomogeneity such as lung is in tract of x-ray beam, tumor dose could be calculated from transmission dose after correction utilizing tissue density.
Purpose Find out about the significance of the GFR values calculated by the kidney depth is measured by comparing the values obtained for kidney depth was measured GFR in the CT image kidney depth and is calculated by Tonnesen law in $^{99m}Tc$-DTPA dynamic kidney scan with each applies. Materials and Methods Among patients with normal value (75~120 mL/min) computed GFR conducted of dynamic renal scan to visit from February 2013 to February 2014 and donor GFR values in patients with normal value. The mean age was 46.9 years with 14 men 13 females. We used abdomen CT image which checked before conducting dynamic Kidney scan for measuring the depth of kidney. We only used CT image that contains renal hilum and measured outermost front of the kidney from the skin surface (a) and the final surface (b) caculated the average depth of [(a + b) / 2] respectively. Using the same ROI in order to limit the change in GFR values by the other additional element was set before and after the depth value was excluded from the GFR falls kidney disease. Results Using Tonnesen law the average value was caculated 5.94 cm from the right kidney 5.90 cm from the left kidney. It was 6.83 cm, 8.71 cm in the left kidney and the right kidney average value of the depth measured on the basis of the CT image. The respective increase in left kidney 0.93 cm and right kidney 2.77 cm calculated on the basis of CT image actually measured values. GFR was calculated as the average depth of the subject calculated by the method Tonnesen $83.3{\pm}9.79mL/min$. $98.6{\pm}14.07mL/min$ GFR was applied to calculate the average depth of the subjects using the CT image, is the difference appears 15.26 mL/min was increased after seting up depth value, P value was less than 0.01 which is significant. Conclusion The difference between GFR before-after setting up depth value cause that the different of depth value. Is a measured depth of the extension value of the calculated estimates Whereas Tonnesen kidney depth method is to use in calculating the value of GFR in a typical dynamic elongation test depth derived using the CT image depth. Is thought to be able to calculate more accurately the GFR value by the distance to the center of kidney more accurately measured in the skin thereby.
Journal of the Korean Society of Fisheries and Ocean Technology
/
v.27
no.1
/
pp.1-12
/
1991
An experiment has been carefully designed and performed to verify the theory for the echointergration technique of estimating the density of fish school by the use of steel spheres in a laboratory tank. The spheres used to simulate a fish school were randomly distributed throughout the insonified volume to produce the acoustic echoes similar to those scattered from real fish schools. The backscattered echoes were measured as a function of target density at tow frequencies of 50kHz and 200kHz. Data acquisition, processing and analysis were performed by means of the microcomputer-based sonar-echo processor including a FFT analyzer. Acoustic scattering characteristics of a 36cm mackerel was investigated by measuring fish echoes with frequencies ranging from 47.8kHz to 52.0kHz. The fluctuation of bottom echoes caused by the effects of fish-school attenuation and multiple scattering which occurred in dense aggregations of fishes was also examined by analyzing the echograms of sardine schools obtained by a 50kHz telesounder in the set-net's bagnet, and the echograms obtained by a scientific echo sounder of 50kHz in the East China Sea, respectively. The results obtained can be summarized as follows: 1. The measured and the calculated echo shapes on the steel sphere used to simulate a fish school were in close agreement. 2. The waveform and amplitude of echo signals by a mackerel without swimbladder fluctuated irregularly with the measuring frequency. 3. When a collection of 30 targets/m super(3) lied the shadow region behind another collection of 5 targets/m super(3), the mean losses in echo energy for the 30 targets/m super(3) were about -0.4dB at 50kHz and about -0.2dB at 200kHz, respectively. 4. In the echograms obtained in the East China Sea, the bottom echoes fluctuated remarkably when the dense aggregations of fish appeared between transducer and seabed. Especially, in the case of the echograms of sardine school obtained in a set-net's bagnet, the disappearance of bottom echoes and the lengthening of the echo trace by fish aggregations were observed. Then the mean density of the sardine school was estimated as 36 fish/m super(3). It suggests that when the distribution density of fishes in oceans is greater than this density, the effects of fish-school attenuation and multiple scattering must be taken into account as a possible source of error in fish abundance estimates. 5. The relationship between mean backscattering strength (, dB) and target density ($\rho$, No./m super(3)) were expressed by the equations: =-46.2+13.7 Log($\rho$) at 50kHz and =-43.9+13.4 Log($\rho$) at 200kHz. 6. The difference between the experimentally derived number and the actual number of targets gradually decreased with an increase in the target density and was within 20% when the density was 30 targets/m super(3). From these results, we concluded that when the number of targets in the insonified volume is large, the validity of the echo-integration technique of estimating the density of fish schools could be expected.
Korean Journal of Agricultural and Forest Meteorology
/
v.21
no.2
/
pp.85-96
/
2019
Solar radiation is an important variable for estimation of energy balance and water cycle in natural and agricultural ecosystems. A deep neural network (DNN) model has been developed in order to estimate the daily global solar radiation. Temperature and precipitation, which would have wider availability from weather stations than other variables such as sunshine duration, were used as inputs to the DNN model. Five-fold cross-validation was applied to train and test the DNN models. Meteorological data at 15 weather stations were collected for a long term period, e.g., > 30 years in Korea. The DNN model obtained from the cross-validation had relatively small value of RMSE ($3.75MJ\;m^{-2}\;d^{-1}$) for estimates of the daily solar radiation at the weather station in Suwon. The DNN model explained about 68% of variation in observed solar radiation at the Suwon weather station. It was found that the measurements of solar radiation in 1985 and 1998 were considerably low for a small period of time compared with sunshine duration. This suggested that assessment of the quality for the observation data for solar radiation would be needed in further studies. When data for those years were excluded from the data analysis, the DNN model had slightly greater degree of agreement statistics. For example, the values of $R^2$ and RMSE were 0.72 and $3.55MJ\;m^{-2}\;d^{-1}$, respectively. Our results indicate that a DNN would be useful for the development a solar radiation estimation model using temperature and precipitation, which are usually available for downscaled scenario data for future climate conditions. Thus, such a DNN model would be useful for the impact assessment of climate change on crop production where solar radiation is used as a required input variable to a crop model.
Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.
It has been suggested that ion transport systems are intimately involved in mediating the effects of growth regulatory factors on the growth of a number of different types of animal cells in vivo. The functional importance of the apical membrane $Na^+/H^+$ antiporter in the renal proximal tubule is evidenced by estimates that this transporter mediates the reabsorption of approximately one third of the filtered load of sodium and the bulk of the secretion of hydrogen ions. This study was designed to investigate the pathway utilized by IGF-I in regulating sodium transport in primary cultured renal proximal tubule cells. Results were as follows : 1. $Na^+$ was observed to accumulate in the primary cells as a function of time. Raising the concentration of extracellular NaCl induced an decrease in $Na^+$ uptake compared with control cells in a dose dependent manner. The rate of $Na^+$ uptake into the primary cells was about two times higher in the absence of NaCl($40.11{\pm}1.76pmole\;Na^+/mg\;protein/min$) than in the presence of 140mM NaCl($17.82{\pm}0.94pmole\;Na^+/mg\;protein/min$) at the 30 minute uptake. 2. $Na^+$ uptake was inhibited by IAA($1{\times}10^{-4}M$) or valinomycin($5{\times}10^{-6}M$) treatment($50.51{\pm}4.04$ and $57.65{\pm}2.27$ of that of control, respectively). $Na^+$ uptake by the primary proximal tubule cells was significantly increased by ouabain($5{\times}10^{-5}M$) treatment($140.23{\pm}3.37%$ of that of control). When actinomycin D($1{\times}10^{-7}M$) or cycloheximide($4{\times}10^{-5}M$) was applied, $Na^+$ uptake was decreased to $90.21{\pm}2.39%$ or $89.64{\pm}3.69%$ of control in IGF-I($1{\times}10^{-5}M$) treated cells, respectively. 3. Extracellular cAMP decreased $Na^+$ uptake in a dose-dependent manner($10^{-8}-10^{-4}M$). IBMX($5{\times}10^{-5}M$) also inhibited $Na^+$ uptake. Treatment of cells with pertussis toxin(50pg/ml) or cholera toxin($1{\mu}g/ml$) inhibited $Na^+$ uptake. Extracellular PMA decreased $Na^+$ uptake in a dose-dependent manner(1-100ng/ml). 100 ng/ml PMA concentration significantly inhibited $Na^+$ uptake in IGF-I treated cells. However, staurosporine($1{\times}10^{-7}M$) had no effect on $Na^+$ uptake. When PMA and staurosporine were added together, the inhibition of $Na^+$ uptake was not observed. In conclusion, sodium uptake in primary cultured rabbit renal proximal tubule cells was dependent on membrane potentials and intracellular energy levels. IGF-I stimulates sodium uptake through mechanisms that involve some degree of de novo protein and/or RNA synthesis, and cAMP and/or PKC pathway mediating the action mechanisms of IGF-I.
본 웹사이트에 게시된 이메일 주소가 전자우편 수집 프로그램이나
그 밖의 기술적 장치를 이용하여 무단으로 수집되는 것을 거부하며,
이를 위반시 정보통신망법에 의해 형사 처벌됨을 유념하시기 바랍니다.
[게시일 2004년 10월 1일]
이용약관
제 1 장 총칙
제 1 조 (목적)
이 이용약관은 KoreaScience 홈페이지(이하 “당 사이트”)에서 제공하는 인터넷 서비스(이하 '서비스')의 가입조건 및 이용에 관한 제반 사항과 기타 필요한 사항을 구체적으로 규정함을 목적으로 합니다.
제 2 조 (용어의 정의)
① "이용자"라 함은 당 사이트에 접속하여 이 약관에 따라 당 사이트가 제공하는 서비스를 받는 회원 및 비회원을
말합니다.
② "회원"이라 함은 서비스를 이용하기 위하여 당 사이트에 개인정보를 제공하여 아이디(ID)와 비밀번호를 부여
받은 자를 말합니다.
③ "회원 아이디(ID)"라 함은 회원의 식별 및 서비스 이용을 위하여 자신이 선정한 문자 및 숫자의 조합을
말합니다.
④ "비밀번호(패스워드)"라 함은 회원이 자신의 비밀보호를 위하여 선정한 문자 및 숫자의 조합을 말합니다.
제 3 조 (이용약관의 효력 및 변경)
① 이 약관은 당 사이트에 게시하거나 기타의 방법으로 회원에게 공지함으로써 효력이 발생합니다.
② 당 사이트는 이 약관을 개정할 경우에 적용일자 및 개정사유를 명시하여 현행 약관과 함께 당 사이트의
초기화면에 그 적용일자 7일 이전부터 적용일자 전일까지 공지합니다. 다만, 회원에게 불리하게 약관내용을
변경하는 경우에는 최소한 30일 이상의 사전 유예기간을 두고 공지합니다. 이 경우 당 사이트는 개정 전
내용과 개정 후 내용을 명확하게 비교하여 이용자가 알기 쉽도록 표시합니다.
제 4 조(약관 외 준칙)
① 이 약관은 당 사이트가 제공하는 서비스에 관한 이용안내와 함께 적용됩니다.
② 이 약관에 명시되지 아니한 사항은 관계법령의 규정이 적용됩니다.
제 2 장 이용계약의 체결
제 5 조 (이용계약의 성립 등)
① 이용계약은 이용고객이 당 사이트가 정한 약관에 「동의합니다」를 선택하고, 당 사이트가 정한
온라인신청양식을 작성하여 서비스 이용을 신청한 후, 당 사이트가 이를 승낙함으로써 성립합니다.
② 제1항의 승낙은 당 사이트가 제공하는 과학기술정보검색, 맞춤정보, 서지정보 등 다른 서비스의 이용승낙을
포함합니다.
제 6 조 (회원가입)
서비스를 이용하고자 하는 고객은 당 사이트에서 정한 회원가입양식에 개인정보를 기재하여 가입을 하여야 합니다.
제 7 조 (개인정보의 보호 및 사용)
당 사이트는 관계법령이 정하는 바에 따라 회원 등록정보를 포함한 회원의 개인정보를 보호하기 위해 노력합니다. 회원 개인정보의 보호 및 사용에 대해서는 관련법령 및 당 사이트의 개인정보 보호정책이 적용됩니다.
제 8 조 (이용 신청의 승낙과 제한)
① 당 사이트는 제6조의 규정에 의한 이용신청고객에 대하여 서비스 이용을 승낙합니다.
② 당 사이트는 아래사항에 해당하는 경우에 대해서 승낙하지 아니 합니다.
- 이용계약 신청서의 내용을 허위로 기재한 경우
- 기타 규정한 제반사항을 위반하며 신청하는 경우
제 9 조 (회원 ID 부여 및 변경 등)
① 당 사이트는 이용고객에 대하여 약관에 정하는 바에 따라 자신이 선정한 회원 ID를 부여합니다.
② 회원 ID는 원칙적으로 변경이 불가하며 부득이한 사유로 인하여 변경 하고자 하는 경우에는 해당 ID를
해지하고 재가입해야 합니다.
③ 기타 회원 개인정보 관리 및 변경 등에 관한 사항은 서비스별 안내에 정하는 바에 의합니다.
제 3 장 계약 당사자의 의무
제 10 조 (KISTI의 의무)
① 당 사이트는 이용고객이 희망한 서비스 제공 개시일에 특별한 사정이 없는 한 서비스를 이용할 수 있도록
하여야 합니다.
② 당 사이트는 개인정보 보호를 위해 보안시스템을 구축하며 개인정보 보호정책을 공시하고 준수합니다.
③ 당 사이트는 회원으로부터 제기되는 의견이나 불만이 정당하다고 객관적으로 인정될 경우에는 적절한 절차를
거쳐 즉시 처리하여야 합니다. 다만, 즉시 처리가 곤란한 경우는 회원에게 그 사유와 처리일정을 통보하여야
합니다.
제 11 조 (회원의 의무)
① 이용자는 회원가입 신청 또는 회원정보 변경 시 실명으로 모든 사항을 사실에 근거하여 작성하여야 하며,
허위 또는 타인의 정보를 등록할 경우 일체의 권리를 주장할 수 없습니다.
② 당 사이트가 관계법령 및 개인정보 보호정책에 의거하여 그 책임을 지는 경우를 제외하고 회원에게 부여된
ID의 비밀번호 관리소홀, 부정사용에 의하여 발생하는 모든 결과에 대한 책임은 회원에게 있습니다.
③ 회원은 당 사이트 및 제 3자의 지적 재산권을 침해해서는 안 됩니다.
제 4 장 서비스의 이용
제 12 조 (서비스 이용 시간)
① 서비스 이용은 당 사이트의 업무상 또는 기술상 특별한 지장이 없는 한 연중무휴, 1일 24시간 운영을
원칙으로 합니다. 단, 당 사이트는 시스템 정기점검, 증설 및 교체를 위해 당 사이트가 정한 날이나 시간에
서비스를 일시 중단할 수 있으며, 예정되어 있는 작업으로 인한 서비스 일시중단은 당 사이트 홈페이지를
통해 사전에 공지합니다.
② 당 사이트는 서비스를 특정범위로 분할하여 각 범위별로 이용가능시간을 별도로 지정할 수 있습니다. 다만
이 경우 그 내용을 공지합니다.
제 13 조 (홈페이지 저작권)
① NDSL에서 제공하는 모든 저작물의 저작권은 원저작자에게 있으며, KISTI는 복제/배포/전송권을 확보하고
있습니다.
② NDSL에서 제공하는 콘텐츠를 상업적 및 기타 영리목적으로 복제/배포/전송할 경우 사전에 KISTI의 허락을
받아야 합니다.
③ NDSL에서 제공하는 콘텐츠를 보도, 비평, 교육, 연구 등을 위하여 정당한 범위 안에서 공정한 관행에
합치되게 인용할 수 있습니다.
④ NDSL에서 제공하는 콘텐츠를 무단 복제, 전송, 배포 기타 저작권법에 위반되는 방법으로 이용할 경우
저작권법 제136조에 따라 5년 이하의 징역 또는 5천만 원 이하의 벌금에 처해질 수 있습니다.
제 14 조 (유료서비스)
① 당 사이트 및 협력기관이 정한 유료서비스(원문복사 등)는 별도로 정해진 바에 따르며, 변경사항은 시행 전에
당 사이트 홈페이지를 통하여 회원에게 공지합니다.
② 유료서비스를 이용하려는 회원은 정해진 요금체계에 따라 요금을 납부해야 합니다.
제 5 장 계약 해지 및 이용 제한
제 15 조 (계약 해지)
회원이 이용계약을 해지하고자 하는 때에는 [가입해지] 메뉴를 이용해 직접 해지해야 합니다.
제 16 조 (서비스 이용제한)
① 당 사이트는 회원이 서비스 이용내용에 있어서 본 약관 제 11조 내용을 위반하거나, 다음 각 호에 해당하는
경우 서비스 이용을 제한할 수 있습니다.
- 2년 이상 서비스를 이용한 적이 없는 경우
- 기타 정상적인 서비스 운영에 방해가 될 경우
② 상기 이용제한 규정에 따라 서비스를 이용하는 회원에게 서비스 이용에 대하여 별도 공지 없이 서비스 이용의
일시정지, 이용계약 해지 할 수 있습니다.
제 17 조 (전자우편주소 수집 금지)
회원은 전자우편주소 추출기 등을 이용하여 전자우편주소를 수집 또는 제3자에게 제공할 수 없습니다.
제 6 장 손해배상 및 기타사항
제 18 조 (손해배상)
당 사이트는 무료로 제공되는 서비스와 관련하여 회원에게 어떠한 손해가 발생하더라도 당 사이트가 고의 또는 과실로 인한 손해발생을 제외하고는 이에 대하여 책임을 부담하지 아니합니다.
제 19 조 (관할 법원)
서비스 이용으로 발생한 분쟁에 대해 소송이 제기되는 경우 민사 소송법상의 관할 법원에 제기합니다.
[부 칙]
1. (시행일) 이 약관은 2016년 9월 5일부터 적용되며, 종전 약관은 본 약관으로 대체되며, 개정된 약관의 적용일 이전 가입자도 개정된 약관의 적용을 받습니다.