• Title/Summary/Keyword: Linear-quadratic model

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Speed, Depth and Steering Control of Underwater Vehicles with Four Stem Thrusters - Simulation and Experimental Results (네 대의 주 추진기를 이용한 무인잠수정의 속도, 심도 및 방위각 제어 - 시뮬레이션 및 실험)

  • JUN BONG-HUAN;LEE PAN-MOOK;LI JI-HONG;HONG SEOK-WON;LEE JIHONG
    • Journal of Ocean Engineering and Technology
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    • v.19 no.2 s.63
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    • pp.67-73
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    • 2005
  • This paper describes depth, heading and speed control of an underwater vehicle that has four stern thrusters of which forces are coupled in the diving and, steering motion, as well as the speed of the vehicle. The optimal linear quadratic controller is designed based on a linearized- state space model, developed by combining the dynamic equations of speed, steering and diving motion. The designed controller gives provides an optimal thrust distribution, minimizing the given performance index to control speed, depth and heading simultaneously. To validate the performance of the controller, a simulation and tank-test are carried out with DUSAUV (Dual Use Semi-Autonomous Underwater Vehicle), developed by KORDI as a test-bed for testing new underwater technologies. Optimal gains of the controller are tuned, using a computer simulation environment with a nonlinear 6-DOF numerical DUSAUV model, developed by PMM (Planner Motion Mechanism) test. To verify the performance of the presented controller in experiment, a tank-test with DUSAUV is carried out in the ocean engineering basin in KORDI. The experimental results are also compared with the simulation results to investigate the accordance of the numerical and the real mode.

Optimization of Ingredient Mixing Ratio for Preparation of Steamed Foam Cake with Barley (Hordeum vulgare L.) Sproutling Powder (어린보릿가루 첨가 거품형 찜케이크의 재료 혼합비율의 최적화)

  • Seo, Min-Ja;Jung, Su-Ji;Jang, Myung-Sook
    • Korean journal of food and cookery science
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    • v.22 no.6 s.96
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    • pp.815-824
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    • 2006
  • This study was performed to determine the optimum ratio of each ingredient in the steamed foam cake with barley (Hordeum vulgare L.) sproutling powder. The experiment was designed according to the D-optimal design of mixture design, which showed 14 experimental points including 4 replicates for three independent variables (sugar 112${\sim}$139%, barley sproutling powder 1${\sim}$8%, and oil 5${\sim}$25%). The compositional and functional properties of test were measured, and these values were applied to the mathematical models. A canonical form and trace plot showed the influence of each ingredient on the mixture final product. The results of F-test, volume, color values (L, a, b), textural properties (hardness, gumminess, chewiness) and sensory characteristics (softness) decided a linear model, while the sensory characteristics (color, smell, taste, overall acceptance) decided a quadratic model. The volume of steamed foam cake was increased by sugar addition, and a negative effect was exerted by barley sproutling powder and oil. L and a of color values increased but the b value decreased with increasing sugar and oil content, whereas barley sproutling powder tended to decrease all color values. The addition of barley sproutling powder also had a positive effect on the textural properties (hardness, gumminess, chewiness). Sensory characteristics (color, smell, softness, taste, overall acceptance) could suffer counter results with the excessive addition of sugar, barley sproutling powder, and oil. The optimum formulations by numerical and graphical methods were similar: sugar, barley sproutling powder, and oil were 130.4%, 4.0%, and 10.7% by numerical method, compared to 130.4%, 4.0%, and 10.7% by graphical method, respectively.

Genetically Optimized Neurofuzzy Networks: Analysis and Design (진화론적 최적 뉴로퍼지 네트워크: 해석과 설계)

  • 박병준;김현기;오성권
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.8
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    • pp.561-570
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    • 2004
  • In this paper, new architectures and comprehensive design methodologies of Genetic Algorithms(GAs) based Genetically optimized Neurofuzzy Networks(GoNFN) are introduced, and a series of numeric experiments are carried out. The proposed GoNFN is based on the rule-based Neurofuzzy Networks(NFN) with the extended structure of the premise and the consequence parts of fuzzy rules being formed within the networks. The premise part of the fuzzy rules are designed by using space partitioning in terms of fuzzy sets defined in individual variables. In the consequence part of the fuzzy rules, three different forms of the regression polynomials such as constant, linear and quadratic are taken into consideration. The structure and parameters of the proposed GoNFN are optimized by GAs. GAs being a global optimization technique determines optimal parameters in a vast search space. But it cannot effectively avoid a large amount of time-consuming iteration because GAs finds optimal parameters by using a given space. To alleviate the problems, the dynamic search-based GAs is introduced to lead to rapidly optimal convergence over a limited region or a boundary condition. In a nutshell, the objective of this study is to develop a general design methodology o GAs-based GoNFN modeling, come up a logic-based structure of such model and propose a comprehensive evolutionary development environment in which the optimization of the model can be efficiently carried out both at the structural as well as parametric level for overall optimization by utilizing the separate or consecutive tuning technology. To evaluate the performance of the proposed GoNFN, the models are experimented with the use of several representative numerical examples.

Optimization of Muffin with Dried Rhynchosia Molubilis Powder Using Response Surface Methodology (반응표면분석법을 이용한 쥐눈이콩가루 첨가 머핀 제조 조건의 최적화)

  • Lee, Sun-Mee;Joo, Na-Mi
    • Korean journal of food and cookery science
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    • v.24 no.5
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    • pp.626-635
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    • 2008
  • The purpose principal objective of this study was to develop a muffin with the addition of jinuni bean powder. The whole Our analysis was conducted by using using Design Expert 7(Stat - Easy Co. Minneapolis). The jinuni bean muffin was produced by varying the contents of jinuni bean powder(A), sugar(B), and butter(C). According to the Response Surface Methodology(RSM), it showed we delineated 16 experimental points, including two replicants. The optimization We attempted to optimize of the jinuni bean muffins was studied with regard to its analysis of rheology and sensory evaluations. As a result of the redness, hardness, and sensory evaluations, characteristics such as color, appearance, flavor, softness, and overall quality showed a varied in accordance with a quadratic model, whereas lightness, yellowness, and cohesiveness, and gumminess showed evidenced a linear model pattern. Lightness, and yellowness decreased with the increases in the of jinuni bean powder content(p<0.0001), while whereas redness increased with the increases of in the content of jinuni bean powder(p<0.001), in case of over 80g, the redness tended to increase decrease again. In addition, hardness(p<0.05), gumminess(p<0.05), and cohesiveness(p<0.01) showed differed significantly differences with the increases of in the jinuni bean powder content. The results of our sensory evaluation results showed demonstrated significant values in color, appearance, flavor, softness,and overall quality values(p<0.05). The optimal formulation, as assessed by numerical and graphical methods, were was determined to be 50.05 g of jinuni bean powder, 78.56 g of sugar, and 90.97 g of butter.

Incremental Regression based on a Sliding Window for Stream Data Prediction (스트림 데이타 예측을 위한 슬라이딩 윈도우 기반 점진적 회귀분석)

  • Kim, Sung-Hyun;Jin, Long;Ryu, Keun-Ho
    • Journal of KIISE:Databases
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    • v.34 no.6
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    • pp.483-492
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    • 2007
  • Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

Evaluation of Biological Characteristics of Neutron Beam Generated from MC50 Cyclotron (MC50 싸이클로트론에서 생성되는 중성자선의 생물학적 특성의 평가)

  • Eom, Keun-Yong;Park, Hye-Jin;Huh, Soon-Nyung;Ye, Sung-Joon;Lee, Dong-Han;Park, Suk-Won;Wu, Hong-Gyun
    • Radiation Oncology Journal
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    • v.24 no.4
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    • pp.280-284
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    • 2006
  • $\underline{Purpose}$: To evaluate biological characteristics of neutron beam generated by MC50 cyclotron located in the Korea Institute of Radiological and Medical Sciences (KIRAMS). $\underline{Materials\;and\;Methods}$: The neutron beams generated with 15 mm Beryllium target hit by 35 MeV proton beam was used and dosimetry data was measured before in-vitro study. We irradiated 0, 1, 2, 3, 4 and 5 Gy of neutron beam to EMT-6 cell line and surviving fraction (SF) was measured. The SF curve was also examined at the same dose when applying lead shielding to avoid gamma ray component. In the X-ray experiment, SF curve was obtained after irradiation of 0, 2, 5, 10, and 15 Gy. $\underline{Results}$: The neutron beams have 84% of neutron and 16% of gamma component at the depth of 2 cm with the field size of $26{\times}26\;cm^2$, beam current $20\;{\mu}A$, and dose rate of 9.25 cGy/min. The SF curve from X-ray, when fitted to linear-quadratic (LQ) model, had 0.611 as ${\alpha}/{\beta}$ ratio (${\alpha}=0.0204,\;{\beta}=0.0334,\;R^2=0.999$, respectively). The SF curve from neutron beam had shoulders at low dose area and fitted well to LQ model with the value of $R^2$ exceeding 0.99 in all experiments. The mean value of alpha and beta were -0.315 (range, $-0.254{\sim}-0.360$) and 0.247 ($0.220{\sim}0.262$), respectively. The addition of lead shielding resulted in no straightening of SF curve and shoulders in low dose area still existed. The RBE of neutron beam was in range of $2.07{\sim}2.19$ with SF=0.1 and $2.21{\sim}2.35$ with SF=0.01, respectively. $\underline{Conclusion}$: The neutron beam from MC50 cyclotron has significant amount of gamma component and this may have contributed to form the shoulder of survival curve. The RBE of neutron beam generated by MC50 was about 2.2.

Effect of Temperature and Various Pre-treatments on Germination of Hippophae rhamnoides Seeds (갈매보리수나무 종자의 온도 및 여러 가지 전처리에 따른 발아반응)

  • Choi, Chung-Ho
    • Korean Journal of Plant Resources
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    • v.25 no.1
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    • pp.132-141
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    • 2012
  • This study was carried out to test seed germination responses to temperatures and pre-treatments in Hippophae rhamnoides, which has many abilities in antioxidant activity, soil improvement and erosion control. H. rhamnoides seeds were placed at 10, 15, 20, 25, 30 and $35^{\circ}C$ under light condition. As the results, germination percentage (GP) was the highest at 15 and $20^{\circ}C$, and mean germination time (MGT), germination rate (GR) and germination value (GV) were the highest at $25^{\circ}C$. Quadratic and linear regression model were used to determine the cardinal temperatures such as base ($T_b$), maximum ($T_m$) and optimum ($T_o$) temperature for germination. In quadratic regression model using PG, $T_b$, $T_m$ and $T_o$ was estimated as 0.6, 36.4 and $18.5^{\circ}C$, respectively, and temperature range for germination was $35.8^{\circ}C$. In linear regression model using GR, $T_b$, $T_m$ and $T_o$ was estimated as 8.3, 35.4 and $25.3^{\circ}C$, respectively, and temperature range for germination was $27.2^{\circ}C$. Germination properties were investigated after H. rhamnoides seeds were treated by prechilling (1, 2, 4, 6 and 8 weeks), stratification (2, 4, 6 and 8 weeks), solid matrix priming (seed : carrier : water = 5 : 1 : 7, 8, 9 and 10), osmo-priming (-0.25, -0.5, -1.0 and -1.5 MPa) and calcium chloride ($CaCl_2$) -priming (100, 200, 300 and 400 mM). The highest GP was observed in $CaCl_2$ 300 and 400 mM treatments, and MGT was the shortest in stratification 6 and 8 weeks treatments. GR and GV were the highest and GP was the second highest when seeds were prechilled for 1 and 2 weeks. Consequently, prechilling 1 or 2 weeks treatment was considered as the appropriate method when we contemplate qualitative and quantitative effects in seedling production.

Induction of Micronuclei in Human and Mouse Lymphocytes Irradiated with Gamma Radiation and Effect of Panax ginseng C.A. Meyer (마우스와 사람 림프구에서 방사선에 의한 미소핵의 형성 및 고려인삼의 효과)

  • Kim, Sung-Ho;Oh, Heon;Lee, Song-Eun;Lee, Yun-Sil;Kim, Tae-Hwan;Jeong, Kyu-Sik;Ryu, Si-Yun
    • Journal of Radiation Protection and Research
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    • v.22 no.3
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    • pp.153-160
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    • 1997
  • The frequencies of ${\gamma}$-ray-induced micronuclei (MN) in cytokinesis-blocked (CB) lymphocytes at several doses were measured in three donors of human and C57BL/6 mice. Measurements performed after irradiation showed a dose-related increases in MN frequency in each of the donors studied. The relative sensitivity of mouse in spleen lymphocytes (SLs) compared with human peripheral blood lymphocytes (PBLs) was estimated by best fitting linear-quadratic model based on the radiation-induced MN data over the range from 0 cGy to 400 cGy. In the case of MN frequency with 0.2 per CB cell, the relative sensitivity of mouse SLs was 1.67. Compared with the radiation-induced MN formation in the PBLs of human, the SLs of mouse were more radiosensitive. Using this MN assay with human PBLs and mouse SLs, studies were performed to determine whether the water fraction of ginseng (Panax ginseng C.A.Meyer) against radiation-induced MN in human PBLs after in vitro irradiation (3Gy) and in SLs of C57BL/6 mice after in vivo irradiation (3Gy). The frequency of MN in human PBLs was reduced by water fraction of ginseng (0.5mg/ml of medium) both pre-and post treatment (p<0.0l) in vitro. In addition, the frequency of MN in mouse SLs was also reduced by pretreatment of ginseng (2mg/ml of drinking water for 7days) in vivo. The data suggested that the ginseng may reduce cell damage caused by ${\gamma}$-rays in vitro and in vivo. Further studies are needed to characterize better the protective nature of ginseng extract, its fractions and compounds.

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The Optimum Methionine to Methionine Plus Cystine Ratio for Growing Pigs Determined Using Plasma Urea Nitrogen and Nitrogen Balance

  • Qiao, Shiyan;Piao, Xiangshu;Feng, Zhanyu;Ding, Yuhua;Yue, Longyao;Thacker, P.A.
    • Asian-Australasian Journal of Animal Sciences
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    • v.21 no.3
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    • pp.434-442
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    • 2008
  • The objective of this study was to determine the optimum ratio of methionine to methionine plus cystine for growing pigs. A nitrogen balance trial was conducted using a total of 21 barrows (Large WhiteLandrace) over two replicates. The initial body weight was $20.36{\pm}1.22kg$ (mean${\pm}$SD) in the first replicate and $23.54{\pm}1.02kg$ (mean${\pm}$SD) in the second. For each replicate, the 21 pigs were randomly assigned to one of seven dietary treatments with three observations per treatment. The diets included a methionine and cystine-deficient basal diet with all other essential nutrients meeting nutrient requirements and six diets formulated with graded levels of DL-methionine (0.00, 0.03, 0.06, 0.10, 0.13, 0.16%) and $L-Cystine{\cdot}HCl{\cdot}H_2O$ (0.19, 0.15, 0.11, 0.07, 0.04, 0.00%). This resulted in ratios of methionine to methionine plus cystine of 41.3, 29.6, 35.3, 41.2, 46.0, 51.6 and 57.5%. Each experimental period lasted 12 days consisting of a seven-day adaptation period followed by a five-day total collection of urine and feces. During the collection period, pigs were fed 900 g/day for the first replicate and 1,200 g/day for the second replicate. The feed was provided in three equal portions at 0800, 1500, and 2200 h daily. Pigs had ad libitum access to water after feeding. There was a linear (p<0.01) and quadratic (p<0.01) effect on daily gain and feed conversion as the ratio of methionine to methionine plus cystine increased. Pigs receiving the diets providing a methionine to methionine plus cystine ratio of 51.6% had the best daily gain and feed conversion. Plasma urea nitrogen was also lowest for this treatment. Nitrogen retention increased (p<0.01) as the relative proportion of methionine increased up to 51.6% and then a downward trend occurred at 57.5%. The quadratic regression model, as well as one- and two- slope regression line models, were used to determine the optimum ratio of methionine to methionine plus cystine. Eliminating the 35.3% methionine to methionine plus cystine treatment resulted in $R^2$ values in excess of 0.92. The optimal ratio of methionine to methionine plus cystine was estimated to be 54.15% for nitrogen retention and 56.72% for plasma urea nitrogen.

A Study on GA-based Optimized Polynomial Neural Networks and Its Application to Nonlinear Process (유전자 알고리즘 기반 최적 다항식 뉴럴네트워크 연구 및 비선형 공정으로의 응용)

  • Kim Wan-Su;Lee In-Tae;Oh Sung-Kwun;Kim Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.7
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    • pp.846-851
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    • 2005
  • In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.