• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.810-818
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• 2020

A moored floating structure may exhibit resonant motion responses to low-frequency excitations. Similar to the resonant responses of many vibration systems, the motion amplitude of a moored floating structure is significantly affected by the damping of the entire system. In such cases, the damping contributed by the mooring lines sometimes accounts for as much as 80% of the total damping. While the damping induced by catenary mooring lines is well-investigated, few studies have been conducted on the damping induced by taut mooring lines, especially one partly embedded in soil. The present study develops a simple but accurate model for estimating the damping contributed by mooring lines. A typical type of taut mooring line was used as the reference and the hydrodynamic drag force and soil resistance were taken into consideration. The proposed model was validated by comparing its predictions with those of a previously developed model and experimental measurements obtained by a physical model. Case studies and sensitivity studies were also conducted using the validated model. The damping induced by the soil resistance was found to be considerably smaller than the hydrodynamic damping. The superposition of the wave frequency motion on the low-frequency motion was also observed to significantly amplify the damping induced by the mooring lines.

• Korean Journal of Poultry Science
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• v.11 no.2
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• pp.86-92
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• 1984

Present study was carried out to estimate phenotypic and genetic parameters influencing body weight (BW) at 4 weeks of age egg breadth (EB), egg length(EL), egg shape index (SI) and egg weight (EW) at 32 weeks of age and egg numbers (EN) up to 38 weeks of age in broiler male and female lines. The data were collected from closed White Plymouth Rock (female line; G) and Cornish (male line; C) flocks involving 1193 pullets from 211 dams and 48 sires in 1982. The results obtained are summarized as follow: 1. General performance for various trails of lines C and G. The means and standard deviations of BW, EB, EL, SI, EW and EN were 668.34${\pm}$47.18, 4.23${\pm}$0,11, 5.49 ${\pm}$0.19, 77.06${\pm}$2.98, 55.73${\pm}$3.54 and 59.72${\pm}$13.39 in line C, respectively and 487.89${\pm}$ 41.43, 4.22${\pm}$0.11, 5.51${\pm}$0.19, 76.72${\pm}$3.20, 55.43${\pm}$3.26 and 76.93${\pm}$12.17 in line G, respectively. 2. Heritability Heritabilities were estimated from sire, dam and combined components. Estimates for BW, EB, EL, SI, EW and EN from combined components were 0.30, 0.29, 0,40, 0.22, 0.45 and 0.60 in line C, respectively and 0.33, 0.23, 0.28, 0.13, 0.49 and. 0.33 in line G, respectively. 3. Correlation Genetic and phenotypic correlations showed similar trend in line C and G. Genetic correlations, estimated EW with EB and EL, were high and positive (line C; 0.99, 0.75, respectively and line G; 0.94, 0.82, respectively), also correlation of EB with EL was 0.58 (both lines; 0.58). High and negative genetic correlations were shown between SI and EL in line C and G (-0.70, -0.65, respectively). Genetic correlations between SI and EW were relatively low and negative in line C and G (-0.11, -0.19, respectively) and between SI and EN were relatively low and positive in line C and G (0.25, 0.17, respectively). Between other traits, low genetic correlations were shown in both lines, High and positive correlation was estimated between hatchability and egg shape index and polynomial regression of egg shape index on hatchability was estimated; Y=-216.77＋7.6216X-0.0146939X$^2$.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.267-274
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• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.168-171
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• 2003

Generally RF AGC (Roll Force Automatic Gauge Control) controls the roll gap using the variation of rolling force caused by the roll eccentricity and the entry thickness of material, but RE AGC takes the bad effect of the roll eccentricity. The Feed-forward (FF) AGC method, which controls the next stand roll gap by the estimation of the thickness variation due to skid mark is needed to supplement the shortage of RF AGC. In this paper, an adaptive filtering method which takes account of the kind of material, the final objective thickness and the rolling speed is proposed to predict skid mark thickness variation. In addition, an improved estimation method of control point using a speedometer and looper angle is suggested. Via on line test, the performance improvement of the suggested FF AGC method is verified.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.50-52
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• 2006

This paper Proposes a new method of on-line estimation for rotor resistance of the induction motor in the indirect vector controlled drive, using artificial neural network (ANN). The back propagation algorithm is used for training of the neural networks. The error between the desired state variable of an induction motor and actual state variable of a neural network model is back propagated to adjust the weight of a neural network model, so that the actual state variable tracks the desired value. The performance of rotor resistance estimator and torque and flux responses of drive, together with these estimators, are investigated variations rotor resistance from their nominal values. The rotor resistance are estimated analytically, using the proposed ANN in a vector controlled induction motor drive.

• Journal of Mechanical Science and Technology
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• v.18 no.4
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• pp.535-549
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• 2004

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator(FAPC) to get a good control performance and reduce the chatter, The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network(FAN) The weighting parameters of the compensate. are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.8
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• pp.1907-1914
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• 1995

An on-line measurement of the workpiece hardened depth in laser surface hardening processes is very much difficult to achieve, since the hardening process occurs in depth wise direction. In this paper, the hardened depth is estimated using a multilayered neural network. Input data of the neural network are the surface temperatures at arbitrary chosen five surface points, laser power and traveling speed of laser beam torch. To simulate the actual hardening process, a finite difference method(FDM) is used to model the process. Since this model yields the calculation results of the temperature distribution around the workpiece volume in the vicinity of the laser torch, this model is used to obtain the network's training data and laser to evaluate the performance of the neural network estimator. The simulation results show that the proposed scheme can be used to estimate the hardened depth with reasonable accuracy.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.