• Korean Journal of Ecology and Environment
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• v.36 no.3 s.104
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• pp.277-285
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• 2003

Temperature profiles were observed to understand seasonal variation of thermal structures in the Juam reservoir from March 2000 to May 2001. Heat flux which affects thermal structures was calculated by observed water temperature and meteorological data. Temperature became homogeneous vertically by convection due to the surface cooling in winter. Maximum heat loss through the surface (109.45W/$m^2$) occurred in December. There was a horizontal gradient of water temperature in winter. The temperature was $3^{\circ}C$ at upstream and $5^{\circ}C$ near the dam. The surface temperature increased by the increase of solar radiation in spring and summer. Maximum heat gained through the surface was 101.95 W/$m^2$ in July. Maximum surface temperature was $29^{\circ}C$ in August, whereas the bottom water was $7^{\circ}C.$ Surface mixed layer became thicker and its temperature decreased by surface heat loss in fall and winter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.554-561
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• 2016

In this study, loading tests and a strength evaluation of the bogie frame were conducted to verify the structural safety of the bogie system in an intermodal tram, which runs with cars on a road track. The loads were calculated taking into account the features of the road track with many sharp curves and steep gradients, which are different from the track of conventional railway. They were compared with the loads specified in the previous standard specifications. After the comparison, it was confirmed that the loads acting on the bogie system operating on a road track are slightly different from the specified loads. The specified vertical load of the standard specification for all kinds of trains is conservative, but the specified lateral and longitudinal loads are less than the calculated loads. The application of the actual loads was proven to be reasonable in the development of a new railway system. Based on the defined loads, the bogie frame was fabricated on which strain gauges were attached. It was set on the large loading frame so that the stresses could be measured when loads were applied by hydraulic actuators. After measuring the stresses, it was shown that they were below the allowable stress, which verified the structural safety of the bogie frame.

• Journal of Radiation Protection and Research
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• v.32 no.4
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• pp.158-167
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• 2007

It is required that the neutron dosimetry be present to monitor the reactor vessel throughout its plant life. The Ex-vessel Neutron Dosimetry Systems which consist of sensor sets, radiometric monitors, gradient chains, and support hardware have been installed for 3-Loop plants after a complete withdrawal of all six in-vessel surveillance capsules. The systems have been installed in the reactor cavity annulus in order to characterize the neutron energy spectrum over the beltline region of the reactor vessel. The installed dosimetry were withdrawn and evaluated after a irradiation during one cycle and then compared to the cycle specific neutron transport calculations. The reaction rates from the measurement and calculation were compared and the results show good agreements each other.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.485-488
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• 2005

Most metals are polycrystalline material whose deformation is dominated by the slip system. During the deformation process, orientation of slip systems is rearranged with preferred orientations, leading to deformation-induced crystallographic texture which is called deformation texture. Depending on the texture development, the property of material can be changed. The rate-independent crystal plasticity which is based on the Schmid law as a yield function causes a non-uniqueness in the choice of active slip systems. In this work, to avoid the slip system ambiguity problem, rate-independent crystal plasticity model based on the smooth yield surface with rounded-off corners is adopted. In order to simulate the polycrystalline material under plastic deformation, we employ the Taylor model of polycrystal behavior that all the grains are assumed to be subjected to the macroscopic velocity gradient. Rigid-plastic finite element program based on this rate-independent crystal plasticity is developed to predict the grain-level deformation behavior of FCC metals during metal forming processes. In the finite element calculation, one integration point is considered as a crystalline aggregate which has a number of crystals. Macroscopic behavior of material can be deduced from the behavior of aggregates. As applications, the extrusion processes are simulated and the changes of mechanical properties are predicted.

• Journal of the Korean Society of Visualization
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• v.14 no.1
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• pp.57-61
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• 2016

We report a finding of fast(exceeding 2,000 K/s) heating of polydimethylsiloxane(PDMS), one of the most commonly-used microchannel materials, under cyclic loadings at high(~MHz) frequencies. A microheater was created based on the finding. The heating mechanism utilized vibration damping of sound waves, which were generated and precisely manipulated using a conventional surface acoustic wave(SAW) microfluidic system, in PDMS. The penetration depths were measured to range from $210{\mu}m$ to $1290{\mu}m$, enough to cover most microchannel heights in microfluidic systems. The energy conversion efficiency was SAW frequency-dependent and measured to be the highest at around 30 MHz. Independent actuation of each interdigital transducer(IDT) enabled independent manipulation of SAWs, permitting spatiotemporal control of temperature on the microchip. All the advantages of this microheater facilitated a two-step continuous flow polymerase chain reaction(CFPCR) to achieve the billion-fold amplification of a 134 bp DNA amplicon in less than 3 min. In addition, a technique was developed for establishing dynamic free-form temperature gradients(TGs) in PDMS as well as in gases in contact with the PDMS.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1381-1388
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• 1993

This paper presents a method for the simultaneous optimal design of structural and control systems. Sensitivities of performance index with respect to structural design variables are analyzed. The structural design variables are optimized to minimize the performance index by use of conjugate gradient method. The method is applied to a half model of an active vehicle suspension system with elastic body moving on a randomly profiled road. The suspension control force of an optimally controlled system in the presence of measurement errors are calculated by use of linear quadratic Gaussian control theory and Kalman filter theory. The performance index contains ride comfort, road holding and working space of suspension. The structural design variables taken are stiffness, daming properties and the position of the suspension system. The random road profile considered as colored noise is shaped from white noise by use of shaping filter. The performance of an optimal simultaneous structure/control system is compared with that of an optimal controlled system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.5
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• pp.592-601
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• 2016

The recurrent neural network (RNN) is a deep learning model which is suitable to sequential or length-variable data. The Long Short-Term Memory (LSTM) mitigates the vanishing gradient problem of RNNs so that LSTM can maintain the long-term dependency among the constituents of the given input sequence. In this paper, we propose a LSTM based language model which can predict following words of a given incomplete sentence to generate a complete sentence. To evaluate our method, we trained our model using multiple Korean corpora then generated the incomplete part of Korean sentences. The result shows that our language model was able to generate the fluent Korean sentences. We also show that the word based model generated better sentences compared to the other settings.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.893-898
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• 2005

Due to the merits that only a small amount of computation is needed for solutions and stochastic policies can be handled explicitly, the actor-critic algorithm, which is a class of reinforcement learning methods, has recently attracted a lot of interests in the area of artificial intelligence. The actor-critic network composes of tile actor network for selecting control inputs and the critic network for estimating value functions, and in its training stage, the actor and critic networks take the strategy, of changing their parameters adaptively in order to select excellent control inputs and yield accurate approximation for value functions as fast as possible. In this paper, we consider a new actor-critic algorithm employing an RLS(Recursive Least Square) method for critic learning, and policy gradients for actor learning. The applicability of the considered algorithm is illustrated with experiments on the two linked robot arm.