• Earthquakes and Structures
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• v.9 no.6
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• pp.1133-1152
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• 2015

A frequency-domain method is developed for evaluating the earthquake input energy to two building structures connected by viscous dampers. It is shown that the earthquake input energies to respective building structures and viscous connecting dampers can be defined as works done by the boundary forces between the subsystems on their corresponding displacements. It is demonstrated that the proposed energy transfer function is very useful for clear understanding of dependence of energy consumption ratios in respective buildings and connecting viscous dampers on their properties. It can be shown that the area of the energy transfer function for the total system is constant regardless of natural period and damping ratio because the constant Fourier amplitude of the input acceleration, relating directly the area of the energy transfer function to the input energy, indicates the Dirac delta function and only an initial velocity (kinetic energy) is given in this case. Owing to the constant area property of the energy transfer functions, the total input energy to the overall system including both buildings and connecting viscous dampers is approximately constant regardless of the quantity of connecting viscous dampers. This property leads to an advantageous feature that, if the energy consumption in the connecting viscous dampers increases, the input energies to the buildings can be reduced drastically. For the worst case analysis, critical excitation problems with respect to the impulse interval for double impulse (simplification of pulse-type impulsive ground motion) and multiple impulses (simplification of long-duration ground motion) are considered and their solutions are provided.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Earthquakes and Structures
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• v.5 no.4
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• pp.461-475
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• 2013

A new experimental system of base-isolated structures is proposed. There are two kinds of dampers usually used in the base-isolated buildings, one is a viscous-type damper and the other is an elastic-plastic hysteretic-type damper. The base-isolated structure with a viscous damper and that with an elastic-plastic hysteretic damper are compared in this paper. The viscous damper is modeled by a mini piston and the elastic-plastic hysteretic damper is modeled by a low yield-point steel. The capacity of both dampers is determined so that the dissipated energies are equivalent at a specified deformation. When the capacity of both dampers is determined according to this criterion, it is shown that the response of the base-isolated structure with the elastic-plastic hysteretic damper is larger than that with the viscous damper. This characteristic is demonstrated through the comparison of the bound of the aspect ratio. It is shown that the bound of aspect ratio for the base-isolated structure with the elastic-plastic hysteretic damper is generally smaller than that with the viscous damper. When the base-isolated structure is subjected to long-duration input, the mechanical property of the elastic-plastic hysteretic damper deteriorates and the response of the base-isolated structure including that damper becomes larger than that with the viscous damper. The effect of this change of material properties on the response of the base-isolated structure is also investigated.

• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.56-61
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• 2017

Purpose: High energy requirement and long cooking time are limiting consumption of pigeon pea (Cajanus cajan), a nutritious food. This study was performed to estimate energy and time demand by different methods of cooking pigeon pea. Methods: Pigeon pea (150 g) was soaked in 2.0 L of water at ambient temperature ($29{\pm} 2^{\circ}C$) to determine hydration behavior. Cooking experiments were conducted using aluminum and pressure-cooking pots. Efficiency of cooking was evaluated using four types of cooking appliances (kerosene, liquefied petroleum gas (LPG), electric, and charcoal stoves). Normal (continuous heating until the food was satisfactorily cooked) and control (controlling the energy input to closely match the actual energy required) cooking were conducted. Energy requirement and duration of cooking were determined using standard procedures. Results: Soaking increased moisture content from 11.99 to 30.01% in 90 min, while water absorption rate decreased with soaking duration. In cooking 150 g of pigeon pea using kerosene stove, presoaked normal pressure-pot cooking method consumed the least energy (10 800 kJ) and time (205 min), while unsoaked normal cooking consumed the highest energy (18 450 kJ) and time (336 min). Using LPG stove, unsoaked normal cooking method required the highest energy (52 470 kJ), while presoaked control pressure-pot required the least energy (14 405 kJ). For electric stove, the lowest energy (15 560 kJ) and shortest duration (105 min) were recorded during control cooking of presoaked sample in the pressure-pot. Conclusions: Control cooking was not practicable using charcoal stove. Generally, kerosene stove consumed the least energy, while electric stove was found to have the shortest duration of cooking.

• Journal of Biomedical Engineering Research
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• v.30 no.4
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• pp.347-354
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• 2009

For the reliable transmission of meaningful visual information using prosthetic electrical stimulation, it is required to develop an effective stimulation strategy for the generation of electrical pulse trains based on input visual information. The characteristics of neuronal activities of retinal ganglion cells (RGCs) evoked by electrical stimulation should be understood for this purpose. In this study, for the development of an optimal stimulation strategy for visual prosthesis, we analyzed the neuronal responses of RGCs in rd1 mouse, photoreceptor-degenerated retina of animal model of retinal diseases (retinitis pigmentosa). Based on the in-vitro model of epiretinal prosthesis which consists of planar multielectrode array (MEA) and retinal patch, we recorded and analyzed multiunit RGC activities evoked by amplitude-modulated electrical pulse trains. Two modes of responses were observed. Short-latency responses occurring at 3 ms after the stimulation were estimated to be from direct stimulation of RGCs. Long-latency responses were also observed mainly at 2 - 100 ms after stimulation and showed rhythmic firing with same frequency as the oscillatory background field potential. The long-latency responses could be modulated by pulse amplitude and duration. From the results, we expect that optimal stimulation conditions such as pulse amplitude and pulse duration can be determined for the successful transmission of visual information by electrical stimulation.

• Earthquakes and Structures
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• v.17 no.6
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• pp.577-590
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• 2019

The seismic response of structures to strong ground motions is a complex problem that has been studied for decades. However, most of current seismic regulations do not assess the potential level of damage that a structure may undergo during a strong earthquake. This will happen in spite that the design objectives for any structural system are formulated in terms of acceptable levels of damage. In this article, we analyze the expected damage in single-degree-of-freedom systems subjected to long-duration ground motions generated in soft soil sites, such as those located in the lakebed of Mexico City. An energy-based methodology is formulated, under the consideration of input energy as the basis for the evaluation process, to estimate expected damage. The results of the proposed methodology are validated with damage curves established directly with nonlinear dynamic analyses.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1913-1917
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• 2000

SMD Equipment convey an electronic parts at high speed, then assemble parts into a circuit board, and it develop a long time-duration dynamic force to be caused by moving mass. Vibration problem to be caused by SMD Equipment have an effect on micro-meter level's precision production process, directly, and dynamic stability of building. In the cause of quantitatively access about its vibration problem, input information(or data) of structure dynamic analysis need accuracy information of dynamic load. Determination of Dynamic load is various kinds of method using experimental and theory. In this paper, we got dynamic load using direct measurement method. We expect that an study on the dynamic load characteristic of SMD can be used to Equipment development of low level vibration and basis information of structure dynamic analysis.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Korean Journal of Optics and Photonics
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• v.12 no.3
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• pp.219-224
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• 2001

Spectral phase interferometry for direct electric-field reconstruction (SPIDER) was fabricated and used to characterize pulses from a Ti:sapphire oscillator. In the SPIDER apparatus, two replicas of the input pulse were generated with a time delay of 200 fs and were upconverted by use of sum-frequency generation with a strongly chirped pulse using a 8-cm-long SFIO glass block at a 30-11m-thick type II BBO (p-BaBz04) crystal. The resulting interferogram was recorded with a UV-enhanced CCD array in the spectrometer. The spectral phase was retrieved by SPIDER algorithm in combination with independently measured pulse spectrum and the corresponding temporal intensity profile was reconstructed with a duration of 19 fs. As an independent cross-check of the accuracy of the method, we compared the interferometric autocorrelation (lAC) signal calculated from the SPIDER data with a separately measured lAC. The conventional, but unjustified, method of fitting a sechz pulse to the autocorrelation deceivingly yielded a pulse duration of 15 fs. This systematic underestimation of the pulse duration affirms the need for a complete characterization method. From the consideration in this paper, we concluded that the SPIDER could provide an accurate characterization of femtosecond pulses. ulses.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.369-369
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• 2016

A nanostructure composite is a highly suitable substance for photoacoustic ultrasound generation. This allows an input laser beam (typically, nanosecond pulse duration) to be efficiently converted to an ultrasonic output with tens-of-MHz frequency. This type of energy converter has been demonstrated by using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) composite film that exhibit high optical absorption, rapid heat transition, and mechanical durability, all of which are necessary properties for high-amplitude ultrasound generation. In order to develop the CNT-PDMS composite film, a high-temperature chemical vapor deposition (HTCVD) method has been commonly used so far to grow CNT and then produce a CNT-PDMS composite structure. Here, instead of the complex HTCVD, we use a mixed solution of hydrophobic multi-walled CNT and dimethylformamid (DMF) and fabricate a solution-processed CNT-PDMS composite film over a spherically concave substrate, i.e. a focal energy converter. As the solution process can be applied over a large area, we could easily fabricate the focal transmitter that focuses the photoacoustic output at the moment of generation from the CNT-PDMS composite layer. With this method, we developed photoacoustic energy converters with a large diameter (>25 mm) and a long focal length (several cm). The lens performance was characterized in terms of output pressure amplitude for an incident pulsed laser energy and focal spot dimension in both lateral and axial. Due to the long focal length, we expect that the new lens can be applied for long-range ultrasonic treatment, e.g. biomedical therapy.