• The Korean Journal of Malacology
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• v.14 no.2
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• pp.121-130
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• 1998

Studies on the distribution and growth of the mud mussel, Musculista senhousia, were carried out in the mud-tidal flat near Mokpo from August 1996 to July 1997. The patch distribution of the mussel was observed in the middle part of the tidalflat during the study period. Annual mean density was 8,215${\pm}$1,394 ind./m2 and annual mean biomass was 1,966.43${\pm}$668.49 g TWwt/m2 in total wet weight, 760.04${\pm}$279.13 gMWwt/m2 in meat wet weight, 209.93 ${\pm}$ 49.41 gMDwt/m2 in meat dry weight, and 109.66${\pm}$58.78 gAFDW/m2 in ash-free dry weight. The monthly mean size of shell length varied from 11.00 mm to 16.97 mm. Relationship between shell length (SL) and shell height (SH) showed a positively significant regression (SH=0.482SL+0.791, R2=0.940, P<0.001). Regressions of total wet weight (TWwt) (TWwt=7.601${\times}$10-5SL3.052, R2=0.905, P<0.001), and meat wet weight (MWwt) (MWwt=1.127${\times}$10-5${\times}$SL3.404, R2=0.784, P<0.001) on shell length were positively allometric, with highly significant correlation coefficient. The relationships between SL and meat dry weight (MDwt), and AFDW were MDwt=9.813${\times}$10-6${\times}$SL2.928 (R2=0.421), and AFDW=1.015${\times}$10-5${\times}$SL2.922(R2=0.810), respectively. The condition factor of the mussel has been increased from March and formed a peak in July and August. It was sharply dropped in September. These results suggest that the gonadal development of the species commenced to be occurred in spring and that main spawning occurred between August and September.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.8 s.173
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• pp.143-150
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• 2005

To verify applicability of multi-dimensional spectral analysis (MDSA) fur noise source identification two different approaches which are frequency response and coherent function have been investigated. The coherence function approach appears able to separate the correlated system when the noise sources were coherent. In this study, we identify contribution of structure-borne-noise of vehicle HVAC system using MDSA method. Firstly, to identify the applicability of MDSA method, 4-inputs of vehicle HVAC system were the signals measured by accelerometers attached on the selected noise sources which were composed of blower, evaporator, heater and duct. While 1-output which was driver's position sound was the SPL signals measured by a remote microphone, when the blower motor was operating. We identify efficiency of systems modeled with four Inputs/single output through ordinary coherence function (OCF) and partial coherence function (PCF). As a result of experiment, the blower accounted for $62-88\%$ of the overall level of sound energy density. Also, according to the analysis of acoustic signal and vibration signals measurement, an investigation of the noise source identification in the vehicle HVAC is presented. With the sound intensity method, the major sources of the vehicle HVAC radiation are verified. Also the method of improving the noise reduction is proposed by attaching damping patch access to blower motor and noise reduction is verified.

• Smart Structures and Systems
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• v.13 no.4
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• pp.587-614
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• 2014

In recent years the interest in online monitoring of lightweight structures with ultrasonic guided waves is steadily growing. Especially the aircraft industry is a driving force in the development of structural health monitoring (SHM) systems. In order to optimally design SHM systems powerful and efficient numerical simulation tools to predict the behaviour of ultrasonic elastic waves in thin-walled structures are required. It has been shown that in real industrial applications, such as airplane wings or fuselages, conventional linear and quadratic pure displacement finite elements commonly used to model ultrasonic elastic waves quickly reach their limits. The required mesh density, to obtain good quality solutions, results in enormous computational costs when solving the wave propagation problem in the time domain. To resolve this problem different possibilities are available. Analytical methods and higher order finite element method approaches (HO-FEM), like p-FEM, spectral elements, spectral analysis and isogeometric analysis, are among them. Although analytical approaches offer fast and accurate results, they are limited to rather simple geometries. On the other hand, the application of higher order finite element schemes is a computationally demanding task. The drawbacks of both methods can be circumvented if regions of complex geometry are modelled using a HO-FEM approach while the response of the remaining structure is computed utilizing an analytical approach. The objective of the paper is to present an efficient method to couple different HO-FEM schemes with an analytical description of an undisturbed region. Using this hybrid formulation the numerical effort can be drastically reduced. The functionality of the proposed scheme is demonstrated by studying the propagation of ultrasonic guided waves in plates, excited by a piezoelectric patch actuator. The actuator is modelled utilizing higher order coupled field finite elements, whereas the homogenous, isotropic plate is described analytically. The results of this "semi-analytical" approach highlight the opportunities to reduce the numerical effort if closed-form solutions are partially available.

• The Korean Journal of Physiology and Pharmacology
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• v.10 no.1
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• pp.19-24
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• 2006

The intermediate conductance $Ca^{2+}-activated$ $K^+$ channels (SK4, IKCa1) are present in lymphocytes, and their membrane expression is upregulated by various immunological stimuli. In this study, the activity of SK4 was compared between Bal-17 and WEHI-231 cell lines which represent mature and immature stages of murine B lymphocytes, respectively. The whole-cell patch clamp with high-$Ca^{2+}$ ($0.8{\mu}M$) KCl pipette solution revealed a voltage-independent $K^+$ current that was blocked by clotrimazole (1 mM), an SK4 blocker. The expression of mRNAs for SK4 was confirmed in both Bal-17 and WEHI-231 cells. The density of clotrimazole-sensitive SK4 current was significantly larger in Bal-17 than WEHI-231 cells ($-11.4{\pm}3.1$ Vs. $-5.7{\pm}1.15$ pA/pF). Also, the chronic stimulation of B cell receptors (BCR) by BCR-ligation (anti-IgM Ab, $3{\mu}g$/ml, 8∼12 h) significantly upregulated the amplitude of clotrimazolesensitive current from $-11.4{\pm}3.1$ to $-53.1{\pm}8.6$ pA/pF in Bal-17 cells. In WEHI-231 cells, the effect of BCR-ligation was significantly small ($-5.7{\pm}1.15$ to $-9.0{\pm}1.00$ pA/pF). The differential expression and regulation by BCR-ligation might reflect functional changes in the maturation of B lymphocytes.

• The Korean Journal of Physiology and Pharmacology
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• v.5 no.5
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• pp.413-421
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• 2001

An impaired smooth muscle cell (SMC) relaxation of coronary artery by alteration of $K^+$ channels would be the most potential explanation for reduced coronary reserve in left ventricular hypertrophy (LVH), however, this possibility has not been investigated. We performed morphometrical analysis of the coronary artery under electron microscopy and measured $Ca^{2+}-activated\;K\;(K_{Ca})$ currents and delayed rectifier K $(K_{dr})$ currents by whole-cell and inside-out patch-clamp technique in single coronary arterial SMCs from rabbits subjected to isoprenaline-induced cardiac hypertrophy. Coronary arterial SMCs underwent significant changes in ultrastructure. The unitary current amplitude and the open-state probability of $K_{Ca}$ channel were significantly reduced in hypertrophy without open-time and closed-time kinetic. The concentration-response curve of $K_{Ca}$ channel to $Ca^{2+}$ is shifted to the right in hypertrophy. The reduction in the mean single channel current and increase in the open channel noise of $K_{Ca}$ channel by TEA were more sensitive in hypertrophy. $K_{dr}$ current density is significantly reduced in hypertrophy without activation and inactivation kinetics. The sensitivity of $K_{dr}$ current on 4-AP is significantly increased in hypertrophy. This is the first study to report evidence for alterations of $K_{Ca}$ channels and $K_{dr}$ channels in coronary SMCs with LVH. The findings may provide some insight into mechanism of the reduced coronary reserve in LVH.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.159-164
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• 2019

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Asian Journal of Turfgrass Science
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• v.20 no.2
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• pp.147-156
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• 2006

This study was initiated to evaluate the growth characteristics of seven creeping bentgrass cultivars in summer, 'Penncross' showed the worst visual quality, whereas 'Penn A-4' and 'Crenshaw' the best quality. 'Putter', which was maintained a fair quality during the test period, was regarded as a good cultivar because of no significant variation in summer as compared to the other caltivars. 'Crenshaw',' L-93' and 'Penn A-4' were greater in chlorophyll content and 'Penncross' lowest during the summer. Also, 'SR1020' had a low content of chlorophyll. 'Putter' greatly increased in chlorophyll content after fertilization. The highest shoot density($19.3/cm^2$) was found with 'L-93' in early August, followed by 'Crenshaw', 'Penn A-4', 'Putter', 'Dominant', and 'SR1020' in that order. However, 'Penncross' was lowest($15.7/cm^2$). As for a root length, 'L-93' was longest, being over an average 5.5cm. 'Penn A-4' and 'Putter' also showed good result in root growth. However, the root length considerably decreased with 'SR1020', 'Penncross' and 'Dominant' in summer. Brown patch was a serious disease for the most cultivars, except 'Penncross'. 'Dominant' had the most serious damage. 'Putter', 'L-93', 'Crenshaw', 'SR1020', and 'Penn A-4' were also greater in damage over the others. In regards of algae occurrence in summer, 'Penn A-4' had the least damage, while 'Dominant' the greatest. In conclusion, 'Crenshaw', 'Penn A-4' and 'L-93' were the best cultivars in terms of summer growth. Conversely, 'Penncross' was the poorest one. However, this study was conducted under the conditions of one-year old green. Accordingly, in-depth experiment should be done over several years to elucidate the characteristics of growth for the wide range of creeping bentgrass cultivars during the summer.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.