• 한국해양학회지
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• v.15 no.2
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• pp.123-128
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• 1980

Variabilities of tidal range at Inchon were described based on observed values. Relationships between tidal ranges and harmonic costants of tide were also examined. Fortnightly variation is predominant and its range is 571.3cm. Mean of maximum spring range(ΔH/sun max/) is 887.2cm and that of minimum neap range(ΔH/sun min/) is 315.9cm. Mean tidal range(ΔH) is 634.3cm. Diurnal inequality is shown about 141cm on an average and monthly inequality is also shown about 100cm. Yearly inquality appears with a range of about 35cm, maxima in March and September, and minima in June and December. There may exist 18 1 years periodicity with a range of about 45cm. There are some relationships between ridal ranges and amplitudes of M$\_$2/ and S$\_$2/, such as ΔH=2.172 H$\_$m/, ΔH$\_$max/=3.043 H$\_$m/, ΔH$\_$min/=1.071 H$\_$m/, ΔH$\_$max/=2.198 (H$\_$m/ + H$\_$s/), and ΔH$\_$min/=1.740 (H$\_$m/ - H$\_$s/).

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.1 s.24
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• pp.13-19
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• 2003

In this paper we discussed the processing procedures of histogram equalization(HE) method and brightness preserving bi-histogram equalization(BBHE) method in the contrast enhancement methods for the performance comparison. With remotely sensed image data of Landsat TM we compared the performances of three methods of Min-Max method, HE method, BBHE method. The experimental results demonstrate that the HE method and BBHE method are more effective in the contrast enhancement performances than the Min-Max method. In the HE method the mean brightness of the resultant output images approached to the middle gray level with regardless of input image mean. In the BBHE method, it is capable of preserving the mean brightness of a original image compared to the HE method while enhancing the contrast of original image effectively. Thus BBHE method is provided more natural enhancement effect than the HE method.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.692-702
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• 2006

In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.669-674
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• 2001

In order to reflect the aggregation situation in the aggregation process, aggregation based on situation assessment (ASA) method was proposed in [1]. It consists of the situation assessment model (SAM) and the ASA algorithm. In the SAM, the value of parameter, p, is transformed into the nearest integer value [1]. The integer-typed output of SAM is used as input for an aggregation. The integer-typed output of SAM indicates the current degree of aggregation situation. The ASA algorithm produces at most finite several aggregation results between min and max. In the sequel, the ASA method can not properly handle the applications with the more sophisticated aggregation results between min and max. In order to solve this problem, we propose two improved ASA (I-ASA) methods. In these I-ASA methods, we allow the value of parameter of SAM to be a real number, and suggest two improved ASA algorithms to make continuous aggregation results between min and max. These I-ASA methods can handle both a precise aggregation and an approximate aggregation. Therefore, when compared to the ASA method [1], the proposed I-ASA methods have advantages in that they can handle the applications with the more sophisticated aggregation results and can be used in the more general applications for aggregations.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.2
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• pp.1-9
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• 2011

An optimal design method for a structural control system considering load variations due to their uncertain characteristics is studied in this paper. The conventional design problem for a control system generally deals with the optimization problem of a structural control system and interaction between the structure and the control device. This study deals with the optimization problem of a load-structure-control system and the more complicated interactions with each other. The problem of finding the load that maximizes the structural responses and the structural control system that minimizes the responses simultaneously is formulated as the min-max problem. In order to effectively obtain the optimal design variables, a co-evolutionary algorithm is adopted and, as a result, an optimal design procedure for the linear structural control system with uncertain dynamic characteristics is proposed. The example design and simulated results of an earthquake excited structure validates the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.1001-1004
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• 2006

Many researches have been processed to reconstruct corrupted an image by noise in fields of signal processing such as image recognition and compute. vision, and AWGN(additive white gaussian noise) and impulse noise are representative. Impulse noise consists of fired-valued(salt & pepper) impulse noise and random-valued impulse noise, and non-linear filters such as SM(standard median) filters are used to remove this noise. But basic SM filters still generate many errors in edge regions of an image, and in order to overcome this problem a variety of methods have been researched. In this paper, we proposed an impulse noise removal algorithm which is superior to the edge preserving capacity. At this tine, after detecting a noise by using the noise detector, we applied a noise removal algorithm based on the min-max operation and compared the capacity with existing methods through simulation.

• Journal of Korean Ophthalmic Optics Society
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• v.7 no.2
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• pp.181-187
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• 2002

Curvature of Crystalline lens changes by Accommdation's change. When Accommdation gives force vertically to Crystalline lens that is elastic body, length increases for vertex direction. Density distribution and form of Crystalline lens that receive force lean to posterior surface, horizontal force of anterior surface direction is bigger more than horizontal force of posterior surface direction. But, if Accommdation begins to grow more than threshold value, expansity reaches in limit on anterior surface. This time, horizontal force of posterior surface direction is great mored more than horizontal force of anterior surface direction, thickness of posterior surface direction increases because is more than anterior surface direction. Anterior and posterior relationship thickness change difference accomplish the 2-nd funtional line(${\Delta}=B_1D+B_2D^2$) about Accommdation. Thickness (${\Delta}t_a$, ${\Delta}t_p$) difference change curved line of anterior pole-border and border-posterior pole by Accommdation is expressed as following. $${\Delta}t_a=t_a-t_{ao}=t_{max}+t_0{\exp}(-A/B)-t_{ao}$$ $${\Delta}t_p=t_p-t_{po}=t_{min}+t_0{\exp}(A/B)-t_{po}$$ The Parameter value that save in human's Crystalline lens obtain $t_{min}=1.1.06$, $t_0=-0.33$, B=9.32 in anterior, and $t_{max}=1.97$, $t_0=0.10$, B=7.96 etc. in posterior. Vertex curvature radius' change is as following Crystalline lens' anterior and posterior by Accommation $$R=R_0+R_1{\exp}(D/k)$$ The Parameter value that save in human's Crystalline lens obtain $R_{min}=5.55$, $R_1=6.87$, k=4.65 in anterior, and $R_{max}=-68.6$, $R_1=76.7$, k=308.5 in posterior, respectively.

• Journal of Astronomy and Space Sciences
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• v.20 no.2
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• pp.109-122
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• 2003

To examine the causal relationship between geomagnetic storm and substorm, we investigate the correlation between dispersionless particle injection rate of proton flux observed from geosynchronous satellites, which is known to be a typical indicator of the substorm expansion activity, and Dst index during magnetic storms. We utilize geomagnetic storms occurred during the period of 1996 ~ 2000 and categorize them into three classes in terms of the minimum value of the Dst index ($Dst_{min}$); intense ($-200nT{$\leq$}Dst_{min}{$\leq$}-100nT$), moderate($-100nT{\leq}Dst_{min}{\leq}-50nT$), and small ($-50nT{\leq}Dst_{min}{\leq}-30nT$) -30nT)storms. We use the proton flux of the energy range from 50 keV to 670 keV, the major constituents of the ring current particles, observed from the LANL geosynchronous satellites located within the local time sector from 18:00 MLT to 04:00 MLT. We also examine the flux ratio ($f_{max}/f_{ave}$) to estimate particle energy injection rate into the inner magnetosphere, with $f_{ave}$ and $f_{max}$ being the flux levels during quiet and onset levels, respectively. The total energy injection rate into the inner magnetosphere can not be estimated from particle measurements by one or two satellites. However, the total energy injection rate should be at least proportional to the flux ratio and the injection frequency. Thus we propose a quantity, “total energy injection parameter (TEIP)”, defined by the product of the flux ratio and the injection frequency as an indicator of the injected energy into the inner magnetosphere. To investigate the phase dependence of the substorm contribution to the development of magnetic storm, we examine the correlations during the two intervals, main and recovery phase of storm separately. Several interesting tendencies are noted particularly during the main phase of storm. First, the average particle injection frequency tends to increase with the storm size with the correlation coefficient being 0.83. Second, the flux ratio ($f_{max}/f_{ave}$) tends to be higher during large storms. The correlation coefficient between $Dst_{min}$ and the flux ratio is generally high, for example, 0.74 for the 75~113 keV energy channel. Third, it is also worth mentioning that there is a high correlation between the TEIP and $Dst_{min}$ with the highest coefficient (0.80) being recorded for the energy channel of 75~113 keV, the typical particle energies of the ring current belt. Fourth, the particle injection during the recovery phase tends to make the storms longer. It is particularly the case for intense storms. These characteristics observed during the main phase of the magnetic storm indicate that substorm expansion activity is closely associated with the development of mangetic storm.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.4
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• pp.188-200
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• 2002

The WRR scheduling algorithm is widely used in ATM networks due to its simplicity and the low cost of hardware implementation. It guarantees minimum cell rate according to the weight of each queue. The fairness is a important factor for ABR service. That is, scheduling algorithm allocates network resources fairly to each VC. However, WRR algorithm shows worse performance on bursty traffic. Because it schedules input traffics according to predetermined weight, it can not satisfies fairness criteria, MCR plus equal share and Maximum of MCR or Max-Min share, defined by ATM Forum TM 4.1 specification. The Nabeshima et al algorithm is not adapt to network status rapidly because it is not compensate the weights of unused bandwidth to VCs and assign the unused bandwidth to VCs by RR method. In this paper, we propose a scheduling algorithm for satisfying the two fairness criteria, MCR plus equal share and Maximum of MCR or Max-Min share, among the six criteria defined by ATM Forum TM 4.1 specification. The WRR, Nabeshima et al, and the proposed scheduling algorithms are compared with respect to fairness and convergence time throughout experimental simulation. According to the simulation results, the proposed algorithm shows higher fairness and more rapid convergence than other algorithms.

• Journal of Ginseng Research
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• v.42 no.2
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• pp.192-198
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• 2018

Background: Ginseng has been used as an ergogenic agent, although evidence for its effectiveness is weak. A randomized, double-blind, placebo-controlled clinical trial was conducted to evaluate the effect of a ginsenoside complex (UG0712) on changes in exercise performance. Methods: Sedentary individuals (n = 117) were randomly assigned into one of three groups: low-dose ginsenoside supplementation (100 mg/d, n = 39), high-dose ginsenoside supplementation (500 mg/d, n = 39), or a placebo group (500 mg/d, n = 39). All participants underwent a supervised 12-wk aerobic and resistance exercise training course. To assess the effects of supplementation on physical performance, maximal oxygen consumption ($VO_2max$), anaerobic threshold (AT), lactic acid, and muscle strength of the dominant knee were measured at baseline, every visit, and after the training program. Results: Both ginsenoside groups showed significant increases in $VO_2max$ and muscular strength during exercise training. There were no definite changes in AT and lactic acid levels over time. After exercise training, there were definite differences in the $VO_2max$ (28.64.9 to $33.7{\pm}4.9ml/kg/min$ in high-dose group vs. $30.4{\pm}6.7$ to $32.8{\pm}6.6ml/kg/min$ in placebo, p = 0.029) and AT ($19.3{\pm}4.2$ to $20.9{\pm}3.5ml/kg/min$ in high-dose group vs. $20.0{\pm}5.1$ to $20.0{\pm}4.9ml/kg/min$ in placebo, p = 0.038) between the high-dose ginsenoside and placebo groups. However, there was no difference in $VO_2max$ between the low-dose ginsenoside and placebo groups (p = 0.254). There were no differences in muscular strength during exercise training among the three groups. Conclusion: High-dose ginsenoside supplementation (UG0712) augmented the improvement of aerobic capacity by exercise training.