• Archives of Plastic Surgery
• /
• v.35 no.5
• /
• pp.514-520
• /
• 2008

Purpose: Many attempts have been made to describe ideal facial proportions for over two thousands year and constantly lasted till now. Dr. Marquardt has derived supposedly ideal facial proportions from the facial golden mask using golden ratio of 1 : 1.618. On the other hand, facial reducing surgeries such as mandible angle reduction are popularized in Asia because the width of mid and lower face of Korean is recognized to be wider. The purpose is to analyze characters of Korean university students' faces in horizontal plane and establish the objective data for facial width distributions and clinical applications. Methods: We applied the facial golden mask to the photographs in 1000 cases, compared the width of mid and lower face between the facial golden mask and Korean university students' faces. And we first calculated the horizontal ratio(HR) of middle and lower face each for using comparative scale of width, facial golden mask. We divided 1,000 cases into 3 groups by degrees of HR and analyzed data of HR on each groups. Using calculated horizontal ratio, we newly invented the cumulative frequency of distribution graphs in Korean university students' faces. Results: Mean data of HR were over 1.0 in all groups, which means that Korean university students' faces are typically wider than facial golden mask in horizontal planes. And this study was statistically significant(p- value < 0.05). Clinically using the cumulative frequency distributions of Korean university students' face width, we can easily explain changes of facial width to patient after facial reducing surgery and describe the changes into objective data. Conclusion: This study concludes thatKorean university students' faces are wider than facial golden mask is significantly true and the cumulative frequency of distribution graphs are expected to be widely used for comparison of results in facial reducing surgery.

• Atmosphere
• /
• v.27 no.1
• /
• pp.67-77
• /
• 2017

In this study, we analyzed the characteristics of flow around the Daeyeon automatic weather station (AWS 942) and established formulas estimating inflow wind speeds at a computational fluid dynamics (CFD) model domain for the area around Pukyong national university using a computational fluid dynamics (CFD) model. Simulated wind directions at the AWS 942 were quite similar to those of inflows, but, simulated wind speeds at the AWS 942 decreased compared to inflow wind speeds except for the northerly case. The decrease in simulated wind speed at the AWS 942 resulted from the buildings around the AWS 942. In most cases, the AWS 942 was included within the wake region behind the buildings. Wind speeds at the inflow boundaries of the CFD model domain were estimated by comparing simulated wind speeds at the AWS 942 and inflow boundaries and systematically increasing inflow wind speeds from $1m\;s^{-1}$ to $17m\;s^{-1}$ with an increment of $2m\;s^{-1}$ at the reference height for 16 inflow directions. For each inflow direction, calculated wind speeds at the AWS 942 were fitted as the third order functions of the inflow wind speed by using the Marquardt-Levenberg least square method. Estimated inflow wind speeds by the established formulas were compared to wind speeds observed at 12 coastal AWSs near the AWS 942. The results showed that the estimated wind speeds fell within the inter quartile range of wind speeds observed at 12 coastal AWSs during the nighttime and were in close proximity to the upper whiskers during the daytime (12~15 h).

• Economic and Environmental Geology
• /
• v.32 no.2
• /
• pp.177-187
• /
• 1999

Gravity, magnetic and VLF surveys were conducted to investigat the structural stability and hazards associated with the Ubong landfill in Pohang City, which has been built to dump industrial wastes. In 1994, the collapse of a bank happened in the 6th landfill site due to sudden heavy rain, and a large quantity of waste materials flowed out to the nearby landfill sites, factories and roads. We used $10{\times}10m$ resolution DEM data for gravity reductions. The maximum variation of the terrain effect in the survey area is about 0.5 mgal and the terrain effect is large in the vicinity of bank boundary. The Bouguer gravity anomaly map shows the effect due to the variatino of thickness and type of waste materials. The small negative gravity anomaly increases from the 9th site to the 6th site. The small negative gravity anomaly of the 9th site reflects the relatively shallow dumping depth of average 14.5 m in this site and increased density of waste materials by the repeated stabilization process of soil overlaying. The 6th site is located at the center of the former valley and rainfall and groundwater are expected to flow from south-east to north-west. Therefore, considering the previous accident of mixing waste and bank materials at the north-west boundary of the landfill, there may be some environmental problems of leakage of contaminated water and bank stability. The complex inversion technique using Simulated annealing and Marquardt-Levenberg methods was applied to calculate three-dimensional density distribution from gravity data. In the case of 6th site, it is apparent that the landfill had been dumped in four sectors. However, most part of the 9th site and showed that high magnetic industrial wastes were concentrated in the 6th site. The result of magnetic survey showing low magnetic anomalies along the boundaries of two sites is similar to that of gravity data. The VLF data also reveals four divided sectors in the 6th site, and overall anomaly trend indicates the directio of former valley.

• Journal of the Society of Naval Architects of Korea
• /
• v.52 no.1
• /
• pp.88-95
• /
• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.9
• /
• pp.820-827
• /
• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.1
• /
• pp.88-95
• /
• 2015

This paper presents the study of the efficient localization algorithm for non-linear least square estimation. Although non-linear least square(NLS) estimation algorithms are more accurate algorithms than linear least square(LLS) estimation, NLS algorithms have more computation loads because of iterations. This study proposed the efficient algorithm which reduced complexity for small accuracy loss in NLS estimation. Simulation results show the accuracy and complexity of the localization system compared to the proposed algorithm and conventional schemes.

• Korean journal of applied entomology
• /
• v.58 no.1
• /
• pp.15-25
• /
• 2019

Mites in the family Parasitidae (Mesostigmata) are important predators in soil ecosystem. During the soil acarine biodiversity study from diverse habitats in the Republic of Korea, we found newly recording species of parasitid mites of genus Cycetogamasus and Neogamasus. This paper reports two species Cycetogamasus coreanus and C. corculatus of genus Cycetogamasus and five species Neogamasus eogenualis, N. tikhomirovi, N. laciniatus, Neogamasus kengicus and Neogamasus mahunkai of genus Neogamasus as new record in the Republic of Korea.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.7
• /
• pp.966-976
• /
• 2019

Objective: The methane mitigating potential of various plant-based polyphenol sources is known, but effects of combinations have rarely been tested. The aim of the present study was to determine whether binary and 3-way combinations of such phenol sources affect ruminal fermentation less, similar or more intensively than separate applications. Methods: The extracts used were from Acacia mearnsii bark (acacia), Vitis vinifera (grape) seed, Camellia sinensis leaves (green tea), Uncaria gambir leaves (gambier), Vaccinium macrocarpon berries (cranberry), Fagopyrum esculentum seed (buckwheat), and Ginkgo biloba leaves (ginkgo). All extracts were tested using the Hohenheim gas test. This was done alone at 5% of dry matter (DM). Acacia was also combined with all other single extracts at 5% of DM each, and with two other phenol sources (all possible combinations) at 2.5%+2.5% of DM. Results: Methane formation was reduced by 7% to 9% by acacia, grape seed and green tea and, in addition, by most extract combinations with acacia. Grape seed and green tea alone and in combination with acacia also reduced methane proportion of total gas to the same degree. The extracts of buckwheat and gingko were poor in phenols and promoted ruminal fermentation. All treatments except green tea alone lowered ammonia concentration by up to 23%, and the binary combinations were more effective as acacia alone. With three extracts, linear effects were found with total gas and methane formation, while with ammonia and other traits linear effects were rare. Conclusion: The study identified methane and ammonia mitigating potential of various phenolic plant extracts and showed a number of additive and some non-linear effects of combinations of extracts. Further studies, especially in live animals, should concentrate on combinations of extracts from grape seed, green tea leaves Land acacia bark and determine the ideal dosages of such combinations for the purpose of methane mitigation.

• Structural Engineering and Mechanics
• /
• v.83 no.3
• /
• pp.327-340
• /
• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Geomechanics and Engineering
• /
• v.28 no.4
• /
• pp.385-396
• /
• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.