• Journal of Fashion Business
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• v.7 no.2
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• pp.55-68
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• 2003

The purpose of this research is to expand the realm of a button for a decorative purpose through embossing the effect and gravity of a button in fashion by designing the new ceramic buttons which are mainly used for decorative function in costume. In order to acquire a motif for the design, I analyzed several works of Gustav Klimt. As the result, those feature can be classified into the use of decorative lines, mosaic forms, and harmony of golden yellow and black, and it can be applied to buttons and clothes design. The sort of clay used in manufacturing the ceramic buttons was white clay to have high density and to diffuse light well, and press shaping techniques using plaster mold were employed. The baking was performed in an electronic kiln at $800^{\circ}C$ for the first time and at $1250^{\circ}C$ for the second time. Based on wearable designs in 2002/2003 F/W Trend of Interfashion Planning, I made three pieces of dress which could express the button's capability of decoration with effect. This is expressing a simplified form which shows up in details of and yellow and red pink were used to harmonize with golden yellow clothes. As an application of shapes of foliage in I transformed its size and form to be consistent with a jacket and a tube top. To accord with golden beige costume, I made a curve, showing up in Klimt's paintings, with golden color on a circle shape which was also a main motif in his paintings.

• Journal of the Korean Society of Costume
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• v.56 no.2 s.101
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• pp.134-146
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• 2006

Modern society is a multi-cultural consumer society, and there are multiple trends to cater to the tastes of diverse consumers with different sociocultural background. To grasp fashion trends in fast-changing society, how consumer life is changing and what sort of trend is prevailing should be understood above all. A major fashion trend keeps on changing in every season, and that is an extensive and compound measure of what affects the lives and values of cultural receivers who take the lead in it. The purpose of this study was to delve into what sorts of trends were presented in the 21st century's different cultures, how those cultures were reflected in fashion trends, and how design elements predicted by fashion trends could serve as the sources of design that could create a new fashion. The findings of the study were as follows: First of all, the theories of popular culture and trends were reviewed to describe how general receivers found meaning and delight in the products of cultural industry in their own way and how the products were converted into diverse cultural media. Secondly, consumer styles were discussed by classifying consumers into six groups, twixter, duppie, Ubi-Nomad, NONOS, LOHAS and chav, who were generated by changing cultural codes. Thirdly, sociocultural trends and consumer changes brought a lot of diverse changes to fashion trends. The visual materials about the 2005 S/S, F/W Collection were examined to track how changing trends affected fashion style.

• Journal of the Korean Wood Science and Technology
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• v.39 no.2
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• pp.163-171
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• 2011

Because Japanese cedar shows lower mechanical performance, glued-laminated timber (glulam) can be a better way to utilize Japanese cedar for structural purpose. However, low yield of higher grade lamination from log makes it difficult to design structural glulam. This study was aimed to increase the yield of higher grade lamination and provide higher efficiency of manufacturing structural lamination by ultrasonic log sorting technology. Logs were sorted by an existing log grading rule regulated by Korea Forest Research Institute (KFRI). It was found that the KFRI log grading rule contributed to finding better logs in viewpoint of the volumetric yield and it can reduce the number of rejected lumber by visual grading. However, it could not identify better logs to produce higher-grade products. To find an appropriate log-sorting-method for structural products, log diameter and ultrasonic time of flight (TOF) for the log were considered as factors to affect mechanical performance of resulting products. However, it was found that influence of log diameter on mechanical performance of resulting products was very small. The TOF showed a possibility to sort logs by mechanical performance of resulting products even though a coefficient of correlation was not strong (R = 0.6). In a case study, the log selection based on the ultrasonic TOF of the log increased the yield of the outermost tension lamination (E8 or better grade, KS F 3021) from 2.6% to 12.5% and reduced LTE5 (lower than E5 grade) lamination from 43.6% to 10.3%, compared with the existing KFRI log grading rule.

• Journal of Conservation Science
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• v.4 no.1 s.4
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• pp.3-10
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• 1995

This study focused on the reconstructional point of Shinan ship-wreck that was excavated between $1976\~1984$. The wreck, which might be sunk in the beginning of the 14th century, is regarded as a vessel of Yuan dynasty, China. This paper tried to find out some structural characteristics and principal dimensions for restoration. The Shinan shipwreck's structural characteristics are summarized as follow, 1) The Shinan shipwreck is formed V-shaped cross section with bar keel, 2) The vessel is divided 8 holds by 7 bulkheads. 3) The ship has flat type stem and transome stern. 4) A rabbeted clinker -built is basically adopted on planking joint. 5) A wooden sheathing, which means a sort of protecting board against marine insects, is covered outside of the main hull, 6) For making an watertight structure, oakum and lime mixtured t'ung-oil are used along the seam of planking and bulkhead. 7) A V-shaped deep water-way exists at both deck side. 8) The shipwreck is believed to have 2 masts at least. 9) The shiptimbers are classified as Chinese Red Pine(Pinus Massonina) which is mainly grown in the southern part of China. Considering as mentioned above the structural characteristics, Shinan ship-wreck could be classified as Chinese Fu-chuan type(복선형) of sea-going ship. The Shinan ship's principal dimensions which are calculated on the basis of Chinese traditional shipbuilding custom, are as follow, Length overall(L.O.A). : 34.80m Length water line(L.W.L) : 24.90m Breadth(B.max.) : 11m Breadth(B) : 10m Depth at keel line(H) : 3.75m Draft(D). : 3.15m Freeboard(F) : 0.65m Ratio, length/breadth(L/B). : 2.26 Ration, breadth/depth(B/D) : 3.5 Height of stem : 7m Height of stern : 10m Displacement : ab.340ton.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Advances in concrete construction
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• v.14 no.3
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• pp.185-194
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• 2022

For producing cement and concrete, the construction field has been encouraged by the usage of industrial soil waste (or) secondary materials since it decreases the utilization of natural resources. Simultaneously, for ensuring the quality, the analyses of the strength along with durability properties of that sort of cement and concrete are required. The prediction of strength along with other properties of High-Performance Concrete (HPC) by optimization and machine learning algorithms are focused by already available research methods. However, an error and accuracy issue are possessed. Therefore, the Enhanced Deep Neural Network (EDNN) based strength along with durability prediction of HPC was utilized by this research method. Initially, the data is gathered in the proposed work. Then, the data's pre-processing is done by the elimination of missing data along with normalization. Next, from the pre-processed data, the features are extracted. Hence, the data input to the EDNN algorithm which predicts the strength along with durability properties of the specific mixing input designs. Using the Switched Multi-Objective Jellyfish Optimization (SMOJO) algorithm, the weight value is initialized in the EDNN. The Gaussian radial function is utilized as the activation function. The proposed EDNN's performance is examined with the already available algorithms in the experimental analysis. Based on the RMSE, MAE, MAPE, and R² metrics, the performance of the proposed EDNN is compared to the existing DNN, CNN, ANN, and SVM methods. Further, according to the metrices, the proposed EDNN performs better. Moreover, the effectiveness of proposed EDNN is examined based on the accuracy, precision, recall, and F-Measure metrics. With the already-existing algorithms i.e., JO, GWO, PSO, and GA, the fitness for the proposed SMOJO algorithm is also examined. The proposed SMOJO algorithm achieves a higher fitness value than the already available algorithm.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.527-536
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• 2018

This study was conducted to classify the factors influencing nursing students' subjectivity toward happiness and to identify differences in department satisfaction according to happiness factors. Q-methodology, which is effective at studying human subjectivity, was employed. Sixty-four students were asked to sort the 34 Q-statements along a 9-point scale ranging from most disagree (-4) to most agree (+4). The Q-sorts were analyzed using pc-QUNAL program, which subjects the data to principle component factor analysis, followed by varimax rotation. Moreover, the data were collected using a questionnaire that consisted of 27 questions pertaining to department satisfaction and analyzed using the SPSS 21.0 program. The result was classified into four factors that explained 58.0% of the total variance. Factor 1 was a progressive self-realization type, factor 2 an isolated flow type, factor 3 an optimistic friendly type, and factor 4 a self-centered relationship seeking type. Moreover, there was a significant difference in department satisfaction according to happiness factors (F=4.53, p=.006). To enhance department satisfaction and nursing professionalism, education and counseling in consideration of these types are needed.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.486-498
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• 2018

This study is a descriptive research study conducted to provide useful preliminary data that is helpful for self-initiated learning through a preference trend-based learning method by analyzing the personality types of university students in an area. In this study, the self-administered MBTI form $M^{(R)}$ test was performed on 695 students of 10 departments at K University, from September 18 to 22, 2017, using an automatic scoring system. Collected data was analyzed with descriptive statistics and Chi-square test, using the SPSS Win 22.0 Program, to sort the target students into one of 16 different personality types and examine psychological function and temperament by their personality. Differences in personality type preference by gender were as follows: for judging function, the male students had a strong preference for the T type (thinking type) while the female students showed a high preference for the F type (feeling type), and in the case of the pattern of behavior and lifestyle, the male students and the female students had a strong preference for the P type (perceiving type) and the J type (judging type), respectively. In addition, there were significant differences for each major and each department in personality type, psychological function and temperament. In conclusion, personality type was found to vary by gender, major and department. It would be necessary to develop a manual for learning methods reflecting individual preference.

• Journal of the Korean Magnetics Society
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• v.1 no.1
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• pp.17-24
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• 1991

Laves phases of $NdFe_2$, $Nd{(Fe_{0.5})}_2$, $SmCo_2$ and $Sm{(Fe_{0.5}Co_{0.5})}_2$ stoichiometry were prepared using a rapid solidification technology. Low temperature magnetic properties show ferromagnetic behaviors for the $Nd{(Fe_{0.5}Co_{0.5})}_2$, $SmCo_2$ and $Sm{(Fe_{0.5}Co_{0.5})}_2$Nd(Feo,Coo,) Laves compounds while a sort of spin reorientation has been suggested for the supposed composition of $NdFe_2$ alloy. This rapidly solidified $NdFe_2$ alloy is believed to consist of metastable rhombohedral $NdFe_7$ phase plus fine particles of Nd-rich phase. Some evidence of phase transition from the mixture of unstable $NdFe_7$ compound plus Nd-rich to $Nd_2Fe_{17}$ plus Fe-Nd-O phase was obtained after annealing the $NdFe_2$, alloy. The pseudo-binary Laves compound, $Sm{(Fe_{0.5}Co_{0.5})}_2$ exhibits a high coercivityof 4 kOe at room temperature with Curie temperature of $400^{\circ}C$ while the $Nd{(Fe_{0.5}Co_{0.5})}_2$ compound shows a magnetic moment of $2.8\;{\mu}_B/f.u.$.