• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Applied Chemistry for Engineering
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• v.30 no.3
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• pp.384-388
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• 2019

In winter season, the snow and ice accretion on the bottom of the high speed railway rolling stock and boogie part has fallen at a high speed from the ballast section (gravel section for the transmission of the rolling stock load received by sleepers and fixing sleepers), causing the gravel to be scattered, thereby damaging the railway rolling stock structures and facilities. In order to solve these problems, the gravel scattering prevention net, manual de-icing, and movable hot air machine were used, but their efficiency was low. For the more efficient de-icing than ever before, an optimum material for de-icing fluid for high speed railway rolling stock was developed by evaluating the ice melting capacity, kinematic viscosity, evaporation of the material used as a chemical de-icing fluid. Four kinds of organic acid salts (sodium formate, sodium acetate, potassium formate and potassium acetate) and two different alcohols (propylene glycol, glycerol) were used as evaluation materials. Potassium formate, potassium acetate, and propylene glycol had similar ice melting capacities in the indoor test, but the propylene glycol showed the best ice melting capacity in spraying the system simulation test. This is because the kinematic viscosity of propylene glycol was 2.989029 St, which is higher than those of other materials therefore, it could stay longer on the ice and de-icing. In addition, potassium formate and potassium acetate were difficult to be used since the crystals precipitated and adversely affected the appearance of the rolling stock. The propylene glycol is the most optimum as an de-icing fluid for the high speed railway rolling stock.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.17 no.2
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• pp.93-103
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• 1981

It is well-known that nowadays heat resisting and anti-corrosive materials have been widely used as the components materials of gas turbines, nuclear power plants and engines etc. In the fields of machine production industry. And materials for engine components, like as the exhaust valve of internal combustion engine, have been required to operate under the high temperature range of $700^{\circ}C$-$800^{\circ}C$ and high pressured gas with repeated mechanical load for the high performance of engines. For these components, friction welding for bonding of dissimilar steels can be applied for in order to obtain process shortening, production cost reduction and excellent bonding quality. And age hardening recently has been noticed to the heat resisting materials for further strengthening of high temperature strength, especially high temperature fatigue strength. However, it is difficult to find out any report concerning the effects of age hardening for strengthening high temperature fatigue strength to the Friction welded heat resisting and anti-corrosive materials. In this study the experiment was carried out as the high temperature rotary bending fatigue testing under the condition of $700^{\circ}C$ high temperature to the friction welded domestic heat resisting steels, SUH3-SUS303, which were 10hr., 100hr. aging heat treated at $700^{\circ}C$ after solution treatment 1hr. at $1, 060^{\circ}C$ for the purpose of observing the effects of the high temperature fatigue strength and fatigue fracture behaviors as well as with various mechanical properties of welded joints. The results obtained are summarized as follows: 1) Through mechanical tests and micro-structural examinations, the determined optimum welding conditions, rotating speed 2420 rpm, heating pressure 8kg/mm super(2), upsetting pressure 22kg/mm super(2), the amount of total upset 7mm (heating time 3 sec and upsetting time 2 sec) were satisfied. 2) The solution treated material SUH 3, SUS 303, have the highest inclination gradient on S-N curve due to the high temperature fatigue testing for long time at $700^{\circ}C$. 3) The optimum aging time of friction welded SUH3-SUS 303, has been recognized near the 10hr. at $700^{\circ}C$ after the solution treatment of 1hr. at $1, 060^{\circ}C$. 4) The high temperature fatigue limits of aging treated materials were compared with those of raw material according to the extender of aging time, on 10hr. aging, fatigue limits were increased by SUH 3 75.4%, SUS 303 28.5%, friction welded joints SUH 3-SUS 303 44.2% and 100hr. aging the rates were 64.9%, 30.4% and 36.6% respectively. 5) The fatigue fractures occurred at the side of the base matal SUS303 of the friction welded joints SUH 3-SUS 303 and it is difficult to find out fractures at the friction welding interfaces. 6) The cracking mode of SUS 303, SUH 3-303 is intergranular in any case, but SUH 3 is fractured by transgranular cracking.

• International Journal of Quality Innovation
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• v.8 no.1
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• pp.40-75
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• 2007

In the early industrial age which with high intensity of machine and labor, using financial measurement index was good enough to tie in company's mechanization and philosophy of management and been in efficiency. But being comply with "New Economic age," a new economic environment is full of knowledge and information, the enterprise competition had changed from tangible assets, plants to intangible innovation ability of knowledge. As recognizing the new tendency by enterprise, they value gradually the growth and influence from learning. Practice of organization learning not only needs firm structure and be in coordination with both hardware and software, but also needs an affect measurement model to offer enterprise to estimate learning performance. It's a good instrument of financial performance measure mold in the past years, But it's for measuring the past, couldn't formulate enterprise trend to future, hard to estimate investment for future, such as development of products, organization learning, knowledge management etc, as which intangible assets and knowledge ability just the key factors of being win around competition environment in the future. In 1992, Kaplan and Norton brought up Balance Scorecard (BSC) on Harvard Business Review, as an instrument helping enterprise to measure performance, which is being considered to be a most influence management instrument. It added non-financial index such as customer, internal process and learning growth besides traditional financial index, as offering enterprise an index to measure and manage intangible assets and intellectual property. As being aware of organization learning is hard to be ignored in the new economic age, this research is based on learning and growth of BSC, and citing one national material company try to let the most difficult measurement performance of organization learning, to be estimate through BSC, analyze of factor and individual case, to discuss the company how to make the related strategy and vision of organization learning to develop learning and growth of the structure of BSC, subject the matter of out put factors to be discussed, and measure the outcomes as a result of research. The research affect offers (1) the base implement procedure of carrying out BSC; (2) the reference of formulating measurement index while enterprise using BSC to estimate performance of organization learning; (3) the possibility bottleneck maybe forcing while carrying out BSC, to be an improvement or preventive for enterprise.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7549-7554
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• 2015

The aim of this study was to develop gastric retentive bi-layered tablet using floating drug delivery technique. Metformin was selected as a model drug due to its narrow absorption window as well as very highly water solubility. These properties of metformin led to be difficult controlling the drug release. The bi-layered tablet was prepared with bi-layered compression machine to minimize interference between floating part and controlling part. The tablet weight, appearance and hardness were evaluated after compression process. The times of 'time to floating' and 'Floating duration' were tested for floating ability and drug release study was also carried out to understand drug release behavior. Furthermore, the drug release of bi-layered tablet was compared with marketed metformin tablet with sustained release pattern (Glucopharge XR$^{(R)}$).The floating ability and drug release behaviors were well controlled by changing amounts of $NaHCO_3$ (floating substance) and hydroxypropyl methylcellulose (HPMC; release control material). Bi-layered tablet had 13s of time to float, over 10h of floating duration and very similar drug release behavior compared with Glucopharge XR$^{(R)}$($f_2$: 89.6). Consequently, the bi-layered tablet with floating ability was successfully prepared and these properties can maximize the efficacy of metformin.

• Journal of the korean academy of Pediatric Dentistry
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• v.25 no.2
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• pp.400-420
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• 1998

We already know that it is very difficult to obtain an "isolated field" for direct bonding during the surgical exposure of unerupted teeth. The aim of this in-vitro study is to simulate the clinical situation of forced eruption and to evaluate the tensile strengths of preligatured button with several types of contamination which can happen during the surgical exposure of unerupted teeth. Four orthodontic direct bonding systems were used. ($Ortho-One^{TM}$, $Rely-a-Bond^{(R)}$, $Ortho-Two^{TM}$, Phase $II^{(R)}$) Each material was divided into four groups(n=20) : Group 1. (Control, no contamination), Group 2. (Rinse etching agent with saline instead of water), Group 3. (Blood contamination of etched surface for 30 seconds), Group 4. (Blood contamination of primed surface for 30 seconds) 320 bovine anterior permanent teeth were divided into the above mentioned 16 groups. Enamel surface was flattened and ground under water coolant. Pre-ligatured buttons were prepared to the same form. (Cut 0.25 ligature wire 10 cm in length. Twist the ligature wire 30 times clockwise. Mark the wire 15mm and 35mm points from button. Make a loop sticking two points together and twist the loop 6 times counterclockwise.) The bonded specimens were stored at $37^{\circ}C$ saline solution for 3 days. Then the tensile strength of each sample was measured with Instron universal testing machine, crosshead speed of 0.5mm/min. The following results were obtained: 1. As compared to control groups (Group 1) of each material, Rely-a-Bond had a significantly lower mean tensile strengths than other material. (p<0.01) 2. In Group 2. of Ortho-One and Rely-a-Bond, the mean tensile strengths decreased about 7.7% and 11.1%, respectively with statistical significances. (p<0.05) 3. In Group 2. of Ortho-Two and Phase II, the mean tensile strengths did not decrease. 4. In Group 3. of Ortho-One, Rely-a-Bond, Ortho-Two, and Phase II, the mean tensile strengths decreased about 60.8%, 56.1%, 60.2%, and 46.0%, respectively with statistical significances. (p<0.01) 5. In Group 4. of Ortho-One and Rely-a-Bond, the mean tensile strengths did not decrease. 6. In Group 4. of Ortho-Two and Phase II, the mean tensile strengths were decreased about 20.95% and 22.28%, respectively with statistical significances. (p<0.01) There were formations of a hump shaped mass from bonding resin under blood contamination which disturbed direct bonding procedure. According to Reynolds, the proper bond strength for clinical manipulation should be at least 45N or about 4.5Kg.F. According to these results, it can be concluded that Ortho-One could be used during surgical exposure of unerupted teeth. In any case, blood contamination of the etched surface should be avoided, but the blood contamination of primed surface of Ortho-One may not decrease bond strength. Just 'blowing-out' is enough to remove blood from primed surface of Ortho-One. You can verify the clean surface of the primer of Ortho-One after blowing out the blood contamination.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• International Journal of Ocean System Engineering
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• v.2 no.3
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• pp.185-194
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• 2012

A Macroscopic combination of two or more distinct materials is commonly referred to as a "Composite Material", having been designed mechanically and chemically superior in function and characteristic than its individual constituent materials. Composite materials are used not only for aerospace and military, but also heavily used in boat/ship building and general composite industries which we are seeing increasingly more. Regardless of the various applications for composite materials, the industry is still limited and requires better fabrication technology and methodology in order to expand and grow. An example of this is that the majority of fabrication facilities nearby still use an antiquated wet lay-up process where fabrication still requires manual hand labor in a 3D environment impeding productivity of composite product design advancement. As an expert in the advanced composites field, I have developed fabrication skills with the use of machinery based on my past composite experience. In autumn 2011, the Korea government confirmed to fund my project. It is the development of a composite sanding machine. I began development of this semi-robotic prototype beginning in 2009. It has possibilities of replacing or augmenting the exhaustive and difficult jobs performed by human hands, such as sanding, grinding, blasting, and polishing in most often, very awkward conditions, and is also will boost productivity, improve surface quality, cut abrasive costs, eliminate vibration injuries, and protect workers from exposure to dust and airborne contamination. Ease of control and operation of the equipment in or outside of the sanding room is a key benefit to end-users. It will prove to be much more economical than normal robotics and minimize errors that commonly occur in factories. The key components and their technologies are a 360 degree rotational shoulder and a wrist that is controlled under PLC controller and joystick manual mode. Development on both of the key modules is complete and are now operational. The Korean government fund boosted my development and I expect to complete full scale development no later than 3rd quarter 2012. Even with the advantages of composite materials, there is still the need to repair or to maintain composite products with a higher level of technology. I have learned many composite repair skills on composite airframe since many composite fabrication skills including repair, requires training for non aerospace applications. The wind energy market is now requiring much larger blades in order to generate more electrical energy for wind farms. One single blade is commonly 50 meters or longer now. When a wind blade becomes damaged from external forces, on-site repair is required on the columns even under strong wind and freezing temperature conditions. In order to correctly obtain polymerization, the repair must be performed on the damaged area within a very limited time. The use of pre-impregnated glass fabric and heating silicone pad and a hot bonder acting precise heating control are surely required.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.31-54
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• 2007

The exchange between buyers and sellers in the industrial market is changing from short-term to long-term relationships. Long-term relationships are governed mainly by formal contracts or informal agreements, but many scholars are now asserting that controlling relationship by using formal contracts under environmental dynamism is inappropriate. In this case, partners will depend on each other's flexibility or interdependence. The former, flexibility, provides a general frame of reference, order, and standards against which to guide and assess appropriate behavior in dynamic and ambiguous situations, thus motivating the value-oriented performance goals shared between partners. It is based on social sacrifices, which can potentially minimize any opportunistic behaviors. The later, interdependence, means that each firm possesses a high level of dependence in an dynamic channel relationship. When interdependence is high in magnitude and symmetric, each firm enjoys a high level of power and the bonds between the firms should be reasonably strong. Strong shared power is likely to promote commitment because of the common interests, attention, and support found in such channel relationships. This study deals with environmental dynamism in high-tech industry. Firms in the high-tech industry regard it as a key success factor to successfully cope with environmental changes. However, due to the lack of studies dealing with environmental dynamism and supply chain commitment in the high-tech industry, it is very difficult to find effective strategies to cope with them. This paper presents the results of an empirical study on the relationship between environmental dynamism and supply chain commitment in the high-tech industry. We examined the effects of consumer, competitor, and technological dynamism on supply chain commitment. Additionally, we examined the moderating effects of flexibility and dependence of supply chains. This study was confined to the type of high-tech industry which has the characteristics of rapid technology change and short product lifecycle. Flexibility among the firms of this industry, having the characteristic of hard and fast growth, is more important here than among any other industry. Thus, a variety of environmental dynamism can affect a supply chain relationship. The industries targeted industries were electronic parts, metal product, computer, electric machine, automobile, and medical precision manufacturing industries. Data was collected as follows. During the survey, the researchers managed to obtain the list of parts suppliers of 2 companies, N and L, with an international competitiveness in the mobile phone manufacturing industry; and of the suppliers in a business relationship with S company, a semiconductor manufacturing company. They were asked to respond to the survey via telephone and e-mail. During the two month period of February-April 2006, we were able to collect data from 44 companies. The respondents were restricted to direct dealing authorities and subcontractor company (the supplier) staff with at least three months of dealing experience with a manufacture (an industrial material buyer). The measurement validation procedures included scale reliability; discriminant and convergent validity were used to validate measures. Also, the reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.70. A series of exploratory factor analyses was conducted. We conducted confirmatory factor analyses to assess the validity of our measurements. A series of chi-square difference tests were conducted so that the discriminant validity could be ensured. For each pair, we estimated two models-an unconstrained model and a constrained model-and compared the two model fits. All these tests supported discriminant validity. Also, all items loaded significantly on their respective constructs, providing support for convergent validity. We then examined composite reliability and average variance extracted (AVE). The composite reliability of each construct was greater than.70. The AVE of each construct was greater than.50. According to the multiple regression analysis, customer dynamism had a negative effect and competitor dynamism had a positive effect on a supplier's commitment. In addition, flexibility and dependence had significant moderating effects on customer and competitor dynamism. On the other hand, all hypotheses about technological dynamism had no significant effects on commitment. In other words, technological dynamism had no direct effect on supplier's commitment and was not moderated by the flexibility and dependence of the supply chain. This study makes its contribution in the point of view that this is a rare study on environmental dynamism and supply chain commitment in the field of high-tech industry. Especially, this study verified the effects of three sectors of environmental dynamism on supplier's commitment. Also, it empirically tested how the effects were moderated by flexibility and dependence. The results showed that flexibility and interdependence had a role to strengthen supplier's commitment under environmental dynamism in high-tech industry. Thus relationship managers in high-tech industry should make supply chain relationship flexible and interdependent. The limitations of the study are as follows; First, about the research setting, the study was conducted with high-tech industry, in which the direction of the change in the power balance of supply chain dyads is usually determined by manufacturers. So we have a difficulty with generalization. We need to control the power structure between partners in a future study. Secondly, about flexibility, we treated it throughout the paper as positive, but it can also be negative, i.e. violating an agreement or moving, but in the wrong direction, etc. Therefore we need to investigate the multi-dimensionality of flexibility in future research.