• Archives of design research
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• v.19 no.4 s.66
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• pp.133-144
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• 2006

According to the latest report by the Customer Protection Board, those who moved into newly constructed buildings are complaining about unidentified pains, asking for more careful selection of constructive materials for prevention of such potential problems. It is internationally recognized today that ecological materials can serve a significant factor for users' health, environmental protection and better industrial competitiveness. This study examined eco-design aspects of each interior material through web site search, in order to help customers learn about and capitalize on eco materials in a proper manner. As a result, 1. It turned out that the domestic industry are giving an impetus to releasing new eco items focusing on lower VOCs emission or addition of functional components as part of the marketing strategy. However, it is recommended that company understand significance of life cycle, and produce eco-concept materials. 2. The reliable standard for choosing the domestic material is EL, HB, GR marks. It is desirable to enhance recycling technologies and expand the sustainable consumption. customer class, since many recycled items are not developed. 3. The sourcing is a vulnerable part in terms of the concept of being environment-friendly material. Therefore, many manufacturers should design the easy knock-down products and produce the good items using recycled materials instead of new raw materials. Also solutions for making the energy from burning material should be studied. 4. The guidebook or manual with correct information about eco-materials is required to promote production and consumption with sustainable concept. 5. Many manufacturers are emphasizing ecological materials for customers, but some of them intended to disrupt customers' proper selection by promoting even unverified items to be environment-friendly.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.5
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• pp.563-572
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• 2006

In order to propose optimum in-situ treatment for reducing phosphorous release from sediment of stationary lakes, a series of column tests were performed. The sediment used in experiment was very fine clay with a mean grain site $7.7{\phi}$ and high $C_{org}$ contents(2.4%). Phosphorous releases were evaluated in two ways : in lake water(with microbial effect) and in distilled water(without microbial effect). As in-situ capping material, sand and loess were used while Fe-Gypsum and $SiO_2$-Gypsum were used for in-situ chemical treatment. In case of lake water considering the effect of microorganism, phosphorous concentration rapidly decreased in the early stage of experiment but it was gradually increased after 10 days. Flux of phosphorous release for control was $3.0mg/m^2{\cdot}d$. Whereas, those for sand layer capping(5 cm) and loess layer capping(5 cm) were $2.5mg/m^2{\cdot}d\;and\;1.8mg/m^2{\cdot}d$, respectively because the latter two were not consolidated sufficiently. For Fe-gypsum and $SiO_2$-gypsum the fluxes were $1.4mg/m^2{\cdot}d$ which meant that reduction efficiency of phosphorous release was more than 40% higher than that of control. The case capping with complex layer was $1.0mg/m^2{\cdot}d$, which showed high reduction efficiency over 60%. The addition of gypsum($CaSO_4{\cdot}2H_2O$) into the sediment reduced release of Phosphorus from the sediments. Gypsum acted as a slow-releasing source of sulphate in sediment, which enhanced the activity of SRB(sulfate reducing bacteria) and improved the overall mineralization rate of organic matter.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.301-310
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• 2023

Ferrihydrite is an iron oxide mineral that is easily found in the natural environment, including acid mine drainage, and has a low crystallinity and high specific surface area, resulting in high reactivity with other ions, and can remove environmentally hazardous substances. However, because ferrihydrite is a metastable mineral, there is a possibility of releasing adsorbed ions by phase transformation to other minerals having low surface area and high crystallinity. In this study, the adsorption characteristics of arsenate, chromate, and selenate on ferrihydrite and the oxyanion removal efficiency of ferrihydrite were studied considering mineral phase transformation. At both pH 4 and 8, the adsorption of oxyanions used in the study were in good agreement with both Langmuir and Freundlich adsorption models except for selenate at pH 8. Due to the difference in surface charge according to pH, at pH 4 a higher amount of ions were adsorbed than at pH 8. The adsorption amount were in the order of arsenate, chromate, and selenate. These different adsorption models and adsorption amounts were due to different adsorption mechanisms for each oxyanions on the surface of ferrihydrite. These adsorption characteristics were closely related to changes in the mineral phase. At pH 4, a phase transformation to goethite or hematite was observed, but only a phase transformation to hematite was observed at pH 8. Among the oxyanion species on ferrihydrite, arsenate showed the highest adsorption capacity and hardly caused phase transformation during the experimental period after adsorption. Contrary to this, chromate and selenate showed faster mineral phase transformation than arsenate, and selenate had the lowest retardation effect among the three oxyanions. Ferrihydrite can effectively remove arsenate due to its high adsorption capacity and low phase transformation rate. However, the removal efficiency for other two oxyanions were low by the low adsorption amount and additional mineral phase transformation. For chromate, the efficient removal is expected only at low concentrations in low pH environments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3A
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• pp.201-209
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• 2010

Generally, PSC girder bridge uses total gross cross section to resist applied loads unlike reinforced concrete member. Also, it is used as short and middle span (less than 30 m) bridges due to advantages such as ease of design and construction, reduction of cost, and convenience of maintenance. But, due to recent increased public interests for environmental friendly and appearance appealing bridges all over the world, the demands for longer span bridges have been continuously increasing. This trend is shown not only in ordinary long span bridge types such as cable supported bridges but also in PSC girder bridges. In order to meet the increasing demands for new type of long span bridges, PSC hollow box girder with H-type steel as compression reinforcements is developed for bridge with a single span of more than 50 m. The developed PSC girder applies compressive prestressing at H-type compression reinforcements using unbonded PS tendon. The purpose of compressive prestressing is to recover plastic displacement of PSC girder after long term service by releasing the prestressing. The static test composed of 4 different stages in 3-point bending test is performed to verify safety of the bridge. First stage loading is applied until tensile cracks form. Then in second stage, the load is removed and the girder is unloaded. In third stage, after removal of loading, recovery of remaining plastic deformation is verified as the compressive prestressing is removed at H-type reinforcements. Then, in fourth stage, loading is continued until the girder fails. The experimental results showed that the first crack occurs at 1,615 kN with a corresponding displacement of 187.0 mm. The introduction of the additional compressive stress in the lower part of the girder from the removal of unbonded compressive prestressing of the H-type steel showed a capacity improvement of about 60% (7.7 mm) recovery of the residual deformation (18.7 mm) that occurred from load increase. By using prestressed H-type steel as compression reinforcements in the upper part of cross section, repair and rehabilitation of PSC girders are relatively easy, and the cost of maintenance is expected to decrease.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.