• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.1
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• pp.41-50
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• 2010

We have developed an in-situ benthic chamber (BelcI) for use in coastal studies that can be deployed from a small boat. It is expected that BelcI will be useful in studying the benthic boundary layer because of its flexibility. BelcI is divided into three main areas: 1) frame and body chamber, 2) water sampler, and 3) stirring devices, electric controller, and data acquisition technology. To maximize in-situ use, the frame is constructed from two layers that consist of square cells. All electronic parts (motor controller, pA meter, data acquisition, etc.) are low-power consumers so that the external power supply can be safely removed from the system. The hydrodynamics of BelcI, measured by PIV (particle image velocimetry), show a typical "radial-flow impeller" pattern. Mixing time of water in the chamber is about 30 s, and shear velocity ($u^*$) near the bottom layer was calculated at $0.32\;cm\;s^{-1}$. Measurements of diffusivity boundary layer thickness showed a range of $180-230\;{\mu}m$. Sediment oxygen consumption rate, measured in-situ，was $84\;mmol\;O_2\;m^{-2}\;d_{-1}$, more than two times higher than on-board incubation results. Benthic fluxes assessed from in-situ incubation were estimated as follows: nitrate + nitrite = $0.18\;{\pm}\;0.07\;mmol\;m^{-2}\;d^{-1}$ ammonium $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$ phosphate = $0.09\;{\pm}\;0.02\;mmol\;m^{-2}\;d^{-1}$ and silicate = $23\;{\pm}\;1\;mmol\;m^{-2}\;d^{-1}$.

• Economic and Environmental Geology
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• v.38 no.5 s.174
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• pp.571-577
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• 2005

Permeable reactive barrier using iron oxide coated sand is one of effective technologies for As(V) contaminated groundwater. However, this method is restricted to As(III), because As(III) species tends to be more weakly bound to adsorbent. In order to overcome the limitation of iron oxide coated sand application to As(III) contaminated groundwater, manganese oxide materials as promoter of As(III) removal were combined to the conventional technology in this study. For combined use of iron oxide coated sand and manganese oxide coated sand, two kinds of removal methods, sequential removal method and simultaneous removal method, were introduced. Both methods showed similar removal efficiency over $85\%$ for 6 hrs. However, the sequential method converted the As contaminated water to acid state (pH 4.5), on the contrary, the simultaneous method maintained neutral state (pH 6.0). Therefore, simultaneous As removal method was ascertained as a suitable treatment technology of As contaminated water. Moreover, for more effective As(III) remediation technique, polypropylene textile which has the characteristics of high surface area, low specific gravity and flexibility was applied as alternative material of sand. The combined use of coated polypropylenes by simultaneous method showed much more prominent and rapid remediation efficiency over $99\%$ after 6 hrs; besides, it has practical advantages in replacement or disposal of adsorbent for simple conventional removal device.

• Journal of Korean Physical Therapy Science
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• v.9 no.3
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• pp.31-49
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• 2002

The purpose of this study was to investigate discomforts and sexual life and to identify the relation between the discomforts and sexual life with low back pain. The data were collected from March 2 through July 31, 2001. Four hundred forty-two questionnaires were returned (response rate=88.0%). Analysis of the data was done with SPSS PC+ and use descriptive statistics, $x^2$-test, t-test, ANOVA. regression. The statistics shows that over than 80% of the adults experienced lumbago at least one time in their life, and Back pain is known as one of the most common complaints made by the patients of all ages in the general hospital or local medical clinics throughout. However, in certain case it leads to a chronic condition which can cause a great deal of problems in management and in financial burden to individuals and society. The result of this study was summarized as follows: 1) It appeared that regarding the distribution of gender, male was the higher(63.6%) then that of female, the portion of forties was 28.5%. Sitting for long time was 23.1% in men and 21.7% in women. Unknown reason including sexual behaviour was 12.9% in men and 15.5% in women. Patients treated medicine and physical therapy were 36.4%. In level of educational background, the rate of high school was 31.0%, technical college was 28.5%. The highest proportion by occupation was 18.3% of office workers, occupation posture was 41.9% of sitting. 2) Men(26.0%) and most of women(34.8%) were not satisfied in the explanation satisfaction rate of sex life concerned disease. 23.8% in men and 23.6% in women considered flexibility of waist good. Man(33.3%) and most of woman(35.0%) considered that Health education is necessary. 32.7% in men and 27.3% in women did't mind educator is whoever. Preventing of lower back pain(LBP) and proper Health education of sex life are demanded in daily life. 3) 58.0% of man and 64.0% of woman mostly had a posture which is man over woman. 28.5% in men and 27.8% in women considered that proper information finding of LBP and sex life was very few and few. 37.7% in men and 42.7% in women have acquired information about sex life flung their friends. 4) The number of sex life was decreased from 2.96 0.98 to 2.61 1.63 and also the time of sex life was decreased from 3.65 1.89 to 226 1.64. The satisfaction rate of sex life changed from 3.60 0.86 to 2.77 1.10. In the number of sex life, The non correct group was 2.62 1.91 and the correct group was higher in 2.68 1.65. In the time of sex life, The non correct group was 2.02 1.47 and the correct group was higher in 229 1.65. The satisfaction rate of sex life was 2.76 0.86 in non correct group and 2.88 1.10 in correct group. So there was a difference. 5) In the satisfaction rate of sex life, Men who have a lower back pain were higher than women and no attack group was higher than attack group. As they had many sex life, the satisfaction rate was higher significantly in statistics. As the time of sex life was short, the satisfaction rate was lower significantly in statistics. As the age was low, the demand rate of Health education was high and as means of patient who had a lower back pain was high, the demand rate of Health education was high. As the patient who had a lower back pain had a long married life, the demand rate of Health education was high and as education level was high, the demand rate of Health education was high. It is necessary to provide patients with conservative treatment, educational teaching, and training to prevent further injuries in the future. In general, it is important to educate the public how to prevent back injuries and how to treat themselves in an onset period to prevent further injuries sliding into a chronic state. Sexuality is an integral part of normal and healthy relationships, but patients are unable to enjoy sex because they are riot able to get into a comfortable position due to back pain. Many conditions of the spine can make certain positions uncomfortable. Health educator should make the education program of the discomforts and the sexual pattern for low back pain in workplace and/or hospital. Further study Is needed on how to integrate the educational program on sexuality into the total rehabilitation program.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.5
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• pp.9-16
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• 2019

In today's global energy market, the importance of green energy is emerging. Hydrogen energy is the future clean energy source and one of the pollution-free energy sources. In particular, the fuel cell method using hydrogen enhances the flexibility of renewable energy and enables energy storage and conversion for a long time. Therefore, it is considered to be a solution that can solve environmental problems caused by the use of fossil resources and energy problems caused by exhaustion of resources simultaneously. The purpose of this study is to efficiently produce hydrogen using plasma, and to study the optimization of DME reforming by checking the reforming reaction and yield according to temperature. The research method uses a 2.45 GHz electromagnetic plasma torch to produce hydrogen by reforming DME(Di Methyl Ether), a clean fuel. Gasification analysis was performed under low temperature conditions ($T3=1100^{\circ}C$), low temperature peroxygen conditions ($T3=1100^{\circ}C$), and high temperature conditions ($T3=1376^{\circ}C$). The low temperature gasification analysis showed that methane is generated due to unstable reforming reaction near $1100^{\circ}C$. The low temperature peroxygen gasification analysis showed less hydrogen but more carbon dioxide than the low temperature gasification analysis. Gasification analysis at high temperature indicated that methane was generated from about $1150^{\circ}C$, but it was not generated above $1200^{\circ}C$. In conclusion, the higher the temperature during the reforming reaction, the higher the proportion of hydrogen, but the higher the proportion of CO. However, it was confirmed that the problem of heat loss and reforming occurred due to the structural problem of the gasifier. In future developments, there is a need to reduce incomplete combustion by improving gasifiers to obtain high yields of hydrogen and to reduce the generation of gases such as carbon monoxide and methane. The optimization plan to produce hydrogen by steam plasma reforming of DME proposed in this study is expected to make a meaningful contribution to producing eco-friendly and renewable energy in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Asia pacific journal of information systems
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• v.20 no.3
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• pp.139-166
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• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).