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

• Management & Information Systems Review
• /
• v.38 no.2
• /
• pp.67-81
• /
• 2019

In accordance with K-IFRS 1109, financial instruments are classified to amortized cost (AC), fair value through other comprehensive income (FVOCI) and fair value through profit or loss (FVPL). And disposal gains are prohibited to be recirculated for net income when FVOCI financial instruments would be sold in the future, so-called recirculation prohibition. This research investigates whether accumulated other comprehensive income of available-for sale financial assets(AFS) under K-IFRS 1039, could affect reclassified amounts to the FVPL securities from the AFS securities. Also, this study investigates the effects of the reported income on the reclassified FVPL, because CEOs are likely to try earnings management when net income is predicted to be less than target or is low, comparing other firms. As a result of empirical analysis, first, I find that accumulated other comprehensive income of the AFS has a positive impact on the reclassified FVPL. Second, level of reporting income has no significant impact on the reclassified FVPL. Third, interaction effects are significantly positive on the firms which have more other comprehensive income and less level of reported income. Fourth, the effects of the bank and securities are more distinct than those of the manufactures. This study is the first research to investigate earnings management through AFS at the timing of the first adoption of K-IFRS 1109. Empirical results of this study provide evidence of earnings management on the reclassification of FVPL which gives meaningful implications to regulators, academic researchers and auditors.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.147-170
• /
• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• The Journal of Internal Korean Medicine
• /
• v.45 no.3
• /
• pp.335-341
• /
• 2024

Objectives: Patients with cancer eventually fail to respond to therapy when malignant cells develop effective ways to evade immunosurveillance. Conventional cancer treatments, such as radiation therapy and chemotherapy, aim to cure the disease or prolong the patient's life. However, the toxicity and side effects of conventional treatments limit their efficacy. Herbal medicine is a typical complementary and integrative form of medicine for cancer treatment in Asia. This protocol evaluates the effectiveness of herbal medicines in improving the immune function of patients with cancer. Methods: The following electronic databases will be searched: MEDLINE via PubMed, EMBASE via Elsevier, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure (CNKI), and Korean databases including Regional Information Sharing Systems (RISS), National Digital Science Library (NDSL), and Oriental Medicine Advanced Searching Integrated System (OASIS). Additionally, prospective randomized controlled trials that evaluate the effectiveness of herbal medicines on immune function in patients with cancer will be included in this review. All outcomes related to the immune function of patients with cancer (e.g., CD3, CD4, CD8, CD4/CD8 ratio, CD19 (B cells), dendritic cells (CD11), CD56 (NK cells), and macrophages) will be included in this review. Results: This review is expected to provide data on the effectiveness of herbal medicines on improving immune functions in patients with cancers. Conclusion: This systematic review will help patients and clinicians establish new management options for cancer treatment.

• Journal of Korea Water Resources Association
• /
• v.57 no.8
• /
• pp.509-518
• /
• 2024

Flash drought is a rapid-onset drought that occurs rapidly over a short period due to abrupt changes in meteorological and environmental factors. In this study, we utilized satellite-based soil moisture product from the Advanced Microwave Scanning Radiometer-2(AMSR2) ascending X-band to calculate the weekly Flash Drought Intensity Index (FDII). We also analyzed the characteristics of flash droughts on the Korean Peninsula over a 10-year period from 2013 to 2022. The analysis of monthly spatial distribution patterns of the irrigation period across the Korean Peninsula revealed significant variations. In North Korea (NK), a substantial increase in the rate of intensification (FD_INT) was observed due to the rapid depletion of soil moisture, whereas South Korea (SK) experienced a significant increase in drought severity (DRO_SEV). Additionally, regional time series analysis revealed that both FD_INT and DRO_SEV were significantly high in the Gangwon province of both NK and SK. The estimation of probability density by region revealed a clear difference in FD_INT between NK and SK, with SK showing a higher probability of severe drought occurrence primarily due to the high values of DRO_SEV. As a result, it is inferred that the occurrence frequency and damage of flash droughts in NK are higher than those in SK, as indicated by the higher density of large FDII values in the NK region. We analyzed the correlation between DRO_SEV and the Evaporative Stress Index (ESI) across the Korean Peninsula and confirmed a positive correlation ranging from 0.4 to 0.6. It is concluded that analyzing overall drought conditions through the average drought severity holds high utility. These findings are expected to contribute to understanding the characteristics of flash droughts on the Korean Peninsula and formulating post-event response plans.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.1-25
• /
• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.141-156
• /
• 2013

Researchers in welfare services of local government in Korea have rather been on isolated issues as disables, childcare, aging phenomenon, etc. (Kang, 2004; Jung et al., 2009). Lately, local officials, yet, realize that they need more comprehensive welfare services for all residents, not just for above-mentioned focused groups. Still cases dealt with focused group approach have been a main research stream due to various reason(Jung et al., 2009; Lee, 2009; Jang, 2011). Social Security Information System is an information system that comprehensively manages 292 welfare benefits provided by 17 ministries and 40 thousand welfare services provided by 230 local authorities in Korea. The purpose of the system is to improve efficiency of social welfare delivery process. The study of local government expenditure has been on the rise over the last few decades after the restarting the local autonomy, but these studies have limitations on data collection. Measurement of a local government's welfare efforts(spending) has been primarily on expenditures or budget for an individual, set aside for welfare. This practice of using monetary value for an individual as a "proxy value" for welfare effort(spending) is based on the assumption that expenditure is directly linked to welfare efforts(Lee et al., 2007). This expenditure/budget approach commonly uses total welfare amount or percentage figure as dependent variables (Wildavsky, 1985; Lee et al., 2007; Kang, 2000). However, current practice of using actual amount being used or percentage figure as a dependent variable may have some limitation; since budget or expenditure is greatly influenced by the total budget of a local government, relying on such monetary value may create inflate or deflate the true "welfare effort" (Jang, 2012). In addition, government budget usually contain a large amount of administrative cost, i.e., salary, for local officials, which is highly unrelated to the actual welfare expenditure (Jang, 2011). This paper used local government welfare service data from the detailed data sets linked to the Social Security Information System. The purpose of this paper is to analyze the factors that affect social welfare spending of 230 local authorities in 2012. The paper applied multiple regression based model to analyze the pooled financial data from the system. Based on the regression analysis, the following factors affecting self-funded welfare spending were identified. In our research model, we use the welfare budget/total budget(%) of a local government as a true measurement for a local government's welfare effort(spending). Doing so, we exclude central government subsidies or support being used for local welfare service. It is because central government welfare support does not truly reflect the welfare efforts(spending) of a local. The dependent variable of this paper is the volume of the welfare spending and the independent variables of the model are comprised of three categories, in terms of socio-demographic perspectives, the local economy and the financial capacity of local government. This paper categorized local authorities into 3 groups, districts, and cities and suburb areas. The model used a dummy variable as the control variable (local political factor). This paper demonstrated that the volume of the welfare spending for the welfare services is commonly influenced by the ratio of welfare budget to total local budget, the population of infants, self-reliance ratio and the level of unemployment factor. Interestingly, the influential factors are different by the size of local government. Analysis of determinants of local government self-welfare spending, we found a significant effect of local Gov. Finance characteristic in degree of the local government's financial independence, financial independence rate, rate of social welfare budget, and regional economic in opening-to-application ratio, and sociology of population in rate of infants. The result means that local authorities should have differentiated welfare strategies according to their conditions and circumstances. There is a meaning that this paper has successfully proven the significant factors influencing welfare spending of local government in Korea.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.193-212
• /
• 2011

Mobile instant messaging service is surfacing to an important keyword in the mobile market together with popularization of Smart phones. Mobile instant messaging service in Korea has become popular to the degree of 87.9% usages from total Smartphone holders, and it is expected that using populations will be more enlarged afterwards if considering a fact that its populations of Smartphone is continuously being increased after exceeding 10 million persons (Trend Monitor, June 2011). In the instant messaging market where competitions have been deepened day by day, raising customer's royalties will be the key for company's business survivals and goals of corporate marketing strategies. It could be said that understanding on which factors affect to customer retentions and royalties is very important. Specially, as changing status is being progressed very quickly in case of innovative mobile services like the instant messaging service, research necessities on how many do consumers use the services after accepting them, how much do consumers use them variously, and whether does it connect to long-term relations have been increased, but studies on such matters are in insufficient situations actually. Therefore, this study examined on which effects were affected to use-diffusion and loyalty factors from perceived customer vales' factors having been occurred after accepting the mobile instant messaging service, namely 'functional value', 'monetary value', 'emotional value', and 'social value'. Also, the study looked into what kind of roles do the service usage and using variety play to service's continued using intents as a loyalty index, recommending intents to others, and brand switching intents. And then the study laid the main purpose in trying to provide implications for enhancing customer securities and royalties on the mobile instant messaging service through research's results. The research hypotheses are as follows; H1: Perceived values will affect influences to royalties. H2: Use-Diffusion will affect influences to loyalty. H3: Perceived value will affect influences to loyalty. H4: The use-diffusion will play intermediating roles between perceived values and loyalty. Total 276 cases among collected 284 ones were used for the statistical analysis by SPSS ver. 15 package. Reliability, Factor analysis, regression were done. As the result of research, 'monetary value' and 'emotional value' affected to 'usage' among perceived value factors, and 'emotional value' was appeared as affecting the largest influence. Besides, the usage affected to constant-using intents and recommending intents to others, and using varieties were displayed as affecting to recommending intents to others. On the other hand, 'Using' and 'Using diversity' were appeared as not affecting to 'brand switching intentions'. Meanwhile, as the result of recognizing about effects of perceived values on the loyalty, it was appeared such like 'continued using intents' affected to'functional value', 'monetary value', and 'social value' first, and also 'monetary value', 'emotional value', and 'social value' affected to 'recommending intents to others'. On the other hand, it was shown such like only 'social value' affected influences to 'brand switching intents', and thus contrary results with the factor 'constant-using intents' were displayed. So, it seems that there are many applications to service provides who are worrying about marketing strategies for making consumer retains (constant-using) and new consumer's inductions (brand-switching intents). Finally, as a result of looking into intermediating roles of the use-diffusion factor in relations between conceived values and royalties at hypothesis 4, 'using' and 'using diversity' were displayed as affecting significant influences all together. Regarding to research result's implications, for expanding and promoting continued uses of the mobile instant messaging service by service providers: First, encouraging recognitions on the perceived value connected to users' service usage are necessary. Second, setting up user's use-diffusion strategies are required so as to enhance the loyalty after understanding a fact that use-diffusion patterns affecting to the service's loyalty are different. Finally, methods of raising customer loyalties and making constant relationships have to be grouped by analyzing on what are the customer value's factors that can satisfy users in competitive alterations.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.57-75
• /
• 2019

In this paper, we conducted a study focusing on the innovation direction of the documentary model on the Internet of Things industry, which is the most actively industrialized among the core technologies of the 4th Industrial Revolution. Policy, economic, social, and technical issues were derived using PEST analysis for global trend analysis. It also presented future prospects for the Internet of Things industry of ICT-related global research institutes such as Gartner and International Data Corporation. Global research institutes predicted that competition in network technologies will be an issue for industrial Internet (IIoST) and IoT (Internet of Things) based on infrastructure and platforms. As a result of the PEST analysis, developed countries are pushing policies to respond to the fourth industrial revolution through cooperation of private (business/ research institutes) led by the government. It was also in the process of expanding related R&D budgets and establishing related policies in South Korea. On the economic side, the growth tax of the related industries (based on the aggregate value of the market) and the performance of the entity were reviewed. The growth of industries related to the fourth industrial revolution in advanced countries overseas was found to be faster than other industries, while in Korea, the growth of the "technical hardware and equipment" and "communication service" sectors was relatively low among industries related to the fourth industrial revolution. On the social side, it is expected to cause enormous ripple effects across society, largely due to changes in technology and industrial structure, changes in employment structure, changes in job volume, etc. On the technical side, changes were taking place in each industry, representing the health and medical sectors and manufacturing sectors, which were rapidly changing as they merged with the technology of the Fourth Industrial Revolution. In this paper, various management methodologies for innovation of existing business model were reviewed to cope with rapidly changing industrial environment due to the fourth industrial revolution. In addition, four criteria were established to select a management model to cope with the new business environment: 'Applicability', 'Agility', 'Diversity' and 'Connectivity'. The expert survey results in an AHP analysis showing that Business Model Canvas is best suited for business model innovation methodology. The results showed very high importance, 42.5 percent in terms of "Applicability", 48.1 percent in terms of "Agility", 47.6 percent in terms of "diversity" and 42.9 percent in terms of "connectivity." Thus, it was selected as a model that could be diversely applied according to the industrial ecology and paradigm shift. Business Model Canvas is a relatively recent management strategy that identifies the value of a business model through a nine-block approach as a methodology for business model innovation. It identifies the value of a business model through nine block approaches and covers the four key areas of business: customer, order, infrastructure, and business feasibility analysis. In the paper, the expansion and application direction of the nine blocks were presented from the perspective of the IoT company (ICT). In conclusion, the discussion of which Business Model Canvas models will be applied in the ICT convergence industry is described. Based on the nine blocks, if appropriate applications are carried out to suit the characteristics of the target company, various applications are possible, such as integration and removal of five blocks, seven blocks and so on, and segmentation of blocks that fit the characteristics. Future research needs to develop customized business innovation methodologies for Internet of Things companies, or those that are performing Internet-based services. In addition, in this study, the Business Model Canvas model was derived from expert opinion as a useful tool for innovation. For the expansion and demonstration of the research, a study on the usability of presenting detailed implementation strategies, such as various model application cases and application models for actual companies, is needed.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.185-209
• /
• 2023

With the advent of the 4th industrial revolution, various technologies have received much attention. Technologies related to the 4th industry include the Internet of Things (IoT), big data, artificial intelligence, virtual reality (VR), 3D printers, and robotics, and these technologies are often converged. In particular, the robotics field is combined with technologies such as big data, artificial intelligence, VR, and digital twins. Accordingly, much research using robotics is being conducted, which is applied to distribution, airports, hotels, restaurants, and transportation fields. In the given situation, research on human-robot interaction is attracting attention, but it has not yet reached the level of user satisfaction. However, research on robots capable of perfect communication is steadily being conducted, and it is expected that it will be able to replace human emotional labor. Therefore, it is necessary to discuss whether the current human-robot interaction technology can be applied to business. To this end, this study first examines the trend of human-robot interaction technology. Second, we compare LDA (Latent Dirichlet Allocation) topic modeling and BERTopic topic modeling methods. As a result, we found that the concept of human-robot interaction and basic interaction was discussed in the studies from 1992 to 2002. From 2003 to 2012, many studies on social expression were conducted, and studies related to judgment such as face detection and recognition were conducted. In the studies from 2013 to 2022, service topics such as elderly nursing, education, and autism treatment appeared, and research on social expression continued. However, it seems that it has not yet reached the level that can be applied to business. As a result of comparing LDA (Latent Dirichlet Allocation) topic modeling and the BERTopic topic modeling method, it was confirmed that BERTopic is a superior method to LDA.