• Information Systems Review
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• v.23 no.1
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• pp.135-154
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• 2021

In the network economy, the informatization of Small and Medium enterprises(SME) plays an important role in determining productivity while being competitive in the businesses. Informatization of SME has become important along with the recent trend of the fourth industrial revolution. Based on the IT Business Value Model, this study analyzes the key factors of information service of SME with the structure model. In addition, multi-level model was conducted by dividing the layers according to the size of the SME. The analysis confirmed that complementary organizational resources are a key factor in determining the informatization of SME. In addition, the effect of informatization of SME on the scale of SME varies depending on the type of entry into the industrial complex.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.31-40
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• 2024

In the realm of domestic online fashion platform industry the manual registration of product information by individual business owners leads to inconvenience and reliability issues, especially when dealing with simultaneous registrations of numerous product groups. Moreover, bias is significantly heightened due to the low quality of product images and an imbalance in data quantity. Therefore, this study proposes a ResNet50 model aimed at minimizing data bias through oversampling techniques and conducting multiple classifications for 13 fashion categories. Transfer learning is employed to optimize resource utilization and reduce prolonged learning times. The results indicate improved discrimination of up to 33.4% for data augmentation in classes with insufficient data compared to the basic convolution neural network (CNN) model. The reliability of all outcomes is underscored by precision and affirmed by the recall curve. This study is suggested to advance the development of the domestic online fashion platform industry to a higher echelon.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.3
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• pp.39-51
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• 2024

To effectively implement the integrated water management policy outlined in the National Water Management Act, it is essential to analyze agricultural water supply and demand at both basin and water district levels. Currently, agricultural water is primarily distributed through open canal systems and controlled by floodgates, yet the utilization-to-supply ratio remains at a mere 48%. In the case of agricultural water, when analyzing water balance through existing national basin water resource models (K-WEAP, K-MODISM), distortion of supply and regression occurs due to calculation of regression rate based on the concept of net water consumption. In addition, by simplifying the complex and diverse agricultural water supply system within the basin into a single virtual reservoir, it is difficult to analyze the surplus or shortage of agricultural water for each field within the basin. There are limitations in reflecting the characteristics and actual sites of rural water areas, such as inconsistencies with river and reservoir supply priority sites. This study focuses on the development of a model aimed at improving the deficiencies of current water balance analysis methods. The developed model aims to provide standardized water balance analysis nationwide, with initial application to the Anseo standard watershed. Utilizing data from 32 facilities within the standard watershed, the study conducted water balance analysis through watershed linkage, highlighting differences and improvements compared to existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• IMMUNE NETWORK
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• v.21 no.6
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• pp.44.1-44.15
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• 2021

Tumor peptides associated with MHC class I molecules or their synthetic variants have attracted great attention for their potential use as vaccines to induce tumor-specific CTLs. However, the outcome of clinical trials of peptide-based tumor vaccines has been disappointing. There are various reasons for this lack of success, such as difficulties in delivering the peptides specifically to professional Ag-presenting cells, short peptide half-life in vivo, and limited peptide immunogenicity. We report here a novel peptide vaccination strategy that efficiently induces peptide-specific CTLs. Nanoparticles (NPs) were fabricated from a biodegradable polymer, poly(D,L-lactic-co-glycolic acid), attached to H-2K^b molecules, and then the natural peptide epitopes associated with the H-2K^b molecules were exchanged with a model tumor peptide, SIINFEKL (OVA_257-268). These NPs were efficiently phagocytosed by immature dendritic cells (DCs), inducing DC maturation and activation. In addition, the DCs that phagocytosed SIINFEKL-pulsed NPs potently activated SIINFEKL-H2K^b complex-specific CD8⁺ T cells via cross-presentation of SIINFEKL. In vivo studies showed that intravenous administration of SIINFEKL-pulsed NPs effectively generated SIINFEKL-specific CD8⁺ T cells in both normal and tumor-bearing mice. Furthermore, intravenous administration of SIINFEKL-pulsed NPs into EG7.OVA tumor-bearing mice almost completely inhibited the tumor growth. These results demonstrate that vaccination with polymeric NPs coated with tumor peptide-MHC-I complexes is a novel strategy for efficient induction of tumor-specific CTLs.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.619-629
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• 2020

The National Groundwater Monitoring Network (NGMN) in South Korea has been implemented in alluvial/ bedrock aquifers for efficient management of groundwater resources. In this study, aquifer types were reclassified with unconfined and confined aquifers based on water-level fluctuation and water quality characteristics. Principal component analysis (PCA) of water-level data from paired monitoring wells of alluvial/bedrock aquifers results in the principal components of both aquifers showing similar water-level fluctuation pattern. There was no significant difference in the rate of water-level rises responding to precipitations and in the NO₃-N concentrations between the alluvial and bedrock aquifers. In contrast, in the results classified with the hydrogeological type, the principal components of water level were different between unconfined and confined conditions. The water-level rises to precipitation events were estimated to be 4.6 (R²=0.8) in the unconfined and 2.1 (R²=0.4) in the confined aquifers, respectively, indicating less impact of precipitation recharge to the confined aquifer. The confined aquifers have the average NO₃-N concentration below 3 mg/L, implying the natural background level protected from the sources at surface. In summary, reclassification of aquifers into hydrogeological types clearly shows the differences between unconfined and confined aquifers in the water-level fluctuation pattern and NO₃-N concentrations. The hydrogeologic condition of aquifer could improve groundwater resource management by providing critical information on groundwater quantity through recharge estimation and quality for protection from potential contamination sources.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.11
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• pp.1102-1110
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• 2008

This research was performed to analyze the characteristics of wood wastes from origin and to suggest grade-classification for them. Korean proximate analysis was conducted, and heating value, heavy metals and Cl concentrations were analyzed for gradeclassification. Wood wastes were sampled from forest, living, construction and demolition, and industrial areas with origin. Moisture content of most wood wastes was ranged in 5$\sim$10%. VS (volatile solids) and ash contents of them showed > 95% and < 5%, respectively. Most wood wastes except wood for growing mushroom permitted the standard (low heating value $\geq$ 3,500 kcal/kg) for refusederived fuel. CCA (Cr, Cu, As) concentration of wood wastes used in bench, wasted fishing boat, and railroad crosstie was higher than that of the other ones. Cl content showed approximately 1.3% in wood box for fish and $\leq$ 0.2% in the other wood wastes. Cl content of all wood wasted used in this research permitted the standard (Cl $\leq$ 0.2%, dry weight basis) for refuse-derived fuel. If the wood wastes were classified in 3-grade, plywoods would be in 2nd grade, and MDF (medium density fiber), wooden bench, painted electric wire drum, wasted fishing boat, and railroad crosstie be in 3rd grade.

• Asia pacific journal of information systems
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• v.22 no.2
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• pp.39-69
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• 2012

With the advent of communication technologies including electronic collaborative tools and conferencing systems provided over the Internet, virtual collaboration is becoming increasingly common in organizations. Virtual collaboration refers to an environment in which the people working together are interdependent in their tasks, share responsibility for outcomes, are geographically dispersed, and rely on mediated rather than face-to face, communication to produce an outcome. Research suggests that new sets of individual skill, knowledge, and ability (SKAs) are required to perform effectively in today's virtualized workplace, which is labeled as individual virtual competence. It is also argued that use of online social networking sites may influence not only individuals' daily lives but also their capability to manage their work-related relationships in organizations, which in turn leads to better performance. The existing research regarding (1) the relationship between virtual competence and task performance and (2) the relationship between online networking and task performance has been conducted based on different theoretical perspectives so that little is known about how online social networking and virtual competence interplay to predict individuals' task performance. To fill this gap, this study raises the following research questions: (1) What is the individual virtual competence required for better adjustment to the virtual collaboration environment? (2) How does online networking via diverse social network service sites influence individuals' task performance in organizations? (3) How do the joint effects of individual virtual competence and online networking influence task performance? To address these research questions, we first draw on the prior literature and derive four dimensions of individual virtual competence that are related with an individual's self-concept, knowledge and ability. Computer self-efficacy is defined as the extent to which an individual beliefs in his or her ability to use computer technology broadly. Remotework self-efficacy is defined as the extent to which an individual beliefs in his or her ability to work and perform joint tasks with others in virtual settings. Virtual media skill is defined as the degree of confidence of individuals to function in their work role without face-to-face interactions. Virtual social skill is an individual's skill level in using technologies to communicate in virtual settings to their full potential. It should be noted that the concept of virtual social skill is different from the self-efficacy and captures an individual's cognition-based ability to build social relationships with others in virtual settings. Next, we discuss how online networking influences both individual virtual competence and task performance based on the social network theory and the social learning theory. We argue that online networking may enhance individuals' capability in expanding their social networks with low costs. We also argue that online networking may enable individuals to learn the necessary skills regarding how they use technological functions, communicate with others, and share information and make social relations using the technical functions provided by electronic media, consequently increasing individual virtual competence. To examine the relationships among online networking, virtual competence, and task performance, we developed research models (the mediation, interaction, and additive models, respectively) by integrating the social network theory and the social learning theory. Using data from 112 employees of a virtualized company, we tested the proposed research models. The results of analysis partly support the mediation model in that online social networking positively influences individuals' computer self-efficacy, virtual social skill, and virtual media skill, which are key predictors of individuals' task performance. Furthermore, the results of the analysis partly support the interaction model in that the level of remotework self-efficacy moderates the relationship between online social networking and task performance. The results paint a picture of people adjusting to virtual collaboration that constrains and enables their task performance. This study contributes to research and practice. First, we suggest a shift of research focus to the individual level when examining virtual phenomena and theorize that online social networking can enhance individual virtual competence in some aspects. Second, we replicate and advance the prior competence literature by linking each component of virtual competence and objective task performance. The results of this study provide useful insights into how human resource responsibilities assess employees' weakness and strength when they organize virtualized groups or projects. Furthermore, it provides managers with insights into the kinds of development or training programs that they can engage in with their employees to advance their ability to undertake virtual work.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.