• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1401-1411
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• 2023

In the face of escalating utilization of the marine spatial domain, conflicts have emerged among stakeholders, necessitating effective management strategies beyond conventional government permits and regulations. Particularly within the domain of aquaculture, operational oversight relies on a localized licensing system, posing challenges in accurately assessing the prevailing circumstances. This research employs synthetic aperture radar (SAR) imagery as a tool to monitor coastal aquaculture fish farms, aimed at enhancing insights into management protocols. Leveraging Sentinel-1A imagery and time series SAR data integration, a superimposition technique is utilized, facilitating noise reduction while retaining crucial information regarding smaller-scale facilities, such as fish farms. Through analysis of VH polarization data, a detection overall accuracy of approximately 88% for coastal fish farms was achieved. The findings of this study offer potential applications in the continuous monitoring of aquaculture farms in correspondence with seasonal variations in aquaculture yields, thereby proposing frameworks for the establishment of effective management cycles for marine space utilization.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.414-427
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• 2023

Implantable bioabsorbable combination products undergo inherent degradation and systemic absorption within the physiological environment, thereby streamlining the therapeutic regimen and obviating the imperative for invasive extraction procedures. This inherent property not only enhances patient convenience and therapeutic efficacy but also underpins a paradigm of support characterized by heightened safety parameters. Within the regulatory landscapes of Korea, the United States, and Europe, implantable bioabsorbable combination products are meticulously classified into distinct categories, either as pharmaceutical implants or as implantable medical devices, depending on their primary mode of action. This scholarly investigation systematically examines the regulatory frameworks governing implantable bioabsorbable combination products in South Korea, the United States, and Europe. Notable discrepancies across national jurisdictions emerge concerning regulatory specifics, including terminology, product classification, and product name associated with these products. The conspicuous absence of standardized approval regulations presents a formidable barrier to the commercialization of these advanced medical devices. This academic discourse passionately emphasizes the critical need for formulating and implementing a sophisticated regulatory framework capable of streamlining the product approval process, thereby paving the way for a seamless path to commercializing implantable bioabsorbable combination products.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Applied Chemistry for Engineering
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• v.34 no.5
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• pp.556-565
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• 2023

Fast industrial and agricultural expansion result in the production of heavy metal ions (HMIs). These are exceedingly hazardous to both humans and the environment, and the necessity to eliminate them from aqueous systems prompts the development of novel materials. In the present study, a UIO-66 (COOH)₂ metal-organic framework (MOF) containing free carboxylic acid groups was post-synthetically modified with L-glutamic acid via the solid-solid reaction route. Pristine and glutamic acid-treated MOF materials were characterized in detail using several physicochemical techniques. Single-ion batch adsorption studies of Pb(II) and Hg(II) ions were carried out using pristine as well as amino acid-modified MOFs. We further examined parameters that influence removal efficiency, such as the initial concentration and contact time. The bare MOF had a higher ion adsorption capacity for Pb(II) (261.87 mg/g) than for Hg(II) ions (10.54 mg/g) at an initial concentration of 150 ppm. In contrast, an increased Hg(II) ion adsorption capacity was observed for the glutamic acid-modified MOF (80.6 mg/g) as compared to the bare MOF. The Hg(II) ion adsorption capacity increased by almost 87% after modification with glutamic acid. Fitting results of isotherm and kinetic data models indicated that the adsorption of Pb(II) on both pristine and glutamic acid-modified MOFs was due to surface complexation of Pb(II) ions with available -COOH groups (pyromellitic acid). Adsorption of Hg(II) on the glutamic acid-modified MOF was attributed to chelation, in which glutamic acid grafted onto the surface of the MOF formed chelates with Hg(II) ions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.175-193
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• 2022

Although there is paradigm shift in art industry and interdisciplinary convergence between art and entrepreneurship, little has been done in "art entrepreneurship." First, this study organized the concepts of art entrepreneurship and conducted literature reviews on the trends of international and domestic research. Second, this paper aimed to understand the concept of art platform business. To do so, authors reviewed the general concept of business model and special features of platform business. Third, this paper categorized and introduced 11 art platform businesses from the based on the purposes of companies (① rental & selling, ②commercialize & selling, ③crowdfunding, ④information sharing & digital exhibition). Forth, this study provided two frameworks (①business model components, ②platform controllability and customers' information asymmetry) and applied them into 11 cases. By systematically reviewing the previous studies, this paper expects to increase scholarly understanding of the field of art entrepreneurship where two different areas (art and entrepreneurship) have studied separately. In addition, introduction and analyses of 11 online art platform have practical implications.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.150-190
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• 2024

This article proposes a framework to elucidate the structural dynamics of the generative AI ecosystem. It also outlines the practical application of this proposed framework through illustrative policies, with a specific emphasis on the development of the Korean generative AI ecosystem and its implications of platform strategies at AI platform-squared. We propose a comprehensive classification scheme within generative AI ecosystems, including app builders, technology partners, app stores, foundational AI models operating as operating systems, cloud services, and chip manufacturers. The market competitiveness for both app builders and technology partners will be highly contingent on their ability to effectively navigate the customer decision journey (CDJ) while offering localized services that fill the gaps left by foundational models. The strategically important platform of platforms in the generative AI ecosystem (i.e., AI platform-squared) is constituted by app stores, foundational AIs as operating systems, and cloud services. A few companies, primarily in the U.S. and China, are projected to dominate this AI platform squared, and consequently, they are likely to become the primary targets of non-market strategies by diverse governments and communities. Korea still has chances in AI platform-squared, but the window of opportunities is narrowing. A cautious approach is necessary when considering potential regulations for domestic large AI models and platforms. Hastily importing foreign regulatory frameworks and non-market strategies, such as those from Europe, could overlook the essential hierarchical structure that our framework underscores. Our study suggests a clear strategic pathway for Korea to emerge as a generative AI powerhouse. As one of the few countries boasting significant companies within the foundational AI models (which need to collaborate with each other) and chip manufacturing sectors, it is vital for Korea to leverage its unique position and strategically penetrate the platform-squared segment-app stores, operating systems, and cloud services. Given the potential network effects and winner-takes-all dynamics in AI platform-squared, this endeavor is of immediate urgency. To facilitate this transition, it is recommended that the government implement promotional policies that strategically nurture these AI platform-squared, rather than restrict them through regulations and stakeholder pressures.

• Journal of The Korean Association For Science Education
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• v.26 no.3
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• pp.342-355
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• 2006

The purpose of this study was to develop a classification scheme for laboratory instruction, which could occupy a central and distinctive role in science education. For this study, literature on laboratory instruction types were analyzed. Utilizing several of these theoretical frameworks, a Classification Scheme for Laboratory Instruction (CSLI), which clearly represents various features of laboratory instruction, was created. The developed CSLI consisted of two descriptors: one is the procedure for laboratory instruction, and the other is a way of approach. The procedure is either designed by the students or provided for them from an external source. A dichotomy also exists for the approach taken toward the activity: deductive or inductive. Validity was established for the CSLI. In addition, laboratory instruction according to CSLI was divided into four types: verification, discovery, exploratory, and investigation. Although this study demonstrated only limited features of laboratory instruction due to the absence of a field test, it serves as a model for more comprehensive studies.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.137-152
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• 2024

In order to derive a plan to increase the field effectiveness of the safety and health management(SHM) system, this study suggested plans for practical application of SHM system to the actual sites managed by the branch office of a public enterprise along with practical implications that should be considered. For this, in-depth interviews were conducted with employees in charge of safety and health work at the sites to analyze SHM system of the branch office, and the implementation processes and frameworks for establishing SHM system were suggested by grasping the actual conditions of the construction company performing the construction ordered by the branch office. This study shows that in order for SHM to be internalized in public enterprises, plans and performance indicators that can be applied in the field should be specifically presented in consideration of the hierarchical structure and processes of the organization performing the work, and a work environment should be created to focus on practical works related to safety and health.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.41-49
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• 2024

In this paper, we validate simulation results for the design optimization of a Superconducting Fault Current Limiter (SFCL) intended for use in Medium Voltage Direct Current systems (MVDC). With the increasing integration of renewable energy and grid connections, researchers are focusing on medium-voltage systems for balancing energy in new and renewable energy networks, rather than traditional transmission or distribution networks. Specifically, for DC distribution networks dealing with fault currents that must be rapidly blocked, current-limiting systems like superconducting current limiters offer distinct advantages over the operation of DC circuit breakers. The development of such superconducting current limiters requires finite element analysis (FEM) and an extensive design process before prototype production and evaluation. To expedite this design process, the design outcomes are assimilated using a Reduced Order Model (ROM). This approach enables the verification of results akin to finite element analysis, facilitating the optimization of design simulations for production and mass production within existing engineering frameworks.