• Journal of Platform Technology
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• v.11 no.5
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• pp.84-96
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• 2023

Network slicing refers to a technology that divides network infrastructure into multiple parts. Network slicing enables flexible network configuration while minimizing the physical resources required for network division. For this reason, network slicing technology has recently been developed and introduced in a form suitable for the 5G environment for efficient management of large-scale network environments. However, systematic analysis of network slicing research in the 5G environment has not been conducted, resulting in a lack of systematic analysis of the technology. Accordingly, in this paper, we provide insight into network slicing technology in the 5G network environment by conducting a comparative analysis of the technology. In this study's comparative analysis, 13 literatures on network slicing in the 5G environment was identified and compared and analyzed through a systematic procedure. As a result of the analysis, three network slicing technologies frequently used for 5G networks were identified: RAN (radio access network) slicing, CN (core network) slicing, and E2E (end-to-end) sliding. These technologies are mainly used for network services. It was confirmed that research is being conducted to achieve quality improvement and network isolation. It is believed that the results of this comparative analysis study can contribute to 6G technology research as a future direction and utilization plan for network slicing research.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.81-86
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• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.

• International Journal of Stem Cells
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• v.15 no.3
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• pp.258-269
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• 2022

Background and Objectives: Currently, safety pharmacological tests for the central nervous system depend on animal behavioral analysis. However, due to the subjectivity of behavioral analysis and differences between species, there is a limit to appropriate nervous system toxicity assessment, therefore a new neurotoxicity assessment that can simulate the human central nervous system is required. Methods and Results: In our study, we developed an in vitro neurotoxicity assessment focusing on neuronal function. To minimize the differences between species and fast screening, hiPSC-derived neurons and a microelectrode array (MEA) that could simultaneously measure the action potentials of the neuronal networks were used. After analyzing the molecular and electrophysiological characters of our neuronal network, we conducted a neurotoxicity assessment on neurotransmitters, neurotoxicants, illicit drugs, and new psychoactive substances (NPS). We found that most substances used in our experiments responded more sensitively to our MEA-based neurotoxicity assessment than to the conventional neurotoxicity assessment. Also, this is the first paper that evaluates various illicit drugs and NPS using MEA-based neurotoxicity assessment using hiPSC-derived neurons. Conclusions: Our study expanded the scope of application of neurotoxicity assessment using hiPSC-derived neurons to NPS, and accumulated evaluation data of various toxic substances for hiPSC-derived neurons.

• Computers and Concrete
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• v.34 no.4
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• pp.489-501
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• 2024

Geopolymers are part of a class of materials characterized by properties combining polymers, ceramics, and cement. These include exceptionally high thermal and chemical stability, excellent mechanical strength and durability in aggressive environments. This work deals with the synthesis, characterization, and sustainability evaluation of GP_GBFS-MK geopolymers by alkaline activation of a granulated blast furnace slag-metakaolin mixture. In the first step, elemental and oxide analyses by XRF and EDS showed that the main constituents of GP_GBFS-MK geopolymers are silicon, sodium, and aluminium oxides. The structural analyses by XRD and FTIR confirmed that the geopolymerization for GP_GBFS-MK geopolymers did occur, accompanied by the formation of disordered networks from the blends and a modification to the microstructure by the geopolymerization process. Similarly, the microstructural study made by SEM showed that the GP_GBFS-MK geopolymers are constituted by aluminosilicates in the form of dense clusters on which are adsorbed particles of unreacted GBFS in the form of spheroids and white residues of the alkaline activating solution. In addition, the study of the sustainability evaluation of GP_GBFS-MK geopolymers showed that the water absorption of geopolymeric materials is lower than that of OPC cement. As for the elevated temperature resistance, the analyses indicated an excellent elevated temperature resistance of GP_GBFS-MK. In the same way, the study of the resistance to chemical aggressions showed that the GP_GBFS-MK geopolymeric materials are unattackable, contrary to the OPC cement-based materials which are strongly altered.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.509-516
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• 2024

We rethink human existence as a assemblage through intersectionality by comparing autopoiesis and sympoiesis systems with reference to science fiction protagonists such as Ghost in the Shell, Neuralink, Camille, a genetic hybrid, and San Ti against the background of neo-materialism. Our findings reveal that, first, radical sympoiesis is characterized by the dissolution of individuals and boundaries, and attempt to explain existence solely through heterogeneous linkage and fusion; second, by ignoring the capacity for autonomous thinking at the individual level, they are unable to fully recognize the destructive nature of hybrid co-production or to develop practical responses to it. Third, we suggest that if the very survival of humanity is threatened by heterogeneous linkage, we should pay more attention to our identity as autonomous members of a autopoietic system rather than to heterogeneous sympoietic networks and we should also pay attention to the role of individual units in stabilizing self-regulation. Through this study, we aimed to contribute to overcoming the limitations of neo-materialism by arguing that it is likely to fail to provide an adequate practical vision if it is limited to describing the hybrid connections that recur through the intersection of beings, and by urging us to define the identity of the human species from a new perspective by utilizing various SF stories that trigger the imagination of destructive interactions between beings, and to explore the autopoiesis in terms of symbiotic interactions based on a certain level of boundary and self-regulation.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.3
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• pp.163-178
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• 2024

Purpose : This study aimed to evaluate the effectiveness of an integrative medical service model applied to breast cancer patients and their caregivers, exploring its feasibility and challenges within the context of South Korean healthcare system. Methods : A case study approach was chosen to assess the integrative medical service model's efficacy, involving one breast cancer patient and her primary caregiver from W University Hospital. The patient had completed reconstructive surgery and chemotherapy and was undergoing radiotherapy. The model included standard treatments alongside psychological counseling, aromatherapy, axillary rehabilitation exercise, make-up program, art therapy, laughter therapy, horticultural therapy, and yoga programs, and meditation programs delivered over eight weeks. Quantitative and qualitative data were collected through surveys, psychological tests, and feedback assessments. Results : The integrative medical service model demonstrated notable improvements in the quality of life for both breast cancer patients and their caregivers. Participants reported enhanced emotional well-being, reduced stress levels, and improved coping mechanisms throughout the treatment journey. Qualitative feedback highlighted the positive impact of holistic interventions in alleviating psychological distress and fostering resilience. Quantitative data corroborated these findings, showing statistically significant improvements in various psychosocial parameters assessed. Conclusions : Our findings underscore the benefits of integrative medical service model with standard medical treatments in the care of breast cancer patients and their caregivers. The holistic approach not only addresses physical symptoms but also enhances overall well-being and quality of life. However, the implementation of such models faces challenges within the South Korean healthcare system, including fragmented service networks and financial constraints. Addressing these structural barriers is crucial for the widespread adoption and sustainability of integrative care models in oncology practice. Future research should focus on larger-scale studies to further validate these findings and inform policy decisions aimed at optimizing cancer care delivery.

• Journal of Bio-Environment Control
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• v.33 no.1
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• pp.1-11
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• 2024

In this study, five artificial intelligence (AI) models: Inception v3, SqueezeNet (local), VGG-16, Painters, and DeepLoc were used to classify tea leaf diseases. Eight image categories were used: healthy, algal leaf spot, anthracnose, bird's eye spot, brown blight, gray blight, red leaf spot, and white spot. Software used in this study was Orange 3 which functions as a Python library for visual programming, that operates through an interface that generates workflows to visually manipulate and analyze the data. The precision of each AI model was recorded to select the ideal AI model. All models were trained using the Adam solver, rectified linear unit activation function, 100 neurons in the hidden layers, 200 maximum number of iterations in the neural network, and 0.0001 regularizations. To extend the functionality of Orange 3, new add-ons can be installed and, this study image analytics add-on was newly added which is required for image analysis. For the training model, the import image, image embedding, neural network, test and score, and confusion matrix widgets were used, whereas the import images, image embedding, predictions, and image viewer widgets were used for the prediction. Precisions of the neural networks of the five AI models (Inception v3, SqueezeNet (local), VGG-16, Painters, and DeepLoc) were 0.807, 0.901, 0.780, 0.800, and 0.771, respectively. Finally, the SqueezeNet (local) model was selected as the optimal AI model for the detection of tea diseases using tea leaf images owing to its high precision and good performance throughout the confusion matrix.

• Resources Recycling
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• v.33 no.4
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• pp.3-14
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• 2024

The preheating and calcination processes in cement manufacturing, which are crucial for producing the cement intermediate product clinker, require a substantial quantity of fossil fuels to generate high-temperature thermal energy. However, owing to the ever-increasing severity of environmental pollution, considerable efforts are being made to reduce carbon emissions from fossil fuels in the cement industry. Several preliminary studies have focused on increasing the usage of alternative fuels like refuse-derived fuel (RDF). Alternative fuels offer several advantages, such as reduced carbon emissions, mitigated generation of nitrogen oxides, and incineration in preheaters and kilns instead of landfilling. However, owing to the diverse compositions of alternative fuels, estimating their calorific value is challenging. This makes it difficult to regulate the preheater stability, thereby limiting the usage of alternative fuels. Therefore, in this study, a model based on deep neural networks is developed to accurately predict the preheater temperature and propose optimal fuel input quantities using explainable artificial intelligence. Utilizing the proposed model in actual preheating process sites resulted in a 5% reduction in fossil fuel usage, 5%p increase in the substitution rate with alternative fuels, and 35% reduction in preheater temperature fluctuations.

• Information Systems Review
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• v.26 no.2
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• pp.155-167
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• 2024

This study recognized the importance of joint research in the field of artificial intelligence and analyzed the characteristics of the industry-academic-research technological cooperation ecosystem focusing on patents from the perspective of the Techno-Economic Segment (TES). To this end, economic entities such as companies, universities, and research institutes within the ecosystem were identified for 7,062 joint research projects out of 113,289 artificial intelligence patents over the past 10 years filed in IP5 countries since 2012. Next, this study identified the topics of technological cooperation and the characteristics of cooperation. As a result of the analysis, technological cooperation is increasing, and the frequency of all types of cooperation was high in industry-to-industry (40%) and industry-to-university (25.2%) relationships. Here, this study confirmed that the role of universities is being strengthened, with an increase in the ratio of companies with strengths in funding and analytical data, industry and universities with excellent research personnel (9.8%), and cooperation between universities (1.9%). In addition, as a result of identifying collaborative patent research areas of interest and collaborative relationships through topic modeling and network analysis, overall similar research interests were derived regardless of the type of cooperation, and applications such as autonomous driving, edge computing, cloud, marketing, and consumer behavior analysis were derived. It was confirmed that the scope of research was expanding, collaborating entities were becoming more diverse, and a large-scale network including Chinese-centered universities was emerging.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.23-28
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• 2024

So far, most of the military trainings were carried out in the field, and were influenced by the various parameters of the weather, the climate and the civil complaints regarding the noise. Also, it's the reality that the considerable time and resources are required to maneuver the weapon system used for the military training. Furthermore, the serious damage and casualties during tha military training are important parameters that can't be ignored. Recently, with the development of 5G communication networks and XR technologies, XR technologies are used in various fields that participate with multilateral parts, i.e. in military technology and training. In this paper, to implement the military education, 5G communication network and military education training system were established. The military education training system were composed that over 10 persons were possible to train in the various circumstances such as counter combat, mountains combat, urban combat and beaches combat. Also it is possible to fight with AI combatants, and train the gun disassembly and assembly, and train the various firing exercise. The military training system of using XR technologies were applied to the multilateral military training, and we analyzed the satisfaction results for the experienced persons of this XR system.