• Journal of The Korean Association For Science Education
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• v.27 no.1
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• pp.93-104
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• 2007

The purpose of this study is to investigate brain activity both during the processing of a scientific hypothesis about biological phenomena and mental arithmetic using 3.0T fMRI at the KAIST. For this study, 16 healthy male subjects participated voluntarily. Each subject's functional brain images by performing a scientific hypothesis task and a mental arithmetic task for 684 seconds were measured. After the fMRI measuring, verbal reports were collected to ensure the reliability of brain image data. This data, which were found to be adequate based on the results of analyzing verbal reports, were all included in the statistical analysis. When the data were statistically analyzed using SPM2 software, the scientific hypothesis generating process was found to have independent brain network different from the mental arithmetic process. In the scientific hypothesis process, we can infer that there is the process of encoding semantic derived from the fusiform gyrus through question-situation analysis in the pre-frontal lobe. In the mental arithmetic process, the area combining pre-frontal and parietal lobes plays an important role, and the parietal lobe is considered to be involved in skillfulness. In addition, the scientific hypothesis process was found to be accompanied by scientific emotion. These results enabled the examination of the scientific hypothesis process from the cognitive neuroscience perspective, and may be used as basic materials for developing a learning program for scientific hypothesis generation. In addition, this program can be proposed as a model of scientific brain-based learning.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.157-172
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• 2024

Autonomous driving technology, when commercialized, has the potential to improve the safety, mobility, and environmental performance of transportation networks. However, safe autonomous driving may be hindered by poor sensor performance and limitations in long-distance detection. Therefore, cooperative autonomous driving that can supplement information collected from surrounding vehicles and infrastructure is essential. In addition, since HDVs, AVs, and CAVs have different ranges of perceivable information and different response protocols, countermeasures are needed for mixed traffic that occur during the transition period of autonomous driving technology. There is a lack of research on traffic flow optimization that considers the penetration rate of autonomous vehicles and the different characteristics of each road segment. The objective of this study is to develop weights based on safety, operational, and environmental factors for each infrastructure control use case and autonomous vehicle MPR. To develop an integrated evaluation index, infra-guidance AHP and hybrid AHP weights were combined. Based on the results of this study, it can be used to give right of way to each vehicle to optimize mixed traffic.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.157-165
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• 2024

The evolution of the Distributed Denial of Service Attack(DDoS Attack) method has increased the difficulty in the detection process. One of the solutions to overcome the problems caused by the limitations of the existing victim-side detection method was the source-side detection technique. However, there was a problem of performance degradation due to network traffic irregularities. In order to solve this problem, research has been conducted to detect attacks using a collaborative network between several nodes based on artificial intelligence. Existing methods have shown limitations, especially in nonlinear traffic environments with high Burstness and jitter. To overcome this problem, this paper presents a collaborative source-side DDoS attack detection technique introduced with an attention mechanism. The proposed method aggregates detection results from multiple sources and assigns weights to each region, and through this, it is possible to effectively detect overall attacks and attacks in specific few areas. In particular, it shows a high detection rate with a low false positive of about 6% and a high detection rate of up to 4.3% in a nonlinear traffic dataset, and it can also confirm improvement in attack detection problems in a small number of regions compared to methods that showed limitations in the existing nonlinear traffic environment.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.29-37
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• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.41-63
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• 2024

This study investigated research trends in digital curation indexed in a prominent domestic academic information database. A systematic literature review was conducted on 39 academic papers published from 2009 to 2023. The review examined indexing status according to publication year, venue, academic discipline, research area distribution, research affiliation and occupation, and research types. In addition, network centrality analysis and cohesive group analysis were performed on 69 author keywords. The findings revealed several key points. First, digital curation research peaked in 2015 and 2016 with 5 publications each year, followed by a slight decrease, and then consistently produced 4 or more publications annually since 2019. Second, among the 39 studies, 25 were conducted in interdisciplinary fields, including library and information science, while 11 were in the humanities, such as miscellaneous humanities. The most prominent research areas were theoretical and infrastructural aspects, information management and services, and institutional domains. Third, digital curation research was predominantly led by university-affiliated professors and researchers, with collaborative research more prevalent than solo research. Lastly, analysis of author keywords revealed that "digital curation," "institution," and "content" were the most influential central keywords within the overall network.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.205-216
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• 2024

Recently, seismic Probabilistic Safety Assessment (PSA) methods have been developed for process plants, such as gas plants, oil refineries, and chemical plants. The framework originated from the PSA of nuclear power plants, which aims to assess the risk of reactor core damage. The original PSA method was modified to adopt the characteristics of a process plant whose purpose is continuous operation without shutdown. Therefore, a fault tree, whose top event is shut down, was constructed and transformed into a Bayesian Network (BN), a probabilistic graph model, for efficient risk-informed decision-making. In this research, the fault tree-based BN from the previous research is further developed to consider the multi-hazard of earthquake-induced fire and explosion (EQ-induced F&E). For this purpose, an event tree describing the occurrence of fire and explosion from a release is first constructed and transformed into a BN. And then, this BN is connected to the previous BN model developed for seismic PSA. A virtual plot plan of a gas plant is introduced as a basis for the construction of the specific EQ-induced F&E BN to test the proposed BN framework. The paper demonstrates the method through two examples of risk-informed decision-making. In particular, the second example verifies how the proposed method can establish a repair and retrofit strategy when a shutdown occurs in a process plant.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.181-191
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• 2024

Ports play a crucial role in the complex global supply chain. While many researchers have used social network analysis (SNA) to study active networks, there is a lack of SNA cohesion analysis specifically related to logistics and trade. Therefore, this study aims to identify time-series structural changes in all domestic import and export logistics networks, including regions, ports, and airports, by utilizing techniques such as k-core and community analysis. To carry out this analysis, we rely on data from the Korea Customs Service's Import and Export Logistics Statistical Yearbook spanning from 2004 to 2022. The findings from the k-core and community analysis indicate that the cohesion of the domestic import and export logistics network has continuously strengthened over time. Moreover, it reveals that regions, ports, and airports are becoming more cohesive and homogeneous, with Busan Port emerging as the central hub of a large community. These insights are expected to enhance our understanding of global logistics dynamics and contribute to the development of policies and sustainable import and export logistics processes.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.3
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• pp.89-102
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• 2024

This study aims to examine the characteristics of the user experience of New Town neighborhood parks and explore issues that diversify the experience of the parks. In order to quantitatively analyze a large amount of park visitors' experiences, text-based Naver blog reviews were collected and analyzed. Among the Phase 1 and 2 New Towns, the parks with the highest user experience postings were selected for each city as the target of analysis. Blog text data was collected from May 20, 2003, to May 31, 2022, and analysis was conducted targeting Ilsan Lake Park, Bundang Yuldong Park, Gwanggyo Lake Park, and Dongtan Lake Park. The findings revealed that all four parks were used for everyday relaxation and recreation. Second, the analysis underscores park's diverse user groups. Third, the programs for parks nearby were also related to park usage. Fourth, the words within the top 20 rankings represented distinctive park elements or content/programs specific to each park. Lastly, the results of the network analysis delineated four overarching types of park users and the networks of four park user types appeared differently depending on the park. This study provides two implications. First, in addition to the naturalistic characteristics, the differentiation of each park's unique facilities and programs greatly improves public awareness and enriches the individual park experience. Second, if analysis of the context surrounding the park based on spatial information is performed in addition to text analysis, the accuracy of interpretation of text data analysis results could be improved. The results of this study can be used in the planning and designing of parks and greenspaces in the Phase 3 New Towns currently in progress.

• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.