• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2648-2665
• /
• 2022

Device-to-device (D2D) multicast has become a promising technology to provide specific services within a small geographical region with a high data rate, low delay and low energy consumption. However, D2D multicast communications are allowed to reuse the same channels with cellular uplinks and result in mutual interference in a cell. In this paper, an intelligent channel assignment algorithm is designed in D2D underlaid cellular networks with the target of maximizing network throughput. We first model the channel assignment problem to be a throughput maximizing problem which is NP-hard. To solve the problem in a feasible way, a novel channel assignment algorithm is proposed. The key idea is to find the appropriate cellular communications and D2D multicast groups to share a channel without causing critical interference, i.e., finding a channel for a D2D multicast group which generates the least interference to network based on current channel assignment status. In order to show the efficacy and effectiveness of our proposed algorithm, a novel search algorithm is proposed to find the near-optimal solution as the baseline for comparisons. Simulation results show that the proposed algorithm improves the network throughput.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.752-759
• /
• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• Korean Journal of Clinical Pharmacy
• /
• v.34 no.2
• /
• pp.118-125
• /
• 2024

Background: Numerous studies have consistently demonstrated that depression can be associated with cognitive function decline, primarily focusing on older adults due to the neurodegenerative characteristics of dementia. With persistent depression frequently reported in patients with early-onset or young-onset dementia, this study aimed to assess the impact of depression, specifically the changes in depressive symptoms over time, on the risk of cognitive function decline in middle-aged adults in Korea. Methods: This retrospective study utilized data from the first four waves (2006-2012) of the Korean Longitudinal Study of Aging (KLoSA), focusing on middle-aged adults with normal cognitive function at baseline. Changes in depressive symptoms were categorized into four groups based on the CES-D score, and their association with cognitive function decline was evaluated using a multivariate logistic regression model. Results: Of the initial 10,254 participants, 3,400 were included in the analysis. Depressive status, particularly newly onset (adjusted odds ratio [aOR] 1.96; 95% confidence interval [CI] 1.32-2.93) and persistent depression groups (aOR 5.59; 95% CI 2.90-10.78), were significantly associated with cognitive function decline. In contrast, recovery from depressive symptoms was not significantly associated with cognitive function decline (p=0.809). Conclusions: Our study showed a significant association between changes in depressive symptoms and cognitive function decline in middle-aged Korean adults. This suggests that management of depressive symptoms could be crucial for the prevention of cognitive function decline in this population.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.9
• /
• pp.2764-2781
• /
• 2024

Federated learning (FL) has been proposed as an emerging distributed machine learning framework, which lowers the risk of privacy leakage by training models without uploading original data. Therefore, it has been widely utilized in the Industrial Internet of Things (IIoT). Despite this, FL still faces challenges including the non-independent identically distributed (Non-IID) data and heterogeneity of devices, which may cause difficulties in model convergence. To address these issues, a local surrogate function is initially constructed for each device to ensure a smooth decline in global loss. Subsequently, aiming to minimize the system energy consumption, an FL approach for joint CPU frequency control and bandwidth allocation, called FCBAFL is proposed. Specifically, the maximum delay of a single round is first treated as a uniform delay constraint, and a limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm is employed to find the optimal bandwidth allocation with a fixed CPU frequency. Following that, the result is utilized to derive the optimal CPU frequency. Numerical simulation results show that the proposed FCBAFL algorithm exhibits more excellent convergence compared with baseline algorithm, and outperforms other schemes in declining the energy consumption.

• Journal of Information Science Theory and Practice
• /
• v.12 no.3
• /
• pp.36-48
• /
• 2024

The information retrieval (IR) process often encounters a challenge known as query-document vocabulary mismatch, where user queries do not align with document content, impacting search effectiveness. Automatic query expansion (AQE) techniques aim to mitigate this issue by augmenting user queries with related terms or synonyms. Word embedding, particularly Word2Vec, has gained prominence for AQE due to its ability to represent words as real-number vectors. However, AQE methods typically expand individual query terms, potentially leading to query drift if not carefully selected. To address this, researchers propose utilizing median vectors derived from deep median networks to capture query similarity comprehensively. Integrating median vectors into candidate term generation and combining them with the BM25 probabilistic model and two IR strategies (EQE1 and V2Q) yields promising results, outperforming baseline methods in experimental settings.

• Journal of Web Engineering
• /
• v.21 no.2
• /
• pp.497-522
• /
• 2021

Audio event classification refers to the detection and classification of non-verbal signals, such as dog and horn sounds included in audio data, by a computer. Recently, deep neural network technology has been applied to audio event classification, exhibiting higher performance when compared to existing models. Among them, a convolutional neural network (CNN)-based training method that receives audio in the form of a spectrogram, which is a two-dimensional image, has been widely used. However, audio event classification has poor performance on test data when it is recorded by a device (unknown device) different from that used to record training data (known device). This is because the frequency range emphasized is different for each device used during recording, and the shapes of the resulting spectrograms generated by known devices and those generated by unknown devices differ. In this study, to improve the performance of the event classification system, a CNN based on the log mel-spectrogram separation technique was applied to the event classification system, and the performance of unknown devices was evaluated. The system can classify 16 types of audio signals. It receives audio data at 0.4-s length, and measures the accuracy of test data generated from unknown devices with a model trained via training data generated from known devices. The experiment showed that the performance compared to the baseline exhibited a relative improvement of up to 37.33%, from 63.63% to 73.33% based on Google Pixel, and from 47.42% to 65.12% based on the LG V50.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.5
• /
• pp.323-334
• /
• 2020

Since boundary layer transition has a significant impact on the aero-thermodynamic performance of hypersonic flight vehicles, capability of accurate prediction of transition location is essential for design and performance analysis. In this study, γ-Re_θt model is improved to predict transition of hypersonic boundary layers and validated. A coefficient in the production term of the intermittency transport equation that affects the transition onset location is constructed and applied as a function of Mach number, wall temperature, and freestream stagnation temperature based on the similarity numerical solution of compressible boundary layer. To take into account a Mach number dependency of transition onset momentum thickness Reynolds number and transition length, additional correlation equations are determined as function of Mach number and applied to Re_θc and F_length correlations of the baseline model. The suggested model is implemented to a commercial CFD code in consideration of practical use. Analysis of hypersonic flat plate and circular cone boundary layers is carried out by using the model for validation purpose. An improvement of prediction capability with respect to variation of Mach number and unit Reynolds number is identified from the comparison with experimental data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.5
• /
• pp.493-500
• /
• 2012

The previous geoid model developed in early 2000s shows 14cm level of precision due to the problems on distribution, and quality of the land gravity and GPS/Leveling data. From 2007, the new land and airborne gravity data as well as GPS/Leveling data having high quality and regular distribution has been obtained. In 2011, a new gravimetric geoid model has been constructed with precision of 5.29cm which was improved about 27% comparing to the previous model. However, much more land gravity data has been collected at the control point, bench marks and triangulation points since 2010. Also, GPS/Leveling data having 10km spacing over whole country has been obtained through the project which is for the construction of new control points. In this study, new gravimetric geoid has been calculated based on the all available gravity data up to present. The geoid height shows the range from 18.05m to 32.70m over whole country and its precision is 5.76cm. The degree of fit and precision of hybrid geoid model are 3.60cm and 4.06cm, respectively. At the end, 3.35cm of the relative precision in 15km baseline has been calculated to confirm its practical usage. Especially, it has been founded that regional bias occurred at the Kangwon and coastal area due to problems on the leveling data. Also, some inland points show inconsistent large difference which needs to be verified by analyzing the unified control points results.

• The KIPS Transactions:PartB
• /
• v.12B no.7 s.103
• /
• pp.829-840
• /
• 2005

Word Sense Disambiguation(WSD) is one of the most difficult problem in Korean information processing. We propose a WSD model with the capability to filter semantic information using the specific characteristics in dictionary dictions, and nth added information, useful to sense determination, such as statistical, distance and case information. we propose a model, which can resolve the issues resulting from the scarcity of semantic information data based on the word hierarchy system (thesaurus) developed by Ulsan University's UOU Word Intelligent Network, a dictionary-based toxicological database. Among the WSD models elaborated by this study, the one using statistical information, distance and case information along with the thesaurus (hereinafter referred to as 'SDJ-X model') performed the best. In an experiment conducted on the sense-tagged corpus consisting of 1,500,000 eojeols, provided by the Sejong project, the SDJ-X model recorded improvements over the maximum frequency word sense determination (maximum frequency determination, MFC, accuracy baseline) of $18.87\%$ ($21.73\%$ for nouns and inter-eojeot distance weights by $10.49\%$ ($8.84\%$ for nouns, $11.51\%$ for verbs). Finally, the accuracy level of the SDJ-X model was higher than that recorded by the model using only statistical information, distance and case information, without the thesaurus by a margin of $6.12\%$ ($5.29\%$ for nouns, $6.64\%$ for verbs).

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.202-217
• /
• 2018

Urban land use changes by human activities affect spatial configuration of urban areas and their surrounding ecosystems. Although it is necessary to identify patterns of urban land use changes and to simulate future changes for sustainable urban management, simulation of land use changes is still challenging due to their uncertainty and complexity. Cellular automata model is widely used to simulate urban land use changes based on cell-based approaches. However, cell-based models can not reflect features of actual land use changes and tend to simulate fragmented patterns. To solve these problems, object-based cellular automata models are developed, which simulate land use changes by land patches. This study simulate future land use changes in Hanam city using an object-based cellular automata model. Figure of merit of the model is 24.1%, which assess accuracy of the simulation results. When a baseline scenario was applied, urban decreased by 16.4% while agriculture land increased by 9.0% and grass increased by 19.3% in a simulation result of 2038 years. In an urban development scenario, urban increased by 22.4% and agriculture land decreased by 26.1% while forest and grass did not have significant changes. In a natural conservation scenario, urban decreased by 29.5% and agriculture land decreased by 8.8% while each forest and grass increased by 6% and 42.8%. The model can be useful to simulate realistic urban land use change effectively, and then, applied as a decision support tool for spatial planning.