• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.285-297
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• 2023

The present study aims to investigate the acoustic performance of the creative convergence classrooms in Korea used by elementary school students under the age of 9 introduced through the school space innovation project. In order to do this, acoustic performances of three creative convergence classrooms were measured. The measured acoustic parameters were background noise levels, Reverberation Time (RT), D₅₀, Speech Transmission Index (STI), and Inter-Aural Cross Correlation (IACC). Also, acoustic parameters including Transmission Loss (TL) and standardized level difference (D_nT) have been measured for the analysis of sound insulation performance of walls. In addition, the noise level was measured according to the opening conditions of doors and windows in the classroom. As a result, background noise level was measured at an average of 28.0 dB(A) to 32.8 dB(A) when the air conditioner was not operated, and the RT did not exceed 0.6 s. There were differences in IACC according to various desk layouts, and IACC values were high in the center line and the seats near the sound source. In particular, higher IACC was measured at the seats on the center line facing the source squarely. Regarding noise level in the classroom according to the opening conditions of doors and windows, the standards were exceeded when all windows, or windows and doors front onto the corridor were opened.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.295-302
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• 2023

Software requirements written in natural language may have different meanings from the stakeholders' viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because the efficient design is possible only when appropriate architectural tactics for each quality attribute are selected. As a result, although many natural language processing models have been studied for the classification of requirements, which is a high-cost task, few topics improve classification performance with the imbalanced quality attribute datasets. In this study, we first show that the classification model can automatically classify the Korean requirement dataset through experiments. Based on these results, we explain that data augmentation through EDA(Easy Data Augmentation) techniques and undersampling strategies can improve the imbalance of quality attribute datasets, and show that they are effective in classifying requirements. The results improved by 5.24%p on F1-score, indicating that handling imbalanced data helps classify Korean requirements of classification models. Furthermore, detailed experiments of EDA illustrate operations that help improve classification performance.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.54-69
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• 2023

This study investigated the distribution of users in urban parks to overcome the limitations of existing research, which made it difficult to determine where data came was collected. It aimed to provide implications for park planning and management based on user distribution using mobile signal data. Five urban parks in Seongdong-gu, Seoul, with various physical characteristics, were selected. Mobile signal data provided by the Seoul Big Data Campus was used to identify the distribution of user inflow through hot spot analysis per park. The relationship between urban context and park influence area was derived. Seoul Forest (P1) and Seongsu Park (P3), which have a high proportion of commercial spaces around the park, showed wider user hotspots compared to Eungbong Park (P2), Dokseodang Park (P4), and Daehyunsan Park (P5), which were located in residential areas. Parks with a significant presence of commercial spaces had a broader influence, while parks with larger sizes and gentle slopes exhibited wider influence areas. This study proposed a novel data-based approach to urban park planning and management based on the inflow distribution of park users. Through this research, valuable insights were derived that could be utilized for urban park planning and management, aiming to enhance the effectiveness and efficiency of park utilization.

• Journal of the Korean Recycled Construction Resources Institute
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• v.11 no.4
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• pp.355-363
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• 2023

In safety assessment of a aged bridge, dynamic characteristics and displacement are directly related to the rigidity of the structural system, especially displacement is the most important factor as the physical quantity that the bridge user can directly detect. However, in order to measure the displacement of the bridge, it is difficult to install displacement sensors at the bottom of the bridge and conduct traffic blocking and loading tests, resulting in increased costs or impossible measurements depending on the bridge's environment. In this study, a method of measuring the displacement of a bridge using only accelerometers without installing displacement sensors and ambient vibration without a loading test was proposed. For the analysis of bridge dynamic characteristics and displacement using ambient vibration, the mode shape and natural frequency of the bridge were extracted using a TDD technique known to enable quick analysis with simple calculations, and the unit load displacement of the bridge was analyzed through flexibility analysis to calculate static displacement. To verify this proposed technology, an on-site test was conducted on C Bridge, and the results were compared with the measured values of the loading test and the structural analysis data. As a result, it was confirmed that the mode shape and natural frequency were 0.42 to 1.13 % error ratio, and the maximum displacement at the main span was 3.58 % error ratio. Therefore, the proposed technology can be used as a basis data for indirectly determine the safety of the bridge by comparing the amount of displacement compared to the design and analysis values by estimating the displacement of the bridge that could not be measured due to the difficulty of installing displacement sensors.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.1-13
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• 2023

As interest in urban safety has increased since COVID-19, various institutions have developed and used indicators that evaluate the safety city model. Yongsan-gu was ranked No. 1 in 2021 by Social Safety Index evaluation and was selected as the safest city in Korea. However, the Itaewon disaster in Yongsan-gu in 2022 caused many casualties. The study of indicators for evaluating cities' safety was necessary. This study aims to examine domestic and foreign safe city models and review the differences between each model and the indicators used to evaluate safe cities. As a result of collecting 11 safe city models and analyzing each evaluation index, safe city models can be classified into program-based safe city models, such as the World Health Organization's International safe community and the UN Office for Disaster Risk Reduction's International Safe city. Considering the diversification of threats to safety, it is reasonable to comprehensively consider digital security, health safety, infrastructure safety, personal safety, environmental safety, traffic safety, fire safety, crime safety, life safety, suicide, and infectious diseases when evaluating safe cities as evaluation parameters.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.617-626
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• 2023

This study aims to investigate the preferred sound level of water sound for various levels of open-plan-office noise regarding soundscape quality and speech privacy. And assessment of the work efficiency of the water sound. For the laboratory experiment, office noise was recorded using a binaural microphone in a real open-plan office. For the assessment of the soundscape quality and speech privacy, Overall Soundscape Quality (OSQ) and Listening Difficulty (LD) were evaluated under three different sound levels (55 dBA, 60 dBA, and 65 dBA) and five different signal-to-noise ratios (SNR -10 dB, -5 dB, 0 dB, +5 dB, and +10 dB). After the evaluation, the preferred SNR was proposed according to OSQ and LD. For the assessment of to work efficiency of water sound, this study evaluated the cognitive performance of both of the condition noise only and combine the water sound with office noise. The results showed that LD increased as the water sound level increased, but OSQ decreased. When the water sound level was more than the office noise level, the OSQ decreased from noise only. Therefore, considering OSQ and LD, the preferred SNR of water sound was -5 dB for all noise levels. At the preferred level of water sound, the cognitive performance results were shown to decrease at 55 dBA compared to noise only, but at 60 dBA and 65 dBA combine the water sound results were increased than the noise only.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.86-93
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• 2023

As the hysteretic behavior of reinforced concrete members under cyclic loading progresses, the energy dissipation ability decreases due to a decrease in stiffness and strength and pinching effects. However, the guideline "Nonlinear Analysis Model for Performance-Based Seismic Design of Reinforced Concrete Building Structures, 2021" requires calculating a single energy dissipation factor for each member and all histeric step, so the decrease in energy dissipation capacity according to histeric step cannot be considered. It is judged that Therefore, in this study, the energy dissipation factor according to the histeric step was examined by comparing the existing experimental results and the nonlinear time history analysis results for a general beam under cyclic loading. The energy dissipation factor was calculated as the ratio of the energy dissipation amount of the actual specimen to the energy dissipation amount of the idealized elastoplastic behavior obtained as a result of nonlinear time history analysis. In the existing experiment results, the energy dissipation factor was derived by calculating one cycle for each histeric step, and the energy dissipation factor was derived based on the nonlinear modeling process in the guidelines. In the existing experimental study, the energy dissipation factor was calculated by setting each histeric step (Y-L-R), and the energy dissipation factor was found to be 0.36 in the Y-L step and 0.28 in the L-R step, and the energy dissipation factor in the guideline was found to be 0.31. This shows that the energy dissipation factor calculation formula in the guidelines does not indicate a decrease in the energy dissipation capacity of reinforced concrete members.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.916-923
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• 2023

Purpose: Looking at the status of fatal accidents in the construction industry in the 2022 Industrial Accident Status Supplementary Statistics, 27.8% of all fatal accidents in the construction industry are caused by construction equipment. In order to overcome the limitations of tours and inspections caused by the enlargement of sites and high-rise buildings, we plan to build a model that can extract construction equipment using computer vision technology and analyze the model's accuracy and field applicability. Method: In this study, deep learning is used to learn image data from excavators, dump trucks, and mobile cranes among construction equipment, and then the learning results are evaluated and analyzed and applied to construction sites. Result: At site 'A', objects of excavators and dump trucks were extracted, and the average extraction accuracy was 81.42% for excavators and 78.23% for dump trucks. The mobile crane at site 'B' showed an average accuracy of 78.14%. Conclusion: It is believed that the efficiency of on-site safety management can be increased and the risk factors for disaster occurrence can be minimized. In addition, based on this study, it can be used as basic data on the introduction of smart construction technology at construction sites.

• Journal of the Korean Institute of Rural Architecture
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• v.25 no.4
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• pp.9-16
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• 2023

In this study, the status and characteristics of fine dust and its impact on neighboring areas were investigated to proactively respond to the government's environmental regulations expected in the future and to minimize the damage by the fine dust generated at construction waste intermediate treatment plants. In addition, since there are no such plants that can affect the surroundings with no houses or other waste treatment sites nearby, an independently located construction waste intermediate treatment plant was selected to compare the characteristics of fine dust with that from the construction waste intermediate treatment sites located in the downtown area. The conclusions of the study are as follows. (1) The measurement results of PM10 at 4 points in the plant showed that the location where the crushing facility was operating had an elevated level of fine dust at 80㎍/m³ on average and a maximum of 124㎍/m³, and the level rose to 110㎍/m³ at points where vehicles frequent. (2) The PM2.5 measurement results inside the plant showed that the average concentration of the reference point was 16㎍/m³ and the maximum value was 20㎍/m³, which was distributed within the management standard. (3) It was found that the average concentration of PM10 in the nearby area ranged from 28 to 38㎍/m³, which was similar to or lower than 36㎍/m³ of the reference point. Therefore, the concentration of the fine dust generated in the plant had a negligible effect on the increase in concentration of fine dust in nearby areas. (4) The heavy metal contents were measured from the filter paper collected from the plant. The PM10 was found to be about 14 to 26ng/m³, and PM 2.5 was 25 to 28ng/m³, which was the average of domestic atmospheric concentrations. (5) The SEM-EDX analysis results showed that the PM10 contained Si and O around 40% similarly for both. The SiO2, a component of silica occupied the most and C was present as CaCO3, which was assumed to be a limestone component. The remaining components included NaO, Al2O3, and CaO as trace oxides. (6) The SEM-EDX analysis results showed that the PM 2.5 contained 5 to 7% of Cl, which is a chlorine ion, and a small amount of K was detected at 2.51% in the sample from the shutdown plant.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.19-26
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• 2023

The increase in the dropout rate of college students nationwide has a serious negative impact on universities and society as well as individual students. In order to proactive identify students at risk of dropout, this study built a decision tree, random forest, logistic regression, and deep learning-based dropout prediction model using academic data that can be easily obtained from each university's academic management system. Their performances were subsequently analyzed and compared. The analysis revealed that while the logistic regression-based prediction model exhibited the highest recall rate, its f-1 value and ROC-AUC (Receiver Operating Characteristic - Area Under the Curve) value were comparatively lower. On the other hand, the random forest-based prediction model demonstrated superior performance across all other metrics except recall value. In addition, in order to assess model performance over distinct prediction periods, we divided these periods into short-term (within one semester), medium-term (within two semesters), and long-term (within three semesters). The results underscored that the long-term prediction yielded the highest predictive efficacy. Through this study, each university is expected to be able to identify students who are expected to be dropped out early, reduce the dropout rate through intensive management, and further contribute to the stabilization of university finances.