• The Journal of the Acoustical Society of Korea
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• 제43권2호
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• pp.243-252
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• 2024

Among the Foley sound generation models that have recently begun to be studied, a sound generation technique using the Vector Quantized-Variational AutoEncoder (VQ-VAE) structure and generation model such as Pixelsnail are one of the important research subjects. On the other hand, in the field of deep learning-based acoustic signal compression, residual vector quantization technology is reported to be more suitable than the conventional VQ-VAE structure. Therefore, in this paper, we aim to study whether residual vector quantization technology can be effectively applied to the Foley sound generation. In order to tackle the problem, this paper applies the residual vector quantization technique to the conventional VQ-VAE-based Foley sound generation model, and in particular, derives a model that is compatible with the existing models such as Pixelsnail and does not increase computational resource consumption. In order to evaluate the model, an experiment was conducted using DCASE2023 Task7 data. The results show that the proposed model enhances about 0.3 of the Fréchet audio distance. Unfortunately, the performance enhancement was limited, which is believed to be due to the decrease in the resolution of time-frequency domains in order to do not increase consumption of the computational resources.

• The Journal of the Convergence on Culture Technology
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• 제10권2호
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• pp.383-390
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• 2024

We conducted this study to identify factors that influence college students' willingness to perform CPR. The subjects of the study were 229 college students from J City and D City. A URL was distributed to those who agreed to the study and a non-face-to-face survey was conducted using a questionnaire form. The research results were analyzed using SPSS/WIN 23.0. 155 out of 229 people (67.7%) responded that they would perform CPR if they found a patient suffering from cardiac arrest. Factors that differences between the CPR performing group and the non-performing group include gender, grade, experience of practice, number of CPR training, period of last CPR training, Have a CPR certification, and knowledge of CPR, Confidence of CPR. There was a statistically significant difference(p<0.05). Factors affecting the willingness to perform CPR include those who have actual experience of CPR, those who have CPR certification, and the higher their confidence in CPR, the higher the willingness to perform CPR. Accordingly, in order to increase confidence in CPR, we emphasize the importance of realistic performance experience and propose the development and application of various educational methods to increase the implementation rate through sufficient simulation learning.

• Journal of the Korea Society for Simulation
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• 제33권1호
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• pp.1-17
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• 2024

Forest management is known to beneficially influence stand structure and wood production, yet quantitative understanding as well as an illustrative depiction of the effects of different management approaches on tree growth and stand dynamics are still scarce. Long-term management of beech forests must balance public interests with ecological aspects. Efficient forest management requires the reliable prediction of tree growth change. We aimed to develop a novel hybrid simulation approach, which realistically simulates short- as well as long-term effects of different forest management regimes commonly applied, but not limited, to German low mountain ranges, including near-natural forest management based on single-tree selection harvesting. The model basically consists of three modules for (a) natural seedling regeneration, (b) mortality adjustment, and (c) tree growth simulation. In our approach, an existing validated growth model was used to calculate single year tree growth, and expanded on by including in a newly developed simulation process using calibrated modules based on practical experience in forest management and advice from the local forest. We included the following different beech forest-management scenarios that are representative for German low mountain ranges to our simulation tool: (1) plantation, (2) continuous cover forestry, and (3) reserved forest. The simulation results show a robust consistency with expert knowledge as well as a great comparability with mid-term monitoring data, indicating a strong model performance. We successfully developed a hybrid simulation that realistically reflects different management strategies and tree growth in low mountain range. This study represents a basis for a new model calibration method, which has translational potential for further studies to develop reliable tailor-made models adjusted to local situations in beech forest management.

• Journal of the Korean Applied Science and Technology
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• 제40권6호
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• pp.1498-1505
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• 2023

This study attempted to investigate the difference in service orientation according to the individual characteristics of hair salon workers, and to identify the internal marketing factors of hair salon that influence service orientation. Questionnaires for empirical research were collected from hair salon workers in Gyeongnam, and the results of analyzing the collected questionnaires through IBM SPSS Statistics 26 are as follows. First, as a result of analyzing the difference in service orientation according to the individual characteristics of hair salon workers, the '40s or older' group and the 'working period of 10 years or longer' group showed statistically higher service orientation than other groups. Second, as a result of analyzing the causal relationship between internal marketing and service orientation, it was found that welfare, compensation system, education and training of internal marketings had the statistical effect on service orientation, and in particular, the compensation system had the strongest effect on service orientation. Therefore, service orientation for customers should be improved through internal marketing activities that take into account the individual characteristics of hair salon workers. The improvement of service orientation means the customer's intention to reuse, suggesting that ultimately the management performance of hair salon companies can be further improved.

• Korean Journal of Radiology
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• 제21권7호
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• pp.869-879
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• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Journal of Korea Water Resources Association
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• 제57권2호
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Journal of the Society of Disaster Information
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• 제20권1호
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• pp.20-31
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• 2024

Purpose: Research and development of high-strength concrete enables high-rise buildings and reduces the self-weight of the structure by reducing the cross-section, thereby reducing the thickness of beams and slabs to build more floors. A large effective space can be secured and the amount of reinforcement and concrete used to designate the base surface can be reduced. Method: In terms of field construction and quality, the effect of reducing the occurrence of drying shrinkage can be confirmed by studying the combination of low water bonding ratio and minimizing bleeding on the concrete surface. Result: The ease of site construction was confirmed due to the high self-charging property due to the increased fluidity by using high-performance water reducing agents, and the advantage of shortening the time to remove the formwork by expressing the early strength of concrete was confirmed. These experimental results show that the field application of ultra-high-strength concrete with a design standard strength of 100 MPa or higher can be expanded in high-rise buildings. Through this study, we experimented and evaluated whether ultra-high-strength concrete with a strength of 130 MPa or higher, considering the applicability of high-rise buildings with more than 120 floors in Korea, could be applied in the field. Conclusion: This study found the optimal mixing ratio studied by various methods of indoor basic experiments to confirm the applicability of ultra-high strength, produced 130MPa ultra-high strength concrete at a ready-mixed concrete factory similar to the real size, and tested the applicability of concrete to the fluidity and strength expression and hydration heat.

• Korean Journal of Cognitive Science
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• 제35권1호
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Journal of Korea Water Resources Association
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• 제57권3호
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Resources Recycling
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• 제33권2호
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• pp.62-72
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• 2024

Although the Paris Agreement in 2015 aimed to limit global temperature increases to below 2℃ and eventually to 1.5℃ to address the climate crisis, global temperature continues to rise. Developed countries have proposed a circular economy as a major strategy to tackle this issue. Detailed implementation methods include reusing, remanufacturing, recycling, and energy recovery. Remanufacturing has a greater potential to achieve high added value and carbon neutrality than other resource circulation methods. However, currently, no standardized method for quantitatively evaluating the greenhouse gas (GHG) reduction effects of remanufacturing exists. This study compares and analyzes recent research trends since 2020 on the calculation of GHG emission reduction effects from remanufacturing. It also examines international standards for environmental impact assessment, including GHGs and environmental performance labeling systems. This study derives the key factors for standardizing the calculation of the GHG emission reduction effects of remanufactured products.