• Journal of the Korean Association of Geographic Information Studies
• /
• v.27 no.3
• /
• pp.76-86
• /
• 2024

This study sought to introduce smart urban regeneration to solve the problem of aging and substandard housing in large cities that occurred during the rapid industrialization and urbanization of local cities in Korea. Accordingly, this study aims to activative the old downtown through the convergence of the existing urban regeneration project and smart city project and to improve the physical, social, and economic aspects. As a research method, the literature related to smart cities and urban regeneration was systematically reviewed, and the possibility of introducing smart city services in the Samho-dong district of Yangsan City was explored through domestic and foreign case analysis. As a result of the research, the necessity of smart urban regeneration was highlighted, and the conclusion was reached that it is important to improve the efficiency of urban regeneration projects by using information and communication technology and strengthen sustainability by urban regeneration. This study is expected to contribute to the activative the old downtown and the improvement of the quality of life of citizens, and it is necessary to strengthen the interaction between smart city and urban regeneration in the future, and the introduction of smart city services suitable for local characteristics is judged to play an important role in sustainable urban development through local community and citizen participation.

• Journal of Radiation Industry
• /
• v.18 no.3
• /
• pp.167-171
• /
• 2024

Technetium-99 (⁹⁹Tc) is recognized as a critical concern in the disposal of spent nuclear fuel due to its long half-life and remarkable stability, existing predominantly as TcO₄^- in the natural environment. The anionic form of technetium is highly soluble and mobile, posing significant environmental risks from the viewpoint of nuclear waste management. Thus, developing efficient and cost-effective sorbents for aqueous Tc(VII) is essential for mitigating relevant contamination. In the present work, the adsorption characteristics of Re(VII), a chemical analog of Tc(VII), were investigated using the clay mineral bentonite, modified with two different organic cations: hexadecylpyridinium (HDPy) and hexadecyltrimethylammonium (HDTMA). Sorption experiments were conducted at a liquid-to-solid ratio of 1 g/L with Re(VII) solutions prepared at concentrations from 10^-4 mol/L to 10^-6 mol/L. The sorption ratio and distribution coefficients were determined with samples collected at reaction times of 10, 50, 100, and 500 minutes after 0.45 ㎛ syringe filtration. In parallel, the modified bentonite samples were further analyzed using the X-ray diffraction (XRD) method to understand the adsorption mechanism of Re(VII) onto the target minerals. According to the quantification analysis results, a rapid equilibrium reaction of aqueous Re(VII) for all modified bentonite samples was identified. Moreover, near-complete adsorption of Re(VII) was observed when the bentonite was modified at 200-400% of its cation exchange capacity (CEC) for both organic cations. For cases of lower modification, the HDTMA-modified bentonite showed relatively higher adsorption efficiency compared with the one modified with HDPy. This result was inferred to be due to the difference in inter-layer spacing based on the characteristics of the organic cations. It is expected that the results obtained through this study will serve as a preliminary case for the synthesis of adsorbents for the retardation of highly mobile anionic radionuclides, such as I and Tc, in the natural environment.

• Journal of the Korean Geotechnical Society
• /
• v.40 no.4
• /
• pp.69-79
• /
• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.52 no.4
• /
• pp.45-55
• /
• 2024

This study aims to distinguish between utilitarian walking and leisure walking activities and analyze the correlation between these types of walking and the walking environment. To measure the walking environment, we utilized Google Street View (GSV) and employed semantic segmentation deep learning techniques to quantitatively assess urban walking environment elements as perceived by pedestrians. A survey was conducted to measure utilitarian walking, leisure walking, and perceived walking environment satisfaction, collecting valid responses from 192 participants. Using the survey data, we visualized utilitarian walking, leisure walking, and perceived walking environment satisfaction, and analyzed the correlation between these variables and the walkability scores. The results indicated that leisure walking had a significant positive correlation with walkability (Pearson's r = 0.121, p-value = 0.012), while there was no significant correlation between utilitarian walking and walkability (Pearson's r = 0.093, p-value = 0.055). These findings suggest that people prioritize mobility efficiency over the walking environment for utilitarian walking, whereas the quality of the walking environment significantly influences the frequency of leisure walking. Based on these results, the study proposes specific strategies to improve the walking environment around residential areas to promote leisure walking. These strategies include creating vertical gardens or various forms of three-dimensional gardens on narrow walkways and improving sidewalk design. The findings of this study can contribute to promoting leisure walking by creating walk-friendly environments, ultimately enhancing urban sustainability and the quality of life for residents.

• Journal of Dental Rehabilitation and Applied Science
• /
• v.40 no.3
• /
• pp.179-188
• /
• 2024

To enhance the predictability of aesthetic treatment outcomes in aesthetic prosthetic restorations, considerations must include analysis of facial features, the relationship between teeth and lips, proportions of tooth width/length, gingival form, and more. Traditional diagnostic wax-ups have limitations in considering the patient's facial features and are unable to facilitate rapid form modifications. With recent advancements in digital technology, it is now possible to digitize the patient's facial features in three dimensions, enabling the design of restorations that harmonize with facial features. These digital workflows not only improve efficiency but also provide patients with faster visualization of treatment outcomes, thereby enhancing motivation. Therefore, in this case, a treatment plan is devised to utilize digital diagnostic wax-ups considering the patient's facial features for the final prosthetic design.

• Information Systems Review
• /
• v.26 no.2
• /
• pp.45-57
• /
• 2024

This study develops a deep learning-based methodology for predicting Crude Palm Oil (CPO) prices. Palm oil is an essential resource across various industries due to its yield and economic efficiency, leading to increased industrial interest in its price volatility. While numerous studies have been conducted on palm oil price prediction, most rely on time series forecasting, which has inherent accuracy limitations. To address the main limitation of traditional methods-the absence of stationarity-this research introduces a novel model that uses the ratio of future prices to current prices as the dependent variable. This approach, inspired by return modeling in stock price predictions, demonstrates superior performance over simple price prediction. Additionally, the methodology incorporates the consideration of lag values of independent variables, a critical factor in multivariate time series forecasting, to eliminate unnecessary noise and enhance the stability of the prediction model. This research not only significantly improves the accuracy of palm oil price prediction but also offers an applicable approach for other economic forecasting issues where time series data is crucial, providing substantial value to the industry.

• Korean Journal of Environment and Ecology
• /
• v.38 no.4
• /
• pp.426-433
• /
• 2024

In this study, to estimate the effects of plant-induced surface area increase and physiological activity on fine dust purification, a control group was set up. We utilized both foliage plants (Spathiphyllum wallisii) and artificial plants (shaped like Spathiphyllum wallisii) to measure and compare the purification time for fine dust. The results showed that the time required for fine dust purification in each experimental group decreased by 57-64% for Type AP and 31-32% for Type P compared to the control group. Subsequently, using a Linear Mixed Model (LMM), we tested the interaction between time and each experimental group, revealing statistically significant interactions between surface area increase and time(PM10 : t=3.123, p<0.05, PM2.5 : t=3.180, p<0.05), as well as physiological activity and time(PM10 : t=4.065, p<0.05, PM2.5 : t=4.307, p<0.05), indicating the presence of interactions between each factor and the time variable. Finally, we estimated the efficiency of fine dust purification by plant factors through nonlinear regression analysis. Compared to the control group without purification factors (Type C), it was estimated that surface area increase shortened the purification time by 1.40 times and physiological activity by an average of 1.95 times, resulting in a total 2.74 times shorter purification time. Based on these results, we hypothesized that physiological activity(transpiration and absorption) has a greater impact on fine dust purification than surface area increase(biosorption). Accordingly, we emphasize the importance of vegetation management practices such as pruning and irrigation management in green spaces aimed at fine dust purification.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.5
• /
• pp.51-65
• /
• 2024

With the advancement of Unmanned Aerial Vehicles (UAV) technology, aerial spraying has been rapidly increasing in the agricultural field. Drones offer many advantages compared to traditional applicators, but they pose challenges such as spray drift risk and spray uniformity. To address these issues, it is essential to understand the characteristics of complex airflow generated by drones and its consequences for the spray performance. This study aims to identify the air velocity distribution of drone downwash and the resulting spray deposition distribution on the ground, ultimately proposing optimized spraying widths and criteria. Experiments were conducted using two agricultural drones with different propeller arrangements under various flight and measurement conditions. The results showed that during hovering, the downward airflow affected the area within a distance of the radius of the blade (R) from the center of the drone. When the drone was flying, the downward airflow was effective up to a distance of 2R. Droplet deposition was concentrated at the center of the drone during hovering. However, during flying, the droplet deposition was more evenly distributed up to the distance of R. The drone downwash and droplet deposition were significantly different during flying compared to the hovering state. At an effective spray width of 3R, the coefficient of variation (CV) was generally less than 16%, indicating a significant improvement in spray uniformity. These findings help optimize effective spraying techniques in drone-based applications.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1329-1329
• /
• 2024

As global imperatives shift toward sustainability and carbon neutrality, the construction industry faces an urgent need to address its environmental impact, particularly within steel construction. Despite the increasing adoption of sustainable practices, a detailed understanding of the entire lifecycle emissions of structural steel, especially within the rapidly evolving Chinese market, remains a significant gap. This study introduces a comprehensive life-cycle assessment (LCA) approach to map the carbon footprint of structural steel construction, with a focus on Chinese structural steel as a case study. By adopting a cradle-to-cradle perspective, the research aims to highlight and address the environmental impact across the entire lifecycle of steel used in construction. Specifically, this study will 1) develop a detailed LCA model that encapsulates the environmental impacts of structural steel from production, use, and recycling phases, 2) dentify and analyze carbon hotspots and inefficiencies within the lifecycle of Chinese structural steel, and 3) evaluate and suggest strategies for stakeholders to minimize carbon emissions, moving towards carbon-neutral steel construction. Leveraging a process-based LCA framework, this study captures the material, energy, and emissions flows associated with the lifecycle of structural steel, including steel production, fabrication, transportation, construction, and recycling, in the context of Chinese construction practices. The model is enriched with data from current Chinese steel construction projects, ensuring its accuracy and applicability. Through systematic analysis, the study pinpoints critical phases where carbon emissions can be significantly reduced. Preliminary Results show significant carbon emission sources within the production, fabrication, and transportation phases of Chinese structural steel. These insights are crucial for devising targeted reduction strategies, such as improving production and fabrication efficiency, optimizing logistics, and enhancing material recyclability. The anticipated impact of this research is multi-faceted: providing a robust framework for assessing and managing the carbon footprint of steel construction, guiding industry and policy-makers towards sustainable practices, and setting a precedent for carbon management in steel construction worldwide. This research marks a significant step towards achieving carbon neutrality in steel construction, with a particular focus on Chinese structural steel. Through a comprehensive LCA model, this study offers a deep dive into the lifecycle emissions of steel construction, paving the way for actionable strategies to reduce the environmental impact, contributing to the global endeavor towards carbon-neutral construction.

• Journal of Internet of Things and Convergence
• /
• v.10 no.4
• /
• pp.1-8
• /
• 2024

As technology advances, Internet of Things (IoT) technology is rapidly evolving as well. Various protocols, including Message Queuing Telemetry Transport (MQTT), are being used in IoT technology. MQTT, a lightweight messaging protocol, is considered a de-facto standard in the IoT field due to its efficiency in transmitting data even in environments with limited bandwidth and power. In this paper, we propose a method to improve the message transmission method in MQTT 5.0, specifically focusing on the shared subscription feature. The widely used round-robin method in shared subscriptions has the drawback of not considering the current state of the clients. To address this limitation, we propose a method to select the optimal transmission method by considering the current state. We model this problem based on Markov decision process (MDP) and utilize Q-Learning to select the optimal transmission method. Through simulation results, we compare our proposed method with existing methods in various environments and conduct performance analysis. We confirm that our proposed method outperforms existing methods in terms of performance and conclude by suggesting future research directions.