• Journal of Venture Innovation
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• v.6 no.3
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• pp.165-184
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• 2023

Since municipal welfare institutions operate for different purposes from general companies or public enterprises, ESG practice items and model construction should be conducted through various and comprehensive social welfare studies. Since there are not many studies available in domestic welfare institutions yet and there are no suitable ESG management utilization indicators, the Cheonan Welfare Foundation's strategy and management strategy system were established to spread the model to other welfare institutions and become a leading foundation through education and training. The foundation and front-line welfare institutions selected issues identification and key issues through the foundation's empirical analysis and criticality analysis, focusing on understanding ESG management and ways to establish a practice model that positively affects institutional image and business performance. Based on this, the promotion system was examined by establishing a performance management plan after deriving appropriate strategies and establishing a strategic system for social welfare institutions. Environmental and social responsibility, transparent management, safety management system establishment, emergency and prevention, user (customer) satisfaction system establishment, anti-corruption prevention and integrity ethics monitoring and evaluation, responsible supply chains, and community contribution programs. This study attempted to specifically present efforts to settle ESG management through the consideration of the Cheonan Welfare Foundation. Therefore, it is considered to be useful data for developing ESG management by referring to the systematic development process of the Cheonan City Restoration Foundation to develop ESG measurement indicators.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.15-21
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• 2024

The purpose of this study is to propose a framework for the introduction and evaluation of artificial intelligence (AI) services not only in general applications but also in public policies. To achieve this, the study explores AI service management and governance toolkits, providing insights into how to introduce AI services in public policies. Firstly, it offers guidelines on the direction of AI service development and what aspects to avoid. Secondly, in the development phase, it recommends using the AI governance toolkit to review content through checklists at each stage of design, development, and deployment. Thirdly, when operating AI services, it emphasizes the importance of adhering to principles related to 1) planning and design, 2) the lifecycle, 3) model construction and validation, 4) deployment and monitoring, and 5) accountability. The governance perspective of AI services is crucial for mitigating risks associated with service provision, and research in risk management aspects should be conducted. While embracing the advantages of AI, proactive measures should be taken to address limitations and risks. Efforts should be made to efficiently formulate policies using AI technology to create high value and provide meaningful societal impacts.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.197-203
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• 2024

The algal blooms in rivers can negatively affect water source management and water treatment processes, necessitating continuous management. In this study, a multi-classification model was developed to predict the concentration of chlorophyll-a (chl-a), one of the key indicators of algal blooms, using Tabular Prior Fitted Networks (TabPFN), a novel deep learning algorithm known for its relatively superior performance on small tabular datasets. The model was developed using daily observation data collected at Buyeo water quality monitoring station from January 1, 2014, to December 31, 2022. The collected data were averaged to construct input data sets with measurement frequencies of 1 day, 3 days, 6 days, 12 days. The performance comparison of the four models, constructed with input data on observation frequencies of 1 day, 3 days, 6 days, and 12 days, showed that the model exhibits stable performance even when the measurement frequency is longer and the number of observations is smaller. The macro average for each model were analyzed as follows: Precision was 0.77, 0.76, 0.83, 0.84; Recall was 0.63, 0.65, 0.66, 0.74; F1-score was 0.67, 0.69, 0.71, 0.78. For the weighted average, Precision was 0.76, 0.77, 0.81, 0.84; Recall was 0.76, 0.78, 0.81, 0.85; F1-score was 0.74, 0.77, 0.80, 0.84. This study demonstrates that the chl-a prediction model constructed using TabPFN exhibits stable performance even with small-scale input data, verifying the feasibility of its application in fields where the input data required for model construction is limited.

• The Journal of Engineering Geology
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• v.7 no.3
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• pp.191-205
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• 1997

Measurements on subsurface movement of the Songsanri tomb site including the Muryong royal tomb was conducted using a tiltmeter system for the period of 15 months form July 7, 1996 to September 30, 1997. Two coordinate tilt monitoring data shows the biggest movement rate of 2.3mm/m/yr toward south in the frontal wall(N-S tilt) of the Muryong royal tomb. Southward tilting of bricks above the southern fire place in the western wall of the Muryong royal tomb is a proof of southward tilting of the royal tomb since its excavation in 1971. The eastern wall of the Muryong royal tomb is also tilting toward inside the tomb with the rate of 1.523mm/m/yr. Furthermore, tilting rate of wall increases twice in rainy season. It is interpreted tbat infiltration of water into the tomb and nearby ground in rainy season results in dangerous status for the safety of tomb structure. On the whole, normal component tilting of the walls of the 5th tomb is large than its shear component. It shows a small displacement toward one direction without no abrupt change in its direction and amount of tilting. The tilting rate of walls of the 6th tomb is about 8.8mm/m/yr in the dry season which is much bigger than those of other tombs in rainy season. Deformation events of walls of the tombs are closely related to amount of precipitation and variation of temperature. In comparison with different weather conditions, tilting is much bigger during the period of rainy weather than sunny weather. It is interpreted that rainwater flew into the turm through faults and nearby ground. High water content in nearby ground resulted strength of ground. The tilting event of walls shows a hysterisis phenomenon in analysis of temperature effect on tilting event. The walls tilt rapidly with steep rising of temperature, but the tilted walls do not come back to original position with temperature falling. Therefore, a factor of steep increase of the temperature must be removed. It means the tomb have to be kept with constant temperature. The observation of groundwater level using three boreholes located in construction site and original ground represented that groundwater level in construction site is higher than that of original ground during the rainy season from the end of June to August. It means that the drainage system of the Muryong royal tomb is worse than original ground, and it is interpreted that the poor drainage system is related to safety of tomb structure. As above mentioned, it is interpreted that artificial changes of the tomb environment since the excavation, infiltration of rainwater and groundwater into the tomb site and poor drainage system had resulted in dangerous situation for the tomb structure. According to the result of the long period observation for the tomb site, it is interpreted that protection of the tomb site from high water content should be carried out at first, and the rise of temperature by means of the dehumidifier inside the tomb must be removed.

• Korean Journal of Environmental Biology
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• v.36 no.1
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• pp.33-42
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• 2018

The tropical peatlands have been deforested and converted to agricultural and plantation areas in Indonesia. To manage water levels and increase the overall productivity of crops, canals have been constructed in tropical peatlands. The canals destructed the structure of the tropical peatlands, and increased the subsidence and fire hazard risks in the region. The Indonesian government enacted regulations and a moratorium on tropical peatlands, in order to reduce degradation. A practical method under the regulations of rewetting tropical peatlands was to permit a canal blocking. In this study, four canal blocking projects were investigated regarding their planning, construction priority, design, building material, construction, monitoring, time and costs associated with the canal blockings. In the protected areas, regulations restricted the development of the tropical peatlands areas that were noted as deeper than 3 m, and the administration stopped issuing new concessions for future work projects for this noted criteria of land use. A noted purpose of canal blockings in these areas was to effectuate the restoration of the lands in the region. The main considerations of the restoration efforts were to maintain a durability of the blockings, and to encourage the participation of the area stakeholders. In the case of a concession area, regulations were set into place to restrict clear-cutting and shifting cultivation, and to maintain groundwater level in the tropical peatland. The most significant priorities identified in the canal blocking project were the efficiency and cost-effectiveness of the project. Nevertheless, the drainage of tropical peatlands has been continued. On the basis of a literature review on regulations and rewetting methods in tropical peatlands of Indonesia, we discussed the improvements of the regulations, and adequate canal blockings to serve the function to rewet the tropical peatlands in Indonesia. Our results would help establishing an adequate direction and recommended guideline on viable rewetting methods for the restoration of drained tropical peatlands in Southeast Asia.

• Asian Journal of Turfgrass Science
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• v.23 no.1
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• pp.143-150
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• 2009

High salt concentration is one of the most important limit factor on plant growth at a disused saltpan for golf course construction site. The control of salt in soil is definitely required and the monitoring of salt concentration in soil and ground water also required to amend soil physiochemical properties. This research was carried out to monitor the pH and salt concentration changes by the height of ground water. By the physiochemical analysis test, the soil contains a high salt concentration and classified as a slight alkaline clay soil. The height of ground water table changed to 1.3m, 3.3m and 2.8m at dry season(mid-late June, 2005), monsoon season(early-mid July) and after monsoon(late July), respectively. Compare to the average ground level of 2.9m, the ground water was over flooded about OAm at monsoon season. The electrical conductivity(ECe) was measured above $4.0dS{\cdot}m^{-1}$ over all areas and however, some areas showed over $20dS{\cdot}m^{-1}$. During a monsoon season, ECe was lowered to $1.2{\sim}15.0dS{\cdot}m^{-1}$, compared with those of the dry season. Therefore, the interception of the capillary connection between planting layer and ground water which contains high salt concentration should be adapted when golf courses are constructed on disused saltpan. The phytotoxicity caused by salt damage may be controled by the interception of capillary fringe of salt flow to the topsoil profile at the upper layer of the ground water table.