• Korean Journal of Heritage: History & Science
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• v.54 no.2
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• pp.56-77
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• 2021

Toponyms are located not only in the site between human cognition and the physical environment but also in the name of cultural heritage. Accordingly, certain identities and ideologies for which human groups and community have sought, their holistic way of life, and all cultural symbols and cosmos, such as sense of place and genius loci, are included in their toponymic heritage. Denoting, symbolizing, integrating and representing the culture and nature belong to the human community. Based on these perceptions of the toponymic heritage, the aims of this article are to examine the values of a toponym as an Intangible Cultural Heritage (ICH) and to suggest the application methods using the toponymic functions for governing of tangible cultural heritage. This article discusses the multivocality, diversity, and non-representational theory of landscape phenomenology intrinsic to the terms of culture and cultural landscape and then the domestic and international issues on the toponymic heritage in the first chapter on the values of toponym as a part of the ICH. In particular, it analyzes the preceding research in the field of toponymy, as well as the Resolutions of UNCSGN and UNGEGN on "Geographical names as culture, heritage and identity" including indigenous, minority and regional language names since 1992, which is related to the UNESCO's Convention for the Safeguarding of the Intangible Cultural Heritage in 2003. Based on this, I suggest that the traits of toponymic cultural heritage and its five standards of selection, i.e., cultural traits of toponyms, historical traits, spatial traits, socio-economic traits and linguistic traits with some examples. In the second chapter discussing on the methods using the toponymic denoting functions for creating and governing of the tangible cultural heritage, it is underlined to maintain the systematic and unified principle regarding the ways of naming in the official cultural heritage and its governing. Lastly, I introduce the possible ways of establishing a conservative area of the historical and cultural environment while using the toponymic scale and multi-toponymic territory. Considering both the spatial and participatory turns in the field of heritage studies in addition to the multiple viewpoints and sense of cultural heritage, I suggest that the conservative area for the cultural heritage and the historical and cultural environment should be set up through choosing the certain toponymic scale and multi-toponymic territory.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.139-148
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• 2021

This study aimed to verify how a multisensory storytelling-based activity-based intervention affects social interaction in children with cerebral palsy. As a quasi-experimental investigation, this study used a single-blind, two-group pre-post test design. This study comprised 24 children aged 7 to 8 y who had been diagnosed with spastic cerebral palsy and were classified as having GMFCS stages I to III. Twelve children were randomly assigned to experimental and control groups, with neither the children nor their guardians knowing which group they were placed in. The group program comprised 16 sessions of 60 min each, twice a week for eight weeks. The experimental group engaged in an activity-centered intervention centered on multisensory storytelling, whereas the control group engaged in structured physical activity. The activities were assessed using the peer relations skills scale to determine the extent to which social interaction had changed prior to and during the child's intervention. The SPSS 25.0 for Windows (IBM Corp, USA) application was used to analyze the data, and the significance level (α) for statistical verification was set to 0.05. Furthermore, the Wilcoxon Signed-Rank and Mann-Whitney U tests were used to assess the differences in social interaction between the experimental and control groups. Significant differences were observed in the total of the peer relationship skill scale and cooperation and empathy areas of the subtest in the intragroup change of the peer relationship skill scale between the experimental and control groups. However, the experimental group demonstrated a significant difference in the initiative area, whereas the control group demonstrated no significant difference. A significant difference was observed in the amount of change between the two groups in the initiative area and total of the subtest of peer relationship skills but no significant difference in the collaboration and empathy areas. We gave a multisensory storytelling-based activity-based intervention based on multisensory storytelling to children with cerebral palsy and saw a significant improvement in peer relationship skills. It may be proposed as an effective intervention strategy for children with cerebral palsy who struggle with social contact.

• The Journal of Engineering Geology
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• v.31 no.4
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• pp.603-620
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• 2021

A land nodal seismic system was employed to acquire seismic reflection data using stand-alone cable-free receivers in a land-river area. Acquiring reliable data using this technology is very cost effective, as it avoids topographic problems in the deployment and collection of receivers. The land nodal airgun system deployed on the mouth of the Hyungsan River (in Pohang, Gyeongsangbuk Province) used airgun sources in the river and receivers on the riverbank, with subparallel source and receiver lines, approximately 120 m-spaced. Seismic data collected on the riverbank are characterized by a low signal-to-noise (S/N) and inconsistent reflection events. Most of the events are represented by hyperbola in the field records, including direct waves, guided waves, air waves, and Scholte surface waves, in contrast to the straight lines in the data collected conventionally where source and receiver lines are coincident. The processing strategy included enhancing the signal behind the low-frequency large-amplitude noise with a cascaded application of bandpass and f-k filters for the attenuation of air waves. Static time delays caused by the cross-offset distance between sources and receivers are corrected, with a focus on mapping the shallow reflections obscured by guided wave and air wave noise. A new time-distance equation and curve for direct and air waves are suggested for the correction of the static time delay caused by the cross-offset between source and receiver. Investigation of the minimum cross-offset gathers shows well-aligned shallow reflections around 200 ms after time-shift correction. This time-delay static correction based on the direct wave is found essential to improving the data from parallel source and receiver lines. Data acquisition and processing strategies developed in this study for land nodal airgun seismic systems will be readily applicable to seismic data from land-sea areas when high-resolution signal data becomes available in the future for investigation of shallow gas reservoirs, faults, and engineering designs for the development of coastal areas.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.69-80
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• 2021

This study tried to suggest improvement measures by discovering problems or matters requiring improvement among the annual regional safety evaluation systems. Briefly introducing the structure and contents of the study, which is the introduction, describes the regional safety evaluation method newly applied by the Ministry of Public Administration and Security in 2020. Utilization plans were also introduced according to the local safety level that was finally evaluated by the local government. In this paper, various views of previous researchers related to regional safety are summarized and described. In addition, problems were drawn in the composition of the index of local safety, the method of calculating the index, and the application of the current index. Next, the problems of specific regional safety evaluation indicators were analyzed and solutions were presented. First, "Number of semi-basement households" is replaced with "Number of households receiving basic livelihood" of 「Social Vulnerability Index」 in the field of disaster risk factors is replaced with "the number of households receiving basic livelihood". In addition, the "Vinyl House Area" is evaluated by replacing "the number of households living in a Vinyl House, the number of container households, and the number of households in Jjok-bang villages" with data. Second, in the management and evaluation of habitual drought disaster areas, local governments with a water supply rate of 95% or higher in Counties, Cities, and Districts are treated as "missing". This is because drought disasters rarely occur in the metropolitan area and local governments that have undergone urbanization. Third, the activities of safety sheriffs, safety monitor volunteers, and disaster safety silver monitoring groups along with the local autonomous prevention foundation are added to the evaluation of the evaluation index of 「Regional Autonomous Prevention Foundation Activation」 in the field of response to disaster prevention measures. However, since the name of the local autonomous disaster prevention organization may be different for each local government, if it is an autonomous disaster prevention organization organized and active for disaster prevention, it would be appropriate to evaluate the results by summing up all of its activities. Fourth, among the Scorecard evaluation items, which is a safe city evaluation tool used by the United Nations Office for Disaster Risk Reduction(UNDRR), the item "preservation of natural buffers to strengthen the protection functions provided by natural ecosystems" is borrowed, which is closely related to natural disasters. The Scorecard evaluation is an assessment index that focuses on improving the disaster resilience of local governments while carrying out the campaign "Creating cities resilient to climate crises and disasters" emphasized by UNDRR. Finally, the names of "regional safety level" and "local safety index" are similar, so the term of local safety level is changed to "natural disaster safety level" or "natural calamity safety level". This is because only the general public can distinguish the local safety level from the local safety index.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.

• Journal of Korean Society of Forest Science
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• v.113 no.1
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• pp.31-39
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• 2024

This study was performed to determine the effects of Four seedling age classes ageon the characteristics of growth and nutrient uptake in Chamaecyparis obtusa container seedlings. Seedlings (1-1, 2-0, 2-1, and 2-2 seedlings) of C. obtusa grown in containers were harvested to measure specific leaf area, height (H)/root collar diameter (D) ratio, dry mass of aboveground (T)/root dry mass (R) ratio, and seedling quality index of seedlings. The specific leaf area was highest in 1-0 seedlings (30.48 cm² g^-1), whereas it decreased (from 28.62 cm² g^-1 to 23.59 cm² g^-1) with increasing seedling age. The H/D ratio increased with increasing seedling age (from 4.41 in 1-0 seedlings to 8.35 in 2-2 seedlings). The T/R ratio decreased as the seedling age increased (from 4.29 in the 1-0 seedling to 2.13 in the 2-1 seedling). The seedling quality index increased with increasing seedling age (from 0.10 for the 1-0 seedling to 3.06 for the 2-2 seedling). The carbon concentrations of seedling components (leaf, branches, stem, and roots) did not differ significantly with seedling age, whereas the nitrogen concentration of seedling components was the lowest in 2-1 seedlings, as no fertilizer was applied to discourage excessive growth of the seedlings. Phosphorus, potassium, and magnesium concentrations in 2-1 seedling components were not affected by the lack of fertilizer application. These results can be applied to determine the optimum morphological characteristics and nutrient management by seedling age in container- grown C. obtusa.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.393-412
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• 2024

In selecting a disposal site for high-level radioactive waste, the hydrogeological research of the site is very important, and the hydraulic conductivity and the storage coefficient are key parameters. In this study, the hydraulic conductivity obtained by two different types of field hydraulic test equipment and methods was compared and analyzed for the deep granite rockmass in the Wonju area to understand the hydraulic characteristics of the deep granite rockmass. One was to perform the lugeon test, constant pressure injection test, and slug test at a maximum depth of 602.0 m by using the auto pressure/flow injection system, and the calculated hydraulic conductivity ranged from 1.26E-9 to 4.16E-8 m/s. In the overall depth, the maximum and minimum differences of the hydraulic conductivity were found to be about 33 times, and in the same test section, the difference by test method or analysis method was 1.13 to 8.25 times. In the other, the hydraulic conductivity calculated by performing a constant pressure injection test and a pulse test at a maximum depth of 705.1 m using the deep borehole hydraulic testing system was found to be 1.60E-10 to 2.05E-8 m/s, and the maximum and minimum differences were found to be about 130 times. In the constant pressure injection test, the difference depending on the analysis method was found to be 1.02 to 2.8 times. The hydraulic conductivity calculated by the two test equipment and methods generally showed similar ranges as E-9 and E-8 m/s, and no clear trend was observed according to depth. It was found that the granite rockmass in the Wonju area where the field hydraulic test was conducted showed low or very low rockmass permeability, and although there are differences in the range of hydraulic conductivity and the depth of application that can be measured depending on the applied test equipment and test method, it is generally believed that reliable results were presented.

• Journal of Nutrition and Health
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• v.50 no.4
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• pp.402-414
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• 2017

Purpose: The purpose of the study was to evaluate the status of safety accidents and importance-performance analysis (IPA) between regular and non-regular employees in industry foodservices. Methods: The participants were regular employees (n = 119) and non-regular employees (n = 163) in industry foodservices in the Jeonbuk area. Demographic characteristics, status of safety accidents, safety education, and importance and performance status were assessed using a self-administered questionnaire. Results: Approximately 66.4% of regular employees and 53.4% of non-regular employees experienced safety accidents (p < 0.05). Types of safety accidents of regular and non-regular employees were mostly burns, and causes were mostly from their own negligence. Approximately 98.3% of regular employees and 95.1% of non-regular employees experienced safety education. Approximately 88.9% of regular employees and 96.8% of non-regular employees received safety education from dietitians. Approximately 41.9% of regular employees and 50.0% of non-regular employees had difficulty applying the contents of safety education due to lack of time during work. As a result of IPA, regular and non-regular employees were aware of the importance of the following and performed them well: 'Clean the floor of the work place', 'Arrange in the work area', 'Wear safety shoes', 'Check for heater cord', and 'Safety cooking when using oil'. On the other hand, they were not aware of the importance of the following and performed them insufficiently: 'Check for the MSDS', 'Aware of chemical signs', 'Wear protection gloves etc.', 'Do stretching exercise', and 'Using ancillary tools'. Conclusion: Therefore, it is necessary to improve the consciousness of dietitians for effective application of safety education contents, development of contents, especially MSDS, and related things.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.