• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.845-853
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• 2023

Recently, along with the increase in chemical accidents, the number of accidents-related disasters has been increasing continuously since 2012, and when looking at the hydrogen fluoride incident which is a representative example of domestic chemical incidents, there is insufficient technology applicable to the incident site. The result was that the damage spread. Therefore, in this paper, we will adapt the water pollution accident response system to a location-based approach, and introduce a measurement method for alternative index tracking using a GPS electronic floater of a location-based index measurement method for real-time response in the water environment when a chemical incident occurs. The research target area is Gumi City, which is the area where the hydrogen fluoride incident occurred, and Gamcheon is selected, and alternative tracking using GPS electronic floater is conducted in the corresponding target area through water quality and flow measurement. As a result, it is possible to measure water quality and flow at the same time in tracker experiments using GPS electronic floater based on the research results, it is believed that using GPS electronic floater will be of great help in disaster response systems for spill incidents in the river.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.239-258
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• 2024

In the face of complex crises such as a shrinking society, regional imbalance, and climate change, there is a need to seek sustainable development in local communities. In the forest sector, attempts are being made to link forest resources with local industries. However, the current support system, which is centered on the central government, has limitations in achieving sustainable forest management. On the other hand, the international community is actively promoting a shift in systems by introducing the concept of local forestry, which emphasizes local initiatives to achieve sustainable forest management. However, in the Republic of Korea, the concept of local forestry is still unclear, which hinders the promotion of a paradigm shift. In this paper, we applied the Delphi technique to conduct three surveys of 29 academics, administrators, and field experts in the Republic of Korea. The aim was to define the concept of local forestry that is suitable for domestic conditions and identify institutional measures to establish and revitalize it. The results showed that local forestry can be defined as a broad concept that is both consultative and systemic in nature and that an institutional approach that supports actors and their activities is necessary to revitalize local forestry.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.1-12
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• 2024

In this paper, we fine-tuned KoAlpaca, a large-scale Korean language model, and implemented a blog text generation system utilizing it. Blogs on social media platforms are widely used as a marketing tool for businesses. We constructed training data of positive reviews through emotion analysis and refinement of collected sponsored blog texts and applied QLoRA for the lightweight training of KoAlpaca. QLoRA is a fine-tuning approach that significantly reduces the memory usage required for training, with experiments in an environment with a parameter size of 12.8B showing up to a 58.8% decrease in memory usage compared to LoRA. To evaluate the generative performance of the fine-tuned model, texts generated from 100 inputs not included in the training data produced on average more than twice the number of words compared to the pre-trained model, with texts of positive sentiment also appearing more than twice as often. In a survey conducted for qualitative evaluation of generative performance, responses indicated that the fine-tuned model's generated outputs were more relevant to the given topics on average 77.5% of the time. This demonstrates that the positive review generation language model for sponsored content in this paper can enhance the efficiency of time management for content creation and ensure consistent marketing effects. However, to reduce the generation of content that deviates from the category of positive reviews due to elements of the pre-trained model, we plan to proceed with fine-tuning using the augmentation of training data.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Knowledge Management Research
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• v.24 no.4
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• pp.103-129
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• 2023

This study aims to analyze the network structure of technological innovation resources in SMEs, especially manufacturing firms, and reveal the differences between innovative and non-innovative firms. The study first analyzes connection centrality, flow-mediated centrality, and power centrality for all firms, and derives structural equivalence through CONCOR analysis. Then, the network structure of innovative and non-innovative firms was compared and analyzed according to innovation performance and creation. The results show that entrepreneurship and corporate innovation strategy have a significant impact on the analysis of technological innovation resources of all firms. According to the CONCOR analysis, the innovation resources of SMEs are organized into seven clusters, which can be defined as intrinsic product innovation resources, competitive advantage promotion resources, cooperative activities resources, information system resources, and innovation protection resources. The network analysis of innovative and non-innovative firms showed that innovative firms focused on enhancing competitiveness and improving quality, while non-innovative firms tended to focus more on existing products and customers. In addition, innovative firms had eight clusters, while non-innovative firms had six clusters, suggesting that innovative firms utilize resources diversely to pursue structural change and new value creation, while non-innovative firms operate technological innovation resources in a more stable form. This study emphasizes the importance of entrepreneurship and corporate innovation strategy in SMEs' technological innovation, and suggests that strong internal efforts are needed to increase innovativeness. These findings have important implications for strategy formulation and policy development for technological innovation in SMEs.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.6
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• pp.555-567
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• 2023

The cracks in the tunnel are currently determined through visual inspections conducted by inspectors based on images acquired using tunnel imaging acquisition systems. This labor-intensive approach, relying on inspectors, has inherent limitations as it is subject to their subjective judgments. Recently research efforts have actively explored the use of deep learning to automatically detect tunnel cracks. However, most studies utilize public datasets or lack sufficient objectivity in the analysis process, making it challenging to apply them effectively in practical operations. In this study, we selected test datasets consisting of images in the same format as those obtained from the actual inspection system to perform an objective evaluation of deep learning models. Additionally, we introduced ensemble techniques to complement the strengths and weaknesses of the deep learning models, thereby improving the accuracy of crack detection. As a result, we achieved high recall rates of 80%, 88%, and 89% for cracks with sizes of 0.2 mm, 0.3 mm, and 0.5 mm, respectively, in the test images. In addition, the crack detection result of deep learning included numerous cracks that the inspector could not find. if cracks are detected with sufficient accuracy in a more objective evaluation by selecting images from other tunnels that were not used in this study, it is judged that deep learning will be able to be introduced to facility safety inspection.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Research in Plant Disease
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• v.30 no.2
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• pp.124-130
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• 2024

Leaf blight caused by Stemphylium vesicarium is one of the most important fungal diseases of garlic (Allium sativum L.) worldwide, which results in a reduction of quality and yield. The breeding of resistant cultivars is an efficient approach to decrease the use of chemical fungicides and minimize crop losses. In this study, to find the resistant garlic resources against S. vesicarium, we evaluated the resistance degree of 20 garlic germplasms. To do this, garlic seedlings at four-leaf stage were rubbed with nonabsorbent cotton and then inoculated with spore suspension (3.0×10⁵ spores/ml of potato dextrose broth) of S. vesicarium by spray method. Three to seven days after inoculation, the infected leaf area (%) of garlic seedling was measured. 'Daeseo' and 'Namdo' were included as susceptible and resistant control cultivars, respectively. After 3 to 7 days of incubation, the infected leaf area (%) of garlic seedling was measured. Our results showed that IT245512, IT245528, and IT244068 lines exhibited the highest resistance against S. vesicarium, whereas IT257134 and IT253043 lines were more susceptible than the susceptible cultivar 'Daeseo'. Based on the results, the resistant genetic resources selected in this study can be used a basic material for resistant garlic breeding system against leaf blight.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.