• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.57-57
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• 2022

In many areas of the world, salt damage and drought have had a negative impact on human survival due to a decrease in agricultural productivity. For instance, about 50% of agricultural land will be affected by salt damage by 2050. Biostimulants such as plant extracts can not only increase the nutrient utilization efficiency of plants, but also promote plant growth and increase resistance to abiotic or biotic stress. Therefore, the objective of this study was to determine how selected plant extracts might reduce levels of stress caused by drought and salt and to better understand the physiological response mechanisms of rice plants. In this study, we used Soybean leaves, Soybean stems and Allium tuberosum, Allium cepa, Hizikia fusiforme, and Gracilaria verrucosa extracts were used. These extracts had been used in previous studies and were found to be effective. The materials were dried in a dry oven at 50℃ for 5 days and ground using a blender. Each 50 g of materials was put in 1 L of distilled water, stirred for 24 hours, filtered using 4 layers of mirocloth, and then concentrated using a concentrator. Rice (cv. Hopumbyeo) seeds were immersed and germinated, and then sown in seedbeds filled with commercial soil. In drought experiments, three rice seedlings at 1 week after seeding was transplanted into 100 ml cups filled with commercial soils and grown until the 4-leaf stage. For this experiment, the soil weight in a cup was equalized, and water was allowed to become 100% saturated and then drained for 24 hours. Thereafter, plant extracts at 3% concentrations were applied to the soils. For NaCl treatments, rice plants at 17 days after seeding were treated with either 100 mM NaCl or plant extracts at 1%+ 100 mM NaCl combinations in the growth chamber. Leaf injury, relative water content, photosynthetic efficiency, and chlorophyll contents were measured at 3, 5, and 6 days after treatments. Shoot fresh weight of rice under drought conditions increased 28-37% in response to treatments of Soybean leaf, Soybean stem, Allium tuberosum, Allium cepa, Hizikia fusiforme, and Gracilaria verrucosa extracts at 3% when compared with control plants. Shoot fresh weight of rice subjected to 100 mM NaCl treatments also increased by 6-24% in response to Soybean leaf, Soybean stem, Allium tuberosum, Allium cepa, Hizikia fusiforme, and Gracilaria verrucosa extracts at 3% when compared with control plants. Compared to the control, rice plants treated with these six extracts and subjected to drought conditions had significantly higher relative water content, Fv/Fm, total chlorophyll and total carotenoids than control plants. With the exception of relative water contents, rice plants treated with the six extracts and subjected to salt stress (100 mM NaCl treatments) had significantly higher Fv/Fm, total chlorophyll and total carotenoids than control plants. However, the type of extract used did not produce significant difference in these parameters. Thus, all the plant extracts used in this study could mitigate drought and NaCl stresses and could also contribute substantially to sustainable crop production.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.

• Korean Journal of Culture and Social Issue
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• v.15 no.1
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• pp.169-186
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• 2009

This study aims to investigate the relationship between personality, especially temperament and subjective well-being. The participants were 681 college students (211 males and 470 females), whose ages ranged from 17 to 37 (M=20.91, SD=2.36). The instruments utilized in the present study were Korean Version of BAS/BIS Scale, The Big Five Locator, Satisfaction with Life Scale, Life Satisfaction Motivation Scale, Life Satisfaction Expectancy Scale, Emotion Frequency Test, and Subjective Happiness Scale. Result indicated that women expected more positive future than men did, while no gender differences were found in any other well-being variables. Correlational analyses revealed that emotional stability and extroversion were closely associated with life satisfaction, happiness, positive and negative emotion, whereas behavioral activation system (BAS) and behavioral inhibition system (BIS) were more closely associated with motivation to live and expectancy of satisfactory life. There was close relationship between conscientiousness and subjective well-being, because they were college students. As a internal factor, personality was better predictor for subjective well-being of female students. For instance, it accounted for around 35% variance of female's subjective happiness. The present findings reiterate the role of personality in quality of life, and it was discussed with characteristics of subjects, situational factors, and previous studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Advances in nano research
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• v.16 no.4
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• pp.325-340
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• 2024

There are several novel uses for dispersing many nanoparticles into a conventional fluid, including dynamic sealing, damping, heat dissipation, microfluidics, and more. Therefore, melting heat and mass transfer characteristics of a 3-D MHD Hybrid Nanofluid flow over a rotating disc with presenting dufour and soret effects are assessed numerically in this study. In this instance, we investigated both ferric sulfate and molybdenum disulfide as nanoparticles suspended within base fluid water. The governing partial differential equations are transformed into linked higher-order non-linear ordinary differential equations by the local similarity transformation. The collection of these deduced equations is then resolved using a Chebyshev spectral collocation-based algorithm built into the Mathematica software. To demonstrate how different instances of hybrid/ nanofluid are impacted by changes in temperature, velocity, and the distribution of nanoparticle concentration, examples of graphical and numerical data are given. For many values of the material parameters, the computational findings are shown. Simulations conducted for different physical parameters in the model show that adding hybrid nanoparticle to the fluid mixture increases heat transfer in comparison to simple nanofluids. It has been identified that hybrid nanoparticles, as opposed to single-type nanoparticles, need to be taken into consideration to create an effective thermal system. Furthermore, porosity lowers the velocities of simple and hybrid nanofluids in both cases. Additionally, results show that the drag force from skin friction causes the nanoparticle fluid to travel more slowly than the hybrid nanoparticle fluid. The findings also demonstrate that suction factors like magnetic and porosity parameters, as well as nanoparticles, raise the skin friction coefficient. Furthermore, It indicates that the outcomes from different flow scenarios correlate and are in strong agreement with the findings from the published literature. Bar chart depictions are altered by changes in flow rates. Moreover, the results confirm doctors' views to prescribe hybrid nanoparticle and particle nanoparticle contents for achalasia patients and also those who suffer from esophageal stricture and tumors. The results of this study can also be applied to the energy generated by the melting disc surface, which has a variety of industrial uses. These include, but are not limited to, the preparation of semiconductor materials, the solidification of magma, the melting of permafrost, and the refreezing of frozen land.

• Journal of The Korean Association For Science Education
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• v.28 no.6
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• pp.565-578
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• 2008

The purpose of this study is to search for the factors that influence students' understanding of the nature of science through the experience of the cognitive processes of authentic open inquiries. The freshmen of a science high school practiced authentic open inquiries reflecting epistemological characteristics of authentic science. The case study was conducted with four focus students who were successful or unsuccessful at learning the nature of science during the authentic open inquiry activity. Questions that the focus students asked during the inquiries as well as students' answers to pre- and post-VNOS (C type) were analysed, and then elaborated in the semi-structured interview. The findings suggest that open inquiry activities provide the inquiry contexts that help science high school students to understand the nature of science, and that the characteristics of students' cognition influence the understanding of the nature of science. For instance, designing experiments with their own research questions had an influence on the students' understanding about the scientific methods and the diversity of research types, and drawing conclusions from their own data made students experience scientific reasoning. In addition, the experience of collecting anomalous data helped students to understand the role of inferences in generating scientific knowledge and the creative nature of scientific knowledge. In this inquiry context, the reflective thinking that came from proactive discussion among students, made students think about the validity of the designing experiments and interpreting data, and helped them to understand the uncertain nature of reasoning and the diverse nature of scientific methods. Moreover, divergent thinking linked to analogical thinking helped students to understand the creative nature of science.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.48-59
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• 2024

As functions that support virtualization on their own in hardware are developed, user applications having various workloads are operating efficiently in the virtualization system. SR-IOV is a virtualization support function that takes direct access to PCI devices, thus giving a high I/O performance by minimizing the need for hypervisor or operating system interventions. With SR-IOV, network I/O acceleration can be realized in virtualization systems that have relatively long I/O paths compared to bare-metal systems and frequent context switches between the user area and kernel area. To take performance advantages of SR-IOV, network resource management policies that can derive optimal network performance when SR-IOV is applied to an instance such as a virtual machine(VM) or container are being actively studied.This paper evaluates and analyzes the network performance of SR-IOV implementing I/O acceleration is compared with Virtio in terms of 1) network delay, 2) network throughput, 3) network fairness, 4) performance interference, and 5) multi-network. The contributions of this paper are as follows. First, the network I/O process of Virtio and SR-IOV was clearly explained in the virtualization system, and second, the evaluation results of the network performance of Virtio and SR-IOV were analyzed based on various performance metrics. Third, the system overhead and the possibility of optimization for the SR-IOV network in a virtualization system with high VM density were experimentally confirmed. The experimental results and analysis of the paper are expected to be referenced in the network resource management policy for virtualization systems that operate network-intensive services such as smart factories, connected cars, deep learning inference models, and crowdsourcing.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.89-112
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• 2024

Metadata is a crucial component of record management, playing a vital role in properly managing and understanding the record. In cases where automatic metadata assignment is not feasible, manual input by records professionals becomes necessary. This study aims to alleviate the challenges associated with manual entry by proposing a method that harnesses ChatGPT technology for extracting records management metadata elements. To employ ChatGPT technology, a Python program utilizing the LangChain library was developed. This program was designed to analyze PDF documents and extract metadata from records through questions, both with a locally installed instance of ChatGPT and the ChatGPT online service. Multiple PDF documents were subjected to this process to test the effectiveness of metadata extraction. The results revealed that while using LangChain with ChatGPT-3.5 turbo provided a secure environment, it exhibited some limitations in accurately retrieving metadata elements. Conversely, the ChatGPT-4 online service yielded relatively accurate results despite being unable to handle sensitive documents for security reasons. This exploration underscores the potential of utilizing ChatGPT technology to extract metadata in records management. With advancements in ChatGPT-related technologies, safer and more accurate results are expected to be achieved. Leveraging these advantages can significantly enhance the efficiency and productivity of tasks associated with managing records and metadata in archives.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.