• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.161-168
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• 2023

This study attempted to determine the relationship between college students' motivation for major selection (personal motivation, social motivation), academic performance, and career maturity, and to identify the influencing factors of career maturity in order to provide basic data for improving career maturity. Data were collected through a structured questionnaire from 199 university students in C city. As a result of examining the correlation between personal motivation for major selection, social motivation, academic achievement, and career maturity, career maturity showed a significant positive correlation with personal motivation for major selection (r=.417, p=.00) and no significant correlation with social motivation for major selection and academic achievement. The influencing factors of career maturity were personal motivation for major selection, economic activity, and major department, and the explanatory power was 24%. Therefore, it seems that university-level support is needed to enable students to engage in economic activities in fields related to their majors. Since personal motivation is important in major selection, we should focus on increasing personal motivation for major selection by providing high school students with a wide range of opportunities, such as career experience and future work experience.

• Journal of the Korea Society for Simulation
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• v.32 no.4
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• pp.77-84
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• 2023

In the agricultural sector, power sources are being developed that use alternative energy sources such as electric tractors and hydrogen tractors, away from internal combustion engine tractors. As parts such as engines and transmissions used in conventional internal combustion engine tractors are replaced with motors and batteries, the center of gravity changes, and thus the risk of rollover should be considered. The purpose of this study is to analyze the overturn angle of the main parts of the electric tractor through dynamic simulation to minimize the overturn accident and to derive the optimal arrangement of parts to improve stability. A total of nine dynamics simulations were conducted by designing three components of the PTO motor, drive motor and the battery pack, and three factors of the arrangement method. As a result of the experiment, it was confirmed that Type3 Level3, in which the drive motor and the PTO motor are located at the front and rear of the tractor, and two battery packs are located in the middle of the tractor, has a high rollover angle. As a result of this study, the stability increased as the center of gravity was placed backward and located below. Future research needs to be done to find the optimal location of parts considering their performance and placement efficiency.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.218-225
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• 2023

An agrivoltaic system (AVS) is a combined system that generates power through photovoltaic panels (PVPs) installed above a field where a crop is cultivated. Although soil moisture is an important limiting factor for open-field crop production, particularly during spring season in Korea, it is not well considered in the utilization of AVS. Indeed, the application of water-energy-food nexus on the AVS should be necessary. In this study, the changes of soil moisture and temperature under the AVS was investigated in fallow paddy field during spring season. The AVS that has partial shading condition by PV panels was decreased soil temperature and increased soil moisture compared to open-field. Furthermore, the maximum of the change in soil moisture to the change in soil temperature had a negative correlation both on open-field and AVS under wet condition. It represents that the micro-climate under the AVS is in energy-limited condition. The open-field of relatively high soil temperature was in water-limited condition. The different behavior of soil moisture on the AVS should be considered for the sustainable agricultural system as related to water-energy-food nexus.

• Journal of the Korea Institute of Building Construction
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• v.23 no.6
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• pp.663-671
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• 2023

This research experimentally evaluated the recyclability of four varieties of lignocellulosic fly ash(FA), a by-product from three power plants employing lignocellulosic biomass(Bio-SRF, wood pellets) as a fuel source. Comprehensive analyses were conducted on FA, encompassing both physical parameters (particle shape, size distribution, fineness, and density) and chemical properties(chemical composition and heavy metal content). Mortar test specimens, with FA mixing ratios ranging from 5 to 20%, were produced in compliance with KS L 5405 standards, and their flow and compressive strength were subsequently measured. The test results indicated that the four types of FA exhibited particle sizes approximately between 20~30㎛, densities around 2.3~2.5g/cm³, and a fineness range of 2,600~4,900cm²/g. The FA comprised approximately 50~90% of components such as SiO₂, Al₂O₃, Fe₂O₃, and CaO, displaying characteristics akin to type-II and type-III FA of KS L 5405 standards, albeit with differences in chlorine and SiO₂ content. From the mortar tests, it was observed that the compressive strength of the mortar ranged between 34~47MPa when the pellet combustion FA was mixed in proportions of 5~20%. FA, produced exclusively from the combustion of 100% lignocellulosic fuel, is assessed to possess high recyclability potential as a substitute for conventional admixtures.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.61-77
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• 2024

The purpose of this study was to develop a method for rapidly diagnosing urban thermal comfort using Unmanned Aerial Vehicle (UAV) based data. The research was conducted at Changwon National University's College of Engineering site and Yongji Park, both located in Changwon, Gyeongsangnam-do. Baseline data were collected using field measurements and UAVs. Specifically, the study calculated field measurement-based thermal comfort indices PET and UTCI, and used UAVs to create and analyze vegetation index (NDVI), sky view factor (SVF), and land surface temperature (LST) images. The results showed that UAV-predicted PET and UTCI had high correlations of 0.662 and 0.721, respectively, within a 1% significance level. The explanatory power of the prediction model was 43.8% for PET and 52.6% for UTCI, with RMSE values of 6.32℃ for PET and 3.16℃ for UTCI, indicating that UTCI is more suitable for UAV-based thermal comfort evaluation. The developed method offers significant time-saving advantages over traditional approaches and can be utilized for real-time urban thermal comfort assessment and mitigation planning

• 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.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.91-98
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• 2024

With the advancement of ICT (Information and Communication Technology), searching for judgments through the internet has become increasingly convenient. However, predicting sentencing based on judgments remains a challenging task for individuals. This is because sentencing involves a complex process of applying aggravating and mitigating factors within the framework of legal provisions, and it often depends on the subjective judgment of the judge. Therefore, this research aimed to develop a model for predicting sentencing using artificial intelligence by focusing on structuring the data from judgments, making it suitable for AI applications. Through theoretical and statistical analysis of previous studies, we identified variables with high explanatory power for predicting sentencing. Additionally, by analyzing 50 legal judgments related to serious crimes that are publicly available, we presented a framework for extracting essential information from judgments. This framework encompasses basic case information, sentencing details, reasons for sentencing, the reasons for the determination of the sentence, as well as information about offenders, victims, and accomplices evident within the specific content of the judgments. This research is expected to contribute to the development of artificial intelligence technologies in the field of law in the future.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• Journal of Ecology and Environment
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• v.47 no.4
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• pp.272-281
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• 2023

Ecosystem observatories are burgeoning globally in an endeavour to detect national and global scale trends in the state of biodiversity and ecosystems in an era of rapid environmental change. In this paper we highlight the additional importance of regional scale outcomes of such infrastructure, through an introduction to the Great Western Woodlands TERN (Terrestrial Ecosystem Research Network) SuperSite, and key findings from three gradient plot networks that are part of this infrastructure. The SuperSite was established in 2012 in the 160,000 km² Great Western Woodlands region, in a collaboration involving 12 organisations. This region is globally significant for its largely intact, diverse landscapes, including the world's largest Mediterranean-climate woodlands and highly diverse sandplain shrublands. The dominant woodland eucalypts are fire-sensitive, requiring hundreds of years to regrow after fire. Old-growth woodlands are highly valued by Indigenous and non-Indigenous communities, and managing impacts of climate change and the increasing extent of intense fires are key regional management challenges. Like other TERN SuperSites, the Great Western Woodlands TERN SuperSite includes a core eddy-covariance flux tower measuring exchanges of carbon, water and energy between the vegetation and atmosphere, along with additional environmental and biodiversity monitoring around the tower. The broader SuperSite incorporates three gradient plot networks. Two of these represent aridity gradients, in sandplains and woodlands, informing regional climate adaptation and biodiversity management by characterising biodiversity turnover along spatial climate gradients and acting as sentinels for ecosystem change over time. For example, the sandplains transect has demonstrated extremely high spatial turnover rates in plant species, that challenge traditional approaches to biodiversity conservation. The third gradient plot network represents a 400-year fire-age gradient in Eucalyptus salubris woodlands. It has enabled characterisation of post-fire recovery of vegetation, birds and invertebrates over multi-century timeframes, and provided tools that are directly informing management to reduce stand-replacing fires in eucalypt woodlands. By building regional partnerships and applying globally or nationally consistent methodologies to regional scale questions, ecological observatories have the power not only to detect national and global scale trends in biodiversity and ecosystems, but to directly inform environmental decisions that are critical at regional scales.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.159-171
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• 2024

This study confirmed the antioxidant activity and antimicrobial efficacy and formulation stability for the effectiveness experiment of Rumex crispus. L root extract. For antioxidant activity, DPPH radical scavenging, FRAP activity, ABTS+ radical scavenging, and SOD-like activity were performed. Antimicrobial activity was evaluated for Staphylococcus epidermidis, Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli and Candida albicans strains. In addition, skin containing Rumex crispus. L root extract is checked over time for pH, temperature, and daylight for 21 days. As a result of antioxidant evaluation, it was confirmed that the activity increased in a concentration-dependent manner at a concentration of 0.0625-1 mg/mL. The clear zones of each bacterium at 100mg/mL concentrations were 10.45±0.34, 9.77±0.59, 9.92±0.22, and 10.08±0.12, which were superior to the control group Methyl paraben, and the antibacterial power of S. aureus and E. coli was confirmed at 100mg/mL concentration for MIC. There was little change in absorbance when the pH of the skin was 4.0, 6.0, and 7.0 and At 4℃, 25℃, and 40℃, it was discolored as the temperature increased. It was also observed that discoloration occurred when exposed to daylight. This is presumed to be able to prevent discoloration when it is shielded and stored at low temperatures. When the results of this study are summarized, Rumex crispus. L root extract is considered to have high value in use as a cosmetic raw material that can expect antioxidant and antibacterial activities.