• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.3
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• pp.159-169
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• 2007

Excessive input of organic matters from fish cage farms to the coastal waters has been considered as one of the major factors disturbing their benthic ecosystem. Sediment samples were taken from around the two fish cage zones (A and B) in Tongyeong coast in June and August 2003, to evaluate the ecological impacts of fish cage farming activity on the macrobenthic polychaete communities. Polychaete accounted for $81{\sim}87%$ of the total macrofauna individuals from each of the sampling stations. The number of species, abundance, diversity and dominant species of polychaete were rapidly changed with the distance from the fish cages. Within 10 m from the fish cages, Capitella capitata, which is a bio-indicator for the highly enriched sediments, was a dominant species and the lowest diversity was recorded. In particular, the maximum density (${\sim}18,410\;ind.m^2$) of C. capitata was found at Farm A where fish cages were more densely established within a semi-enclosed bay system. The sampling zone between 10 m and 15 m showed a rapid decrease of C. capitata with a rapid increase of the numbers of species, implying that this zone may be an ecotone point from a highly to a slightly enriched area. In the sampling zone between 15 m and 60 m, a transitional zone, which represents slightly enriched condition before normal one, was observed with additional increase and maintenance of the number of species and density of polychaete. In addition, the potential bio-indicators of organic enrichment, such as Lumbrineris longifolia and Aphelochaeta monilaris were the predominant species in the sampling zone. Multidimensional scaling (MDS) ordination plots and k-dominance curves confirmed the above results on the gradual changes in the macrobenthic polychaete communities. Our findings suggest that the magnitude of impact of fish cage farming activity on polychaete communities is probably governed by a distance from fish cage, density of fish cage and geomorphological characteristics around fish cage farm.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.110-126
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• 2024

This study conducted a classification of small-scale biological habitats created in Seoul to analyze and synthesize location characteristics, habitat structure, biological habitat functions, and threat factors of representative sites, as well as derive creation and management problems according to the ecological characteristics. The aim was to suggest improvement measures and management items. Data collected through a field survey was used to categorize 39 locations, and 8 representative sites were selected by dividing them into location, water system, and size as classification criteria for typification. Due to the characteristics of each type, the site was created in an area where amphibian movement was disadvantageous due to low or disconnected connectivity with the hinterland forest, and the water supply was unstable in securing a constant flow and maintaining a constant water depth. The habitat structure has a small area, an artificial habitat structure that is unfavorable for amphibians, having the possibility of sediment inflow, and damage to the revetment area. The biological habitat function is a lack of wetland plants and the distribution of naturalized grasses, and threats include the establishment of hiking trails and decks in the surrounding area. Artificial disturbances occur adjacent to facilities. When creating habitats according to the characteristics of each type, it was necessary to review the possibility of an artificial water supply and introduce a water system with a continuous flow in order to connect the hinterland forest for amphibian movement and locate it in a place where water supply is possible. The habitat structure should be as large as possible, or several small-scale habitats should be connected to create a natural waterfront structure. In addition, additional wetland plants should be introduced to provide shelter for amphibians, and facilities such as walking paths should be installed in areas other than migration routes to prevent artificial disturbances. After construction, the management plan is to maintain various water depths for amphibians to inhabit and spawn, stabilize slopes due to sediment inflow, repair damage to revetments, and remove organic matter deposits to secure natural grasses and open water. Artificial management should be minimized. This study proposed improvement measures to improve the function of biological habitats through the analysis of problems with previously applied techniques, and based on this, in the future, small-scale biological habitat spaces suitable for the urban environment can be created for local governments that want to create small-scale biological habitat spaces, including Seoul City. It is significant in that it can provide management plans.

• Journal of the Daesoon Academy of Sciences
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• v.48
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• pp.305-335
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• 2024

In the 7th and 8th centuries, Chinese Buddhism was at its peak, and major sects emerged and began to differ from one another in significant ways. This fact was also revealed through several versions of Biographies of the Eminent Monks and changes observable in the peculiarity of their formats. In the early 6th century, Huijiao (慧皎) compiled Gaosengzhuan (高僧傳, Biographies of Eminent Monks) which contains the history of Buddhism after it was introduced to China. At this time, he established a new format called the ten-subjects (十科). In 645, Daoxuan (道宣) used these ten-subjects as the basic framework to compile Xu-Gaosengzhuan (續高僧傳). However, by modifying and supplementing some parts of the ten-subjects, he standardized the ten-subjects into a format suitable for historiography. After the Xu-Gaosengzhuan, several versions of Biographies of the Eminent Monks were compiled in a format that further modified the ten-subjects. Fazang (法藏) wrote Huayanjing zhuanji (華嚴經傳記, 690?) which consisted of the ten-subjects, but the names and meanings of the subjects changed significantly to emphasize the Avatamsaka philosophy. Subsequently, while compiling Hongzan fahuazhuan (弘贊法華傳, 706?), Huixiang (惠詳) compiled a newly modified list of eight-subjects based on the ten-subjects of Gaosengzhuan and Xu-Gaosengzhuan. Sengxiang (僧詳) compiled Fahua xhuanji (法華傳記, 750?) in the format of twelve-subjects which added two new subjects to the ten-subjects of the Huayanjing zhuanji. These two formats focused on faith rather than philosophy. Even in the Chan (Zen) schools, a series of Biographies of the Eminent Monks was compiled from the beginning of the 8th century. Chuan fabaoji (傳法寶紀, 713?), Lengqui shiziji (楞伽師資記, 713?), Lidai fabaoji (歷代法寶記, 774), and Baolin zhuan (寶林傳, 801) are all examples of such compilations. However, the format of these four Biographies of the Eminent Monks was completely different from prior versions. Without setting any subjects, the authors established and described a dharma lineage transmitted continually from master to disciple. This is because Chan Buddhism does not rely on Buddhist texts but focuses on monks achieving realization through other means. At first, only the Chinese patriarchs were listed, but starting with Baolin zhuan, 27 patriarchs including Buddha and Kasyapa were included in the dharma lineage and presented as history. This fictional lineage was based on the need to secure sectarian superiority and legitimacy as Chan Buddhism flourished.

• The Mathematical Education
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• v.63 no.2
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• pp.255-272
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• 2024

This study aims to design mathematics-integrated classes that cultivate artificial intelligence (AI) thinking and to analyze students' AI thinking within these classes. To do this, four classes were designed through the integration of the AI4K12 Initiative's AI Big Ideas with the 2015 revised elementary mathematics curriculum. Implementation of three classes took place with 5th and 6th grade elementary school students. Leveraging the computational thinking taxonomy and the AI thinking components, a comprehensive framework for analyzing of AI thinking was established. Using this framework, analysis of students' AI thinking during these classes was conducted based on classroom discourse and supplementary worksheets. The results of the analysis were peer-reviewed by two researchers. The research findings affirm the potential of mathematics-integrated classes in nurturing students' AI thinking and underscore the viability of AI education for elementary school students. The classes, based on AI Big Ideas, facilitated elementary students' understanding of AI concepts and principles, enhanced their grasp of mathematical content elements, and reinforced mathematical process aspects. Furthermore, through activities that maintain structural consistency with previous problem-solving methods while applying them to new problems, the potential for the transfer of AI thinking was evidenced.

• The Journal of Engineering Geology
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• v.34 no.2
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• pp.279-294
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• 2024

Large-scale civil engineering structures such as dams require a systematic approach to jointed rock-mass grouting to prevent water leakage into the foundations and to ensure safe operation. In South Korea, rock grouting design often relies on the experience of field engineers that was gained in similar projects, highlighting the need for a more systematic and reliable approach. Rock-mass grouting is affected mainly by hydrogeology and the presence of discontinuities, involving factors such as the rock quality designation (RQD), joint spacing (Js), Lugeon value (Lu), and secondary permeability index (SPI). This study, based on data from field investigations of 14 domestic sites, analyzed the correlation between hydrogeological factors (Lu and SPI), discontinuity characteristics (RQD and Js), and grout take, and systematically established a design method for rock grouting. Analysis of correlation between the variables RQD, Js, Lu, and SPI yielded Pearson correlation (r) values as follows: Lu-SPI, 0.92; RQD-Lu, -0.75; RQD-Js, 0.69; RQD-SPI, -0.65; Js-Lu, -0.47; and SPI-Js, -0.41. The grout take increases with Lu and SPI values, but there is no significant correlation between RQD and Js. The proposed approach for grouting design based on SPI values was verified through analysis and comparison with actual curtain-grouting construction, and is expected to be useful in practical applications and future studies.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Korean Journal of Ecology and Environment
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• v.57 no.2
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• pp.102-110
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• 2024

This study presents a fine scale distribution of the endangered species, Odontobutis obscura, through field surveys and literature reviews. Using the mark-recapture method, the population size in major habitats was determined. Field surveys conducted on 18 streams in Geoje Island revealed that the O. obscura was only found in the main streams and tributaries of the Sanyang, Gucheon, and Buchun Streams, which are part of the Sanyang Stream watershed. The O. obscura exhibited relative abundances ranging from 0.5% to 35.3% at different locations, with certain spots showing higher relative abundances (18.8% to 35.3%), indicating major habitat areas. A review of six literature studies confirmed the presence of the O. obscura, although there were differences in occurrence status depending on the purpose, scope, and duration of the studies. Combining the results of field and literature surveys, it was found that the O. obscura inhabits the main and tributary streams of the Sanyang, Gucheon, and Buchun Streams, from the upper to lower reaches. Currently, the O. obscura population in the Sanyang Stream watershed maintains a stable habitat, but its limited distribution range suggests potential issues such as genetic diversity deficiency in the long term. The population size of the O. obscura was confirmed at two specific locations, with densities of 0.5 to 1.5 individuals per m². The average movement distance from the release point was 13.1 m, indicating the limited mobility characteristic of ambush predators. Understanding the distribution and population size of endangered species is the first step towards their conservation and protection. Based on this information, further research could significantly contribute to the conservation of the O. obscura.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.4
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• pp.18-31
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• 2024

This study examines the inscription trends and characteristics of 121 cultural landscapes inscribed on the UNESCO World Heritage List to gain a comprehensive understanding of their inherent values and attributes. By employing a dual methodology involving descriptive statistical analysis and in-depth case studies, this research investigates the geographical distribution, temporal inscription patterns, selection criteria, and typologies of these landscapes. The data for this study were collected from official documents and databases available on the UNESCO World Heritage Center website, ensuring the reliability and authenticity of the information. The analysis reveals that cultural landscapes are predominantly concentrated in Europe and Asia, with a steady increase in inscriptions since 1992. These landscapes are primarily recognized for their uniqueness in reflecting human-nature interactions, as well as the importance of traditional culture and land-use practices, resulting in their inscription mainly under criteria (iv), (iii), (v), and (ii). Furthermore, cultural landscapes can be broadly categorized into three types: designed landscapes, organically evolved landscapes, and associative landscapes. Among these, organically evolved landscapes, formed through long-term interactions between human activities such as agriculture and industry and the natural environment, constitute a significant proportion. These findings suggest that UNESCO World Heritage cultural landscapes possess a complex value system encompassing nature and culture, tangible and intangible elements, and material and non-material aspects. This necessitates a fundamental shift in the perception and preservation approaches to cultural heritage, requiring an integrated approach that emphasizes the overall context rather than individual elements and focuses on the dynamic process of landscape evolution itself. Moreover, cultural landscapes have the potential to contribute to sustainable development models by fostering regional identity, strengthening community resilience, and promoting sustainable economic growth. Therefore, the preservation and management of cultural landscapes require a perspective that holistically views the dynamic evolution process of the landscape and a governance system based on the active participation of local communities and stakeholders. This study contributes to enhancing the in-depth understanding of the characteristics and values of cultural landscapes and provides a foundation for the selection and management of future cultural landscape heritage sites.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.