• Advanced Industrial SCIence
• /
• v.3 no.2
• /
• pp.17-22
• /
• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.27 no.1
• /
• pp.17-29
• /
• 2024

This study was conducted to select the restoration priority of forest damage sites in Gangwon Province. We first identified the status of damaged areas. We then selected restoration evaluation indicators through a literature review. We then set weights for these indicators through expert surveys. We next acquired data that can represent these indicators and spatially mapped them. Finally, we prioritized the restoration target sites by taking the weights. The results of the study showed that disaster sensitivity and ecologicality are important criteria for selecting the restoration priority of damage sites. The analysis showed that damage sites in Doam, Jeongseon, Samcheok and Inje are in urgent need of restoration. The results of this study are significant in that they selected the restoration priority of damage sites in Gangwon Province based on the restoration priority evaluation criteria selected based on expert surveys. However, the priority restoration areas derived from the results of this study are not actually implementing restoration projects at present. Therefore, it is judged that it would be efficient in various aspects to establish the restoration priority area based on scientific analysis techniques and carry out the project for efficient implementation of the restoration project. In this study, it can be pointed out that the priority of restoration of damage sites was derived based on data from the past due to the limitation of data acquisition. However, the fact that the priority restoration area inferred based on past data has been restored over time has improved the reliability of the study by verifying the usefulness of the priority extraction technique. In the future, if the priority of damage sites is extracted by extracting the restoration target area boundary through the latest data based on the methodology applied in this study, it is considered that it will be available as a result that can be applied to the field.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.50 no.3
• /
• pp.123-133
• /
• 2024

Dentoalveolar (DA) trauma, which can involve tooth, alveolar bone, and surrounding soft tissues, is a significant dentofacial emergency. In emergency settings, physicians might lack comprehensive knowledge of timely procedures, causing delays for specialist referral. This systematic review assesses the literature on isolated DA fractures, emphasizing intervention timing and splinting techniques and duration in both children and adults. This systematic review adhered to PRISMA guidelines and involved a thorough search across PubMed, Google Scholar, Semantic Scholar, and the Cochrane Library from January 1980 to December 2022. Inclusion and exclusion criteria guided study selection, with data extraction and analysis centered on demographics, etiology, injury site, diagnostics, treatment timelines, and outcomes in pediatric (2-12 years) and adult (>12 years) populations. This review analyzed 26 studies, categorized by age into pediatrics (2-12 years) and adults (>12 years). Falls were a common etiology, primarily affecting the anterior maxilla. Immediate management involved replantation, repositioning, and splinting within 24 hours (pediatric) or 48 hours (adult). Composite resin-bonded splints were common. Endodontic treatment was done within a timeframe of 3 days to 12 weeks for children and 2-12 weeks for adults. Tailored management based on patient age, tooth development stage, time elapsed, and resource availability is essential.

• Journal of Advanced Navigation Technology
• /
• v.28 no.2
• /
• pp.238-245
• /
• 2024

In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.

• Journal of Korean Society for Quality Management
• /
• v.52 no.2
• /
• pp.185-199
• /
• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Economic and Environmental Geology
• /
• v.57 no.4
• /
• pp.371-386
• /
• 2024

This paper presents analytical insights regarding into the occurrence of gold within organic matter, which is hosted by solid bitumen and closely associated with uranium ores in the Late Permian Kővágószőllős Sandstone Formation in Western Mecsek, South-West Hungary. The study utilizes a range of analytical techniques, including X-ray powder diffraction (XRPD) and wavelength dispersive X-ray fluorescence (WD-XRF) for comprehensive mineralogical and elemental analysis; organic petrography and electron microprobe analysis for characterizing organic matter; and an organic elemental analyzer for identifying organic compounds. A three-step sequential extraction method was used to liberate gold from organic matter and sulfide minerals, employing KOH, HCl, and aqua regia, followed by inductively coupled plasma optical emission spectroscopy (ICP-OES) to quantify gold contents. The organic matter is identified as comprising two vitrinite types (telinite V1 and reworked V2) and three solid bitumen forms: nonfluorescing (B1) and fluorescing (B2) fillings within the V1, as well as homogenous pyrobitumen (PB) occupying narrow cracks and voids within globular quartz. Despite the samples exhibiting low total organic carbon content (<1 wt%), they display high sulfur content (up to 6 wt%) and the sequentially extracted noble metal content from the organic matter is found to total 7.45 ppm gold. The research findings suggest that organic matter plays crucial roles in ore mineralization processes. Organic matter acts as an active component in the migration of gold, uranium, and hydrocarbons within sulfur-rich hydrothermal fluids. Additionally, organic matter contributes to the entrapment and enrichment of gold in hetero-atomic organic fractions, forming metal-organic compounds. Moreover, uranium inclusions are observed as oxide/phosphate minerals within solid bitumen and associated vitrinite particles. These insights into the occurrence and distribution of gold within organic matter highlight substantial exploration potential, guiding additional research activities focused on organic matter within the Kővágószőllős Sandstone Formation at the Western Mecsek deposit.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1505-1514
• /
• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.408-415
• /
• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.

• Economic and Environmental Geology
• /
• v.35 no.4
• /
• pp.325-337
• /
• 2002

We examined the contamination of stream water and stream sediments by heavy metal elements with respect to distance from the abandoned Backun Au-Ag-Cu mine. High contents of heavy metals (Pb, Zn, Cu, Cd, Mn, and Fe) and aluminum in the waters connected with mining and associated deposits (dumps, tailings) reduce water quality. In the mining area, Ca and SO$_4$ are predominant cation and anion. The mining water is Ca-SO$_4$ type and is enriched in heavy metals resulted from the weathering of sulfide minerals. This mine drainage water is weakly acid or neutral (pH; 6.5-7.1) because of neutralizing effect by other alkali and alkaline earth elements. The effluent from the mine adit is also weakly acid or neutral, and contains elevated concentrations of most elements due to reactions with ore and gangue minerals in the deposit. The concentration of ions in the Backun mining water is high in the mine adit drainage water and steeply decreased award to down stream. Buffering process can be reasonably considered as a partial natural control of pollution, since the ion concentration becomes lower and the pH value becomes neutralized. In order to evaluate mobility and bioavailability of metals, sequential extraction was used for stream sediments into five operationally defined groups: exchangeable, bound to carbonates, bound to FeMn oxide, bound to organic matter, and residual. The residual fraction was the most abundant pool for Cu(2l-92%), Zn(28-89%) and Pb(23-94%). Almost sediments are low concentrated with Cd(2.7-52.8 mg/kg) than any other elements. But Cd dominate with non stable fraction (68-97%). Upper stream sediments are contaminated with Pb, and down area sediments are enriched with Zn. It is indicate high mobility of Zn and Cd.

• Journal of Korean Society of Environmental Engineers
• /
• v.31 no.10
• /
• pp.845-854
• /
• 2009

There has been increased concern regarding the release of antibiotics to different environmental compartments due to the possibility of the development of antibiotic resistant bacteria. However, limited information is available regarding the occurrence, fate, and transport of antibiotics in Korea in both the aqueous phase and in solid phases such as sediment and soil. Therefore, this study was conducted to monitor the concentration of released antibiotics in surface water, sediment, and soil adjacent to a cattle manure composting facility in Korea. Specifically, the following six antibiotics were monitored: tetracycline (TC), chlortetracycline (CTC), oxytetracycline (OTC), sulfamethazine (SMT), sulfamethoxazole (SMX), and sulfathiazole (STZ). To extract and quantify the antibiotics from different environmental compartments, solid phase extraction (SPE) and high performance liquid chromatography mass spectrometry (HPLC/MS) techniques were adopted. The concentration of the six antibiotics ranged from below the detection limit (BDL) to 0.71 ${\mu}g$/L in surface water, from BDL to 27.61 ${\mu}g$/L in sediment, and from 0.12 to 157.33 ${\mu}g$/L in soil. In addition, higher concentrations of antibiotics were observed in surface water and sediment at locations closer to the composting facility indicating that composting is the source of the antibiotics found in the environment. Furthermore, higher concentrations of antibiotics were observed in the solid phase (sediment and soil) than the aqueous phase. These findings indicate that the possibility of antibiotic resistant bacteria is increased because such bacteria are more stable in the solid phase. Overall, longterm monitoring of the aqueous phase and solid phase is necessary to gain a better understanding of the impact of antibiotics from source on the environment in Korea.