• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.374-374
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• 2023

Groundwater is the main source of water on which relies many countries in case of emergency, this is the case of Japan in 2011 after the great Sendai Earthquake. This important resource is found to be heavily influenced by human induced factors such as wetland area reduction. For groundwater sustainable management in perfect cohesion with wetland it is important to understand the relationship between both resources. Wetlands have a strong interaction with both groundwater and surface water, influencing catchment hydrology and water quality. Quantifying groundwater-wetland interactions can help better identify locations for wetlands restoration and/or protection. This study uses observation data from piezometers and wetland to study the qualitative and quantitative aspects of the correlation. Groundwater level, wetland area, chemical, organic and inorganic contaminants are the important parameters used. the results proved that few contaminants in the wetland are found in groundwater and in general the wetland quality does not affect that much groundwater quality. The strong linear relationship found between wetland water level and nearest groundwater level proved that, in term of quantity, groundwater and wetland are strongly correlated. While wetland becoming dry, groundwater level has dropped in the region about 0.52m. The area of wetland was found to be lightly correlated with groundwater level, proving that wetlands dry has contributed to groundwater level declining. This study has showed that whilst rainfall variability contributed to the decline and loss of wetlands, the impacts from landuse changes and groundwater extraction were likely to be significant contributors to the observed losses.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.1
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• pp.2-12
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• 2023

The current study aimed to explore the types and frequencies of uncommon complications associated with third molar extractions based on a scoping review of case reports and case series. The study used an electronic literature search based on PubMed and Embase up to March 31, 2020, with an update performed on October 22, 2021. Any case reports and case series that reported complications associated with third molar extractions were included. The types of complications were grouped and the main symptoms of each type of complication were summarized. A total of 51 types of uncommon complications were identified in 248 patients from 186 studies. Most types of complications were post-operative. In the craniofacial and cervical regions, the most frequent complications included iatrogenic displacement of the molars or root fragments in the craniofacial area, late mandibular fracture, and subcutaneous emphysema. In other regions, the most frequent complications include pneumomediastinum, pneumorrhachis, pneumothorax, and pneumopericardium. Of the patients, 37 patients had life-threatening uncommon complications and 20 patients had long-term/irreversible uncommon complications associated with third molar extractions. In conclusion, a variety of uncommon complications associated with third molar extractions were identified. Most complications occurred in the craniofacial and cervical regions and were mild and transient.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.1-6
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• 2023

Morphological analysis is a branch of natural language processing, is now a rapidly growing field. The fundamental tenet of morphological analysis is that it can establish the roots or stems of words and enable comparison to the original term. Arabic is a highly inflected and derivational language and it has a strong structure. Each root or stem can have a large number of affixes attached to it due to the non-concatenative nature of Arabic morphology, increasing the number of possible inflected words that can be created. Accurate verb recognition and extraction are necessary nearly all issues in well-known study topics include Web Search, Information Retrieval, Machine Translation, Question Answering and so forth. in this work we have designed and implemented an algorithm to detect and recognize Arbic Verbs from Arabic text.The suggested technique was created with "Python" and the "pyqt5" visual package, allowing for quick modification and easy addition of new patterns. We employed 17 alternative patterns to represent all verbs in terms of singular, plural, masculine, and feminine pronouns as well as past, present, and imperative verb tenses. All of the verbs that matched these patterns were used when a verb has a root, and the outcomes were reliable. The approach is able to recognize all verbs with the same structure without requiring any alterations to the code or design. The verbs that are not recognized by our method have no antecedents in the Arabic roots. According to our work, the strategy can rapidly and precisely identify verbs with roots, but it cannot be used to identify verbs that are not in the Arabic language. We advise employing a hybrid approach that combines many principles as a result.

• Smart Media Journal
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• v.12 no.5
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• pp.28-35
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• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• The Journal of the Acoustical Society of Korea
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• v.27 no.4
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• pp.200-206
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• 2008

This paper presents a method of quantization to extract quantization parameters using the straight-line and DCT (Discrete Cosine Transform) for two splited frequency bands. As the number of harmonic is variable frame to frame, harmonics in low frequency band is oversampled to fix the dimension and straight-lines present a spectral envelope, then the discontinuous points of straight-lines in low frequency is sent to quantizer. Thus, extraction of quantization parameters using the straight-line provides a fixed dimension. Harmonics in high frequency use variable DCT to obtain quantization parameters and this paper proposes a method of quantization combining the straight-line with DCT. The measurement (If proposed method of quantization uses spectral distortion (SD) for spectral magnitudes. As a result, The proposed method of quantization improved 0.3dB in term of SD better than HVXC.

• Journal of Veterinary Clinics
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• v.29 no.6
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• pp.474-482
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• 2012

Elongation of cheek teeth roots in pet rabbits is very common, and is associated with malocclusion followed by reduced appetite, salivation, periapical abscess, and epiphora. Conservative methods including medication, drainage, irrigation, tooth trimming, intraoral and extraoral extraction, surgical treatment of periapical abscessation, and diet control have been adapted as the only treatments to resolve elongation of teeth roots. However, remaining challenges include the long-term period of cure and recurrence. This study was performed to investigate the possibility of nonvital pulp therapy on elongation of the mandibular cheek teeth roots in pet rabbits. Thirty-one pet rabbits with dental problems due to root elongation were submitted. Ten pet rabbits among them were treated by nonvital pulp therapy procedures (group A), while the others were treated by conservative methods (group B). Appetite improved within 1-5 days after nonvital pulp therapy and the treatment was discontinued 1 month postoperatively in group A. Abscess occurred in another site not treated with nonvital pulp therapy in only two rabbits. Growth of the mandibular cheek teeth treated with nonvital pulp therapy stopped, resulting in malocclusion, intraoral inflammation from the enamel spur, and abscess of the teeth roots. In the group B rabbits treated with conservative therapy, partial drainage, long term medication, recurrent oral trimming and control of repeated oral inflammation occurred. Consequently, buccotomy or tooth extraction was performed in group B. Owners were satisfied with nonvital pulp therapy preventing dental root abscess and repeated troubles including inflammation and malocclusion and reduction of the treatment period. These results suggest that nonvital pulp therapy can be performed on pet rabbits with elongation of mandibular cheek teeth roots.

• Economic and Environmental Geology
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• v.48 no.4
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• pp.337-349
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• 2015

This study has focused on investigation of correlation for As and heavy metals in paddy soil and rice crops sampled in the vicinity of the abandoned Munmyung Au-Ag mine. Soil samples extracted by various methods including aqua regia, 1 M $MgCl_2$, 0.01 M $CaCl_2$ and 0.05 M EDTA were analyzed for As and heavy metals (Cd, Cu, Pb and Zn). Rice grain samples grown on the soils were also analyzed for the same elements to evaluate the relationships between soils and rice crops. According to soil extraction methods, As and heavy metal contents in the soils were decreased in the order of aqua regia > 0.01 M $CaCl_2$ > 1 M $MgCl_2$ > 0.05 M EDTA. In addition to correlation analysis, statistically significant correlation with the four extraction methods (p<0.01) were found in the soil and rice samples. As calculation of biological accumulation coefficients (BACs) of the rice crops for As and heavy metals, the BACs for Cd, Zn and Cu were relatively higher than those for As and Pb. This study also carried out a stepwise multiple linear regression analysis to identify the dominant factors influencing metal extraction rates of the paddy soils. Furthermore, daily intakes of As and heavy metals from regularly consumed the rice grain (287 g/day) grown on the contaminated soils by the mining activities were estimated, and found that Cd and As intakes from the rice reached up to 73.7% and 51.8% for maximum allowance levels of trace elements suggested by WHO, respectively. Therefore, long-term consumption of the rice poses potential health problems to residents around the mine, although no adverse health effects have yet been observed.

• Korean Journal of Environmental Agriculture
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• v.34 no.3
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• pp.161-167
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• 2015

BACKGROUND: For the heavy metal cotamination sites, it is very important to estimate the human bioavailability quotients for heavy metals in paddy soils released from mine tailings, which is a major source of contamination in Korea, and to assess the human health risks of heavy metals. METHODS AND RESULTS: This experiment was carried out to investigate the human bioavailability quotient of the heavy metals in paddy soils below part of the closed metalliferous mine. For estimating the human bioavailability quotients for heavy metals, 30 paddy soils below part of the closed mine were collected, and analyzed for Cd, Cu, Pb, Zn, and As using simple bioavailability extraction test(SBET). The quantities of Cd, Cu, Pb, Zn and As extracted from paddy soils below part of the mine by using the SBET analysis were 28.1, 17.3, 34.1, 14.6 and 2.3% respectively. Specially, the maximum values of Cd, Pb and Zn were 73.3, 81.5 and 58.1% of human bioavailability quotient, respectively, and varied considerably among the sampling sites. The human bioavailability quotient of Cd, Cu, Pb and Zn in soils near the closed mine showed significant positive correlation among soil pH value, O.M. and Ex. Ca. contents, while it correlated negatively between soil Ex. K and Ex. Mg contents in paddy soils. Also, its of Cd, Cu, Pb and Zn in paddy soils showed significant positive correlation with 0.1M HCl extractable and total contents, while in soils, it correlated negatively with As content in soil near the closed mine. CONCLUSION: The results of the simple bioavailability extraction test (SBET) indicate that regular ingestion of soils by the local population could be closed a potential health threat due to long-term heavy metals exposure in these mine areas.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.