• Journal of Climate Change Research
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• v.8 no.4
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• pp.297-303
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• 2017

Considering Japan's Greenhouse Gas (GHG) emissions reduction target for Fiscal Year (FY) 2030, the Joint Crediting Mechanism (JCM) was analyzed in order to estimate its significant contribution to Japan's Nationally Determined Contribution (NDC) and check its availability as a new mechanism to achieve Korea's 2030 mitigation target of 11.3% using carbon credits from international market mechanisms. The total budget for JCM Model Projects (1.2 billion JPY/yr) and JCM REDD+ Model Projects (0.8 billion JPY/yr), which are expected to deliver at least 50% of issued credits to Japan, is estimated about 21.6 billion JPY by the year 2030. This budget is about one third of the purchase of carbon credits from international carbon markets. So far, JCM credits of $378tCO_2-eq$. have been allocated to Japan, which are about 77% of the total issued credit through five-JCM Model Projects implemented from the year 2014. It is expected that Japan will obtain about $0.5MtCO_2-eq$. credits more from 100-ongoing JCM Projects, which are only 1% of Japan's NDC target through JCM credits. With regard to regular issued credits from implemented projects, expected new issued credits from pipeline projects and the less budget for JCM implementation as compared to purchasing carbon credits, JCM credits can be reached a resonable level of Japan's NDC target of $50{\times}100MtCO_2-eq$. through JCM until FY 2030.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.11-19
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• 2023

The paper proposes a smart mirror system that recommends fragrances based on user emotion analysis. This paper combines natural language processing techniques such as embedding techniques (CounterVectorizer and TF-IDF) and machine learning classification models (DecisionTree, SVM, RandomForest, SGD Classifier) to build a model and compares the results. After the comparison, the paper constructs a personal emotion-based fragrance recommendation mirror model based on the SVM and word embedding pipeline-based emotion classifier model with the highest performance. The proposed system implements a personalized fragrance recommendation mirror based on emotion analysis, providing web services using the Flask web framework. This paper uses the Google Speech Cloud API to recognize users' voices and use speech-to-text (STT) to convert voice-transcribed text data. The proposed system provides users with information about weather, humidity, location, quotes, time, and schedule management.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.69-78
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• 2024

Recently, consumers have shown an increasing tendency to seek information related to environmental, social, and governance (ESG) aspects in order to choose products with higher social value and environmental friendliness. In this paper, we proposes a product recommendation system applying ESG indicators tailored to the recent consumer trend of value-based consumption, utilizing a model called MultiSAGE that combines GraphSAGE and GAT. To achieve this, ESG rating data for 1,033 companies in 2022 collected from the Korea ESG Standard Institute and actual product data from N companies were transformed into a Heterogeneous Graph format through a data processing pipeline. The MultiSAGE model was then applied in machine learning to implement a recommendation system that, given a specific product, suggests eco-friendly alternatives. The implementation results indicate that consumers can easily compare and purchase products with ESG indicators applied, and it is anticipated that this system will be utilized in recommending products with social value and environmental friendliness.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.311-324
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• 2023

As an advanced study on the method of calculating the target revenue water ratio of local waterworks through the leakage component analysis method proposed by Kim et al. (2022), this study developed a model to calculate the achievable revenue water ratio within the specified project cost, the required project cost to achieve the specified target revenue water ratio, and the economically appropriate target revenue water ratio level by considering the leakage reduction cost and leakage reduction benefit for each revenue water ratio improvement strategy, and conducted an applicability evaluation of the developed model using actual field data. The procedure for calculating the target revenue water ratio of local waterworks considering economics proposed in this study consists of three stages: physical data linkage model construction, leakage component analysis, and economic analysis, and the applicability was evaluated for Zone H with branch type and the Zone M network type. As a result of the application, it was calculated that approximately 32.5 billion won would be required to achieve the target revenue water ratio of 70% in the Zone H, and approximately KRW 10.5 billion would be required to achieve the target revenue water ratio of 75% in the Zone M. If the business scale of Zones H and M was corrected to 10,000 m³/day of water usage, the required project cost for a 1% improvement in the revenue water ratio of Zone H was calculated to be 0.7642 billion won and 0.4715 billion won for Zone M.

• Journal of Korea Water Resources Association
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• v.57 no.8
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• pp.561-569
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• 2024

In this study, a damage estimation model for water distribution system was developed to quantitatively calculate the cumulative damage of water distribution system. And it was applied to real water distribution system to analyze the cumulative damage of water distribution system. To analyze the overall damage rate of the water distribution system, the cumulative damage analysis formula of individual pipes was established. And the aging index that affects the damage rate was analyzed using MCS (Monte Carlo Simulation), and Romanoff's measured data was used to calculate the thickness change due to corrosion. In addition, a cumulative damage estimation model was applied to unit network such as small and medium block network, and the cumulative damage of the unit network for up to 50 years was calculated. From the results, it was found that the cumulative damage rate is increased from 7% to 79% for the water distribution system of Naeduk 1-dong, Cheongju City, as the age of the pipeline is increased from 20 years to 50 years.

• Journal of Microbiology and Biotechnology
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• v.34 no.10
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• pp.2033-2040
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• 2024

Deep learning presents a promising approach to complex biological classifications, contingent upon the availability of well-curated datasets. This study addresses the challenge of analyzing three-dimensional protein structures by introducing a novel pipeline that utilizes open-source tools to convert protein structures into a format amenable to computational analysis. Applying a two-dimensional convolutional neural network (CNN) to a dataset of 12,143 avian influenza virus genomes from 64 countries, encompassing 119 hemagglutinin (HA) and neuraminidase (NA) types, we achieved significant classification accuracy. The pathogenicity was determined based on the presence of H5 or H7 subtypes, and our models, ranging from zero to six mid-layers, indicated that a four-layer model most effectively identified highly pathogenic strains, with accuracies over 0.9. To enhance our approach, we incorporated Principal Component Analysis (PCA) for dimensionality reduction and one-class SVM for abnormality detection, improving model robustness through bootstrapping. Furthermore, the K-nearest neighbor (K-NN) algorithm was fine-tuned via hyperparameter optimization to corroborate the findings. The PCA identified distinct clustering for pathogenic HA, yielding an AUC of up to 0.85. The optimized K-NN model demonstrated an impressive accuracy between 0.96 and 0.97. These combined methodologies underscore our deep learning framework's capacity for rapid and precise identification of pathogenic avian influenza strains, thus providing a critical tool for managing global avian influenza threats.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.361-371
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• 2000

A demand of marine outfall system has been much increased for the effective disposal of the wastewater due to population and industrial development at the coastal areas. The outfall system discharges primary or secondary treated effluent into the coastline, or at the deep water, or between these two. The discharge is carried out by constructing a pipeline on the sea bed with a diffuser or with a tunnel, risers and appropriate. The effluent, which has a density similar to that of fresh water, rises to the sea surface forming plume or jet, together with entraining the surrounding salt water and becomes very dilute. Thus there have been growing interests about plume behaviour around the outfall system. Plume or jet discharged from single-port or multi-port diffuser might cause certain impacts on coastal environment. Near field mixing characteristics of discharged water field using CORMIX model have been studied for effective outfall design various conditions on ambient current, depth, flow rate, effluent concentration, diffuser specification, port specification etc.. This kind of analysis is necessary to deal with water quality problems caused by the ocean discharge. The analyzed result was applied to the Pusan Jungang effluent outfall system plan.

• Structural Monitoring and Maintenance
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• v.3 no.4
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• pp.377-393
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• 2016

The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature ${\theta}=25^{\circ}C$, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.8
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• pp.1071-1080
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• 2003

A national standard system was developed in order to calibrate and test the oil flowmeters for the petroleum field. A stop valve and a gyroscopic weighing scale were employed for the primary standard of the flow quantity. It is operated by the standing start and finish mode and the static weighing method. The model equation for uncertainty evaluation was based on the calibration principle of standard system. The sources of the uncertainties were quantified and combined according to the GUM(Guide to the Expression of Uncertainty in Measurement). It was found that the standard system had the relative expanded uncertainty of 0.04 % in the range of 18 - 350 ㎥/ｈ. According to the uncertainty budget, the uncertainties of the fluid density and the volume of pipeline, which were temperature dependent, contributed 92% of final uncertainty in the oil flow standard system.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.247-250
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• 2008

Biomass gasification is a promising technology for producing a fuel gas which is useful for power generation systems. In biomass gasification processes, tar formation often causes some problems such as pipeline plugging. Thus, proper tar treatment is necessary. So far, nickel (Ni)-based catalysts have been intensively studied for the catalytic tar removal. However, the deactivation of Ni-based catalysts takes place because of coke deposition and sintering of Ni metal particles. To overcome these problems, we have been using ruthenium (Ru)-based catalyst for tar removal. It is reported by Okada et al., that a Ru/$Al_2O_3$ catalyst is very effective for preventing the carbon deposition during the steam reforming of hydrocarbons. Also, this catalyst is more active than the Ni-based catalyst at a low steam to carbon ratio (S/C). Benzene was used for the tar model compound because it is the main constituent of biomass tar and also because it represents a stable aromatic structure apparent in tar formed in biomass gasification processes. The steam reforming process transforms hydrocarbons into gaseous mixtures constituted of carbon dioxide ($CO_2$), carbon monoxide (CO), methane ($CH_4$) and hydrogen ($H_2$).