• Korean Journal of Radiology
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• v.22 no.9
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• pp.1497-1513
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• 2021

The diagnostic and treatment methods of multiple myeloma (MM) have been rapidly evolving owing to advances in imaging techniques and new therapeutic agents. Imaging has begun to play an important role in the management of MM, and international guidelines are frequently updated. Since the publication of 2015 International Myeloma Working Group (IMWG) criteria for the diagnosis of MM, whole-body magnetic resonance imaging (MRI) or low-dose whole-body computed tomography (CT) and ¹⁸F-fluorodeoxyglucose positron emission tomography/CT have entered the mainstream as diagnostic and treatment response assessment tools. The 2019 IMWG guidelines also provide imaging recommendations for various clinical settings. Accordingly, radiologists have become a key component of MM management. In this review, we provide an overview of updates in the MM field with an emphasis on imaging modalities.

• Nuclear Engineering and Technology
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• v.56 no.10
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• pp.4127-4133
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• 2024

Noise reduction in low-dose positron emission tomography (PET) is a well-researched topic aimed at reducing patient radiation doses and improving diagnosis. Software-based noise reduction mainly improves the contrast between regions by reducing the variation of the acquired image. However, it should be performed under appropriate parameters to reduce discrimination. We propose a method that derives optimal noise-reduction parameters using the multi-scale structural similarity index measure and visual information fidelity, which are metrics for image quality assessment. Simulation and experimental studies demonstrated the viability of the proposed algorithm. The contrast-to-noise ratio value of the denoised reconstruction slice, which was used as the optimal parameter, increased approximately three times compared to that of the low-dose slice while preserving the resolution. The results indicate that the proposed method successfully predicted the parameters according to the noise-reduction algorithm and PET system conditions in the sinogram domain. The proposed algorithm should help prevent misdiagnosis and provide standardized medical images for clinical application by performing appropriate noise reduction.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

• Proceedings of the Korean Environmental Health Society Conference
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• 2003.06a
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• pp.89-93
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• 2003

Daily average concentrations of fine particulates have been measured simultaneously in Seoul and Asan area by using PM minivolTM portable air sampler(Air Metrics, U.S.A) from September 2001 to August 2002. The sampler were analyzed by ICP-OES(inductively coupled plasma optical emission spectrometry, optima 3000DV, Perkin Elmor) to determine the fine particulate concentrations of metallic elements(As, Mn. Ni, Fe, Cr, Cu, Cd, Pb, Zn, Si). The concentration of PM$\sub$2.5/ showed a high trend in the Seoul area. Zn showed a similar distribution ratio for the fine particle in both Seoul and Asan. Mn and Fe, Cr, Cd are highly correlated in the Seoul and Asan area(P<0.05).

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.109-113
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• 2016

Techno-economic evaluation of Non-Capture $CO_2$ Utilization (NCCU) technology for the production of high-value-added products using greenhouse gas ($CO_2$) was performed. The general scheme of NCCU process is composed of $CO_2$ carbonation and brine electrolysis process. Through a carbonation reaction with sodium hydroxide that is generated from brine electrolysis and $CO_2$ of the flue gas, it is possible to get high-value-added products such as sodium bicarbonate, sodium hydroxide, hydrogen & chloride and also to reduce the $CO_2$ emission simultaneously. For the techno-economic study on NCCU technology, continuous operation of bench-scale facility which could treat $2kgCO_2/day$ was performed. and based on the key performance data evaluated, the economic evaluation analysis targeted on the commercial chemical plant, which could treat 6 tons $CO_2$ per day, was performed using the net present value (NPV) metrics. The results showed that the net profit obtained during the whole plant operation was about 7,890 mKRW (million Korean Won) on NPV metrics and annual $CO_2$ reduction was estimated as about $2,000tCO_2$. Also it was found that the energy consumption of brine electrolysis is one of the key factors which affect the plant operation cost (ex. electricity consumption) and the net profit of the plant. Based on these results, it could be deduced that NCCU technology of this study could be one of the cost-effective $CO_2$ utilization technology options.

• Biomedical Science Letters
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• v.24 no.4
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• pp.418-425
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• 2018

Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the ${\beta}$-Amyloid ($A{\beta}$) deposition. We designed a convolutional neural network (CNN) model that predicts the $A{\beta}$-positive and $A{\beta}$-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the $A{\beta}$-positive and $A{\beta}$-negative status.

• New & Renewable Energy
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• v.18 no.2
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• pp.60-72
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• 2022

The environmental, social, and governance (ESG) score is gaining recognition as important nonfinancial investment criteria. With climate change emerging as a global issue, energy companies must pay attention to the ESG impact on corporate performance. In this study, the ESG impact on the performance of energy companies was analyzed based on 23 companies selected from the S&P 500. The panel corrected standard error methodology was used. The Refinitiv ESG score was the independent variable, and financial performance metrics, such as Tobin's Q, return on assets, and return on equity, were the dependent variables. It was found that the ESG score is positively associated with long-term corporate value but not with short-term profitability in the electricity utility industry. Among the subcategories of ESG, the environmental and social scores also showed positive correlations with long-term corporate value. A direct incentive policy is recommended that can offset expenses for ESG activities to reduce carbon emission in the energy sector.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.