• Economic and Environmental Geology
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• v.42 no.5
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• pp.457-469
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• 2009

Applicability of bioleaching techniques using a sulfur-oxidizing bacteria, Acidithiobacillus thiooxidans, for remediation of shooting range soil contaminated with toxic heavy metals was investigated. The effects of sulfur concentration, the amount of bacterial inoculum and operation temperature on the efficiency of heavy metal solubilization were examined as well. As sulfur concentration and the amount of bacterial inoculum increased, the solubilization efficiency slightly increased; however, significant decrease of heavy metal extraction was observed with no addition of sulfur or bacterial inoculum. Bacteria solubilized the higher amount of heavy metals at $26^{\circ}C$ than $4^{\circ}C$. Lead showed the highest removal amount from the contaminated soil but the lowest removal efficiency when compared with Zn, Cu and Cr. It was likely due to formation of insoluble $PbSO_{4(s)}$ as precipitate or colloidal suspension. Sequential extraction of the microbially treated soil revealed that the proportion of readily extractable phases of Zn, Cu and Cr increased by bacterial leaching, and thus additional treatment or optimization of operation conditions such as leaching time were required for safe reuse of the soil. Bioleaching appeared to be a useful strategy for remediation of shooting range soil contaminated with heavy metals, and various operating conditions including concentration of sulfur input, inoculum volume of bacteria, and operation temperature exerted significant influence on bioleaching efficiency.

• Journal of radiological science and technology
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• v.25 no.2
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• pp.57-64
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• 2002

The recent development of embodiment technology of the medical images makes most medical institutions introduce PACS(Picture Archiving and Communication System) in haste. However lots of PACS solutions, currently developed and distributed, haven't been able to serve the convenience of users and to satisfy user's demand because of economic limitations and administrator-oriented considerations in the process of development. So we have developed MIH(Medical Image History), by which we can search and refer to the patient's medical images and information with few restrictions of time and space for diagnosis and treatment. This program will contribute to the improvement in the medical environment and meet the clients' need. We'll make more effort to develop the application which insures the better quality of medical images. MIH manages the patient's image files and medical records like film chart in connection with time. This trial will contribute to the reduction of the economical loss caused by unnecessary references and improve the quality in the medical services. The demand on the development or the program which refers to the medical +ata quickly and keeps them stable will be continued by the medical institute. This will satisfy the client's demand and improve the service to the patients in that tile program will be modified from the standpoint of the users. MIH is trying to keep user-oriented policy and to apply the benefit of the analog system to the digital environment. It is necessary to lead the public to the better understanding that the systematic management and referring of the medical images is as important as the quality of the images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.175-182
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• 2017

In light emitting diode (LED) chip packaging, wire bonding is an important process that connects the LED chip on the lead frame pad with the Au wire and enables electrical operation for the next process. The wire bonding process is divided by two types: thermo compression bonding and ultrasonic bonding. Generally, the wire bonding process consists of three steps: 1st ball bonding that bonds the shape of the ball on the LED chip electrode, looping process that hangs the wire toward another connecting part with a loop shape, and 2nd stitch bonding that forms and bonds to another electrode. This study analyzed the factors affecting the LED die bonding processes to optimize the process capability that bonds a small Zener diode chip on the PLCC (plastic-leaded chip-carrier) LED package frame, and then applied response surface analysis. The design of experiment (DOE) was established considering the five factors, three levels, and four responses by analyzing the factors. As a result, the optimal conditions that meet all the response targets can be derived.

• Resources Recycling
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• v.31 no.4
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• pp.12-18
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• 2022

Korea depends on the import of raw materials such as iron ore and coal for the steel industry. These raw materials have a major impact on the cost, productivity, and quality competitiveness in the global steel industry. To secure the competitiveness of steel companies, it is necessary to reduce the country's dependence on raw materials. This can be achieved using byproducts with a high Fe content, which are primarily generated by the steel industry. These byproducts are available in the form of a very fine powder, which can disperse as dust when used directly in plant processes. Dust dispersion has a negative impact on the environment and can lead to the loss of raw materials. To enable the use of a wide range of Fe-containing byproducts, it is necessary to pretreat them in the form of larger aggregates such as pellets and briquettes. There are several methods to achieve such aggregates. There are two ways to produce briquettes: using a hot briquette, which supplies additional heat to produce briquettes, or using a cold briquette, which does not use heat. A method for producing cold briquettes using Fe-containing byproducts was investigated in this study. The yield ratio and briquette strength were examined under various manufacturing conditions.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.209-235
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• 2022

Starting from 2010, blockchain technology, along with the development of artificial intelligence, has been in the spotlight as the latest technology to lead the 4th industrial revolution. Furthermore, previous research regarding blockchain's technological applications has been ongoing ever since. However, few studies have been examined the standards for classifying the blockchain economic ecosystem from a capital market perspective. Our study is classified into a collection of interviews of software developers, entrepreneurs, market participants and experts who use blockchain technology to utilize the blockchain economic ecosystem from a capital market perspective for investing in stocks, and case study methodologies of blockchain economic ecosystem according to application fields of blockchain technology. Additionally, as a way that can be used in connection with equity investment in the capital market, the blockchain economic ecosystem classification methodology was established to form an investment universe consisting of global blue-chip stocks. It also helped construct an intelligent portfolio through quantitative and qualitative analysis that are based on quant and artificial intelligence strategies and evaluate its performances. Lastly, it presented a successful investment strategy according to the growth of blockchain economic ecosystem. This study not only classifies and analyzes blockchain standardization as a blockchain economic ecosystem from a capital market, rather than a technical, point of view, but also constructs a portfolio that targets global blue-chip stocks while also developing strategies to achieve superior performances. This study provides insights that are fused with global equity investment from the perspectives of investment theory and the economy. Therefore, it has practical implications that can contribute to the development of capital markets.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

• Convergence Security Journal
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• v.22 no.4
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• pp.35-44
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• 2022

Among the five promotion strategies of Defense Innovation 4.0(DI 4.0), the military structure/operation optimization strategy aims to innovate the military structure based on advanced science&technology(S&T), and to integrate advanced S&T in the field of defense operation such as education&training and human resource development. As the future battlefield expands to AI-based unmanned/robot combat systems, space, cyberspace, and electromagnetic fields, it is necessary to train officers with the capabilities required in these battlefields. It is necessary to develop capabilities from junior officers who will lead the future battlefield to operating core advanced power based on the 4^th industrial revolution S&T. We review the education system of the military in universities and propose a method of redesigning the education system that is compatible with DI 4.0 and can develop technology-intensive capabilities based on advanced S&T. We propose a operation plan of major and extra-programs that can develop the capabilities of junior officers required for the future battlefield, and also suggest ways to support the army's practical training.

• Journal of Powder Materials
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• v.31 no.2
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• pp.169-179
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• 2024

Colloidal quantum dot (QDs) have emerged as a crucial building block for LEDs due to their size-tunable emission wavelength, narrow spectral line width, and high quantum efficiency. Tremendous efforts have been dedicated to improving the performance of quantum dot light-emitting diodes (QLEDs) in the past decade, primarily focusing on optimization of device architectures and synthetic procedures for high quality QDs. However, despite these efforts, the commercialization of QLEDs has yet to be realized due to the absence of suitable large-scale patterning technologies for high-resolution devices., This review will focus on the development trends associated with transfer printing, photolithography, and inkjet printing, and aims to provide a brief overview of the fabricated QLED devices. The advancement of various quantum dot patterning methods will lead to the development of not only QLED devices but also solar cells, quantum communication, and quantum computers.

• The Journal of Bigdata
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• v.9 no.1
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• pp.101-113
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• 2024

Federated learning has garnered attention as an efficient method for training machine learning models in a distributed environment while maintaining data privacy and security. This study proposes a novel FedRFBagging algorithm to optimize the performance of random forest models in such federated learning environments. By dynamically adjusting the trees of local random forest models based on client-specific data characteristics, the proposed approach reduces communication costs and achieves high prediction accuracy even in environments with numerous clients. This method adapts to various data conditions, significantly enhancing model stability and training speed. While random forest models consist of multiple decision trees, transmitting all trees to the server in a federated learning environment results in exponentially increasing communication overhead, making their use impractical. Additionally, differences in data distribution among clients can lead to quality imbalances in the trees. To address this, the FedRFBagging algorithm selects only the highest-performing trees from each client for transmission to the server, which then reselects trees based on impurity values to construct the optimal global model. This reduces communication overhead and maintains high prediction performance across diverse data distributions. Although the global model reflects data from various clients, the data characteristics of each client may differ. To compensate for this, clients further train additional trees on the global model to perform local optimizations tailored to their data. This improves the overall model's prediction accuracy and adapts to changing data distributions. Our study demonstrates that the FedRFBagging algorithm effectively addresses the communication cost and performance issues associated with random forest models in federated learning environments, suggesting its applicability in such settings.