Search

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks’ formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

This research investigates the simultaneous reduction of nitric oxide (NOx) and toluene using Pt-based catalysts, aiming at applications in small-scale industrial environmental processes. Catalysts with varying Pt loadings from 0.01 to 3 % were synthesized through wet impregnation, and their dispersion was assessed across several characterizations, revealing effective Pt dispersion even at loadings above 1 %. Under conditions of high toluene feed, the 3 % Pt-loaded catalyst emerged as the most efficient, achieving significant NOx and toluene conversion rates. The performance of this catalyst was thoroughly examined under various operational parameters including NO, toluene, O2 concentrations, gas hourly space velocity (GHSV) and reaction temperature. A short-stress test further underscored its effectiveness, with NOx and toluene conversions reaching 89.7 % and 97.6 %, respectively. Additionally, a preliminary process simulation of the NOx reduction by toluene indicated an energy efficiency improvement of over 8 % compared to conventional RCO and de-NOx processes. The findings suggest the catalyst's potential for NOx reduction in industrial settings, utilizing toluene present in flue gas without the need for additional commercial reducing agents.

In response to global efforts to deal with climate change, various renewable energy policies are being implemented. Among these, renewable portfolio standards (RPS) and renewable energy 100 (RE100) regulate the obligated supply of renewable energy to ensure compliance with set quotas by nations and institutions. In this context, the renewable energy certificate (REC) system is employed to assist obligated entities in meeting their renewable energy generation quotas. Obligated entities can purchase REC from renewable energy generators to obtain recognition for their renewable energy allocation. However, the current REC system is insufficient in addressing issues related to procedural complexity and cyber security. This study aims to overcome its limitations by applying blockchain technology. Blockchain, a distributed financial network, serves as a digital ledger, enabling peer-to-peer transactions in a simple, transparent, and secure way. The proposed new REC system based on blockchain moves away from the complex structure of the traditional REC system, simplifying the system into four processes: participation, issuance, transaction, and authentication. Moreover, applying blockchain algorithms can address the cyber security issues of the traditional system. The case study using Hyperledger Besu, or one of the blockchain platforms, demonstrates that the aspects of procedural complexity and cyber security are improved in the proposed system.

This paper proposes a visual map-based position and heading estimation system that is invariant to image rotation and consistent over time, which is achieved by exploiting the radial and azimuthal distributions of semantic segments. To characterize the specific position and heading, a novel concept termed “visual semantic context” is applied, which collects semantics in a polar-coordinated fashion in collaboration with measures of discrepancy. The system then matches visual semantic contexts: one from a semantically segmented aerial image aided by deep learning technology and others from a semantics-labeled database. Two-stage minimization alleviates the expensive computation of an exhaustive search. The first stage marginalizes the heading and coarsely searches for positions. At the same time, the Kolmogorov–Smirnov test significantly reduces the search domain by rejecting unlikely candidates, and the second stage refines the estimates. Numerical experiments show that the proposed algorithm fixes the position and heading, is invariant to image rotation, and is also robust to imprecise scale information.

News shows often deliver crosscutting information to their audiences by inviting commentators from rival political parties. If these news shows foster the formation of informed and balanced views of the audience, mass media could provide countermeasures against political polarization. To test the effect of such news shows, this study conducted an experiment with two variants of a simulated radio talk show. In the partisan scenario, the two guest commentators’ affiliations suggested their ideological orientation. In the non-partisan scenario, the commentators had neutral affiliations. We divided participants into two ideology groups, liberals and conservative, and compared each group’s evaluation of the commentators in the two scenarios. Two multivariate analysis of variance (MANOVA) tests were conducted to analyze the effect of the perceived ideology of the commentators on respondents’ attitudes toward the commentators’ arguments depending on their own ideological inclinations. The analyses results did not support the hypothesis that anticipated partisan attitudes towards the commentators’ arguments. It was only the liberal respondents who showed statistically significant different attitudes toward commentators’ arguments in each of the two scenarios. The findings suggest that such broadcast shows do not automatically trigger partisan message processing and may help the audience to develop informed and balanced opinions. While the current study failed to find conclusive evidence to support the hypotheses, it also found that the perceived ideology of the information source may trigger partisan attitudes for certain types of issues. Future studies with different experiment designs are needed to investigate the issue further.

Cyberspace has expanded as a result of the rapid spread of the Internet. This expansion has led to a change from conventional warfare to a form that includes cyber warfare. Cyber warfare, which occurs in cyberspace, consists of numerous cyberattacks using the vulnerabilities of cyber assets. Cyberattacks have increased with increases in the number of assets connected to the network and the information held by these assets; information can control the outcome of a war. Therefore, it is very important to assess the damage from cyberattacks between physical operations because such cyberattacks may be directly linked to the failure of an operation. This paper presents a cyber damage assessment framework for assessing the damage from cyberattacks between physical operations.

Usually, the level of asymmetry between the left and right brain caused by the difference between left and right brain functions is seen in an EEG analysis, as we see in the literature. A prerequisite for optimizing brain function is appropriate symmetry between the left brain and the right brain. In this paper, we developed a neurofeedback system to improve emotional disorders, such as anxiety and depression, and to optimize the brain function. Our experiment confirmed an enhancement in the symmetry of left/right brain activities and EEGs in each training section. A comparison of the results for the first and third weeks of the experiment showed a considerable increase in alpha and beta waves, and we found that the symmetry between the left and right brain activities was improved. The system might be applied in various fields, including education programs improving concentration for children and medical treatments to improve depression and insomnia for adults.

The number of elderly people suffering from dementia, a senile disease, is increasing day by day due to the rapid aging of the population. As a result, social and economic costs are also gradually increasing. To prevent such monetary losses, a system that can operate at a low cost is needed to care for dementia patients. Therefore, this research proposes a sensor-based deviant behavior detection system that allows caregivers to easily manage dementia patients even if they are not in the same location as their dementia patients at a low cost. In this research, the autoencoder and the LSTM models were used together, because deviance behavior is difficult to obtain labeled data. The autoencoder model is a representative unsupervised learning model, which can be used to extract characteristics of data, and was used to learn characteristics of normal behavioral data. The LSTM model is used to determine the deviant behavior from output outlier data that exceeds the threshold in the autoencoder. As a result of the experiment, each model achieved more than 96% and more than 99% accuracy. This research is expected to help caregivers of dementia patients manage the elderly with dementia more inexpensively and efficiently.

Cyberspace has expanded due to the rapid spread of the Internet. This expansion of cyberspace has resulted in a change in war patterns from conventional warfare to cyberwarfare. Especially in the defense field, cyberspace was established as the fifth battlefield, following land, sea, air, and space. Cyberwarfare in cyberspace is caused by numerous cyberattacks. However, the current defense system to effectively defend against cyber threats is not sufficient. A new cyberwarfare framework is needed to complement current defense system. In this paper, we will construct the required concepts according to the cyber operation execution process into an integrated framework, conduct experiments on evaluating cyber battle damage assessment among the frameworks, and propose a cyberwarfare operation framework.

Distributed energy resources (DERs), such as small-scale renewable energy generators, storage systems, and controllable loads, have been attracting great attention. Accordingly, interest in peer-to-peer (P2P) energy trading between prosumers with DERs is growing. The prosumers may perform the P2P electricity trading within the loss-guided framework, where network losses are primarily considered during the peer matching process. However, the loss-guided framework has limitations in that prosumer welfare is neglected in favor of prioritizing the network losses caused by the P2P transactions. Thus, in this study, a price-based framework for P2P electricity trading is suggested, where the prosumer welfare is considered by including not only network loss costs but also energy costs in the matching procedure. The effects of the suggested price-based framework on network efficiency, prosumer welfare, and social welfare are examined by comparing simulation results with the loss-guided framework and the random transactions. Further, how those three properties are affected by the change in loss price is analyzed and a guideline for the suitable choice of the loss price is suggested.

With the advancement of information technology (IT), the importance of cyber security is increasing because of the expansion of software utilization in the development of weapon systems. Civilian embedded systems and military weapon systems have cybersecurity-related symmetry that can increase vulnerabilities in the process of advanced information technology. Many countries, including the United States, are exploring ways to improve cybersecurity throughout the lifecycle of a weapon system. The South Korean military is applying the U.S. standard risk management framework (RMF) to some weapon systems to improve cybersecurity, but the need for a model that is more suitable for the South Korean military has been emphasized. This paper presents the results of a mission-based cybersecurity test, along with an evaluation model that can be applied to South Korean military weapon systems in parallel with the RMF. This study first examined the related international research trends, and proposed a test and evaluation method that could be utilized with the RMF throughout the entire life cycle of a weapon system. The weapon system was divided into asset, function, operational task, and mission layers based on the mission, and a mutually complementary model was proposed by linking the RMF and cybersecurity test and evaluation according to the domestic situation. In order to verify the proposed cybersecurity test and evaluation model, a simulation was developed and performed targeting the Close Air Support (CAS) mission support system, which is a virtual weapon system. In this simulation, the nodes performances by layer before and after a cyberattack were calculated, and the vulnerabilities and protection measures identified in the cyber security test and evaluation were quantified. This simulation made it possible to evaluate and derive protection measures in consideration of mission performance. It is believed that the proposed model could be used with some modifications, depending on the circumstances of each country developing weapon systems in the future.

Decreasing response rates to traditional survey methods, like face-to-face and telephone interviews, have led survey practitioners around the world to seek new ways of conducting surveys in recent years. The COVID-19 pandemic exacerbated this problem because it made conducting face-to-face interviews even more difficult than before. For example, it made conducting face-to-face surveys infeasible in 2020 in South Korea, and so the Korean Academic Multimode Open Survey (KAMOS) was unable to conduct a planned face-to-face survey to recruit new panel members. The entire 8,514-member panel, established via two-stage probability-based sampling from 2016 to 2019, was invited to take three online/telephone surveys in 2020. Of these panel members, 1,352 responded to at least one survey in 2020. To test to what extent the panel remained representative of the adult South Korean population, we compared the two groups of panel members: those who responded to at least one survey in 2020 and those who did not. After weighting both groups on the basis of age, sex, and geographical area, we analyzed their responses to some of the questions that were asked during multiple rounds of the face-to-face panel-recruiting interviews. Using Cohen’s _d_ for survey items that could be analyzed numerically and Cramér's V for categorical items, we were able to conclude that the respondents to the 2020 surveys were equivalent to the non-respondents in terms of both demographics and in the answers they originally gave to substantive questions on a variety of topics related to social science or public opinion research, including questions about quality of life, societal issue, and politics (Cohen's _d_ items

Due to the growth of the internet and communication technologies, electronic financial systems are becoming popular. Physical cash is losing its preeminence, and digital numbers on computers represent money. However, electronic financial systems, mostly operated by private entities, have defects to be compensated for, such as high charges for using the system, security issues, and the problem of exclusion. As a solution, many countries around the world are considering central bank digital currency. For central bank digital currency to be utilized as a national legal tender, it must be universal and accessible regardless of time and place, similar to physical cash. Therefore, offline payment functions that extend the accessibility of central bank digital currency are becoming attractive. However, due to the characteristics of the electronic financial system, central bank digital currency is vulnerable to possible malicious behaviors in offline situations, such as blackouts and system shutdowns. This paper reviews research studies that deal with security matters related to the offline payment function of central bank digital currency. Offline payment solutions, including central bank digital currency and other electronic financial systems, such as electronic cash and cryptocurrency, are reviewed, and supplemental methods to improve the offline payment solutions of central bank digital currency based on trusted execution environment devices are suggested.

The relationship between compliance with behaviors recommended to prevent the spread of COVID-19 and media exposure, negative emotions, and risk perception was examined using regression analyses of data from KAMOS, a nationally representative survey of South Korean adults. The strongest predictor of preventive behaviors in general was negative emotions, which had the largest *βh* (.22) among the independent variables considered. The eight negative emotions, identified using factor analysis of a series of 11 emotions, were anger, annoyance, fear, sadness, anxiety, insomnia, helplessness, and stress. Negative emotions themselves were influenced most strongly by the respondent’s anxiety over social safety (*βf*=.286), followed by prediction of COVID-19 spread (*β*=.121, *p*

In this study, we examined the impacts of peer-to-peer (P2P) electricity trading on the power losses in the network, which is one of the objectives optimized in the centralized approach. For this purpose, we reviewed the conventional loss management schemes and suggested the requirements to be considered in the design of P2P electricity trading. Then, we described a new loss management framework for P2P transactions and introduced the concept of the transaction guide. Based on the proposed framework, we simulated the P2P transactions with and without the transaction guide and examined the variation in the network losses. Three noteworthy remarks are derived from the simulation in this paper. First, the random characteristics of P2P trading itself do not guarantee favorable transaction ordering in terms of network losses, but when the new loss management framework is applied, the network losses can be effectively decreased. Second, through the new loss management framework, loss costs can be fairly allocated to individual prosumers. Third, to invigorate the P2P electricity trading, an incentive program should be considered to alleviate the burden of loss costs of the first trader in the P2P electricity trading.

The development of information and communication technology (ICT) is making daily life more convenient by allowing access to information at anytime and anywhere and by improving the efficiency of organizations. Unfortunately, malicious code is also proliferating and becoming increasingly complex and sophisticated. In fact, even novices can now easily create it using hacking tools, which is causing it to increase and spread exponentially. It has become difficult for humans to respond to such a surge. As a result, many studies have pursued methods to automatically analyze and classify malicious code. There are currently two methods for analyzing it: a dynamic analysis method that executes the program directly and confirms the execution result, and a static analysis method that analyzes the program without executing it. This paper proposes a static analysis automation technique for malicious code that uses machine learning. This classification system was designed by combining a method for classifying malicious code using a portable executable (PE) structure and a method for classifying it using a PE structure. The system has 98.77% accuracy when classifying normal and malicious files. The proposed system can be used to classify various types of malware from PE files to shell code.