• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.896-908
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. In this regard, it is vital to provide credible HEPs based on firm technical underpinnings including (but not limited to): (1) how to collect HRA data from available sources of information, and (2) how to inform HRA practitioners with the collected HRA data. Because of these necessities, the U.S. Nuclear Regulatory Commission and the Korea Atomic Energy Research Institute independently developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. These systems provide unique frameworks that can be used to secure HRA data from full-scope training simulators of NPPs (i.e., simulator data). In order to investigate the applicability of these two systems, two papers have been prepared with distinct purposes. The first paper, entitled "SACADA and HuREX: Part 1. The Use of SACADA and HuREX Systems to Collect Human Reliability Data", deals with technical issues pertaining to the collection of HRA data. This second paper explains how the two systems are able to inform HRA practitioners. To this end, the process of estimating HEPs is demonstrated based on feed-and-bleed operations using HRA data from the two systems.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.9-22
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• 2023

In this paper, we propose a balanced approach to AI regulation, focused on harnessing the potential benefits of artificial intelligence while upholding fairness and ethical responsibility. With the increasing integration of AI systems into daily life, it is essential to develop regulations that prevent harmful biases and the unfair disadvantage of certain demographics. Our approach involves analyzing regulatory frameworks and case studies in AI applications to ensure responsible development and application. We aim to contribute to ongoing discussions around AI regulation, helping to establish policies that balance innovation with fairness, thereby driving economic progress and societal advancement in the age of artificial intelligence.

• Journal of Electrochemical Science and Technology
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• v.15 no.2
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• pp.207-219
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• 2024

Metal-N-C (MNC) catalysts have been anticipated as promising candidates for oxygen reduction reaction (ORR) to achieve low-cost polymer electrolyte membrane fuel cells. The structure of the M-N_x moiety enabled a high catalytic activity that was not observed in previously reported transition metal nanoparticle-based catalysts. Despite progress in non-precious metal catalysts, the low density of active sites of MNCs, which resulted in lower single-cell performance than Pt/C, needs to be resolved for practical application. This review focused on the recent studies and methodologies aimed to overcome these limitations and develop an inexpensive catalyst with excellent activity and durability in an alkaline environment. It included the possibility of non-precious metals as active materials for ORR catalysts, starting from Co phthalocyanine as ORR catalyst and the development of methodologies (e.g., metal-coordinated N-containing polymers, metal-organic frameworks) to form active sites, M-N_x moieties. Thereafter, the motivation, procedures, and progress of the latest research on the design of catalyst morphology for improved mass transport ability and active site engineering that allowed the promoted ORR kinetics were discussed.

• Journal of Korea Trade
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• v.24 no.7
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• pp.1-28
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• 2020

Purpose - This paper empirically investigates the predictors and main determinants of consumers' ratings of mobile applications in the Google Play Store. Using a linear and nonlinear model comparison to identify the function of users' review, in determining application rating across countries, this study estimates the direct effects of users' reviews on the application rating. In addition, extending our modelling into a sentimental analysis, this paper also aims to explore the effects of review polarity and subjectivity on the application rating, followed by an examination of the moderating effect of user reviews on the polarity-rating and subjectivity-rating relationships. Design/methodology - Our empirical model considers nonlinear association as well as linear causality between features and targets. This study employs competing theoretical frameworks - multiple regression, decision-tree and neural network models - to identify the predictors and main determinants of app ratings, using data from the Google Play Store. Using a cross-validation method, our analysis investigates the direct and moderating effects of predictors and main determinants of application ratings in a global app market. Findings - The main findings of this study can be summarized as follows: the number of user's review is positively associated with the ratings of a given app and it positively moderates the polarity-rating relationship. Applying the review polarity measured by a sentimental analysis to the modelling, it was found that the polarity is not significantly associated with the rating. This result best applies to the function of both positive and negative reviews in playing a word-of-mouth role, as well as serving as a channel for communication, leading to product innovation. Originality/value - Applying a proxy measured by binomial figures, previous studies have predominantly focused on positive and negative sentiment in examining the determinants of app ratings, assuming that they are significantly associated. Given the constraints to measurement of sentiment in current research, this paper employs sentimental analysis to measure the real integer for users' polarity and subjectivity. This paper also seeks to compare the suitability of three distinct models - linear regression, decision-tree and neural network models. Although a comparison between methodologies has long been considered important to the empirical approach, it has hitherto been underexplored in studies on the app market.

• School Mathematics
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• v.5 no.1
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• pp.27-42
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• 2003

The current 7th National Mathematics Curriculum had been developed as a learner-centered curriculum and begun to apply to high school since 2002. This paper discusses issues related to the high school mathematics curriculum application into high school. The mathematics curriculum for grades 11 and 12 was developed primarily as a learner-centered one to provide five elective courses according to the needs of students based on their future occupation and attitudes. Discussion starts with the differences of the five elective courses: the three of them have dependent and sequential structure and the two are totally different with regards to their levels of difficulty and the content they span. It is claimed that the frameworks of the 2005 National Ability Test for the College Entrance and the minimal enrollment requirements of several influential colleges' admission policy make the high school mathematics education very rigid, unflexible, and anti-educational. Several suggestions to recover and imp-rove the high school mathematics education and the spirit of the 7th curriculum are presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1139-1144
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• 2008

In this paper, we propose an automatic generation method of transaction level(TL) model from algorithmic model to verify system specification fast and effectively using virtual platform. The TL virtual platform including structural properties such as timing, synchronization and real-time is one of the effective verification frameworks. However, whenever change system specification or HW/SW mapping, we must rebuild virtual platform and additional design/verification time is required. And the manual description is very time-consuming and error-prone process. To solve these problems, we build TL library which consists of basic components of virtual platform such as CPU, memory, timer. We developed a set of design/verification tools in order to generate a virtual platform automatically. Our tools generate a virtual platform which consists of embedded real-time operating system (RTOS) and hardware components from an algorithmic modeling. And for communication between HW and SW, memory map and device drivers are generated. The effectiveness of our proposed framework has been successfully verified with a Joint Photographic Expert Group (JPEG) and H.264 algorithm. We claim that our approach enables us to generate an application specific virtual platform $100x{\tims}1000x$ faster than manual designs. Also, we can refine an initial platform incrementally to find a better HW/SW mapping. Furthermore, application software can be concurrently designed and optimized as well as RTOS by the generated virtual platform

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.10a
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• pp.150-156
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• 2004

Though the concept of Life-Cycle Cost (LCC) itself is not new, its effectiveness for planning, design, rehabilitation and maintenance/management of civil infrastructures is becoming increasingly recognized. For the decision problems as in the case of the LCC of plant facilities, equipments, bridge decks, pavements, etc., the Life-Cycle Cost Analysis (LCCA) is relatively simple, and thus its practical implementation is rather straightforward. However, when it comes to major infrastructures such as bridge, tunnels, underground facilities, etc., the LCCA problem becomes extremely complex because lack of cost data associated with various direct and indirect losses, and the absence of uncertainty data available for the assessment as well. As a result, the LCC studies have been largely limited only to those relatively simple LCCA problems of planning or conceptual design for making decisions. Accordingly, in the recent years, the researchers have pursued extensive studies on the LCC effectiveness mostly related to LCC models and frameworks for civil infrastructures. Moreover, recently the demand on the practical application of LCC effective decisions in design and maintenance is rapidly growing unprecedently in civil engineering practice. Indirction cost is very important on LCC formulation. But that is very difficult and complicate the estimation every LCC. The objective of this paper is to suggest efficient regression model for the estimation of indirect cost approach to the practical application of LCC for the design and rehabilitation of civil. infrastructures considering traffic, traffic network, detour condition, and workzone condition. In this paper, it performed the sensitivity analysis and correlation analysis of parameter for development of regression model of inflection cost.

• Journal of Internet Computing and Services
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• v.10 no.4
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• pp.71-85
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• 2009

The demand for mobile database applications has been rapidly increased with the growth of mobile users and the development of wireless Internet technology. But the productivity of mobile applications is low and it takes much time to manage the versions of applications because the user interface and query processing code of applications is manually written. In this paper, we describe the design and implementation of the MobileGen that is a script generator for mobile database applications. The generated scripts enhance mobile application productivity by providing the code for operating with a database and processing user queries. Each script provides a corresponding deck that is a set of related cards as user interface. The MobileGen supports easy version management of generated applications and the MobileGen itself because it is based on the templates that are frameworks for scripts. Moreover, the MobileGen provides not only the interested entity but also the entities that are related directly and indirectly with the interested entity unlike the most commercial mobile script generators.

• Journal of Communications and Networks
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• v.13 no.6
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• pp.591-601
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• 2011

Today's Internet which provides communication channels with best-effort end-to-end performance is rapidly evolving into an autonomic global computing platform. Achieving autonomicity in the Future Internet will require a performance architecture that (a) allows users to request and own 'slices' of geographically-distributed host and network resources, (b) measures and monitors end-to-end host and network status, (c) enables analysis of the measurements within expert systems, and (d) provides performance intelligence in a timely manner for application adaptations to improve performance and scalability. We describe the requirements and design of one such "Future Internet performance architecture" (FIPA), and present our reference implementation of FIPA called 'OnTimeMeasure.' OnTimeMeasure comprises of several measurement-related services that can interact with each other and with existing measurement frameworks to enable performance intelligence. We also explain our OnTimeMeasure deployment in the global environment for network innovations (GENI) infrastructure collaborative research initiative to build a sliceable Future Internet. Further, we present an applicationad-aptation case study in GENI that uses OnTimeMeasure-enabled performance intelligence in the context of dynamic resource allocation within thin-client based virtual desktop clouds. We show how a virtual desktop cloud provider in the Future Internet can use the performance intelligence to increase cloud scalability, while simultaneously delivering satisfactory user quality-of-experience.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.71-77
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• 2021

Recently, there is a demand for efficient program development of an IoT application support frameworks considering heterogeneous hardware characteristics. In addition, the scope of hardware support is expanding with the development of neuromorphic architecture that mimics the human brain to learn on their own and enables autonomous computing. However, most existing IoT IDE(Integrated Development Environment), it is difficult to support AI(Artificial Intelligence) or to support services combined with various hardware such as neuromorphic architectures. In this paper, we design an AI component abstract model that supports the second-generation ANN(Artificial Neural Network) and the third-generation SNN(Spiking Neural Network), and implemented an autonomous IoT IDE based on the proposed model. IoT developers can automatically create AI components through the proposed technique without knowledge of AI and SNN. The proposed technique is flexible in code conversion according to runtime, so development productivity is high. Through experimentation of the proposed method, it was confirmed that the conversion delay time due to the VCL(Virtual Component Layer) may occur, but the difference is not significant.