• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.436-443
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• 1992

Presented in the paper is a systematic approach to constructing a Petri net model of FAS (flexible assembly system). Petri net is widely used in modeling automated manufacturing systems. But, it found to be very difficult for an FA engineer to build a correct model of an FAS with Petri net symbols (ie, place, transition, and token) from the beginning. An automated manufacturing system in general is built from a set of "standard" hardware components. An FAS in particular is usually composed of assembly robots, work tables, conveyor lines, buffer storages, part feeders, etc. In the proposed modeling scheme, each type of standard resources is represented as a standard "module" which is a sub Petri net. Then, the model of a FAS can be conveniently constructed using the predefined modules the same way the FAS itself is built from the standard components. The network representation of a FAS is termed a JR-net (job resource relation net) which is easy to construct. This JR net is then mechanically converted to a formal Petri net (to simulate the behavior of the FAS). The proposed modeling scheme may easily be extended to the modeling of other types of automated manufacturing systems such as FMS and AS/RS.ch as FMS and AS/RS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.5
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• pp.434-445
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• 1990

Protocol testing techniques are expanded from the traditional simple function- testing based on the OSI model, to sophisticated performance testing, conformance testing and interoperability testing. In addition, both point-to-point and point-to-multipoint protocols are to be covered. This paper presents a new multiple-purpose protocol test system where the common platform includes the test sequence generation and test result analysis, and the modular test execution part is selectively adjusted according to the test purposes and protocols under test. This paper describes test system for network routing protocol and test system for transport protocol, designed upon the ideas of the multiple-purpose protocol test system.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.251-258
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• 2014

This paper proposes an efficient token flow design methodology for a decoder in the MPEG Reconfigurable Media Coding (RMC) framework. The MPEG RMC framework facilitates a decoder to be configured with a set of modules called functional units (FUs) that are connected by tokens. Such a modular design philosophy of the MPEG RMC framework enables the reusability and reconfigurability of FUs. One drawback of the MPEG RMC framework is that the decoder performance can be affected by increasing the token transmissions between FUs. The proposed method improves the design of the FU network in the RMC framework toward real-time decoder implementation. In the proposed method, the merging of FU, the separation of token flow, and the merging of token transactions are applied to minimize the token traffic between FUs. The experimental results of the MPEG-4 SP decoder show that the proposed method reduces the total decoding time by up to 77 percent compared to the design of the RMC simulation model.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1881-1892
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• 1997

This paper proposes the method for recognition of printed chinese characters by probabilistic VQ networks and multi-stage recognizer has hierarchical structure. We use modular neural networks, because it is difficult to construct a large-scale neural network. Problems in this procedure are replaced by probabilistic neural network model. And, Confused Characters which have significant ratio of miss-classification are reclassified using the entropy theory. The experimental object consists of 4,619 chinese characters within the KSC5601 code except the same shape but different code. We have 99.33% recognition rate to the training data, and 92.83% to the test data. And, the recognition speed of system is 4-5 characters per second. Then, these results demonstrate the usefulness of our work.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7B
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• pp.450-463
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• 2005

This paper aims to find influencing factors for firms to adopt network equipments which based on Advanced Telecom Computing Architecture (ATCA). ATCA suggests a standardized specification for telecom equipments design. This new paradigm of developing network equipment provides benefits for network equipment manufacturers by reducing development time for new equipments with lower CapEx and OpEx. It also deliver oportunities for telecom services providers to exploit or test new services by replacing or upgrading part of total system with modular based network equipments. The research model basically depends on various researches based on Rogers' Innovation and Diffusion theory and it is verified through an empirical study for ninety-one domestic forms. Binary logistic regression was conducted to find the relationship between purchase intention and factors affecting new technology adoption. As a result, two factors such as scalability and cost/benefit effectiveness of the new system were statistically significant. Cluster analysis followed with those two variables. This helps TEMs (Telecom Equipments Manufacturers) get some implications on timing and target customers for diffusing the ATCA based technologies in the market.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.153-162
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• 2022

Relevance. Innovative activities in education should be aimed at ensuring the comprehensive development of the individual and professional development of students. The main idea of modular technology is that the student should learn by himself, and the teacher manages his learning activities. The advantage of modular technology is the ability of the teacher to design the study of the material in the most interesting and accessible forms for this part of the study group and at the same time achieve the best learning results. Innovative Moodle technology. it is gaining popularity every day, significantly expanding the space of teaching and learning, allowing students to study inter-faculty university programs in depth. The purpose of this study is to assess the quality of implementation of the e-learning system Moodle. The study was conducted at the South Ukrainian National Pedagogical University named after K. D. Ushinsky in order to identify barriers to the effective implementation of innovative distance learning technologies Moodle and introduce a new model that will have a positive impact on the development of e-learning. Methodology. The paper used a combination of theoretical and empirical research methods. These include: scientific analysis of sources on this issue, which allowed us to formulate the initial provisions of the study; analysis of the results of students 'educational activities; pedagogical experiment; questionnaires; monitoring of students' activities in practical classes. Results. This article evaluates the implementation of the principles of distance learning in the process of teaching and learning at the University in terms of quality. The experiment involved 1,250 students studying at the South Ukrainian National Pedagogical University named after K. D. Ushinsky. The survey helped to identify the main barriers to the effective implementation of modern distance learning technologies in the educational process of the University: the lack of readiness of teachers and parents, the lack of necessary skills in applying computer systems of online learning, the inability to interact with the teaching staff and teachers, the lack of a sufficient number of academic consultants online. In addition, internal problems are investigated: limited resources, unevenly distributed marketing advantages, inappropriate administrative structure, and lack of innovative physical capabilities. The article allows us to solve these problems by gradually implementing a distance learning model that is suitable for any university, regardless of its specialization. The Moodle-based e-learning system proposed in this paper was designed to eliminate the identified barriers. Models for implementing distance learning in the learning process were built according to the CAPDM methodology, which helps universities and other educational service providers develop and manage world-class online distance learning programs. Prospects for further research focus on evaluating students' knowledge and abilities over the next six months after the introduction of the proposed Moodle-based program.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.639-645
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• 2018

This paper is intended to develop a decision model that can be applied to autonomous vehicles and autonomous mobile vehicles. The developed module has an independent configuration for application in various driving environments and is based on a platform for organically operating them. Each module is studied for decision making on lane changes and for securing safety through reinforcement learning using a deep learning technique. The autonomous mobile moving body operating to change the driving state has a characteristic where the next operation of the mobile body can be determined only if the definition of the speed determination model (according to its functions) and the lane change decision are correctly preceded. Also, if all the moving bodies traveling on a general road are equipped with an autonomous driving function, it is difficult to consider the factors that may occur between each mobile unit from unexpected environmental changes. Considering these factors, we applied the decision model to the platform and studied the lane change decision system for implementation of the platform. We studied the decision model using a modular learning method to reduce system complexity, to reduce the learning time, and to consider model replacement.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.993-1001
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• 2024

The TANDEM project is a European initiative funded under the EURATOM program. The project started on September 2022 and has a duration of 36 months. TANDEM stands for Small Modular ReacTor for a European sAfe aNd Decarbonized Energy Mix. Small Modular Reactors (SMRs) can be hybridized with other energy sources, storage systems and energy conversion applications to provide electricity, heat and hydrogen. Hybrid energy systems have the potential to strongly contribute to the energy decarbonization targeting carbon-neutrality in Europe by 2050. However, the integration of nuclear reactors, particularly SMRs, in hybrid energy systems, is a new R&D topic to be investigated. In this context, the TANDEM project aims to develop assessments and tools to facilitate the safe and efficient integration of SMRs into low-carbon hybrid energy systems. An open-source "TANDEM" model library of hybrid system components will be developed in Modelica language which, by coupling, will extend the capabilities of existing tools implemented in the project. The project proposes to specifically address the safety issues of SMRs related to their integration into hybrid energy systems, involving specific interactions between SMRs and the rest of the hybrid systems; new initiating events may have to be considered in the safety approach. TANDEM will study two hybrid systems covering the main trends of the European energy policy and market evolution at 2035's horizon: a district heating network and power supply in a large urban area, and an energy hub serving energy conversion systems, including hydrogen production; the energy hub is inspired from a harbor-like infrastructure. TANDEM will provide assessments on SMR safety, hybrid system operationality and techno-economics. Societal considerations will also be encased by analyzing European citizen engagement in SMR technology safety.