• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1999.04a
• /
• pp.57-60
• /
• 1999

Amplification factor spectrum using the observed strong ground motions database in the Korean Peninsula has been obtained and compared with Standard Rpectrum which wa suggested by United States Nuclear Regulatory Committee. The observed ground motions from the Yongwol and the Kyoungju Earthquakes respectively which are supposed to represent domestic seismotectonic characteristics such as seismic source attenuation of the propagation meium and site specific effect are used for the analysis of amplification factor spectrum,. The database are slightly different from the those of the second study. Amplification factors have been calculated by comparing the observed peak ground motions with results from responses to the observed horizontal na vertical ground motions. The comparison have shown that the amplification factors resultant from this study exceeds those of Standard Response Spectrum The results suggest that the characteristics of seismic strong ground motion which are supposed to represent the domestic seismotectonic characteristics differs from those of Standard Response Spectrum especially at higher frequencies. The results from the 2nd study are similar to those of 1st analysis.

• Journal of the Korea Concrete Institute
• /
• v.20 no.1
• /
• pp.51-58
• /
• 2008

Analytical probabilistic vulnerability analysis requires extensive computing effort as a result of the randomness in both input motion and response characteristics. In this study, a new methodology whereby a set of vulnerability curves are derived based on the fundamental response quantities of stiffness, strength and ductility is presented. A response database of coefficients describing lognormal vulnerability relationships is constructed by employing aclosed-form solution for a generalized single-degree-of-freedom system. Once the three fundamental quantities of a wide range of structural systems are defined, the vulnerability curves for various limit states can be derived without recourse to further simulation. Examples of application are given and demonstrate the extreme efficiency of the proposed approach in deriving vulnerability relationships.

• The KIPS Transactions:PartC
• /
• v.12C no.3 s.99
• /
• pp.309-316
• /
• 2005

Recently, RFID system is a main technology to realize ubiquitous computing environments, but the feature of the RFID system may bring about various privacy problem. So, many kinds of protocols To resolve this problem are researched. In this paper, we analyse the privacy problem of the previous methods and propose more secure and effective authentication protocol to protect user's privacy. Then we prove that the proposed protocol is secure and effective as we compare the proposed protocol with previous methods. The proposed protocol is based on Challenge-Response using one-way hash function and random number. The proposed protocol is secure against replay attack, spoofing attack and so on. In addition, the proposed protocol is proper for distributed database environment.

• Journal of the Korean Society of Systems Engineering
• /
• v.18 no.2
• /
• pp.75-93
• /
• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Proceedings of the Korea Database Society Conference
• /
• 2002.10a
• /
• pp.544-558
• /
• 2002

High Quality Strategic Information: 과학적, 논리적인 경영계획 수립. 다양한 Assumption을 통한 Simulation 및 Forecast. Effective Decision Making Support: 적시성(Response time). Analysis/Reporting (중략)

• Proceedings of the SAREK Conference
• /
• 2008.06a
• /
• pp.776-781
• /
• 2008

A ground heat exchanger in a GSHP system is an important unit that determines the thermal performance of a system and its initial cost. The Size and performance of this heat exchanger is highly dependent on the thermal properties. A proper design requires certain site-specific parameters, most importantly the ground effective thermal conductivity, the borehole thermal resistance and the undisturbed ground temperature. This paper is part of a research project aiming at constructing a database of these site-specific properties, especially ground effective thermal conductivity. The objective was to develop and evaluation method, and to provide this knowledge to design engineers. To achieve these goals, thermal response tests were conducted using a testing device at nearly 150 locations in Korea. The in-situ thermal response is the temperature development over time when a known heating load imposed, e.g. by circulating a heat carrier fluid through the test exchangers. The line-source model was then applied to the response test data because of its simplicity. From the data analysis, the range of ground effective thermal conductivity at various sites is $1.5{\sim}4.0\;W$/mK. The results also show that the ground effective thermal conductivity varies with grouting materials as well as regional geological conditions and groundwater flow.

• The KIPS Transactions:PartD
• /
• v.16D no.3
• /
• pp.295-306
• /
• 2009

Query execution time minimization is an important objective in distributed database design. While total time minimization is an objective for On Line Transaction Processing (OLTP), response time minimization is for Decision Support queries. We formulate the sub-query allocation problem using analytical models and solve with genetic algorithm (GA). We show that query execution plans with total time minimization objective are inefficient from response time perspective and vice versa. The procedure is tested with simulation experiments for queries of up to 20 joins. Comparison with exhaustive enumeration indicates that GA produced optimal solutions in all cases in much less time.

• Journal of the Korean Society of Systems Engineering
• /
• v.19 no.2
• /
• pp.18-31
• /
• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Herbal Formula Science
• /
• v.18 no.2
• /
• pp.227-239
• /
• 2010

Objective : The purpose of this study was to develop the simulator which can analyze the anti-inflammatory effects of herbs based on e-cell, or the virtual cell. Method : We have ensured the medical herbs and its active compounds by investigating the oriental medicine records and NBCI(Biomedicine database). Also we have developed the web-based search system for confirming database related to anti-inflammation. We have researched the cell signal pathway related with inflammatory response control and established the mathematical model of herb interaction on selected signal pathway in e-cell. Finally we have developed the prototype which can confirm the result of this model visibly. Results : We constructed the database of 62 cases of anti-inflammatory active compounds in 61 cases of medical herbs which have been known anti-inflammation effects in the paper, 16 cases of inflammatory factors, 10 cases of signal pathways related with inflammatory response and 6,834 cases of URL(Uniform Resource Locator) of referenced papers. And we embodied the web-based research system, which can research this database. User can search basic and detailed information of medical plants related with anti-inflammatory by using information system. And user can acquire information on an active compounds, a signal pathway and a link URL of related paper. Among investigated ten pathways, we selected NF-${\kappa}B$, which plays important role in activation of immune system, and we searched the mechanisms of actions of proteins which could be components of this pathway. We reduced total network into IKK-$I{\kappa}B$ - NF-${\kappa}B$, and completed mathematic modeling by using ordinary differential equations and response variables of $I{\kappa}B-NF-{\kappa}B$ signaling model network which is suggested by Baltimore Group. We designed OED(Ordinary Differential Equation) for response of IKK, $I{\kappa}B$, $NF-{\kappa}B$ in e-cell's cytoplasm and nucleus, and measured whether an active compound of medicinal plants which is inputted by an user would have a anti-inflammation effects in obedience to change in concentration over time. The proposed model was verified by using experimental results of the papers which are listed on NCBI.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.744-747
• /
• 2017

The Internet of things (IoT) needs to be an event-driven approach for efficient real time response and processing. An IoT gateway is sometimes employed to provide the connection and translation between devices and the cloud. Storing data in the local database, and then forwarding it on the cloud is a task to be relegated to a gateway device In this paper, we propose the design of the IoT gateway with Fog computing for storing data from sensors into a local database. In the procedure of designing storing tasks, we propose to use the interfacing software known as Ardulink MQTT bridge. MQTT is a protocol for sensors to publish data to the clients. When it comes to needing historical data, MQTT connector can push MQTT data into SQL database. We write an MQTT client and based on the message topic insert the values into a SQL Database The design of IoT gateway with Fog computing adds value because it provides processing of the data across multiple devices before it sends to the cloud.