• Nuclear Engineering and Technology
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• v.36 no.5
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• pp.420-429
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• 2004

The local power density should be estimated accurately to prevent fuel rod melting. The local power density at the hottest part of a hot fuel rod, which is described by the power peaking factor, is more important information than the local power density at any other position in a reactor core. Therefore, in this work, the power peaking factor, which is defined as the highest local power density to the average power density in a reactor core, is estimated by fuzzy neural networks using numerous measured signals of the reactor coolant system. The fuzzy neural networks are trained using a training data set and are verified with another test data set. They are then applied to the first fuel cycle of Yonggwang nuclear power plant unit 3. The estimation accuracy of the power peaking factor is 0.45% based on the relative $2_{\sigma}$ error by using the fuzzy neural networks without the in-core neutron flux sensors signals input. A value of 0.23% is obtained with the in-core neutron flux sensors signals, which is sufficiently accurate for use in local power density monitoring.

• The Journal of Industrial Distribution & Business
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• v.13 no.10
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• pp.15-22
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• 2022

Purpose: In the manufacturing sector, VDI (Virtual Desktop Infrastructure) offers advantages to the organizations, such as allowing manufacturers access to the system from any location. The most important things are understanding what the user needs, avoiding under-provisioning, network preparation. This research is to provide useful practical l implementations of VDI in manufacturing industry based on numerous prior studies. Research design, data and methodology: This research has conducted the qualitative content analysis (QCA). When conducting this research, the present author assumed that it is crucial to create the procedures and processes that will be used to acquire the text data needed to structure or solve problems. Results: According to the prior literature analysis, there are five suggestions to implement successful VDI for manufacturing sector. The five solutions are (1) Creation of the machines, (2) Direct users to an available 'Virtual Machine', (3) 'Virtual Machine Power Management', (4) Performance monitoring, and (5) Review security. Conclusions: The research clearly details how VDI can be implemented on a manufacturer platform and how it can be connected to hundreds of users. The author can conclude that connecting hundreds of users can be done using the remote connection of devices and encourage manufacturers to work from different areas.

• Annual Conference of KIPS
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• 2009.04a
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• pp.377-378
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• 2009

최근 환경 문제와 고유가 시대에 따른 문제가 전세계적인 이슈로 떠오르면서, 신재생에너지 자원의 개발에 대한 필요성이 대두되고 있다. 이와 함께 신재생에너지 자원에 대한 국내외 연구가 활발히 진행되고 있다. 최근에는 IT 기술이 발달함에 따라 센서와 같은 최신기술을 신재생에너지 자원관리를 위한 모니터링에 적용하고 있다. 이러한 환경에서 실시간적이고 무제한적인 신재생에너지 자원 정보를 표현하기 위한 기술과 센서의 특성에 맞는 시스템 구조가 필요하다. 이 논문에서는 효율적인 신재생에너지 자원 관리를 위한 센서 네트워크 기반 모니터링 시스템 구조를 설계한다.

• Smart Structures and Systems
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• v.33 no.4
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• pp.313-323
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• 2024

Deterioration of buildings is one of the biggest problems in modern society, and the importance of a safety diagnosis for old buildings is increasing. Therefore, most countries have legal maintenance and safety diagnosis regulations. However, the reliability of the existing safety diagnostic processes is reduced because they involve subjective judgments in the data collection. In addition, unstructured tasks increase rework rates, which are time-consuming and not cost-effective. Therefore, This paper proposed the method that can calculate the safety grade of deterioration automatically. For this, a DNN structure is generated by using existing precision inspection data and precision safety diagnostic data, and an objective building safety grade is calculated by applying status evaluation data obtained with a UAV, a laser scanner, and reverse engineering 3D models. This automated process is applied to 20 old buildings, taking about 40% less time than needed for a safety diagnosis from the existing manual operation based on the same building area. Subsequently, this study compares the resulting value for the safety grade with the already existing value to verify the accuracy of the grade calculation process, constructing the DNN with high accuracy at about 90%. This is expected to improve the reliability of aging buildings in the future, saving money and time compared to existing technologies, improving economic efficiency.

• Journal of the Korean Institute of Gas
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• v.12 no.2
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• pp.38-41
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• 2008

In this research, we propose a new method for tracking the release rate using the concentration data obtained from the sensor. We used a sensor network that has already been set surrounding the area where hazardous gas releases can occur. From the real-time sensor data, we detected and analyzed releases of harmful materials and their concentrations. Based on the results, the release rate is estimated using the neural network. This model consists of 14 input variables (sensor data, material properties, process information, meteorological conditions) and one output (release rate). The dispersion model then performs the simulation of the expected dispersion consequence by combining the sensor data, GIS data and the diagnostic result of the source term. The result of this study will improve the safety-concerns of residents living next to storage facilities containing hazardous materials by providing the enhanced emergency response plan and monitoring system for toxic gas releases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1122-1127
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• 2014

WLAN is affordability, flexibility, and ease of installation, use the smart device due to the dissemination and the AP (Access Point) to the simplification of the Office building, store, at school. Wi-Fi radio waves because it uses the medium of air transport to reach areas where security threats are always exposed to illegal AP installation, policy violations AP, packet monitoring, AP illegal access, external and service access, wireless network sharing, MAC address, such as a new security threat to steal. In this paper, signature-based of wireless intrusion detection system for Snort to suggest how to develop. The public can use hacking tools and conduct a mock hacking, Snort detects an attack of hacking tools to verify from experimental verification of the suitability of the thesis throughout.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2167-2175
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• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.327-341
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• 2006

As we are getting to deal with more applications that generate streaming data such as sensor network, monitoring, and SDI (selective dissemination of information), active research is being conducted to support efficient processing of queries over streaming data. The applications on the Web environment like SDI, among others, require query processing over streaming XML data, and its investigation is very important because XML has been established as the standard for data exchange on the Web. One of the major problems with the previous systems that support query processing over streaming XML data is that they cannot deal adaptively with dynamically changing stream because they rely on static query plans. On the other hand, the stream query processing systems based on relational data model have achieved adaptiveness in query processing due to query operator routing. In this paper, we propose a system of adaptive query processing over streaming XML data in which the model of adaptive query processing over streaming relational data is applied. We compare our system with YFiiter, one of the representative systems that provide XML stream query processing capability, to show efficiency of our system.

• Convergence Security Journal
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• v.8 no.4
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• pp.73-80
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• 2008

In this paper, we developed a WMS (Warehouse Management System) Simulator. There is no Simulator that supports optimized design for Warehouse, consider goods which storage in warehouse and using RFID and USN based on cable wireless network. Also, there is no tool for monitoring which decides delivery time with information about temperature, humidity and illumination, after goods are stocked into warehouse. In this paper, WMS Simulator Implements function of drawing a blueprint. The Simulator that can analyze moving information of Palette with RFID tags and the change about temperature, humidity and illumination is developed in this paper. Inventory accuracy, space equipment practical use, and decreasing of picking time, faulty storage and product loss by product processing ability elevation are expected by designing the way of operating of warehouse for most suitable use of system in physical distribution through these treatise.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.