• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.200.1-200.1
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• 2010

Solar energy is one of the most promising energy resources in the future. For the application and dissemination of solar energy technologies in various fields, reliable data sets of solar irradiation are needed for engineers, researchers, businessmen, and policy makers. Global horizontal solar radiation is needed for the use of flat plate collector, solar domestic hot water system, photovoltaic devices and passive systems like green house. In many countries, solar radiation data accumulated for more then 40 or 50 years and typical weather data are published with average of more then 30 years. In Korea, those global total radiations are measured for about 30 years. With the connections of computer network, measured data could be transmitted to the central control system at key station through Ethernet lines. The data acquisition systems are connected to be automatically controlled by the monitoring network. Global horizontal solar radiation data 16 locations were measured and averaged from 1982 to 2008.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.401-404
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• 2011

Considerations for green house gas (GHG) monitoring over next generation network (NGN) are regarded as a green convergence service for the successful reduction of GHG emission leading to resolve global warming issue in that NGN is expected to provide secure connections to fixed-and-mobile converged (FMC) features. Model-based scenario approach is an appropriate way to standardize and actualize the desired service. This paper first describes the service scenario of GHG monitoring service over NGN.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.74-85
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• 2021

Global Navigation Satellite System (GNSS) receivers are intrinsically vulnerable to radio frequency jamming signals due to the fundamental property of radio navigation systems. A GNSS jamming monitoring system that is capable of jamming detection, classification and localization is essential for infrastructure for autonomous driving systems. For these 3 functionalities, a GNSS jamming monitoring network consisting of a multiple of low-cost GNSS receivers distributed in a certain area is needed, and the precise time synchronizaion between multiple independent GNSS receivers in the network is an essential element. This paper presents a precise time synchronization method based on the direct use of Time Difference of Arrival (TDOA) technique in signal domain. A block interpolation method is additionally incorporated into the method in order to maintain the precision of time synchronization even with the relatively low sampling rate of the received signals for computational efficiency. The feasibility of the proposed approach is verified in the numerical simualtions.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1251-1258
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• 2023

Traditional markets are infrastructure with facilities and characteristics of very high population density. Recently, arcades have been installed through traditional market modernization policies, and aging infrastructure has been repaired. However, gas and electrical facilities of traditional markets cannot be easily replaced because of its high density. And because regular inspections are not conducted, management of facilities is on very poor condition. In addition, when a fire occurs in a traditional market, the fire easily spreads to nearby stores and is likely to spread to a large fire because of a lot of highly flammable substances. Smoke detectors and heat detectors are installed in most traditional markets to monitor fires, but malfunctions are frequent due to the nature of smoke detectors and heat detectors, and network facilities are not properly maintained. Therefore, in this study, gas detection sensors and flame detectors are additionally installed in Gwangmyeong Traditional Market, and a digital twin-based traditional market fire monitoring system is implemented in conjunction with existing sensors in the market's 3D model. With this digital twin based fire monitoring system, we can reduce the malfunctions of fire detect sensors, and can easily guide the evacuation route.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.293-308
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• 2014

In the aeronautical environment, numerous regulatory and communication protocols exist that cover interconnection of on-board equipment inside the aircraft. Developed and implemented by the airlines since the 1960s, these communication systems are reliable, strong, certified and able to contact different sensors distributed throughout the aircraft. However, the scenario is slightly different in the structural health monitoring (SHM) field as the requirements and specifications that a global SHM communication system must fulfill are distinct. The number of SHM sensors installed in the aircraft rises into the thousands, and it is impossible to maintain all of the SHM sensors in operation simultaneously because the overall power consumption would be of thousands of Watts. This design of a new communication system must consider aspects as management of the electrical power supply, topology of the network for thousands of nodes, sampling frequency for SHM analysis, data rates, selected real-time considerations, and total cable weight. The goal of the research presented in this paper is to describe and present a possible integration scheme for the large number of SHM sensors installed on-board an aircraft with low power consumption. This paper presents a new communications system for SHM sensors known as the Bi-Instruction Link Bi-Operator (BILBO).

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.915-922
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• 2003

The Grid is an infrastructure to connect heterogeneous resources that are scattered over areas with high-speed network and to cooperate with each other. To support Grid applications, network resources should be managed, since the network has to be safe and reliable. The Grid Monitoring Architecture Working Group (GMAWG) of the Global Grid Forum (GGF) proposed an effective architecture to be scalable across wide-area networks and encompass a large number of heterogeneous resources. In this paper, we describe the design and implementation of Grid network monitoring system based on the GMA for practical network management. By this system, network operations center can form a management system flexibly and scalably for Grid network.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.10
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• pp.10-20
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• 1994

This paper presents a CATV SMS (Status Monitoring System) using HDLC (High Level Data Link Contorl) protocol and the system implementation.The system specifications were derived from the analysis of technical status and requirement of the domestic CATV industry. For the interoperability with a global network management system in the future, HDLC protocol was adopted in the system. The system performance was improved by using the communication controller chip and the large data buffer. For reducting the communication problems induced by accumulated noise in up-stream data channels, the system was designed that the different communication channel can be assigned to each proper mass of terminal. The operating software was designed with menu driven user interface and have various functions for the convenience of users. The test result of the implemented system at the experimental network showed good performance and suitability for a coaxial CATV Status Monitoring System.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.308-314
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• 2022

As a result of the global effect of infectious diseases like COVID-19, remote patient monitoring has become a vital need. Surgical ICU monitors are attached around the clock for patients in critical care. Most ICU monitor systems, on the other hand, lack an output port for transferring data to an auxiliary device for post-processing. Similarly, strapping a slew of wearables to a patient for remote monitoring creates a great deal of discomfort and limits the patient's mobility. Hence, an unique remote monitoring technique for the ICU monitor's physiologically vital readings has been presented, recognizing this need as a research gap. This mechanism has been put to the test in a variety of modes, yielding an overall accuracy of close to 90%.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.95-99
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect. locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal. Rising time. Half period. and Global time, they are used as the inputs to neural network . Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising clime. Half Period amplitude. The result shored that the neural network would be applied to LPMS. Also, applying the neural network to thin practical false alarm data during startup and impact test signal at nuclear power plant, the false alarms are reduced effectively.