• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.57-61
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• 2015

Current research and development efforts and other projects for energy storage systems (ESS) have recently been gaining attention. This is due to the many applications where ESS, particularly batteries, can be used. Among these are peak shaving, frequency regulation, and stabilization of renewable energy output. KEPCO has completed the construction and demonstration of a 4-MW battery energy storage system (BESS) located in Jeju City to verify its practicability in the power grid. KEPCO Research Institute has also been developing technology for the commercialization of BESS, and has been conducting a trial run of a 52-MW ESS (28MW + 24MW of Seo-Anseong and Shin-Yongin substations) constructed in 2014 for frequency regulation. This paper discusses the development of operation methods, as well as an operation user interface, for the safe operation and monitoring of BESS used for frequency regulation in a power system. Included are operation and simulation methods for various normal and transient frequency situations that can be experienced by a power system. Also discussed are the results obtained after applying these methods to the 4-MW BESS and the 52-MW ESS, both used for frequency regulation. The technology in this paper will be applied to 500MW ESS for frequency regulation of KEPCO by 2017. It is expected that this technology helps a safe and reliable operation and control of ESS for frequency regulation through its continuous upgrade.

• Journal of Advanced Navigation Technology
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• v.8 no.2
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• pp.105-111
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• 2004

This paper describes the design of navigation system for an unmanned target drone which is operated by Korean army as for anti-air gun shooting training. Current target drone is operated by pilot control of on-board servo motor via remote control system. Automatic flight control system for the target drone greatly reduces work load of ground pilot and can increase application area of the drone. Most UAVs being operated nowdays use high-priced sensors as AHRS and IMU to measure the attitude, but those are costly. This paper introduces the development of low-cost automatic flight control system with low-cost sensors. The integrated automatic flight control system has been developed by integrating combining power module, switching module, monitoring module and RC receiver as an one module. The performance of navigation for low cost unmanned aerial vehicle, unmanned target drone as our test bed in this paper is verified by both Hardware in the loop simulation(HILS) to test performance of GPS as GPS output frequency high and results of flight test.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.580-585
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• 2010

In this paper, The object Tracking System is designed by SOPC based Nios II embedded processor and C2H compiler. And this system using single PTZ camera can effectively control IPs in the platform of SOPC based Nios II Embedded Processor and creating IP by C2H(C-To-Hardware) compiler for image-in/output, image-processing and devices of communication that can supply various monitoring information to network or serial. Accordingly, Special quality and processing speed of object tracking using high-quality algorism in the system is improved by hardware/software programming methods.

• Structural Engineering and Mechanics
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• v.67 no.2
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• pp.105-113
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• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.1954-1960
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• 2008

Modem Robot is used in all industries. Previous Robot was used by simplicity work, at recent times, robot is developed in form that can do action such as a person. Robot's action runs according to command repeat or in the every moment according to sensor's output value, achieve other action. In this raper, we studied about self-regulation transfer robot that follow Object autonomously. This robot can be used by purpose that carry heavy burden instead of human. Robot's composition is drive part which run object's position awareness Sensor, Processor that control action and Motor part. After robot is connects with Network, we did robot remote control and monitor the action situation of robot. For the methode to reduce drive error, we developed algorithm for outside environment. For an experiment we made the self-regulation robot. We showed the directivity of sensor, error of directivity and soft moving of robot. We showed the monitoring system and the execution screen for communication between robot and PC.

• Journal of Forest and Environmental Science
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• v.27 no.3
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• pp.205-210
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• 2011

This study aims to assess MEE(management effectiveness evaluations) on PAs(protected areas) for forest genetic resources which play an important role in biodiversity conservation, and then to suggest better ways to manage PAs for forest genetic resources. This study applies same indicators of the MEE on PAs as the ones described in the prior study(Ryu et al. 2011). The indicators applied are composed of five elements, thirty-two indicators which all would be grouped into one element by each traits. Overall indicators belonging to the element of output and outcome are comparatively low. Especially the ones related to the change of biodiversity, degree of ecosystem health, variation of civil complaint and visitor satisfaction are ranked mostly low. The element of input shows the low rank on the number of staff and budget. The score of indicators related to the identification of the threats of PAs and local communities' supports turn out to be low in the element of Context. The element of process, however, has scored low on Staff management, Education/Awareness programs and Governance, while the law enforcement, management regulation and capacity to prevent forest disasters have made relatively high score. Meanwhile, all indicators in the element of planning have scored relatively high as compared to the indicators belonged to other elements. This study suggests to strengthen a few constructive proposals, such as facilitating efficient management framework for PAs, developing local community cooperation program, establishing survey, research and monitoring system, and registering PAs to the WDPA(World Database on Protected Areas) according to the IUCN categories.

• Journal of IKEEE
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• v.22 no.4
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• pp.953-958
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• 2018

In this study, a portable detection system was developed that can detect harmful gas and signals simultaneously in an enclosed space of industrial sites and underground facilities. The developed system is a sensor module for gas detection, a patch type 1 channel small ECG sensor, a module for three-axial acceleration detection sensor, and a system for statistics. In order to verify the performance of the system modules, the digital resolution, signal frequency, output voltage, and ultra-small modules were evaluated. As a result of the performance of the developed system, the digital resolution was 300 (rps) and the signal amplification gain was 500 dB or more, and the ECG module was manufactured with $50mm{\times}10mm{\times}10mm$ to increase patch utilization. It is believed that the product of this research will be valuable if it is used as an IoT-based management system for real-time monitoring of industrial workers.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.139-143
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• 2019

Measurement of flow rate is required in various fields. Water meters are often used at home, and flow meters are used in water and sewage plants, petrochemical industries and so on.. A system is needed to monitor the flow rate in real time and notify immediately when flow rate is abnormal. Recently, with the development of the IoT it is possible to construct such devices at low cost. WeMos can be programmed with Arduino IDE as a mini wifii IoT module. The flow sensor can output a digital pulse proportional to the flow rate. In this paper, we developed the flow monitoring and warning system using WeMos and IoT technology. When the system operates, it calculates the flow rate, sends the value as JSON format to the server, monitors the flow rate as graph from the remote with the smartphone. We also implement the system to promptly send alert message to the smart phone using Pushbullet when the flow rate is abnormal.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.