• Proceedings of the Korean Nuclear Society Conference
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• 2004.05a
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• pp.106-106
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• 2004

• Nuclear Engineering and Technology
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• v.42 no.1
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• pp.115-124
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• 2010

According to wide-spread experience in many industries, a procedure is one of the most effective countermeasures to reduce the possibility of human related problems. Unfortunately, a systematic framework to evaluate the complexity of procedural tasks seems to be very scant. For this reason, the TACOM measure, which can quantify the complexity of procedural tasks, has been developed. In this study, the appropriateness of the TACOM measure is investigated by comparing TACOM scores regarding the procedural tasks of high speed train drivers with the associated workload scores measured by the NASA-TLX technique. As a result, it is observed that there is a meaningful correlation between the TACOM scores and the associated NASA-TLX scores. Therefore, it is expected that the TACOM measure can properly quantify the complexity of procedural tasks.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4170-4180
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• 2022

The task complexity (TACOM) measure was previously developed to quantify the complexity of proceduralized tasks conducted by nuclear power plant operators. Following the development of the TACOM measure, its appropriateness has been validated by investigating the relationship between TACOM scores and three kinds of human performance data, namely response times, human error probabilities, and subjective workload scores. However, the information reflected in quantified TACOM scores is still insufficient to determine the levels of complexity of proceduralized tasks for human reliability analysis (HRA) applications. In this regard, the objective of this study is to suggest criteria for determining the levels of task complexity based on logistic regression between human error occurrences in digitalized main control rooms and TACOM scores. Analysis results confirmed that the likelihood of human error occurrence according to the TACOM score is secured. This result strongly implies that the TACOM measure can be used to identify the levels of task complexity, which could be applicable to various research domains including HRA.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2018.11a
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• pp.527-528
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• 2018

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.550.2-550
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• 2001

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.5
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• pp.499-505
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• 2015

To properly design and assess a piezoelectric vibration energy harvester, it is necessary to consider the application of an efficiency measure of energy conversion. The energy conversion efficiency is defined in this work as the ratio of the electrical output power to the mechanical input power for a piezoelectric vibration energy harvester with an impedance-matched load resistor. While previous research works employed the electrical output power for approximate impedance-matched load resistance, this work derives an efficiency measure considering optimally matched resistance. The modified efficiency measure is validated by comparing it with finite element analysis results for piezoelectric vibration energy harvesters with three different values of the electro-mechanical coupling coefficient. New findings on the characteristics of energy conversion and conversion efficiency are also provided for the two different impedance matching methods.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.650-655
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• 2001

Wind speeds can vary rapidly and wind turbines cannot easily follow these variations because of their inertia and aerodynamic characteristics. For maximum energy extraction. the turbine blades should operate at their optimum tip speed ratio, but with rapid changes in wind speed. this is usually not possible. To improve the energy extraction from turbulent wind, it is necessary to establish an effective measure of the high frequency component of the wind. and then to use this measure to optimise the operation of the turbine controller for maximum energy extraction. This paper presents an approach for combining readings from three anemometers into a composite wind speed measurement. and using this signal to control the operation of a permanent magnet generator to achieve maximum energy extraction. The method combines simulation and experimental investigations into a heuristic algorithm. and demonstrates its effectiveness with field trials.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.37-43
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• 2012

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

• Proceedings of the Korea Database Society Conference
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• 2010.06a
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• pp.33-41
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• 2010

Recently the number of IT equipment have increased. It consumes large amounts of energy and is emissions of greenhouse gases. Co2 emissions with the PC and the monitor has the highest percentage to 39% more than other IT equipment. In addition, Plan for your PC's power management and technology development is being pursued in developed countries. To reduce energy costs of organizations with large numbers of the PC and to cut down on Co2 emissions, the energy load control technology of ACPI standards-based PC IS suggested. AMI-based PC power-management system was constructed, Approximately 20% of operating a result of the test power consumption was reduced. Looking at the case of the United States, PC monitors from the University of Wisconsin-Oshkosh was Sleep mode. As a result, the monitor on a, $ 20 for a year reduced energy costs. In GE(General Electronic), Approximately 75,000 PC's power setting time was Monitor Off :15 minutes/ Hard Drives Off 30 minutes/ System Standby 30 minutes/ Hibernation mode 2 hours. 1 year, electric bill was $ 2.5 million savings and 3 years electric bill was $ 6.5 million savings. Measuring energy usage data, using the measured data, electric energy management technology is not. Platform development to measure energy usage for Individual energy-consuming equipment is urgently required.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.