• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.493-499
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• 2017

This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.38-42
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• 2012

This paper describes electrical fire on residential environment such as apartment and detached house caused by defect of interior wiring. We carried out experimental study on short circuit characteristics by the interior wiring length. We were measured arc current, arc energy and interrupting time of earth leakage current circuit breaker(ELB), when an interior wiring break out short circuit in residential environment. From the experiment results, the longer of the interior wiring, the magnitude of arc current decreased and the interrupting time of ELB increased. When applied the A maker's ELB, the strength of arc current and interrupting time of ELB was 254 A and 245 ms respectively at 30 m interior wiring length. In 3 m interior wiring length, arc current and interrupting time was 716 A and 4.24 ms respectively. Arc energy was dependent on the magnitude of arc current and the interrupting time of ELB, the longer the interrupting time, arc energy increasing. In this paper, minimum arc energy was 277 J using C maker's ELB and 3 m interior wiring length(arc current 283 A, interrupting time of breaker 6.28 ms). Therefore in the residential environment, short circuit caused by defect of the interior wiring lead to electrical fire.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.37-39
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. Since the background noise presenting to record will introduce disturbance while we just want to get the stationary parameters to represent the corresponding speech section, in particular, a major source of error in automatic recognition system of isolated words is the inaccurate detection of beginning and ending boundaries of test and reference templates, thus we must find potent method to remove the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two simple time-domain measurements - short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Comparing with the conventional methods based on short-time energy and short-time zero-crossing rate, the novel approach based on time-domain Lyapunov Exponents(LEs) is low complexity and suitable for Digital Isolated Word Recognition System.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1195-1205
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• 2022

Recent developments on spectral diffusion algorithms, i.e., algorithms which exploit the projection of the solution on the eigenfunctions of the Laplacian operator, demonstrated their effective applicability in fast transient conditions. Nevertheless, the numerical error introduced by these algorithms, together with the uncertainties associated with model parameters, may impact the reliability of the predictions on short-lived volatile fission product release from nuclear fuel. In this work, we provide an upper bound on the numerical error introduced by the presented spectral diffusion algorithm, in both constant and time-varying conditions, depending on the number of modes and on the time discretization. The definition of this upper bound allows introducing a methodology to a priori bound the numerical error on short-lived volatile fission product retention.

• Journal of Korean Society on Water Environment
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• v.34 no.1
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• pp.96-108
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• 2018

Large efforts have recently been made on research and development of sustainable and energy-efficient short-cut nitrogen removal processes owing to strong attention to the energy neutral/positive wastewater treatment system. Anaerobic ammonium oxidizing bacteria (anammox bacteria) have been highlighted since 1990's due to their unique advantages including 60% less energy consumption, nearly 100% reduction for carbon source requirement, and 80% less sludge production. Side-stream short-cut nitrogen removal using anammox bacteria and partial nitritation anammox (PN/A) has been well established, whereas substantial challenges remain to be addressed mainly due to undesired main-stream conditions for anammox bacteria. These include low temperature, low concentrations of ammonia, nitrite, free ammonia, free nitrous acid or a combination of those. In addition, an anammox side-stream nitrogen management is insufficient to reduce overall energy consumption for energy-neutral or energy positive water resource recovery facility (WRRF) and at the same time to comply with nitrogen discharge regulation. This implies the development of the successful main-stream anammox based technology will accelerate a conversion of current wastewater treatment plants to sustainable water and energy recovery facility. This study discusses the status of the research, key mechanisms & interactions of the protagonists in the main-stream PN/A, and control parameters and major challenges in process development.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• Journal of Korean Biological Nursing Science
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• v.22 no.2
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• pp.111-118
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• 2020

Purpose: The purpose of this study was to understand the impact of long- and short-term energy drinks on anxiety-like, depressionlike, and cognitive behavior in adolescent rats. Methods: Adolescent rats (age six weeks) were randomly classified into a control group (CON), a long-term administration group (LT), and a short-term administration group (ST). The LT group was orally administered 1.5 mL/100 g (body weight) of energy drink twice daily for 14 days, the ST group was orally administered for one day, and the control group applied the same amount of normal saline. Later, an open-field test, a forced swim test, novel object recognition test, and an 8-arm radial maze test was conducted to assess the rats' anxiety, depression, and cognitive function. Results: There were different effects in the long- and short-term groups of energy drink administration. In the LT group, anxiety- and depressive-like behavior increased because of increased movement in the side corner and decrease of immobility time. Also, the time to explore novel objects decreased, and the number of correct responses was reduced, indicating a learning and memory function disorder. However, the ST group was not different from the control group. Conclusion: These results indicate that long-term consumption of energy drinks can increase anxiety-like, depression-like behavior, and this can lead to decrease in learning and memory functions. Thus, nurse and health care providers should understand the impact of energy drink consumption in adolescence to provide appropriate practices and education.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.1
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• pp.49-56
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• 2015

As WSN is energy constraint so energy efficiency of nodes is important. Because avoiding long distance communication, clustering operating in rounds is an efficient algorithm for prolonging the lifetime of WSN and its performance depends on duration of a round. A short round time leads to frequent re-clustering while a long round time increases energy consume of cluster heads more. So existing clustering schemes determine proper round time, based on the parameters of initial WSN. But it is not appropriate to apply the round time according to initial value throughout the whole network time because WSN is very dynamic networks nodes can be added or vanished. In this paper we propose a new algorithm which calculates the round time relying on the alive node number to adapt the dynamic WSN. Simulation results validate the proposed algorithm has better performance in terms of energy consumption of nodes and loss rate of data.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1419-1424
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• 2018

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the Kalman filter algorithm is proposed. In order to apply the Kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the Kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2017.10a
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• pp.371-372
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• 2017