• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.501-503
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• 2017

In the water sector, various data such as flow rate, pressure, water quality and water level are collected during the whole process of water purification plant and piping system. The collected data is stored in each water treatment plant's DB, and the collected data are combined in the regional DB and finally stored in the database server of the head office of the Korea Water Resources Corporation. Various abnormal data can be generated when a measuring instrument measures data or data is communicated over various processes, and it can be classified into missing data and wrong data. The cause of each abnormal data is different. Therefore, there is a difference in the method of detecting the wrong side and the missing side data, but the method of cleansing the data is the same. In this study, a program that can automatically refine missing or wrong data by applying deep learning LSTM (Long Short Term Memory) algorithm will be studied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.141-146
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• 1999

This paper describes the communication method of an auto-startup system. The Auto-Startup system is designed to operate a nuclear power plant automatically during the startup operation . In general , the operations during startup in existing plant have only been manually controlled by the operator. The manual operation caused to the operator mistake. The Auto-Startup system consists of the Distributed Control System(DCS) and G2 (Expert System). Also, Functional Test Facility(FTF) provides the plant's real-data for an Auto-Startup system. So, it is necessary to develop the communication method between these systems. We developed two methods ; one is a network and the other is a hardwire line. To communicate between these systems (DCS-G2 and DCS-FTF) , we developed the communication program. In case of DCS-FTF, we used the TCP/IP and VXI. BUt, in case of DCS-G2 , we , what it called , developed the bridge program using the GSI(G2 Standard Interface). We test to check the function of the important parameter, in time, for analysis of the developed communication method. The results are a good performance when we check the communication time of important parameter. We conclude that Auto-startup system could save heat-up time about at least 5 hours and reduced the change of the reactor operation and trip.

• 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.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2266-2273
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• 2024

A machine learning platform is proposed for the diagnosis of a severe accident progression in a nuclear power plant. To predict the key parameters for accident management including lost signals, a long short term memory (LSTM) network is proposed, where multiple accident scenarios are used for training. Training and test data were produced by MELCOR simulation of the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident at unit 3. Feature variables were selected among plant parameters, where the importance ranking was determined by a recursive feature elimination technique using RandomForestRegressor. To answer the question of whether a reduced order ML model could predict the complex transient response, we performed a systematic sensitivity study for the choices of target variables, the combination of training and test data, the number of feature variables, and the number of neurons to evaluate the performance of the proposed ML platform. The number of sensitivity cases was chosen to guarantee a 95 % tolerance limit with a 95 % confidence level based on Wilks' formula to quantify the uncertainty of predictions. The results of investigations indicate that the proposed ML platform consistently predicts the target variable. The median and mean predictions were close to the true value.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2074-2079
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• 2018

Global state space's optimal policy is used for offline controller in the form of table by using Dynamic Programming. If an optimal policy table has a large amount of control data, it is difficult to use the system in a low capacity system. To resolve these problem, controller using the compressed optimal policy table is proposed in this paper. A DCT is used for compression method and the cosine function is used as a basis. The size of cosine function decreased as the frequency increased. In other words, an essential information which is used for restoration is concentrated in the low frequency band and a value of small size that belong to a high frequency band could be discarded by quantization because high frequency's information doesn't have a big effect on restoration. Therefore, memory could be largely reduced by removing the information. The compressed output is stored in memory of embedded system in offline and optimal control input which correspond to state of plant is computed by interpolation with Inverse DCT in online. To verify the performance of the proposed controller, computer simulation was accomplished with a one link inverted pendulum.

• The Korean Journal of Physiology and Pharmacology
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• v.21 no.6
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• pp.579-589
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• 2017

Anesthetics are used extensively in surgeries and related procedures to prevent pain. However, there is some concern regarding neuronal degeneration and cognitive deficits arising from regular anesthetic exposure. Recent studies have indicated that brain-derived neurotrophic factor (BDNF) and cyclic AMP response element-binding protein (CREB) are involved in learning and memory processes. Genistein, a plant-derived isoflavone, has been shown to exhibit neuroprotective effects. The present study was performed to examine the protective effect of genistein against isoflurane-induced neurotoxicity in rats. Neonatal rats were exposed to isoflurane (0.75%, 6 hours) on postnatal day 7 (P7). Separate groups of rat pups were orally administered genistein at doses of 20, 40, or 80 mg/kg body weight from P3 to P15 and then exposed to isoflurane anesthesia on P7. Neuronal apoptosis was detected by TUNEL assay and FluoroJade B staining following isoflurane exposure. Genistein significantly reduced apoptosis in the hippocampus, reduced the expression of proapoptotic factors (Bad, Bax, and cleaved caspase-3), and increased the expression of Bcl-2 and Bcl-xL. RT-PCR analysis revealed enhanced BDNF and TrkB mRNA levels. Genistein effectively upregulated cAMP levels and phosphorylation of CREB and TrkB, leading to activation of cAMP/CREB-BDNF-TrkB signaling. PI3K/Akt signaling was also significantly activated. Genistein administration improved general behavior and enhanced learning and memory in the rats. These observations suggest that genistein exerts neuroprotective effects by suppressing isoflurane-induced neuronal apoptosis and by activating cAMP/CREB-BDNF-TrkB-PI3/Akt signaling.

• Convergence Security Journal
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• v.16 no.7
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• pp.173-180
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• 2016

There has been a growing interest in automation of factories in the country. And, the development in this regard has been actively attempted. In particular, on the basis of the "innovation 3.0 strategy of manufacturing industry", interest in the smart of the manufacturing plant of small and medium-sized enterprises has increased rapidly. As well as policy for building smart plant, technical, seeking a strategic approach. But, in order to introduce such a smart plant or factory automation systems, manufacturing plant security with vulnerability and personal information protection problems, it should always be top priority there. Accordingly, we provide the applicable lightweight secure protocols in RFID communication. It is a wireless communication technology that is most often introduced for factory automation. Our proposed lightweight secure protocol in this study, less the number of calculations in comparison with the existing public key-based and the symmetric key encryption algorithm. And it is fast in compare with the existing protocol. Furthermore, we design that it system can support to low power consumption and small consume the memory size.

• Korean Journal of Medicinal Crop Science
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• v.12 no.1
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• pp.1-9
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• 2004

Prolyl endopeptidase (PEP) is proline-specific serine protease, cleaving peptide bonds on the biologically active neuropeptides such as substance P, vassopressin, and thyrotropin-releasing hormone and is, therefore, suggested to play important roles in learning and memory process. In this work, the inhibitory effect of plant extracts on PEP was investigated. Out of 200 plant extracts, Prunus mume, Pyrola. japonica, Hypericum ascyron, Astilbe chinensis var. typica, and Elaeagnus umbellata inhibited more than 90% of PEP activity at the concentration of 5 ppm.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.123-132
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• 2019

This study is to estimate the spatial soil moisture using Terra MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data and machine learning technique. Using the 3 years (2015~2017) data of MODIS 16 days composite NDVI (Normalized Difference Vegetation Index) and daily Land Surface Temperature (LST), ground measured precipitation and sunshine hour of KMA (Korea Meteorological Administration), the RDA (Rural Development Administration) 10 cm~30 cm average TDR (Time Domain Reflectometry) measured soil moisture at 78 locations was tested. For daily analysis, the missing values of MODIS LST by clouds were interpolated by conditional merging method using KMA surface temperature observation data, and the 16 days NDVI was linearly interpolated to 1 day interval. By applying the RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) artificial neural network model, 70% of the total period was trained and the rest 30% period was verified. The results showed that the coefficient of determination ($R^2$), Root Mean Square Error (RMSE), and Nash-Sutcliffe Efficiency were 0.78, 2.76%, and 0.75 respectively. In average, the clay soil moisture was estimated well comparing with the other soil types of silt, loam, and sand. This is because the clay has the intrinsic physical property for having narrow range of soil moisture variation between field capacity and wilting point.