• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.483-494
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• 2023

Recently, gas turbine generators are widely used for frequency control of power systems. Although the inlet temperature of a gas turbine is a key factor related to the performance and lifespan of the device, the inlet temperature is not measured directly for reasons such as the turbine structure and operating environment. In particular, the inlet temperature of the reheating gas turbine is very important for stable operation management, but field workers are experiencing a lot of difficulties because the manufacturer does not provide information on the calculation formula. Therefore, in this study, we propose a method for estimating the inlet temperature of a gas turbine using a machine learning-based linear regression analysis method based on a polytropic process equation. In addition, by proposing an inlet temperature calculation algorithm through the usefulness analysis and verification of the inlet temperature calculation model obtained through linear regression analysis, it is intended to help to improve the level of reheat gas turbine combustion tuning technology.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.699-706
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• 2023

Recently, the 4 generation industry revolution is developed with advanced and combined with a variety of new technologies. Conventional healthcare system is applied with IoT application. It provides many advantages with mobility and swift data transfers to patient and doctor. In despite of these kinds of advantages, it occurred security issues between basic devices and protocols in their applications. Especially, internet of things have restricted and limited resources such as small memory capacity, low capability of computing power, etc. Therefore, we can not utilize conventional mechanism. In this paper, we analyzed attacks and vulnerability in terms of security issues. To analyze security structure, features, demands and requirements, we solve the methods to be reduced security issues.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.31-34
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• 2024

태양광 에너지는 탄소 중립 이행을 위한 주요 방안으로 많은 주목을 받고 있다. 태양광 발전량은 여러 환경적 요인에 따라 크게 달라질 수 있으므로, 정확한 발전량 예측은 전력 네트워크의 안정성과 효율적인 에너지 관리에 근본적으로 중요하다. 대표적인 인공지능 기술인 신경망(Neural Network)은 불안정한 환경 변수와 복잡한 상호작용을 효과적으로 학습할 수 있어 태양광 발전량 예측에서 우수한 성능을 도출하였다. 하지만, 신경망은 모델의 구조나 초매개변수(Hyperparameter)를 최적화하는 것은 복잡하고 시간이 많이 드는 작업이므로, 에너지 분야에서 실제 산업 적용에 한계가 존재한다. 본 논문은 2단계 신경망 최적화를 통한 태양광 발전량 예측 기법을 제안한다. 먼저, 태양광 발전량 데이터 셋을 훈련 집합과 평가 집합으로 분할한다. 훈련 집합에서, 각기 다른 은닉층의 개수로 구성된 여러 신경망 모델을 구성하고, 모델별로 Optuna를 적용하여 최적의 초매개변숫값을 선정한다. 다음으로, 은닉층별 최적화된 신경망 모델을 이용해 훈련과 평가 집합에서는 각각 5겹 교차검증을 적용한 발전량 추정값과 예측값을 출력한다. 마지막으로, 스태킹 앙상블 방식을 채택해 기본 초매개변숫값으로 설정해도 우수한 성능을 도출하는 랜덤 포레스트를 이용하여 추정값을 학습하고, 평가 집합의 예측값을 입력으로 받아 최종 태양광 발전량을 예측한다. 인천 지역으로 실험한 결과, 제안한 방식은 모델링이 간편할 뿐만 아니라 여러 신경망 모델보다 우수한 예측 성능을 도출하였으며, 이를 바탕으로 국내 에너지 산업에 이바지할 수 있을 것으로 기대한다.

• Journal of Advanced Navigation Technology
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• v.7 no.1
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• pp.59-71
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• 2003

The channel scheduler is suggested the radio resource management method in order to provide service with guaranteeing fairness and throughput to the users who use limited wireless channel. Proportional fairness scheduling algorithm is the channel scheduler used in the AMC(Adaptive Modulation and Coding)/TDM system, and this algorithm increases the throughput considering the user's time fairness. In this paper is suggested the channel scheduler combining CDM scheme available in AMC/TDM/CDM system. Unlike the system which only uses TDM which provide the only one user at the same slot, this scheduler can service a lot of users since this uses the CDM scheme with multi-cord channel. At every moment, allocation of transmission power to multi-channel users is problematic because of CDM scheme. In this paper, we propose a water-filling scheduling algorithm to solve the problem. Water-filling fairness(WF2) scheduling algorithm watches the average channel environment. So, this modified method guarantees fairness for each user in terms of power and service time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.301-308
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• 2021

It is important to develop a digital SOC (Social Overhead Capital) maintenance system for preemptive maintenance in response to the rapid aging of social infrastructures. Abnormal signals induced from structures can be detected quickly and optimal decisions can be made promptly using IoT sensors deployed on the structures. In this study, a digital SOC monitoring system incorporating a multimetric IoT sensor was developed for long-term monitoring, for use in cloud-computing server for automated and powerful data analysis, and for establishing databases to perform : (1) multimetric sensing, (2) long-term operation, and (3) LTE-based direct communication. The developed sensor had three axes of acceleration, and five axes of strain sensing channels for multimetric sensing, and had an event-driven power management system that activated the sensors only when vibration exceeded a predetermined limit, or the timer was triggered. The power management system could reduce power consumption, and an additional solar panel charging could enable long-term operation. Data from the sensors were transmitted to the server in real-time via low-power LTE-CAT M1 communication, which does not require an additional gateway device. Furthermore, the cloud server was developed to receive multi-variable data from the sensor, and perform a displacement fusion algorithm to obtain reference-free structural displacement for ambient structural assessment. The proposed digital SOC system was experimentally validated on a steel railroad and concrete girder bridge.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.49-62
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• 2024

Reckless development of the underground by rapid urbanization causes inspection delay on replacement of existing structure and installation new facilities. However, frequent accidents occur due to deviation in construction design planned by inaccurate location information of underground structure. Meanwhile, the electrical resistivity survey, knowns as non-destructive method, is based on the difference in the electric potential of electrodes to measure the electrical resistance of ground. This method is significantly advanced with multi-electrode and deep learning for analyzing strata. However, there is no study to quantitatively assess change in electrical resistance according to geometric conditions of structures. This study evaluates changes in electrical resistance through geometric parameters of electrodes and structure. Firstly, electrical resistance numerical module is developed using generalized mesh occurring minimal errors between theoretical and numerical resistance values. Then, changes in resistances are quantitatively compared on geometric parameters including burial depth, diameter of structure, and distance electrode and structure under steady current condition. The results show that higher electrical resistance is measured for shallow depth, larger size, and proximity to the electrode. Additionally, electric potential and current density distributions are analyzed to discuss the measured electrical resistance around the terminal electrode and structure.

• Korean Journal of Remote Sensing
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• v.33 no.5_3
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• pp.773-786
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• 2017

This study was to analyze data of climate change impacts on social infrastructure using text-mining methodology, and to visualize the spatial information by integrating those with regional data layers. First of all, the study identified that the following social infrastructure; power, oil and resource management, transport and urban, environment, and water supply infrastructures, were affected by five kinds of climate factors (heat wave, cold wave, heavy rain, heavy snow, strong wind). Climate change impacts on social infrastructure were then analyzed and visualized by regions. The analysis resulted that transport and urban infrastructures among all kinds of infrastructure were highly impacted by climate change, and the most severe factors of the climate impacts on social infrastructure were heavy rain and heavy snow. In addition, it found out that social infrastructure located in Seoul and Gangwon-do region were relatively largely affected by climate change. This study has significance that atypical data in media was used to analyze climate change impacts on social infrastructure and the results were translated into spatial information data to analyze and visualize the climate change impacts by regions.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.1051-1064
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• 2015

Currently, the H purification plant determines the hourly water intake amount based on operator experience and skill. Therefore, inevitably, there are deviations among operators. While meeting time-varying demand and maintaining the proper water level in the clean water reservoir, the methodology for minimizing electricity cost, when dealing with different electricity rate time zones, is a very complicated problem, which is beyond an operator's capability. To solve this problem, a linear programming (LP) model is proposed, which can determine the optimal hourly water intake amount for minimizing the daily electricity cost. It is shown that an inaccurate estimate for the hourly water usage in the demand areas causes the water level constraint to be violated, which is the weak point of the proposed LP method. However, several examples with real-field data show that we can practically and safely solve this problem with safety margins. It is also shown that the safety margin method still works effectively whether the estimate is accurate or not. The operators need not attend the site at all times under the proposed LP method, and we can additionally expect reductions in labor costs.