• Journal of Korea Water Resources Association
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• v.57 no.4
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• pp.249-261
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• 2024

Climate change has intensified the severity of extreme floods, presenting substantial challenges to dam management and operation. Traditionally, flood control strategies for dam operations have been based on theoretical scenarios, such as designed floods, without taking into account downstream conditions. However, in practice, managing floods involves operating dams based on climate forecasts. This strategy encounters challenges due to the limited predictability of climate forecasts, which in turn leads to uncertainty in decisionmaking among dam managers. This study proposes a flood control approach for dam operations that involves gradually increasing the outflow, considering the operational constraints and potential downstream damage, based on inflow data. The effectiveness of this method was assessed through simulation, employing both a designed flood and data from the most significant historical flood. The dam operation strategy for flood control presented in this study provides a framework for dam operators, facilitating consistent decisionmaking in flood management by integrating realistic dam operational conditions.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.4
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• pp.143-150
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• 2013

The School Curriculum amend in 2007 mandates the contents from which students can learn the principles and concepts of computer science. Computer Science is one of the most rapidly changing subjects, and the Informatics textbook should accurately explain the basic principles and concepts based on the latest technology. However, we found that the middle school textbooks in circulation lack accuracy and consistency in describing CPU. This paper attempted to discover the root-cause of the fallacy and suggest timely and appropriate explanation based on the historical and technical analysis. According to our study, it is appropriate to state that CPU is composed of datapath and control unit. The Datapath performs operations on data and holds data temporarily, and it is composed of the hardware components such as memory, register, ALU and adder. The Control unit decides the operation types of datapath elements, main memory and I/O devices. Nevertheless, considering the technological literacy of middle school students, we suggest the terms, 'arithmetic part' and 'control part' instead of datapath and control unit.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.6
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• pp.381-389
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• 2021

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.23-28
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• 2021

The article discusses the features of adaptation of the parameters of the physical layer of data transmission in self-organizing networks based on unmanned aerial vehicles operating in the conditions of "smart cities". The concept of cities of this type is defined, the historical path of formation, the current state and prospects for further development in the aspect of transition to "smart cities" of the third generation are shown. Cities of this type are aimed at providing more comfortable and safe living conditions for citizens and autonomous automated work of all components of the urban economy. The perspective of the development of urban mobile automated technical means of infocommunications is shown, one of the leading directions of which is the creation and active use of wireless self-organizing networks based on unmanned aerial vehicles. The advantages of using small-sized unmanned aerial vehicles for organizing networks of this type are considered, as well as the range of tasks to be solved in the conditions of modern "smart cities". It is shown that for the transition to self-organizing networks in the conditions of "smart cities" of the third generation, it is necessary to ensure the adaptation of various levels of OSI network models to dynamically changing operating conditions, which is especially important for the physical layer. To maintain an acceptable level of the value of the bit error probability when transmitting command and telemetry data, it is proposed to adaptively change the coding rate depending on the signal-to-noise ratio at the receiver input (or on the number of channel decoder errors), and when transmitting payload data, it is also proposed to adaptively change the coding rate together with the choice of modulation methods that differ in energy and spectral efficiency. As options for the practical implementation of these solutions, it is proposed to use an approach based on the principles of neuro-fuzzy control, for which examples of determining the boundaries of theoretically achievable efficiency are given.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• ETRI Journal
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• v.42 no.4
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• pp.585-595
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• 2020

Integration of multiple communication technologies in a smart grid (SG) enables employing cognitive radio (CR) technology for improving reliability and security with low latency by adaptively and effectively allocating spectral resources. The versatile features of the CR enable the smart meter to select either the unlicensed or the licensed band for transmitting data to the utility company, thus reducing communication outage. Demand response management is regarded as the control unit of the SG that balances the load by regulating the real-time price that benefits both the utility company and consumers. In this study, joint allocation of the transmission power to the smart meter and consumer's demand is formulated as a two stage multi-armed bandit game in which the players select their optimal strategies noncooperatively without having any prior information about the media. Furthermore, based on historical rewards of the player, a real-time pricing adaptation method is proposed. The latter is validated through numerical results.

• Proceedings of the Optical Society of Korea Conference
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• 2000.02a
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• pp.68-68
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• 2000

In this paper a very brief review of historical development of optical science and technology in China is presented. More attention has been pain on Modem Optics, which developed since 1950s. The recent development of optical science and technology in following fields are introduced. 1. Optical engineering and instrumentation (tracking theodolites, high speed cameras, satellite laser ranging systems, satellite flying attitude control, cameras for remote sensing, astronomical optical instrument) 2. Applied optics (adaptive optics, optical metrology, infrared optics, optical processing, optical holography) 3. Laser science and technology (ultrashort pulse lasers, UV-X ray lasers, high power laser facilities and laser fusion, laser isotope separation) 4. Laser and nonlinear materials (rare earth elements doped laser glasses and crystals, tunable laser crystals, borate series and organic nonlinear crystals) 5. Optoelectronic science and technology (Optical communication, optical data storage, optical computing) The current situation and developing prospect of optical and optoelectronic industry in China are presented. Furthermore it points out that the optical industry could be developed vigorously only if products development capacity is enhanced and new products industrialization is heightened. The main research and education institutions in the optics field in China, as well as the Chinese Optical Society (COS) are introduced.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.233-240
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• 2000

This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1051-1053
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• 2012

SCADA, DCS, PLC 등 산업제어시스템은 전기, 수도, 수송, 가스 및 석유와 같은 국가기반시설의 감시 및 제어를 통해 위험의 조기 예측, 대응, 각 공정의 품질 향상 등에 기여하고 있다. 산업제어시스템은 HMI(Human Machine Interface), 이력 데이터베이스, 각 센서 H/W 및 S/W 기술로 구성되는데, 그 중 이력 데이터베이스는 실시간으로 들어오는 디지털 및 아날로그 형태의 이력 데이터에 대한 효과적으로 처리하기 위한 주요 요소이다. 현재, 국내에서는 히스토리안 등 주로 외산 제품에 의존하고 있어 이에 대한 기반 기술 연구 및 관련 산업화가 요구된다. 또한, 이력 데이터베이스의 종류 및 특성에 대한 연구가 선행되어야 한다. 본 논문에서는 산업제어시스템에 주로 적용된 이력 데이터베이스들에 대해 자세히 살펴보고, 일반적으로 사용되는 데이터와 산업제어시스템에서 사용하는 이력 데이터와 트랜잭션의 특징을 살펴봄으로써 산업제어 응용에서 요구되는 이력 데이터베이스가 어떤 모습을 갖추어야 할 지에 대한 이해를 높이고자 한다.