• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.405-410
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• 1994

The purpose of this paper is to present the details of design procedure of a nonlinear regulator by Riemannian geometric approach and to applied it to the case of a double-effect evaporator. A nonlinear geometric model is proposed on a direct sum space of a state vector and a control vector as well as in the previous parers by the authors. The geometric model is derived by replacing the orthogonal straight coordinate axes of a linear system on the direct sum space with the curvilinear coordinate axes. The integral manifold of the geometric model becomes homeomorphic to that of fictitious linear system. For the geometric model a nonlinear regulator with a performance index is designed renewedly by the procedure of optimization. The construction method of the curvilinear coordinate axes on which the nonlinear system behaves as a linear system is discussed. To apply the above regulator theory to double-effect evaporators especially to the pilot plant at the University of Alberta, a suitable nonlinear model is determined by the plant dynamics. The optimal control law is derived through the calculation of the homeomorphism. As a result it is confirmed that the regulator is effective and superior to that of the conventional control.

• 전자공학회논문지SC
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• 제46권5호
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• pp.38-43
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• 2009

본 논문에서는 자기 벡터 포텐셜 해석을 통해 3-코일 구조를 갖는 금속 검출기 헤드가 수신 코일에 미치는 유도 전압을 구하고, 금속 물체의 특성이 송신 코일의 임피던스에 미치는 영향을 유도하였다. 이러한 해석을 통하여 최적의 검출 성능을 위한 송신 코일과 수신 코일 간의 거리를 구하였고, 최대 유도 전압을 출력하는 금속의 위치를 유도하였다. 또한 기존의 수식과 비교하여 본 논문의 자기 벡터 포텐셜 해석에 대한 타당성을 입증하였다.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• Structural Engineering and Mechanics
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• 제75권5호
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• pp.585-594
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• 2020

Detecting the damage of indeterminate trusses is of major importance in the literature. This paper proposes a quick approach in this regard, utilizing a precise mathematical approach based on Finite Element Method. Different to a general two-step method defined in the literature essentially based on optimization approach, this method consists of three steps including Damage-Suspected Element Identification step, Imminent Damaged Element Identification step, and finally, Damage Severity Detection step and does not need any optimizing algorithm. The first step focuses on the identification of damage-suspected elements using an index based on modal residual force vector. In the second step, imminent damage elements are identified among the damage-suspected elements detected in the previous step using a specific technique. Ultimately, in the third step, a novel relation is derived to calculate the damage severity of each imminent damaged element. To show the efficiency and quick function of the proposed method, three examples including a 25-bar planar truss, a 31-bar planar truss, and a 52-bar space truss are studied; results of which indicate that the method is innovatively capable of suitably detecting, for indeterminate trusses, not only damaged elements but also their individual damage severity by carrying out solely one analysis.

• Smart Structures and Systems
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• 제2권3호
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.565-570
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Korean Chemical Engineering Research
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• 제46권4호
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• pp.764-768
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• 2008

소 고환 유래의 hyaluronidase 효소 PH-20을 pPIC9 expression vector를 사용하여 Pichia pastoris에서 발현하였다. 생산된 재조합 단백질 rPH-20b는 75 kDa의 분자량을 보였고 7460 units/L의 효소활성을 보였다. 세포내보다 세포외의 효소활성이 두배 높았다. pH 의 경우 buffer를 사용 안 한 경우가, 또한 $30^{\circ}C$ 배양 조건에서 높은 활성을 보였다. 1 M sorbitol 삼투압조건과 0.3% 메탄올의 생산유도인자를 사용시 성장과 생산에 유리하였으며 0.4 M 아르기닌을 첨가시 재조합 단백질의 분해가 감소하였다.