• 대한수학교육학회지:학교수학
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• 제5권4호
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• pp.477-506
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• 2003

본 연구는 중학교 3학년 학생들이 문자와 식, 방정식, 함수에 대한 문제 해결과정에서 미지수, 변수, 매개변수로 사용되는 문자의 의미를 어떻게 이해하고 있는지를 살펴봄으로써, 매개변수로서 문자가 이해되는 과정을 분석한다. 그리고 학생들이 문제를 해결할 때 매개변수로서의 문자의 의미를 이해하면서 유연하게 변환할 수 있도록 메타인지 사고전략을 활용한 수업 설계 모형인 '자기질문에 의한 자기조정형 수업모형을 제안한다. 분석결과, 학생들은 문제의 문맥에서 매개변수의 역할을 미지수, 변수의 역할과 비교해 볼 때 매개변수는 상수를 대신하는 문자로 인식하는 경향이 강했으며, 주어진 방정식의 매개변수였던 문자는 구문론적 조작을 거치면서 변수나 미지수의 역할로 변환하는 경우에 그 의미와 역할을 불확실하게 이해하고 있었다. 그리고, 문맥상 매개변수의 의미를 파악하여 생각하기보다는 문맥의 전후관계를 살피지 않고 연산과 기호조작을 이용하여 파악하는 경향이 강했으며, 직선의 그래프로 제시했을 때 학생들은 매개변수의 의미를 좌표평면 상에서 직선의 위치를 결정하는 요소로서 해석하는 능력이 부족하였다.

• 영어어문교육
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• 제12권2호
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• pp.177-194
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• 2006

Snyder (2001) proposes that complex predicate constructions are interrelated by shared dependence on a single parameter, the Compounding Parameter, and that the global application of the parameter explains the simultaneous acquisition of the complex predicate constructions and N-N compounds in L1 acquisition of English. Slabakova (2002) examined the status of the Compounding Parameter in the acquisition of L2 Spanish by instructed learners. The result of the study, however, was not compatible with the prediction of the Compounding Parameter, possibly due to the availability of negative evidence in the input. Building upon Slabakova's study, this paper examines the status of the Compounding Parameter in naturalistic L2 learning. It is shown that the naturalistic L2 learners do not acquire the complex predicate constructions and N-N compounds concurrently contra to the prediction of the Compounding Parameter. It is suggested that the validity of the Compounding Parameter as a theoretical construct be reconsidered.

• 제어로봇시스템학회논문지
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• 제10권5호
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• pp.395-401
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• 2004

We present an AILC(Adaptive Iterative Learning Control) scheme and a sufficient condition for system parameter identification for uncertain robotic systems that perform the same tasks repetitively. It is guaranteed that the joint velocity and position asymptotically converge to the reference joint velocity and position, respectively. In addition, it is proved that a sufficient condition for parameter identification is the PE(Persistent Excitation) condition on the regressor matrix evaluated at the reference trajectory during the operation period. Since the regressor matrix on the reference trajectory can be easily computed prior to the real robot operation, the proposed algorithm provides a useful method to verify whether the parameter error converges to zero or not.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.446-448
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• 2021

In training a deep learning model, it is crucial to tune various hyperparameters and gain speed and accuracy. While hyperparameters that mathematically induce convergence impact training speed, system parameters that affect host-to-device transfer are also crucial. Therefore, it is important to properly tune and select parameters that influence the data loader as a system parameter in overall time acceleration. We propose an automated framework called Num Worker Tuner (NWT) to address this problem. This method finds the appropriate number of multi-processing subprocesses through the search space and accelerates the learning through the number of subprocesses. Furthermore, this method allows memory efficiency and speed-up by tuning the system-dependent parameter, the number of multi-process spawns.

• 한국산업융합학회 논문집
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• 제27권1호
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• pp.161-172
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• 2024

Approximately 89% of all capsizing accidents involve small vessels, and despite their relatively high accident rates, small vessels are not subject to ship stability regulations. Small vessels, where the provision of essential basic design documents for stability calculations is omitted, face challenges in directly calculating their stability. In this study, considering that the majority of domestic coastal small vessels are of the Chine-type design, the goal is to establish the major hull form characteristic data of vessels, which can be identified from design documents such as the general arrangement drawing, as input data. Through the application of a deep learning approach, specifically a multilayer neural network structure, we aim to infer hydrostatic curves, operational draft ranges, and more. The ultimate goal is to confirm the possibility of directly calculating the initial stability of small vessels.

• Nuclear Engineering and Technology
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• 제56권6호
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• pp.1989-2001
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• 2024

Artificial intelligence (AI) techniques are now being considered in the nuclear field, but application faces with the lack of actual plant data. For this reason, most previous studies on AI applications in nuclear power plants (NPPs) have relied on simulators or thermal-hydraulic codes to mimic the plants. However, it remains uncertain whether an AI model trained using a simulator can properly work in an actual NPP. To address this issue, this study suggests the use of metadata, which can give information about parameter trends. Referred to here as robust AI, this concept started with the idea that although the absolute value of a plant parameter differs between a simulator and actual NPP, the parameter trend is identical under the same scenario. Based on the proposed robust AI, this study designs an event diagnosis algorithm to classify abnormal and emergency scenarios in NPPs using prototypical learning. The algorithm was trained using a simulator referencing a Westinghouse 990 MWe reactor and then tested in different environments in Advanced Power Reactor 1400 MWe simulators. The algorithm demonstrated robustness with 100 % diagnostic accuracy (117 out of 117 scenarios). This indicates the potential of the robust AI-based algorithm to be used in actual plants.

• ETRI Journal
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• 제15권2호
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.755-766
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• 2008

This study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.

• Journal of Communications and Networks
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• 제14권5호
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Nuclear Engineering and Technology
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• 제53권6호
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.