• Journal of Hydrospace Technology
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• v.2 no.2
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• pp.24-35
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• 1996

This paper describes parameter identification techniques formulated for the estimation of maneuvering coefficients of a submerged body. The first part of this paper is concerned with the identifiability of the system parameters. The relationship between a stochastic linear time-invariant system and the equivalent dynamic system is investigated. The second is concerned with the development of the numerically stable identification technique. Two identification techniques are tested; one is the ma7mum likelihood (ML) methods using the Holder & Mead simplex search method and using the modified Newton-Raphson method, and the other is the modified extended Kalman filter (MEKF) method with a square-root algorithm, which can improve the numerical accuracy of the extended Kalman filter. As a results, it is said that the equations of motion for a submerged body have higher probability to generate simultaneous drift phenomenon compared to general state equations and only the ML method using the Holder & Mead simplex search method and the MEKF method with a square-root algorithm gives acceptable results.

• Journal of the Korean Society for Advanced Composite Structures
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• v.3 no.3
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• pp.38-46
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• 2012

The method based on various mathematical characteristic equations for identifying tensile forces in the cable structure system are used as response data to reflect the properties of the dynamic sensitivity. The vibration tests have been conducted with respect to levels of applied weight for the sagged cable. In this study, a set of natural frequencies are extracted from the measured dynamic data. Next, existing characteristic equation methods based these extracted natural frequencies are applied to identify tensil forces of the sagged cable system. Through several verification procedures, the proposed methods could be applied to a sagged cable system when the initial material data are insufficiency.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4684-4692
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• 2022

Radionuclide identification is an important part of the nuclear material identification system. The development of artificial intelligence and machine learning has made nuclide identification rapid and automatic. However, many methods directly use existing deep learning models to analyze the gamma-ray spectrum, which lacks interpretability for researchers. This study proposes an explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping. This method shows the area of interest of the neural network on the gamma-ray spectrum by generating a class activation map. We analyzed the class activation map of the gamma-ray spectrum of different types, different gross counts, and different signal-to-noise ratios. The results show that the convolutional neural network attempted to learn the relationship between the input gamma-ray spectrum and the nuclide type, and could identify the nuclide based on the photoelectric peak and Compton edge. Furthermore, the results explain why the neural network could identify gamma-ray spectra with low counts and low signal-to-noise ratios. Thus, the findings improve researchers' confidence in the ability of neural networks to identify nuclides and promote the application of artificial intelligence methods in the field of nuclide identification.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.375-382
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• 2006

This paper is about how to identify emotional information and how to detect a specific emotion from speech signals. For emotion identification and detection task. we use long-term acoustic feature parameters and select the optimal Parameters using the feature selection technique based on F-score. We transform the conventional SVM into probabilistic output SVM for our emotion identification and detection system. In this paper we propose three approximation methods for log-likelihoods in a hypothesis test and compare the performance of those three methods. Experimental results using the SUSAS database showed the effectiveness of both feature selection and Probabilistic output SVM in the emotion identification task. The proposed methods could detect anger emotion with 91.3% correctness.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.37-43
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• 2011

In pattern analysis, dimensionality reduction and reasonable identification rule generation are very important parts. This paper performed effectively the dimensionality reduction by grouping the sensors of which the measured patterns are similar each other, where genetic algorithms were used for combination optimization. To identify the gas type, this paper constructed the hierarchically identifiable rule base with two frames by using rough set theory. The first frame is to accept measurement characteristics of each sensor and the other one is to reflect the identification patterns of each group. Thus, the proposed methods was able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, this paper demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• 전기의세계
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• v.18 no.6
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• pp.53-57
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• 1969

적응성 혹은 비선형 계통의 제어가 발달됨에 따라 평상시 작동상태에서 그 계통의 특성을 아는 것이 중요시 된다. 이 특성은 계통의 입력과 출력에서 얻게 되는데 이런 과정을 식별(Identification)이라고 하며 이중 특히 이미 그 계통의 "토포로지"는 알려지고 파라미터만을 구하는 문제에 대해서 사용코저 제시된 여러가지 방식을 종합하고 검토해보았다. 방식의 구분은 다소 임의적이나 편의상 사용키로한다.의상 사용키로한다.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.413-413
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• 2004

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.89-97
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• 2021

Voice interface can not only make daily life more convenient through artificial intelligence speakers but also improve the working environment of the factory. This paper presents a voice-assisted work management system that supports both speech and speaker recognition. This system is able to provide machine control and authorized worker authentication by voice at the same time. We applied two speech recognition methods, Google's Speech application programming interface (API) service, and DeepSpeech speech-to-text engine. For worker identification, the SincNet architecture for speaker recognition was adopted. We implemented a prototype of the work management system that provides voice control with 26 commands and identifies 100 workers by voice. Worker identification using our model was almost perfect, and the command recognition accuracy was 97.0% in Google API after post- processing and 92.0% in our DeepSpeech model.

• Journal of Korean Society of Steel Construction
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• v.12 no.1 s.44
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• pp.83-91
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• 2000

The first Procedure to identify structural system by using measured data is transformation of data to frequency domain and try to recognize modal characteristics in graphical condition. Those methods are doubted about the reliability to the civil structures, especially bridges which has coupled and close modal characteristics. In this paper, feasibility of time domain modal Identification methods were examined and applied double least square method to overcome bias characteristics of the identification methods. To show the advantage of proposed method, simulation were carried out for mass-spring model. And to examine the usage of the method in realistic case, sensitivity of the methods to noise was performed.