• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.83-85
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• 2007

A new approach on identifying a first-order plus time-delay (FOPTD) model using finite--duration pulse inputs has been presented recently [1]. The identification methods are very simple because it is enough to observe only two extremes and the time when they occur in the transient response to pulse input. However, when there is mismatch between actual system and FOPTD model. how sensitive the methods are has not been studied. In this paper, we investigate robustness issue of those identification algorithms in the presence of the model structure mismatch and uncertainties. Through an example we will demonstrate it.

• Smart Structures and Systems
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• v.21 no.5
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• pp.677-686
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• 2018

The structural responses are often used to identify the structural local damages. However, it is usually difficult to gain the responses of the track, as the sensors cannot be installed on the track directly. The vehicles running on a track excite track vibration and can also serve as response receivers because the vehicle dynamic response contains the vibration information of the track. A damage identification method using the vehicle responses and sensitivity analysis is proposed for the vehicle-track coupling system in this paper. Different from most damage identification methods of vehicle-track coupling system, which require the structural responses, only the vehicle responses are required in the proposed method. The local damages are identified by a sensitivity-based model updating process. In the vehicle-track coupling system, the track is modeled as a discrete point supported Euler-Bernoulli beam, and two vehicle models are proposed to investigate the accuracy and efficiency of damage identification. The measured track irregularity is considered in the calculation of vehicle dynamic responses. The measurement noises are also considered to study their effects to the damage identification results. The identified results demonstrate that the proposed method is capable to identify the local damages of the track accurately in different noise levels with only the vehicle responses.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.3
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• pp.419-426
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• 2018

An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.40-46
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• 2000

In this paper, a transient identification based on a Hidden Markov Model (HMM) has been suggested and evaluated experimentally for the classification of transients in the dynamic process. The transient can be identified by its unique time dependent patterns related to the principal variables. The HMM, a double stochastic process, can be applied to transient identification which is a spatial and temporal classification problem under a statistical pattern recognition framework. The HMM is created for each transient from a set of training data by the maximum-likelihood estimation method. The transient identification is determined by calculating which model has the highest probability for the given test data. Several experimental tests have been performed with normalization methods, clustering algorithms, and a number of states in HMM. Several experimental tests have been performed including superimposing random noise, adding systematic error, and untrained transients. The proposed real-time transient identification system has many advantages, however, there are still a lot of problems that should be solved to apply to a real dynamic process. Further efforts are being made to improve the system performance and robustness to demonstrate reliability and accuracy to the required level.

• 대한상한금궤의학회지
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• v.9 no.1
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• pp.85-99
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• 2017

Objective : The objective of this study is to define the conceptional meaning of Shanghanlun provision while applying Shanghanlun disease pattern identification diagnostic system. Methods : 2 clinical cases, whose patients were treated by Injinho-tang, which was selected according to Shanghanlun provisions dualized with Je-Gang(提綱) and Jo-Moon(條文), were analyzed. Results : According to the results of analysis of 2 cases, the patients' diseases were improved when the treatment was applied according to Shanghanlun disease pattern identification diagnostic system and the interpretation of Shanghanlun provisions according to the etymology of Chinese character. Conclusions : These results suggests that Shanghanlun provisions be applied according to etymological interpretation of Chinese character in Shanghanlun disease pattern identification diagnostic system.

• Water for future
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• v.36 no.5
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• pp.72-76
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• 2003

• Water for future
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• v.36 no.3
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• pp.86-95
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• 2003

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.531-536
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• 2005

The purpose of this paper is to improve the hysteresis characteristics of a stack type piezoelectric actuator using system identification and tracking control. Recently, several printing methods that cost less and are faster than previous semiconductor processes have been developed for the production of electric paper and RFID. The system proposed in this study prints by spraying the molten metal, and consists of a nozzle, heating furnace, operating actuator, and an XYZ 3-axis stage, As an operating system, the piezoelectric(PZT) method has very valuable uses. However, the PZT actuator has a very big hysteresis characteristic due to the ferroelectric characteristics of the PZT element. This causes problems in the system position control characteristics and deteriorates the performance of the system. In this study, an investigation was conducted to improve the hysteresis characteristics of the PZT actuator that has an output displacement for the input voltage. The study proposed a inverse hysteresis model, a mathematic modeling method that can express the geometric relationship between voltage and displacement, in order to reduce the hysteresis of the PZT actuator. In addition, system identification and PID control methods were examined. Also, it was confirmed that the proposed control strategy gives good precision position control performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.600-607
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• 2011

As network traffic is dramatically increasing due to the popularization of Internet, the need for application traffic identification becomes important for the effective use of network resources. In this paper, we present an Internet application traffic identification method based on flow correlation to overcome limitation of signature-based identification methods and to improve performance (completeness) of it. The proposed method can identify unidentified flows from signature-based method using flow correlation between identified and unidentified flows. We propose four separate correlation methods such as Server-Client, Time, Host-Host, and Statistic correlation and describe a flow correlation-based identification system architecture which incorporates the four separate methods. Also we prove the feasibility and applicability of our proposed method by an acceptable experimental result.