• 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.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.2 s.344
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• pp.39-43
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• 2006

This paper deals with the AR Identification of unknown system using cumulant, which is the 3rd order statistics of output signal in the presence of the noise signal. The algorithms for identification of unknown system we applies to the AR identification method using the cumulant which is possible to the guarantees of global convergence and the representation of amplitude and phase information of system among with the method of parametric modeling. In the process of identification, we considered unknown system to the one of AR system. After the generation of input signal, it was being passed through the system then We use the its output signal that the noise is added. As a result of identification of AR system by changing the signal to noise ratio, we get the fairly good results compared to original system output values and confirmed that the pole was located in the unit circle of z transform.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.317-324
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• 1995

A need exists for the accurate identification of time series models having time varying parameters, as is important in the case of real time identification of nonstationary EMG signal. Thls paper describes real time identification and muscle fatigue monitoring method of nonstationary EMG signal. The method is composed of the efficient identifier which estimates the autoregressive parameters of nonstationary EMG signal model, and its real time implementation by using T805 parallel processing computer. The method is verified through experiment with real EMG signals which are obtained from surface electrode. As a result, the proposed method provides a new approach for real time Implementation of muscle fatigue monitoring and the execution time is 0.894ms/sample for 1024Hz EMG signal.

• Journal of Veterinary Clinics
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• v.13 no.2
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• pp.171-176
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• 1996

In this study, dldctronic identification system of individual dairy cow was developed for autocatization of stoxkvreeding management. To automize the breeding management, it is necessary to obtain and analyze the individual information distinguished from others perferentially. Electronic identification system can distinguish individual livestock from others with electromagnetic wave signal recognition system. Electoronic identification system consists of transmitter transmitting the oscillated signal and receiver set. The transmitted signal from transmitter clung to individual livestock is received from the receiving antenna and the signal in different according to the established value of the register. By distinct signal recieved from the reciever, wi can distinguish the identity of a livestock from others clearly. This system can manage $2^{12}$ individuals with a reciever theoretically. However in order to reduce the errors by analogous signal, this system uses only triple number and can manage 1365 individuals with a reciever practically. This system can be connevtted to Max 232 and microcomputer for the breeding management efficiently.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.1-13
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• 2020

In this paper, the author proposed a nuclear power plant (NPP) instrumentation signal faults identification algorithm. A variational autoencoder (VAE)-based model is trained by using only normal dataset as same as existing anomaly detection method, and trained model predicts which signal within the entire signal set is anomalous. Classification of anomalous signals is performed based on the reconstruction error for each kind of signal and partial derivatives of reconstruction error with respect to the specific part of an input. Simulation was conducted to acquire the data for the experiments. Through the experiments, it was identified that the proposed signal fault identification method can specify the anomalous signals within acceptable range of error.

• Smart Structures and Systems
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• v.15 no.1
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• pp.209-225
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• 2015

Compared to ambient vibration testing, impact testing has the merit to extract not only structural modal parameters but also structural flexibility. Therefore, structural deflections under any static load can be predicted from the identified results of the impact test data. In this article, a signal processing procedure for structural flexibility identification is first presented. Especially, practical issues in applying the proposed procedure for structural flexibility identification are investigated, which include sensitivity analyses of three pre-defined parameters required in the data pre-processing stage to investigate how they affect the accuracy of the identified structural flexibility. Finally, multiple-reference impact test data of a three-span reinforced concrete T-beam bridge are simulated by the FE analysis, and they are used as a benchmark structure to investigate the practical issues in the proposed signal processing procedure for structural flexibility identification.

• Journal of Sensor Science and Technology
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• v.25 no.5
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• pp.337-343
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• 2016

In this paper, we newly introduce a wireless identification system using a solar cell and RF transceivers. The reader sends interrogating signal to a transponder using LED visible light, and the transponder responds to the reader using RF signal. The transponder consists of a solar cell, an amplifier, a microprocessor, and an RF transmitter. The solar cell receives the visible light from the reader and generates current to supply electric power to the other devices in the transponder. At the same time, the solar cell detects interrogating signal in the reader light. The microprocessor senses the interrogating signal and generates a responding signal. The RF transmitter radiates the responding signal to the reader. The transponder is a passive circuit because it operates without external power. In experiments, the maximum read distance between a reader and a transponder was about 1.6 meter.

• Journal of Sensor Science and Technology
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• v.30 no.4
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• pp.196-203
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• 2021

In this study, an optical identification system was developed, wherein visible light is used to transmit the interrogating signal, and infrared is used to send the response signal. In the reader, visible light from a light emitting diode (LED) array was modulated via modified pulse width modulation for flicker-free illumination and dimming control. Moreover, the duty factor of the dimming control time was employed to control the illumination from the LED. In the transponder, the spike signal in the output of the high-pass filter was utilized to recover the interrogating signal while preventing interference from the 120-Hz noise from adjacent lighting lamps. The illumination was controlled in 26-86% range of the constant wave LED illumination by changing the duty factor from 20% to 90%. This configuration is advantageous for the construction of optical identification systems for automatic security check and car fare calculation at toll gates or parking facilities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.412-419
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• 2011

In this paper, we used the inter-bit noise detection method in order to reduce the effects of noise light in a visible light identification system that uses a visible LED as a carrier source. A visible light identification system consists of a reader and a transponder. When the enable signal from the reader is detected, the transponder encodes the response data in RZ(Return-to-Zero) bit stream and sends response signal by modulating a visible LED. The reader detects the response signal mixed with noise light, samples the noise voltage in each blank low time between data bits of the RZ signal, and recovers the original data by subtracting the sampled noise from the received signal. In experiments, we improved the signal-to-noise ratio by 20dB using the inter-bit noise detection method.