• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.2
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• pp.149-159
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• 2016

Purpose : To evaluate the usefulness of the breathing exercise,we analyzed the change in the RPM signal and the diaphragm imagebefore 4D respiratory gated radiation therapy planning of lung cancer patients. Materials and Methods : The breathing training was enforced on 11 patients getting the 4D respiratory gated radiation therapy from April, 2016 until August. At the same time, RPM signal and diaphragm image was obtained respiration training total three steps in step 1 signal acquisition of free-breathing state, 2 steps respiratory signal acquisition through the guide of the respiratory signal, 3 steps, won the regular respiration signal to the description and repeat training. And then, acquired the minimum value, maximum value, average value, and a standard deviation of the inspiration and expiration in RPM signal and diaphragm image in each steps. Were normalized by the value of the step 1, to convert the 2,3 steps to the other distribution ratio (%), by evaluating the change in the interior of the respiratory motion of the patient, it was evaluated breathing exercise usefulness of each patient. Results : The mean value and the standard deviation of each step were obtained with the procedure 1 of the RPM signal and the diaphragm amplitude as a 100% reference. In the RPM signal, the amplitudes and standard deviations of four patients (36.4%, eleven) decreased by 18.1%, 27.6% on average in 3 steps, and 2 patients (18.2%, 11 people) had standard deviation, It decreased by an average of 36.5%. Meanwhile, the other four patients (36.4%, eleven) decreased by an average of only amplitude 13.1%. In Step 3, the amplitude of the diaphragm image decreased by 30% on average of 9 patients (81.8%, 11 people), and the average of 2 patients (18.2%, 11 people) increased by 7.3%. However, the amplitudes of RPM signals and diaphragm image in 3steps were reduced by 52.6% and 42.1% on average from all patients, respectively, compared to the 2 steps. Relationship between RPM signal and diaphragm image amplitude difference was consistent with patterns of movement 1, 2 and 3steps, respectively, except for No. 2 No. 10 patients. Conclusion : It is possible to induce an optimized respiratory cycle when respiratory training is done. By conducting respiratory training before treatment, it was possible to expect the effect of predicting the movement of the lung which could control the patient's respiration. Ultimately, it can be said that breathing exercises are useful because it is possible to minimize the systematic error of radiotherapy, expect more accurate treatment. In this study, it is limited to research analyzed based on data on respiratory training before treatment, and it will be necessary to verify with the actual CT plan and the data acquired during treatment in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.5
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• pp.30-37
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• 2015

The study for enhancing the data transmission rate of the next generation wireless communication system using MIMO system operating in the frequency selective fading environment is currently actively conducted. Mixed signal from each transmitted antennas are received at antennas. The training signal with orthogonal property is needed to separate the mixed signal and enable to estimate channel and time synchronization. In this paper we introduce several training sequences used in MIMO communication system and proposed the modified WeCAN sequence with good auto-correlation property in interested area. We compared auto-correlation property of each sequence via simulation and compared the performance of sequences in doppler shift and multipath fading channel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2818-2825
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• 2013

In this paper, a channel estimation method by orthogonalizing of the time domain training signal in MIMO-OFDM systems is proposed. It has shown that Jeon's method[8] cannot be directly used in 4 Tx antenna MIMO-OFDM systems since the delayed Rx signals interfere the orthogonal property of the time domain training signals. As a possible solution to the problem, in this paper, a guard interval is inserted into the center of the training signals so that the orthogonal property between the Rx training signals can be maintained. It is shown by using computer simulations that the proposed method can estimate the channel response in time domain in 4 Tx antenna MIMO-OFDM systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2883-2902
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• 2023

Since the existing anti-jamming methods, including intelligent methods, have difficulty against high-speed reactive jamming, we studied a new methodology for jamming utilization instead of avoiding jamming. Different from the existing jamming utilization techniques that harvest energy from the jamming signal as a power supply, our proposed method can take the jamming signal as a favorable factor for frequency detection. Specifically, we design an intelligent differential frequency hopping communication framework (IDFH), which contains two stages of training and communication. We first adopt supervised learning to get the jamming rule during the training stage when the synchronizing sequence is sent. And then, we utilize the jamming rule to improve the frequency detection during the communication stage when the real payload is sent. Simulation results show that the proposed method successfully combated high-speed reactive jamming with different parameters. And the communication performance increases as the power of the jamming signal increase, hence the jamming signal can help users communicate in a low signal-to-noise ratio (SNR) environment.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.71-76
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• 2001

In this paper we propose the Digital Automatic Gain Controller for IEEE 802.11a-High-speed Physical Layer in the 5 GHz Band. The input gain it estimated by calculating the energy of the training symbol that it a synchronizing signal. The renewal gain is calculated by comparing the estimated gain with the ideal gain. The renewal gain is converted into the controlled voltage for GCA to reduce or amplify the input signals. We used a piecewise-linear approximation to reduce the hardware size. The gain control is performed seven times to provide more accurate gain control. The proposed automatic gain controller is designed with VHDL and verified by using the Xilinx FPGA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.574-577
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• 2002

In this paper, we propose a new symbol time synchronization scheme suitable for the OFDM system with an interpolator. The proposed performs the following three steps. In the first step, the coarse symbol time synchronization is achieved by continuously measuring the average power of the received envelope signal. Based on this average power, the detection possibility for the symbol time synchronization is determined. If the signal is sufficient for synchronization, we next perform a relatively accurate symbol time synchronization by measuring the correlation a short training signal and the received envelope signal. Finally, an additional frequency synchronization is performed with a long training signal to correct symbol synchronization errors caused by the phase rotation. From the simulation results, one can see that the proposed synchronization scheme provides a good synchronization performance over frequency selective channels.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.253-256
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• 1997

In this study, we developed a portable device which monitors and analyzes a vibration signal happened to machines. This device is based on PDA which is smaller thant a palm of the hand, but it has powerful computing ability as much s a computer with 100MHz CPU and an operating system called Windows CE. As a preprocess for a diagnosis of a rotating machine, training artificial neural network based on PC is performed, and the device will diagnose the condition of a rotating machine using weight values as a result of the training ANN.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.457-467
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• 2001

It is well known that the performance of a fuzzy neural network strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural network and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PCA), genetic algorithms (CA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods.