• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1815-1820
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• 2017

In this paper, a study was conducted to estimate the maximum muscle strength which is a standard for selecting exercise intensity in weight training. We designed a device that estimates the muscle fatigue from the EMG signal, expecting to show a correlation between peak muscle strength and fatigue. Curl - Dumbbell was performed using a 4 kg dumbbell and the frequency change of the EMG was observed. At this time, the designed device acquires the signal using the MCU and finally Matlab was used to confirm the change in the center frequency value. The results of 10 subjects were analyzed using SPSS regression analysis. The statistical results showed a correlation of $R^2$ 0.583 and Significant probability of 0.010, and the relation of Y = 8.144-2.097 (slope (MDF)) was obtained. In conclusion, if the wearable device is manufactured in the form of a wearable device and the user can recommend the exercise intensity, the system will be able to retry the more efficient exercise.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.33 no.6
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• pp.510-514
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• 2020

Vocational education needs to be transformed to cultivate talents with diverse fusion competencies, which is in line with the recent changes that have become a part of the complex technological developments in the 4th Industrial Revolution. Therefore, it is very important for college graduates to obtain employment skills as they are required to prepare for careers within the complex environments of future societies. With the transition to the Internet of Things (IoT)-based control in the manufacturing industry, the development of technological education and related training programs is required to cultivate practical talents for students who have acquired not only the information on existing programmable logic controller (PLC)-based technology, but also that on embedded programming technology. Therefore, to develop an NCS-based education program for analog signal processing to ensure that programming can easily be learned for cultivating practical talent, this study summarizes the opinions of field experts, selects the appropriate NCS competency unit, and designs an adequate technology education training program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.206-221
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• 2019

Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Journal of Positioning, Navigation, and Timing
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• v.8 no.4
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• pp.193-200
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• 2019

The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.

• International Journal of Aerospace System Engineering
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• v.2 no.2
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• pp.44-49
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• 2015

A gust response analyses method based on Reduced Order Models (ROMs) was developed in the paper. Firstly, taken random signal as the input signal and adopt Single Input-Multi-Output (SIMO) training fashion, a ROM based on Auto-Regressive and Moving Average model (ARMA) was established and validated with the comparison of CFD/CSD and experiment. Then, by introducing control surface deflection and control laws, flutter active suppress was studied. Lastly, through filtering and transferring function, the gust temporal signal is obtained based on Dryden gust model, and gust response and suppress were simulated.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.1-78.1
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• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.

• Journal of Biomedical Engineering Research
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• v.32 no.1
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• pp.1-6
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• 2011

An active training system has been developed to assist the upper extremity function in patients with spasticity. We also evaluated the performance of the developed assistive system in five normal subjects and one hemiplegic patient. The maximum voluntary contraction (MVC) tests for biceps brachii and triceps brachii were performed and the relationship between linear enveloped EMG signal and the elbow joint torque was found. In order to implement an active training, our system was designed to allow isokinetic movement only when the subject generates elbow joint motion larger than the pre-fixed threshold level. The proposed EMG-feedback control method could provide active exercises, resulting in better rehabilitation protocol for spastic patients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.473-479
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• 2005

A key issue toward mobile multimedia communications is to create technologies for broadband signal transmission that ran support high quality services. Such a broadband mobile communications system should be able to overcome severe distortion caused by time-varying multi-path fading channel, while providing high spectral efficiency and low power consumption. For these reasons, an adaptive suboptimum decision feedback equalize. (DFE) for the single-carrier short-burst transmissions system is considered as one of the feasible solutions. For the performance improvement of the system with the short-burst format including the short training sequence, in this paper, the multiple-training least mean square (MTLMS) based DFE scheme with soft decision feedback is proposed and its performance is investigated in mobile wireless channels throughout computer simulation.