• Journal of the Korean Geotechnical Society
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• v.39 no.9
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• pp.13-24
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• 2023

To investigate the stability of temporary retaining walls during excavation, it is essential to develop reverse analysis technologies capable of precisely evaluating the properties of the ground and a learning model that can assess stability by analyzing real-time data. In this study, we targeted excavation sites where the C.I.P method was applied. We developed a Deep Neural Network (DNN) model capable of evaluating the stability of the retaining wall, and estimated the physical properties of the ground being excavated using a Differential Evolution Algorithm. We performed reverse analysis on a model composed of a two-layer ground for the applicability analysis of the Differential Evolution Algorithm. The results from this analysis allowed us to predict the properties of the ground, such as the elastic modulus, cohesion, and internal friction angle, with an accuracy of 97%. We analyzed 30,000 cases to construct the training data for the DNN model. We proposed stability evaluation grades for each assessment factor, including anchor axial force, uneven subsidence, wall displacement, and structural stability of the wall, and trained the data based on these factors. The application analysis of the trained DNN model showed that the model could predict the stability of the retaining wall with an average accuracy of over 94%, considering factors such as the axial force of the anchor, uneven subsidence, displacement of the wall, and structural stability of the wall.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.348-350
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• 2021

When predicting the concentration of fine dust using deep learning, there is a problem that the characteristics of a high concentration of 81㎍/m³ or more are not well reflected in the prediction model. In this paper, a comparison through predictive performance was conducted to confirm the results of reflecting the characteristics of fine dust in the high concentration area according to the deep learning algorithm. As a result of performance evaluation, overall, similar levels of results were shown, but the RNN model showed higher accuracy than other models at concentrations of "very bad" based on AQI. This confirmed that the RNN algorithm reflected the characteristics of the high concentration better than the DNN and LSTM algorithms.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.47-50
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• 2019

This paper proposes an acoustic model adaptation method for effective speech recognition in noisy environments. In the proposed algorithm, the noise corruption function is estimated employing DNN (Deep Neural Network), and the function is applied to the model parameter estimation. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed model adaptation method shows more effective in known and unknown noisy environments compared to the conventional methods. In particular, the experiments of the unknown environments show 15.87 % of relative improvement in the average of WER (Word Error Rate).

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1491-1499
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• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.506-511
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• 2018

This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1811-1819
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• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.23-30
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• 2022

The failure of the reefer container causes a great loss of cost, but the current reefer container alarm system is inefficient. Existing studies using simulation data of refrigeration systems exist, but studies using actual operation data of refrigeration containers are lacking. Therefore, this study classified the causes of failure using actual refrigerated container operation data. Data imbalance occurred in the actual data, and the data imbalance problem was solved by comparing the logistic regression analysis with ENN-SMOTE and class weight with the 2-stage algorithm developed in this study. The 2-stage algorithm uses XGboost, LGBoost, and DNN to classify faults and normalities in the first step, and to classify the causes of faults in the second step. The model using LGBoost in the 2-stage algorithm was the best with 99.16% accuracy. This study proposes a final model using a two-stage algorithm to solve data imbalance, which is thought to be applicable to other industries.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.73-78
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• 2023

Diabetes is chronic disease that is rapidly increasing in prevalence around the world, and mortality from complications continues to rise. This has made blood glucose management a critical challenge for modern society. The main methods used to manage blood glucose are diet, exercise, and medication. Among these, diet is one of the fundamental foundations of blood glucose management, avoiding foods that cause high blood glucose and minimizing blood glucose fluctuations, and is more accessible to people with diabetes as well as the general population. Currently, several platforms, both domestic and international, offer meal planning services, but this is mainly done by users or professional coaches. Accordingly, this paper implements an accurate Kcal calculation model based on DNN and presents a series of dietary algorithms for blood glucose management based on this.

• ETRI Journal
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• v.43 no.5
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• pp.881-890
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• 2021

Photoplethysmography (PPG) is a noninvasive technique that can be used to conveniently measure heart rate (HR) and thus obtain relevant health-related information. However, developing an automated PPG system is difficult, because its waveforms are susceptible to motion artifacts and between-patient variation, making its interpretation difficult. We use deep neural network (DNN) filters to mimic the cognitive ability of a human expert who can distinguish the features of PPG altered by noise from various sources. Systolic (S), onset (O), and first derivative peaks (W) are recognized by three different DNN filters. In addition, the boundaries of uninformative regions caused by artifacts are identified by two different filters. The algorithm reliably derives the HR and presents recognition scores for the S, O, and W peaks and artifacts with only a 0.7-s delay. In the evaluation using data from 11 patients obtained from PhysioNet, the algorithm yields 8643 (86.12%) reliable HR measurements from a total of 10 036 heartbeats, including some with uninformative data resulting from arrhythmias and artifacts.