• Journal of IKEEE
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• v.23 no.4
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• pp.1273-1279
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• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1209-1223
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• 2022

Blood pressure is one of the key physiological parameters for determining human health, and can prove whether human cardiovascular function is healthy or not. In general, what we call blood pressure refers to arterial blood pressure. Blood pressure fluctuates greatly and, due to the influence of various factors, even varies with each heartbeat. Therefore, achievement of continuous blood pressure measurement is particularly important for more accurate diagnosis. It is difficult to achieve long-term continuous blood pressure monitoring with traditional measurement methods due to the continuous wear of measuring instruments. On the other hand, radar technology is not easily affected by environmental factors and is capable of strong penetration. In this study, by using machine learning, tried to develop a linear blood pressure prediction model using data from a public database. The radar sensor evaluates the measured object, obtains the pulse waveform data, calculates the pulse transmission time, and obtains the blood pressure data through linear model regression analysis. Confirm its availability to facilitate follow-up research, such as integrating other sensors, collecting temperature, heartbeat, respiratory pulse and other data, and seeking medical treatment in time in case of abnormalities.

• Geomechanics and Engineering
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• v.18 no.1
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• pp.11-20
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• 2019

The purpose of this study was to propose a new approach for quantifying in situ rock mass damage, which would include a degree-of-damage and the degraded strength of a rock mass, along with its prediction based on real-time Acoustic Emission (AE) observations. The basic approach for quantifying in-situ rock mass damage is to derive the normalized value of measured AE energy with the maximum AE energy, called the degree-of-damage in this study. With regard to estimation of the AE energy, an AE crack source location algorithm of the Wigner-Ville Distribution combined with Biot's wave dispersion model, was applied for more reliable AE crack source localization in a rock mass. In situ AE wave attenuation was also taken into account for AE energy correction in accordance with the propagation distance of an AE wave. To infer the maximum AE energy, fractal theory was used for scale-independent AE energy estimation. In addition, the Weibull model was also applied to determine statistically the AE crack size under a jointed rock mass. Subsequently, the proposed methodology was calibrated using an in situ test carried out in the Underground Research Tunnel at the Korea Atomic Energy Research Institute. This was done under a condition of controlled incremental cyclic loading, which had been performed as part of a preceding study. It was found that the inferred degree-of-damage agreed quite well with the results from the in situ test. The methodology proposed in this study can be regarded as a reasonable approach for quantifying rock mass damage.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.127-135
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• 2024

The purpose of this paper is to establish the relationship between compression wave velocity and porosity in unsaturated soil using a deep neural network (DNN) algorithm. Input parameters were examined using the error norm method to assess their impact on porosity. Compression wave velocity was conclusively found to have the most significant influence on porosity estimation. These parameters were derived through both field and laboratory experiments using a total of 266 numerical data points. The application of the DNN was evaluated by calculating the mean squared error loss for each iteration, which converged to nearly zero in the initial stages. The predicted porosity was analyzed by splitting the data into training and validation sets. Compared with actual data, the coefficients of determination were exceptionally high at 0.97 and 0.98, respectively. This study introduces a methodology for predicting dependent variables through error norm analysis by disregarding fewer sensitive factors and focusing on those with greater influence.

• Earthquakes and Structures
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• v.18 no.2
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• pp.163-170
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• 2020

Floor acceleration plays a major role in the seismic design of nonstructural components and equipment supported by structures. Large floor acceleration may cause structural damage to or even collapse of buildings. For precision instruments in high-tech factories, even small floor accelerations can cause considerable damage in this study. Six P-wave parameters, namely the peak measurement of acceleration, peak measurement of velocity, peak measurement of displacement, effective predominant period, integral of squared velocity, and cumulative absolute velocity, were estimated from the first 3 s of a vertical ground acceleration time history. Subsequently, a new predictive algorithm was developed, which utilizes the aforementioned parameters with the floor height and fundamental period of the structure as the new inputs of a support vector regression model. Representative earthquakes, which were recorded by the Structure Strong Earthquake Monitoring System of the Central Weather Bureau in Taiwan from 1992 to 2016, were used to construct the support vector regression model for predicting the peak floor acceleration (PFA) of each floor. The results indicated that the accuracy of the predicted PFA, which was defined as a PFA within a one-level difference from the measured PFA on Taiwan's seismic intensity scale, was 96.96%. The proposed system can be integrated into the existing earthquake early warning system to provide complete protection to life and the economy.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.163-171
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• 2024

Structural health monitoring for ships and offshore structures is important in various aspects. Ships and offshore structures are continuously exposed to various environmental conditions, such as waves, wind, and currents. In the event of an accident, immense economic losses, environmental pollution, and safety problems can occur, so it is necessary to detect structural damage or defects early. In this study, structural response data of multi-linked floating offshore structures under various wave load conditions was calculated by performing fluid-structure coupled analysis. Furthermore, the order reduction method with distortion base mode was applied to the structures for predicting the structural response by using the results of numerical analysis. The distortion base mode order reduction method can predict the structural response of a desired area with high accuracy, but prediction performance is affected by sensor arrangement. Optimization based on a genetic algorithm was performed to search for optimal sensor arrangement and improve the prediction performance of the distortion base mode-based reduced-order model. Consequently, a sensor arrangement that predicted the structural response with an error of about 84.0% less than the initial sensor arrangement was derived based on the root mean squared error, which is a prediction performance evaluation index. The computational cost was reduced by about 8 times compared to evaluating the prediction performance of reduced-order models for a total of 43,758 sensor arrangement combinations. and the expected performance was overturned to approximately 84.0% based on sensor placement, including the largest square root error.

• Journal of the Korean Society for Marine Environment & Energy
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• v.19 no.2
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• pp.159-164
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• 2016

It is one of the important processes to predict the resistance of the towed ship when towing a disabled ship on the sea. Besides the basic resistance of hull itself, there are various added resistance, especially the added resistance due to waves can be considered one of the biggest component. In this paper, the algorithm which predict the added resistance due to waves of a disabled ship by theoretical analysis method of ISO 15016 standard was established, and realized as a computer program. The calculated result for an example ship was compared with existing standard one, and it is considered that this algorithm and computer program are appropriate to use for predicting the resistance and towing force of the disabled ship actually.

• Ocean Systems Engineering
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• v.12 no.2
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• pp.159-172
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• 2022

To ensure stable performance, adaptive regulators with new theories are designed for steel-covered offshore platforms to withstand anomalous wave loads. This model shows how to control the vibration of the ocean panel as a solution using new results from Lyapunov's stability criteria, an evolutionary bat algorithm that simplifies computational complexity and utilities. Used to reduce the storage space required for the method. The results show that the proposed operator can effectively compensate for random delays. The results show that the proposed controller can effectively compensate for delays and random anomalies. The improved prediction method means that the vibration of the offshore structure can be significantly reduced. While maintaining the required controllability within the ideal narrow range.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.129-137
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• 2016

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown's Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.296-300
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• 1989

The paper proposes a method to eliminate harmonics of PWM inverter fed induction motor system using Walsh series. In other words, this paper presents technique of the selective harmonics elimination(SHE) by W-FT series in three phase PWM inverter output waveform. A microprocessor(8086 CPU) - controlled three phase induction motor system in order to verify this algorithm is present. It is designed for a three output voltage in the 1$\sim$60 Hz inverter with the 5th and 7th harmonics, 5th, 7th, 11th, and 13th, harmonics eliminated, and with the fundamental wave amplitude proportional to the output frequency. In the PWM inverter, dead time circuit is inserted in the switching si gnats to prevent the de link shortage. This paper is deals with quantative prediction of dead-time effect and its compensation in PWM inverters. The performance of the compensation circuits is confirmed by the experiment.