• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.6
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• pp.399-405
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• 2015

In this work we use the mode decomposition technique employing chirplet transform, which is able to separate the individual modes from dispersive and multimodal waveform measured with the magnetostrictive sensor. The mode decomposition technique is also used to estimate the time-frequency centers and individual energies of the reflection, which would be used to locate and characterize axial defects. The arrival times of the separated modes are calculated and the axial defect lengths can be evaluated by using the estimated arrival time. Results from an experiment on a carbon steel pipe are presented and it is shown that the accurate and quantitative defect characterization could become enabled using the proposed technique.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.18-24
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• 2001

This paper presents a model and a heuristic algorithm for the Traveling Salesman Problem with Time Windows(TSPTW). The main difference of our model compared with the previous ones lies in that the time windows we are concerned are more flexible and realistic than the previous ones. In the typical TSPTW, the service at a node must begin within the time grid called the time window that is defined by the earliest and the latest time to start the service at each node. But, in real business practice, a lateness cost is usually penalized rather than the service is prohibited at all when a vehicle arrives after the latest time. Considering this situation, we develop a model with a new time window that allows an arrival after the latest time and penalizes the late arrival by charging a lateness cost. A two-phased heuristic algorithm is proposed for the model and is extensively tested to verify the accuracy and efficiency of the algorithm.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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• pp.14-19
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• 2010

Baroreflex sensitivity (BRS) is a parameter of the cardiovascular system that is reflected in changes in pulse interval (PD and systolic blood pressure (SBP). BRS contains information about how the autonomic nervous system regulates hemodynamic homeostasis. Normally the beat-to-beat SBP measurement and the pulse interval measured from the electrocardiogram (ECG) are required to estimate the BRS. We investigated the possibility of measuring BRS in the absence of a beat-to-beat SBP measurement device. Pulse arrival time (PAT), defined as the time between the R-peak of the ECG and a single characteristic point on the pulse wave recorded from any arterial location was measured by photoplethysmography. By comparing the BRS obtained from conventional measurements with our method during controlled breathing, we confirmed again that PAT and SBP are closely correlated, with a correlation coefficient of -0.82 to -0.95. The coherence between SBP and PI at a respiration frequency of 0.07-0.12 Hz was similar to the coherence between PAT and PI. Although the ranges and units of measurement are different (ms/mmHg vs. ms/ms) for BRS measured conventionally and by our method, the correlation is very strong. Following further investigation under various conditions, BRS can be reliably estimated without the inconvenient and expensive beat-to-beat SBP measurement.

• Journal of electromagnetic engineering and science
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• v.18 no.2
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• pp.94-100
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• 2018

Partial discharges (PDs) are electrical sparks that occur inside insulation between two conducting electrodes and can lead to the disastrous failure of insulation systems. To determine the location of a PD, a distributed array of UHF PD sensors is used to detect the electromagnetic (EM) signals emitted from the PD source, and the localization of the PD source can be estimated using the time difference of arrival (TDOA) between EM signals captured by the UHF PD sensor array. There are four popular methods to estimate the TDOA-the first peak method, the cross-correlation method, the energy criterion method, and the average time window threshold method. In this work, we numerically investigate the influence of noise on estimating the TDOA for the four different methods. Numerical results show that the energy criterion method is more robust against noise than other methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5208-5213
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• 2011

In this paper, we proposed a new direction finding(DF) technology using TDOA(time-difference of arrival) and PDOA(phase difference of arriving signal) method. The proposed technology has a good DF accuracy without DF ambiguity. TDOA or PDOA technology is used to the most of intelligence systems in 21 century. The principle of TDOA is to receive a signal with two parallel antennas, measure the time difference of arrival signal, and converse the time difference to the direction of incident signal. Those technology make a DF system small size but the DF accuracy is low into short antenna installation distance. The principle of PDOA is similar to TDOA except measuring the phase difference of arrival signal, These technology get a good DF accuracy in short antenna installation distance but have a DF ambiguity. The proposed DF method is simulated into DF system operation environment with noise, and has a good DF accuracy.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.135-142
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• 2010

The convenient techniques for predicting the bus arrival time have used the data obtained from the buses belong to the same company only. Consequently, the conventional techniques have often failed to predict the bus arrival time at the downstream bus stops due to the lack of the data during congestion time period. The primary objective of this study is to overcome the weakness of the conventional techniques. The estimation model developed based on the data obtained from Bus Information System(BIS) and Bus management System(BMS). The proposed model predicts the bus arrival time at bus stops by using the data of all buses travelling same roadway section during the same time period. In the tests, the proposed model had a good accuracy of predicting the bus arrival time at the bus stops in terms of statistical measurements (e.g., root mean square error). Overall, the empirical results were very encouraging: the model maintains a prediction job during the morning and evening peak periods and delivers excellent results for the severely congested roadways that are of the most practical interest.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.115-135
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• 2008

An interesting problem in capacitated supply chains is how to guarantee customer service levels with limited resources. One of the common approaches to solve the problem is differentiating service depending on customer classes. High-priority customers receive a better service at the cost of low-priority customers' service level. One of common criteria to determine a customer's priority in practice is whether he has made a reservation before arrival or not. Customers with a reservation usually receive service based on the time the reservation was made. We examine the effect this advance information of customer arrival has on customer service levels. We show the trade-offs between the leadtime and other system characteristics such as the proportion of high-priority customers. when the service level is high. We also suggest how to differentiate service using the asymptotic ratio of the service levels for both types of customers.

• Convergence Security Journal
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• v.22 no.2
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• pp.105-114
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• 2022

This paper is presents an intelligent control system that visualizes the direction of arrival for hearing impaired using personal mobility, and aims to recognize and prevent dangerous situations caused by sound such as alarm sounds and crack sounds on roads. The position estimation method of sound source uses a machine learning classification model characterized by generalized correlated phase transformation based on time difference of arrival. In the experimental environment reproducing the road situations, four classification models learned after extracting learning data according to wind speeds 0km/h, 5.8km/h, 14.2km/h, and 26.4km/h were compared with grid search cross validation, and the Muti-Layer Perceptron(MLP) model with the best performance was applied as the optimal algorithm. When wind occurred, the proposed algorithm showed an average performance improvement of 7.6-11.5% compared to the previous studies.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.2
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• pp.1-8
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• 2014

This paper introduces a new framework of predicting the arrival time of an aircraft by incorporating the probabilistic information of what type of trajectory pattern will be applied by human air traffic controllers. The proposed method is based on identifying the major patterns of vectored trajectories and finding the statistical relationship of those patterns with various traffic complexity factors. The proposed method is applied to the traffic scenarios in real operations to demonstrate its performances.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.53
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• pp.13-22
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• 1999

This paper presents a model and dynamic programming based algorithm for the Traveling Salesperson Problem with Time Windows (TSPTW). The main difference of our model compared with the previous ones lies in that the time windows we are concerned are far more flexible and realistic. In the typical TSPTW, the service at a node must begin within the time grid called the time window that is defined by the earliest and the latest time to start the service at the node. But, in real business practices, a lateness cost is usually penalized rather than the service is prohibited at all when a vehicle arrives after the latest time. Considering this situation, we propose a model with a new time window that allows an arrival after the latest time and penalizes the late arrival by charging a lateness cost. An algorithm introduced for the model is extensively tested to verify the accuracy and efficiency.