• Smart Structures and Systems
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• v.12 no.3_4
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• pp.251-269
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• 2013

Measurement of deflections of certain bridges is usually hampered by corruption of the GPS signal by multipath associated with passing vehicles, resulting to unrealistically large apparent displacements. Field data from the Gorgopotamos train bridge in Greece and systematic experiments revealed that such bias is due to superimposition of two major effects, (i) changes in the geometry of satellites because of partial masking of certain satellites by the passing vehicles (this effect can be faced with solutions excluding satellites that get temporarily blocked by passing vehicles) and (ii) dynamic multipath caused from reflection of satellite signals on the passing trains, a high frequency multipath effect, different from the static multipath. Dynamic multipath seems to have rather irregular amplitude, depending on the geometry of measured satellites, but a typical pattern, mainly consisting of a baseline offset, wide base peaks correlating with the sequence of main reflective surfaces of the vehicles passing next to the antenna. In cases of limited corruption of GPS signal by dynamic multipath, corresponding to scale distortion of the short-period component of the GPS waveforms, we propose an algorithm which permits to reconstruct the waveform of bridge deflections using a weak fusion of GPS and RTS data, based on the complementary characteristics of the two instruments. By application of the proposed algorithm we managed to extract semi-static and dynamic displacements and oscillation frequencies of a historical railway bridge under train loading by using noisy GPS and RTS recordings. The combination of GPS and RTS is possible because these two sensors can be fully collocated and have complementary characteristics, with RTS and GPS focusing on the long- and short-period characteristics of the displacement, respectively.

• Journal of Navigation and Port Research
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• v.38 no.5
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• pp.479-484
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• 2014

This paper presents the crew members fatigue survey in order to understand the current state of various fatigue causal factors and personnel fatigue subjective symptoms, and then analyzes the survey items. The results of this survey are as follows. Firstly, many crew members were struggling with the lack of sleep and rest hour. Secondly, environmental factors such as weather, ship motion and vibration, noise, accommodation condition etc. disturbed the sleep of crew members. In third, their duty hours were more than 10 hours per day in certain types of ship. In fourth, they felt fatigue a lot when they were on board because of the workload and stress. Lastly, in some measurement items of fatigue symptoms(physical, mental, emotional), many crew members were experiencing more than moderate fatigue symptoms.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.293-298
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• 2013

This research develops an ultrasonic sensor to measure the distance in underwater. The ultrasonic transducer transmits an acoustic signal to an object and receives the echo signal reflected from the object. The ultrasonic driver calculates a distance by multiplying the acoustic speed to the time of flight(TOF) which is the time necessary for the acoustic signal to travel from the transducer to the object. We apply a thresholding and a cross correlation methods to detect the TOF and show their results. When an echo pulse is corrupted with noise and its shape is distorted, the cross correlation method is used to find the TOF based on the maximum similarity between the reference and the delayed echo signals. The echoes used for the reference signal are achieved at the different environments, which improves the performance of the sensor. This paper describes the driver of the acoustic sensor and analyzes the performance of sensors in different measurement environments.

• Journal of the Korean Institute of Educational Facilities
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• v.25 no.2
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• pp.3-9
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• 2018

This study is designed to measure the indoor environment and research on the environmental situation in the lecture room where the lecture is conducted during the winter time in order to understand the level of environment in the lecture room and then suggest the method of improving the environment in the lecture room in the future. The findings are as follows. First, the number of ventilation measured at Lecture Room 1 was 1.2 times/hour while that at Lecture Room 2 was 2.2 times/hour. Second, the lighting at Lecture Room 1 and 2 was 650~700 lux while the noise at Lecture Room 1 and 2 was not more than 60dB. Third, Group 1 and Group 2 felt in the same way that the air quality in the lecture room was not good when the air quality was measured in 30 minutes after the start of lecture. Fourth, both Group 1 and Group 2 showed the lowered concentration on the class in 30 minutes after the start of the class when the room was heated. But Group 1 got less drop in the concentration when they was put in the non-heated room. Fifth, As for the change in the carbon dioxide volume during lecture, the carbon dioxide volume in the room where the windows was closed rose 1,000~1,400ppm from that at the time of start, thus showing that the indoor air quality got worsened. In addition, it is hard to control the indoor temperature due to the heating and non-heating. Accordingly, it is necessary to get the heating system which can make the ventilation in order to keep the environmental level in the lecture room to a certain level and keep the proper indoor temperature.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.1049-1061
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• 2018

As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users' brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user's content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.181-189
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• 2004

This paper describes the method for detecting vehicles in the rear and rear-side at night by using headlight features. A headlight is the outstanding feature that can be used to discriminate a vehicle from a dark background. In the segmentation process, a night image is transformed to a binary image that consists of black background and white regions by gray-level thresholding, and noise in the binary image is eliminated by a morphological operation. In the feature extraction process, the geometric features and moment invariant features of a headlight are defined, and they are measured in each segmented region. Regions that are not appropriate to a headlight are filtered by using geometric feature measurement. In region classification, a pair of headlights is detected by using relational features based on the symmetry of a pair of headlights. Experimental results show that this method is very applicable to an approaching vehicle detection system at nighttime.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.2
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• pp.162-169
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• 2011

The flight control system utilize RSS(Relaxed Static Stability) criteria in both longitudinal axes to achieve performance enhancements and improve stability. The aircraft using digital flight-by-wire flight control system receives aircraft flight conditions such as pitch, roll and yaw rate, normal acceleration from RSA(Rate Sensor Assembly) and ASA(Acceleration Sensor Assembly). These sensors has permissible measurement error related to system safety of an aircraft but, unexpected flight motions are happened by sensing errors such as offset, noise and etc. The unexpected pitch down tendency occurred by ASA sensor bias in 1g level flight with pilot hands-off. This paper addresses the design and verification of flight control law to improve of pitch down or up tendency caused by ASA sensor bias. The result of analysis and flight test reveals that pitch down tendency can be improved by pitch attitude feedback system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.8
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• pp.783-789
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• 2017

In terms of 3D orientation estimation based on nine-axis IMMU(inertial and magnetic measurement unit), there are two disturbance components decreasing estimation accuracy: one is external acceleration disturbing accelerometer's signals and the other is magnetic disturbance related to magnetometer's signals. In order to minimize effects by these two disturbances, two approaches including switching approach and model-based approach have been suggested and further research comparing these two has also been conducted. Nevertheless, effect of disturbance modeling differences on orientation estimation accuracy in model-based approach has not been studied before. This paper compares the recently reported two orientation estimation algorithms that have difference in disturbance models, in order to investigate the effect of disturbance models on accuracy of IMMU-based orientation estimation under various operating conditions. This research shows that the difference in disturbance models leads to difference in process noise covariance matrix. Consequently, this affected the orientation estimation, i.e., the estimation differences between the algorithms were root mean square errors of $1.35^{\circ}$ in average and $3.63^{\circ}$ in yaw estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.