• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.72-80
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• 2019

In this task, the tool dynamometer design and manufacture, and the Ansys S/W structural analysis program for tool attachment that satisfies the cutting force measurement requirements of the tool dynamometer system are used to determine the cutting force generated by metal cutting using 3-axis static structural analysis and the LabVIEW system. The cutting power in a cutting process using a milling tool for processing metals provides useful information for understanding the processing, optimization, tool status monitoring, and tool design. Thus, various methods of measuring cutting power have been proposed. The device consists of a strain-gauge-based sensor fitted to a new design force sensing element, which is then placed in a force reduction. The force-sensing element is designed as a symmetrical cross beam with four arms of a rectangular parallel line. Furthermore, data duplication is eliminated by the appropriate setting the strain gauge attachment position and the construction of a suitable Wheatstone full-bridge circuit. This device is intended for use with rotating spindles such as milling tools. Verification and machining tests were performed to determine the static and dynamic characteristics of the tool dynamometer. The verification tests were performed by analyzing the difference between strain data measured by weight and that derived by theoretical calculations. Processing test was performed by attaching a tool dynamometer to the MCT to analyze data generated by the measuring equipment during machining. To maintain high productivity and precision, the system monitors and suppresses process disturbances such as chatter vibration, imbalances, overload, collision, forced vibration due to tool failure, and excessive tool wear; additionally, a tool dynamometer with a high signal-to-noise ratio is provided.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.409-416
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• 2016

The contact force characteristic between the pantograph and the catenary wire represents the current collection quality of trains; it should be precisely controlled under international standard. Recently, a noise reduction cover has been installed around the pantograph of high speed trains. However, little study on the contact force by the pantograph cover has been conducted. In this study, the impact on the current collection performance of the pantograph cover was analyzed by dynamic contact force measurement using a next generation high speed train (HEMU-430X). As a result, it was confirmed that the attachment of a pantograph cover could lower the mean contact force by approximately 50N at 300km/h. In addition, the pure difference of the average contact force by the presence of pantograph cover, except for the static pressure, was measured and found to be up to 110N at 300km/h. It was also found that the standard deviation of the contact force of 3~5N could be changed by use of a pantograph cover.

• Smart Structures and Systems
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• v.19 no.1
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• pp.1-10
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• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.27-34
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• 2021

This study analyzes machining characteristics and presents optimal cutting conditions by measuring the surface roughness, dimensional accuracy, and dimension straightness based on the feed rate after processing the inner diameter hall of SCM415 steel using an automatic CNC(Computerized Numerical Control) lathe. The testing material was cut using an 11.8 mm-diameter Chamdrill after mounting the 32 mm-diameter round bar on an automatic CNC lathe. The cut depth was set at 3 mm, and the cutting speed was fixed at 1500 rpm. The surface roughness, dimensional accuracy, and dimension straightness of 15 testings were measured by changing the feed rate to 0.05, 0.1, and 0.15 mm/rev, respectively. It was difficult to process more than 15 tests during the maching due to noise or break. Additionally, the optimum cutting of SCM415 steel showed excellent surface roughness in the 10^th and 11^th of testing at cutting speed and feed speed of 1500 rpm and 0.05 mm/rev, respectively. The dimensional accuracy was measured in three dimensions after drilling, which showed good results with an average range of 0.0138-0.0208 mm. Moreover, the lower the feed speed, the higher the accuracy. Additionally, the measurement results of the dimensional straightness showed that the straightness is the straightness was the best at the 1^th and 2^th cutting regardless of the feed speed.

• Journal of Sensor Science and Technology
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• v.27 no.6
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• pp.397-402
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• 2018

Proper seat design is critical to the safety, comfort, and ergonomics of automotive driver's seats. To ensure effective seat design, quantitative methods should be used to evaluate the characteristics of automotive seats. This paper presents a system that is capable of simultaneously monitoring body pressure distribution and surface deformation in a textile material. In this study, a textile-based capacitive sensor was used to detect the body pressure distribution in an automotive seat. In addition, a strain gauge sensor was used to detect the degree of curvature deformation due to high-pressure points. The textile-based capacitive sensor was fabricated from the conductive fabric and a polyurethane insulator with a high signal-to-noise ratio. The strain gauge sensor was attached on the guiding film to maximize the effect of its deformation due to bending. Ten pressure sensors were placed symmetrically in the hip area and six strain gauge sensors were distributed on both sides of the seat cushion. A readout circuit monitored the absolute and relative values from the sensors in realtime, and the results were displayed as a color map. Moreover, we verified the proposed system for quantifying the body pressure and fabric deformation by studying 18 participants who performed three predefined postures. The proposed system showed desirable results and is expected to improve seat safety and comfort when applied to the design of various seat types. Moreover, the proposed system will provide analytical criteria in the design and durability testing of automotive seats.