• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.2
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• pp.87-92
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• 2019

In this paper, we have verified a low-voltage EM(elasto-magnetic) sensing technique for tensile force management of PSC(prestressed concrete) internal tendon in order to apply the technique to actual construction sites where stable power supply is difficult. From observation of past domestic and overseas PSC structural accident cases, it was found that PS tension is very important to maintain structural stability. In this paper, we have tried to measure the tensile force from a magnetic hysteresis curve through EM sensors according to voltage value by using relation between magnetostriction and stress of ferromagnetic material based on elastic-magnetic theory. For this purpose, EM sensor of double cylindrical coil type was fabricated and tensile force test equipment for PS tendon using hydraulic tensioning device was constructed. The experiment was conducted to confirm relationship between changes of permeability and tensile force from the measurement results of the maximum / minimum voltage amount. The change of magnetic hysteresis curve with magnitude of tensile force was also measured by reducing amount of voltage step by step. As a result, the slope of estimation equation in accordance with magnitude of magnetic field decreases with the voltage reduction. But it was confirmed a similar pattern of change of magnetic permeability for the magnetic hysteresis loop. So, in this study, it is considered that it is possible to manage the tensions of PSC internal tendon using EM sensing technique in low-voltage state.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.22-28
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• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.107-116
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• 2021

This study aims to investigate the effect of multisensory stimulation on relieving the stress experienced by drivers. The photoplethysmograms (PPGs) of 30 healthy subjects were measured, and their subjective response to stressful situations and normal driving were evaluated. The subjects underwent nonstimulation and multisensory stimulation in stressful driving situations. Heart rate estimation from the PPG was collected via an ear-type sensor to reduce movement noise. The signals acquired were sampled at 200 Hz using BIOPAC PPG100C. Heart rate variability (HRV) was analyzed to compare the effect of multisensory stimulation on stress situations. In the multisensory stimulation, blue, green, and yellow were used for the visual sensory system; white, pink, and brown noises were used for the auditory sensory system; and lavender, lemon, and rosemary were used for the olfactory sensory system. No difference was observed in the subjective evaluation; however, the HRV results showed an increased HF (%) and decreased LF (%) and LF/HF (%) in the multisensory stimulation (e.g., green, pink noise, and rosemary) when compared to the nonstimulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.555-562
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• 2021

Recently, in the field of geospatial information construction, unmanned aerial vehicles have been increasingly used because they enable rapid data acquisition and utilization. In this study, photogrammetry was performed using fixed-wing, rotary-wing, and VTOL (Vertical Take-Off and Landing) unmanned aerial vehicles, and geospatial information was constructed using two types of unmanned aerial vehicle LiDAR (Light Detection And Ranging) sensors. In addition, the accuracy was evaluated to present the utility of spatial information constructed through unmanned aerial photogrammetry and LiDAR. As a result of the accuracy evaluation, the orthographic image constructed through unmanned aerial photogrammetry showed accuracy within 2 cm. Considering that the GSD (Ground Sample Distance) of the constructed orthographic image is about 2 cm, the accuracy of the unmanned aerial photogrammetry results is judged to be within the GSD. The spatial information constructed through the unmanned aerial vehicle LiDAR showed accuracy within 6 cm in the height direction, and data on the ground was obtained in the vegetation area. DEM (Digital Elevation Model) using LiDAR data will be able to be used in various ways, such as construction work, urban planning, disaster prevention, and topographic analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.18-29
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• 2022

Recently, cooperative intelligent transport systems (C-ITS) testbeds have been deployed in great numbers, and advanced autonomous driving research using V2X communication technology has been conducted actively worldwide. In particular, the broadcasting services in their beginning days, giving warning messages, basic safety messages, traffic information, etc., gradually developed into advanced network services, such as platooning, remote driving, and sensor sharing, that need to perform real-time. In addition, technologies improving these advanced network services' throughput and latency are being developed on many fronts to support these services. Notably, this research analyzed the network latency requirements of the advanced network services to develop a remote driving service for the droid type low-speed robot based on the 3GPP C-V2X communication technology. Subsequently, this remote driving service's performance was evaluated using system modeling (that included the operator behavior) and simulation. This evaluation showed that a respective core and access network latency of less than 30 ms was required to meet more than 90 % of the remote driving service's performance requirements under the given test conditions.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.4
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• pp.17-34
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• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.691-695
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• 2023

Currently, the utilization of measurement results of the automated measurement system is very low and is at the level of providing only fragmentary measurement results. In this study, we are going to study a structure behavior analysis 3D display system with high precision and reliability for automated measurement data obtained by constructing big data by transmitting massive data values measured in real time to the cloud and using a Python-based algorithm. As a result of the study, as a system that can evaluate the behavior of a structure to a manager in real time, it provides analysis data in real time without significant restrictions regardless of the type of measurement data and sensor, and derived it as a 3D display. In addition, it was analyzed that the manager could grasp the behavior graph of the structure in real time and more easily judge the derivation of the weak part of the structure through data analysis. In the future, by analyzing the behavior of structures in three dimensions using past and present data, it is expected that more effective measurement results can be obtained in terms of repair, reinforcement, and maintenance of realistic structures.

• Composites Research
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• v.19 no.5
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• pp.7-11
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• 2006

The improved SWNTs/PANi composite actuator films applicable to an artificial muscle were fabricated successfully using a new process of manufacture that consists of 90% pure single-walled carbon nanotubes (SWNT) and chemical polymerization. PANi is electrically conducting polyaniline polymer. The conductivities of the composite SWNTs/PANi film-type actuators and the pure PANi films fabricated were measured as 56.15 S/cm and 17.38 S/cm, respectively, by the 4-prove method. The conductivity of the composite actuator is 3.2 times higher than the pure PANi film. The fabricated composite actuator showed higher conductivity than any other similar ones. The quality of samples was investigated by an electron scanning microscope (SEM). To measure the actuating strains, a specially designed beam balance apparatus was developed and strains of the composite actuators was measured by a laser displacement sensor subjected to electric currents. During the operation, the sample was soaked in the $NaNO_3$ solution and the sine-wave voltage in the range of $+1V{\sim}-1V$ was applied. The length of the composite actuator changed from $l_0=12.690$ mm to $l_1=12.733$ so that the change of length was l=0.043 mm and the strain was 0.34 %. This is a very high strain for this kind of a composite actuator. Other result reported by Tahhan showed 0.23 % strain, so that the present result is improved by 48%.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.172-181
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• 2023

In this study, real-time damage evaluation of cable-stayed bridges was conducted for cable damage. ICP type acceleration sensors were used for real-time damage assessment of cable-stayed bridges, and Kinetic Energy Optimization Techniques (KEOT) were used to select the optimal conditions for the location and quantity of the sensors. When a structure vibrates by an external force, KEOT measures the value of the maximum deformation energy to determine the optimal measurement position and the quantity of sensors. The damage conditions in this study were limited to cable breakage, and cable damage was caused by dividing the cable-stayed bridge into four sections. Through FE structural analysis, a virtual model similar to the actual model was created in the real-time damage evaluation method of cable. After applying random oscillation waves to the generated virtual model and model structure, cable damage to the model structure was caused. The two data were compared by defining the response output from the virtual model as a corruption-free response and the response measured from the real model as a corruption-free data. The degree of damage was evaluated by applying the data of the damaged cable-stayed bridge to the Improved Mahalanobis Distance (IMD) theory from the data of the intact cable-stayed bridge. As a result of evaluating damage with IMD theory, it was identified as a useful damage evaluation technology that can properly find damage by section in real time and apply it to real-time monitoring.