• Composites Research
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• v.18 no.3
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• pp.38-46
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• 2005

In order to characterize the outstanding performance of three-dimensional (3D) composites, the low velocity impact test has been carried out. 3D fiber structures have been achieved by using the automated tape placement (ATP) process and a stitching method. Materials for the ATP and the stitching process were carbon/epoxy prepreg tapes and Kevlar fibers, respectively. Two-dimensional composites with the same stacking sequence as 3D counterparts have also been fabricated for the comparison of damage tolerance. For the assessment of damage after the impact loading, specimens were subjected to C-Scan nondestructive inspection. Compression after impact (CAI) tests were conducted to evaluate residual compressive strength. The damage area of 3D composites was greatly reduced $(30-40\%)$ compared with that of 2D composites. Although the CAI strength did not show drastic improvement for 3D composites, the ratio of retained strength was $5-10\%$ higher than 2D samples. The effect of stitching on the impact performance was negligible above the energy level of 35 Joules.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.66-73
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• 2024

Stay-cable is one of the most important load carrying members in cable-stayed bridges. Monitoring structural integrity of stay-cables is crucial for evaluating the structural condition of the cable-stayed bridge. For stay-cables, tension and damping ratio are estimated based on modal properties as a measure of structural integrity. Since the monitoring system continuously measures the vibration for the long-term period, data acquisition systems should be stable and power-efficiency as the hardware system. In addition, massive signals from the data acquisition systems are continuously generated, so that automated analysis system should be indispensable. In order to fulfill these purpose simultaneously, this study presents an autonomous cable monitoring system based on domain-knowledge using IoT for continuous cable monitoring systems of cable-stayed bridges. An IoT system was developed to provide effective and power-efficient data acquisition and on-board processing capability for Edge-computing. Automated peak-picking algorithm using domain knowledge was embedded to the IoT system in order to analyze massive data from continuous monitoring automatically and reliably. To evaluate its operational performance in real fields, the developed autonomous monitoring system has been installed on a cable-stayed bridge in Korea. The operational performance are confirmed and validated by comparing with the existing system in terms of data transmission rates, accuracy and efficiency of tension estimation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.3
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• pp.147-153
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• 2018

Cracking in concrete structure must be examined according to appropriate methods, to ensure structural serviceability and to prevent structural deterioration, since cracks opened wide for a long time expedite corrosion of rebar. A site investigation is conducted in a regular basis to monitor structural deterioration by tracking growing cracks. However, the visual inspection are labor intensive. and judgment are subject. To overcome the limit of the on-site visual investigation image processing for identifying the cracks of concrete structures by analyzing 2D images has been developed. This study develops a unique 3D technique utilizing a line laser and its projection image onto concrete surfaces. Automated process of crack detection is developed by the algorithms of automatizing crack map generation and image data acquisition. Performance of the developed method is experimentally evaluated.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.12
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• pp.1511-1519
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• 2012

Pipe structures contain many welded zones, and ultrasonic tests are increasingly being performed by using automated testing devices in order to evaluate the weld integrity. An electromagnetic acoustic transducer (EMAT) is a noncontact transducer that can transmit or receive ultrasonic waves without a couplant. Furthermore, it can easily generate specific guided waves such as SH (shear horizontal) or Lamb waves by altering the design of the coil and magnet. Therefore, an EMAT should be useful for application to an automated ultrasonic inspection system. In this study, SH waves generated using an EMAT were applied to inspect the pipe-weld zone. To analyze the specific SH mode (SH0) from the SH wave signals, wavelet transform was applied. It was found that flaws could be detected precisely because the intensity of the $SH_0$ mode-frequency, which is analyzed by using wavelet transform, is proportional to the length of the flaw.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.111-116
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• 2021

This study is a study on improving the pedestrian Care system, which delivers jaywalking events in real time to the autonomous driving control center and Autonomous driving vehicles in operation and issues warnings and announcements to pedestrians based on pedestrian signals. In order to secure reliability of object detection method of pedestrian Care system, the inspection method combined with camera sensor with Lidar sensor and the improved system algorithm were presented. In addition, for the occurrence events of Lidar sensors and intelligent CCTV received during the operation of autonomous driving vehicles, the system algorithm for the elimination of overlapping events and the improvement of accuracy of the same time, place, and object was presented.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.161-168
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• 2021

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.139-145
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• 2022

As products that consumers want become more diverse, the types of automation equipment are becoming more diverse and advanced to produce competitive products. In general, equipment is manufactured with built-in interface devices (HMI) for users so that operators can efficiently monitor and operate equipment quickly. Because HMI devices are connected to various industrial controllers, elements such as communication protocols of various controllers must be understood and set up in the design stage. Non-experts not only have difficulty choosing compatible items among various protocols, but also have limitations in integrating and operating on one device because screens and settings are statically assigned. This paper proposes a model that can scan information such as equipment ID and communication protocol with QR code using a mobile device, access industrial controller, and remotely operate the displayed equipment screen. The proposed model is expected to increase efficiency in inspection and management of automated equipment as it can easily set, monitor, and operate the communication environment of various automated equipment using one mobile device.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.165-173
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• 2023

This research proposed a model for automatically monitoring the quality of insulation adhesive application in external insulation construction. Upon case implementation, the area segmentation model demonstrated a 92.3% accuracy, while the area and distance calculation accuracies of the proposed model were 98.8% and 96.7%, respectively. These findings suggest that the model can effectively prevent the most common insulation defect, insulation failure, while simultaneously minimizing the need for on-site supervisory personnel during external insulation construction. This, in turn, contributes to the enhancement of the external insulation system. Moving forward, we plan to gather construction images of various external insulation methods to refine the image segmentation model's performance and develop a model capable of automatically monitoring scenarios with a considerable number of insulation materials in the image.

• Nuclear Engineering and Technology
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• v.20 no.3
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• pp.170-178
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• 1988

An automated ultrasonic flaw detection system was developed for thin-walled and short tubes such as Zircaloy-4 tubes used for cladding heavy-water reactor fuel. The system was based on the two channels immersion pulse-echo technique using 14 MHz shear wave and the specially developed helical scanning technique, in which the tube to be tested is only rotated and the small water tank with spherical focus ultrasonic transducers is translated along the tube length. The optimum angle of incidence of ultrasonic beam was 26 degrees, at which the inside and outside surface defects with the same size and direction could be detected with the same sensitivity. The maximum permissible defects in the Zircaloy-4 tubes, i.e., the longitudinal and circumferential v notches with the length of 0.76mm and 0.38mm, respectively and the depth of 0.04 mm on the inside and outside surface, could be easily detected by the system with the inspection speed of about 1 m/min and the very excellent reproducibility. The ratio of signal to noise was greater than 20 dB for the longitudinal defects and 12 dB for the circumferential defects.