• Journal of the Society of Naval Architects of Korea
• /
• v.59 no.4
• /
• pp.192-199
• /
• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.511-516
• /
• 2024

The recently enacted detailed guidelines on the performance evaluation of track facilities presented the necessary requirements regarding the evaluation procedures and implementation methods of track performance evaluation. However, the grade of rail surface damage is determined by external inspection (visual inspection), and there is no choice but to rely only on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we attempted to develop a diagnostic application that can diagnose rail internal defects using rail surface damage. In the field investigation, rail surface damage was investigated and patterns were analyzed. Additionally, in the indoor test, SEM testing was used to construct image data of rail internal damage, and crack length, depth, and angle were quantified. In this study, a deep learning model (Fast R-CNN) using image data constructed from field surveys and indoor tests was applied to the application. A rail surface damage diagnosis application (App) using a deep learning model that can be used on smart devices was developed. We developed a smart diagnosis system for rail surface damage that can be used in future track diagnosis and performance evaluation work.

• Korean Journal of Optics and Photonics
• /
• v.29 no.1
• /
• pp.19-27
• /
• 2018

A change in object distance would generally change the magnification of an optical system. In this paper, we have proposed and designed a double-Gauss optical system with a fixed magnification and image surface regardless of any change in object distance, according to moving the lens groups a little bit to the front and rear of the stop, independently parallel to the direction of the optical axis. By maintaining a constant size of image formation in spite of various object-distance changes in a projection system such as a head-up display (HUD) or head-mounted display (HMD), we can prevent the field of view from changing while focusing in an HUD or HMD. Also, to check precisely the state of the wiring that connects semiconductor chips and IC circuit boards, we can keep the magnification of the optical system constant, even when the object distance changes due to vertical movement along the optical axis of a testing device. Additionally, if we use this double-Gauss optical system as a vision system in the testing process of lots of electronic boards in a manufacturing system, since we can systematically eliminate additional image processing for visual enhancement of image quality, we can dramatically reduce the testing time for a fast test process. Also, the Gaussian bracket method was used to find the moving distance of each group, to achieve the desired specifications and fix magnification and image surface simultaneously. After the initial design, the optimization of the optical system was performed using the Synopsys optical design software.

• Smart Structures and Systems
• /
• v.12 no.1
• /
• pp.55-71
• /
• 2013

Structural health monitoring (SHM) is vital for detecting the onset of damage and for preventing catastrophic failure of civil infrastructure systems. In particular, piezoelectric transducers have the ability to excite and actively interrogate structures (e.g., using surface waves) while measuring their response for sensing and damage detection. In fact, piezoelectric transducers such as lead zirconate titanate (PZT) and poly(vinylidene fluoride) (PVDF) have been used for various laboratory/field tests and possess significant advantages as compared to visual inspection and vibration-based methods, to name a few. However, PZTs are inherently brittle, and PVDF films do not possess high piezoelectricity, thereby limiting each of these devices to certain specific applications. The objective of this study is to design, characterize, and validate piezoelectric nanocomposites consisting of zinc oxide (ZnO) nanoparticles assembled in a PVDF copolymer matrix for sensing and SHM applications. These films provide greater mechanical flexibility as compared to PZTs, yet possess enhanced piezoelectricity as compared to pristine PVDF copolymers. This study started with spin coating dispersed ZnO- and PVDF-TrFE-based solutions to fabricate the piezoelectric nanocomposites. The concentration of ZnO nanoparticles was varied from 0 to 20 wt.% (in 5 % increments) to determine their influence on bulk film piezoelectricity. Second, their electric polarization responses were obtained for quantifying thin film remnant polarization, which is directly correlated to piezoelectricity. Based on these results, the films were poled (at 50 $MV-m^{-1}$) to permanently align their electrical domains and to enhance their bulk film piezoelectricity. Then, a series of hammer impact tests were conducted, and the voltage generated by poled ZnO-based thin films was compared to commercially poled PVDF copolymer thin films. The hammer impact tests showed comparable results between the prototype and commercial samples, and increasing ZnO content provided enhanced piezoelectric performance. Lastly, the films were further validated for sensing using different energy levels of hammer impact, different distances between the impact locations and the film electrodes, and cantilever free vibration testing for dynamic strain sensing.

• Structural Monitoring and Maintenance
• /
• v.2 no.1
• /
• pp.19-34
• /
• 2015

One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck.

• Structural Engineering and Mechanics
• /
• v.54 no.2
• /
• pp.257-289
• /
• 2015

Shear connectors are generally used to link the slab and girders together in slab-on-girder bridge structures. Damage of shear connectors in such structures will result in shear slippage between the slab and girders, which significantly reduces the load-carrying capacity of the bridge. Because shear connectors are buried inside the structure, routine visual inspection is not able to detect conditions of shear connectors. A few methods have been proposed in the literature to detect the condition of shear connectors based on vibration measurements. This paper proposes a different dynamic condition assessment approach to identify the damage of shear connectors in slab-on-girder bridge structures based on power spectral density transmissibility (PSDT). PSDT formulates the relationship between the auto-spectral densities of two responses in the frequency domain. It can be used to identify shear connector conditions with or without reference data of the undamaged structure (or the baseline). Measured impact force and acceleration responses from hammer tests are analyzed to obtain the frequency response functions at sensor locations by experimental modal analysis. PSDT from the slab response to the girder response is derived with the obtained frequency response functions. PSDT vectors in the undamaged and damaged states can be compared to identify the damage of shear connectors. When the baseline is not available, as in most practical cases, PSDT vectors from the measured response at a reference sensor to those of the slab and girder in the damaged state can be used to detect the damage of shear connectors. Numerical and experimental studies on a concrete slab supported by two steel girders are conducted to investigate the accuracy and efficiency of the proposed approach. Identification results demonstrate that damages of shear connectors are identified accurately and efficiently with and without the baseline. The proposed method is also used to evaluate the conditions of shear connectors in a real composite bridge with in-field testing data.

• Journal of Biosystems Engineering
• /
• v.40 no.1
• /
• pp.19-27
• /
• 2015

Purpose: A system to measure the visibility of agricultural tractor operators was designed and evaluated according to ISO standards, and a blind area diagram around the tested tractor was created based on the manual method recommended by the National Institute for Occupational Safety and Health (NIOSH). Methods: A visibility measurement system was designed and evaluated based on the ISO 5006 and ISO 5721-1 standards. Two bulbs used to simulate the operator's eyes were mounted on a bar with a supporting frame. A wooden frame was used to determine the seat index point position. The 12-m visibility test circle was divided into six sectors of vision, and the test tractor was placed at the center of the circle. Artificial light was supplied in the darkened environment, and shadow or masking effects were measured manually around the 12-m circle. Results: When the bulbs were placed at the operator's eye level, front visibility was good; no masking was found in the "A" vision sector, but larger masking widths were found in the "B" and "C" vision sectors. Since the masking width exceeded 700 mm, additional tests, such as movement of the light sources to both sides of the operator's eye level, were performed. Less than six masking effects were found in the semi-circle of vision to the front, and more than one masking was found in the "B" and "C" visual fields. The minimum distance between the centers of two masking effects exceeded 2500 mm when measured as a chord on the semi-circle of vision. A blind area diagram was created to define the exact nature of the blind spots and mirror visibility. Conclusions: Visibility evaluation is an effective way to enable proper and safe operation for agricultural tractor operators. Inclusion of this visibility evaluation test in the general testing process might aid tractor manufacturers.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.10
• /
• pp.6282-6288
• /
• 2014

$CO_2$ welding is used widely in the field. On the other hand, welding defects occur when welders cannot adjust the current and voltage needed for welding and have to stop working to adjust the current and voltage, causing sudden cooling down of the welding structure inside a vehicle or tank where the control panel is invisible or when work site is far. This study used three types of existing $CO_2$ welders. This also applied SS400 rolled steel for welding structural purposes for remote control torch welding, perform a welding test through v-groove butt welding with a remote control torch and existing $CO_2$ welding torch, conducted visual inspection on the appearance of a welded top bead. In addition, the appearance quality of the welding part was monitored mainly through penetrant testing and a bending test to evaluate the welding defect reduction and the effect on the performance and compatibility by replacing the existing welder.

• Journal of Korean Society on Water Environment
• /
• v.37 no.4
• /
• pp.275-285
• /
• 2021

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.29 no.2
• /
• pp.217-225
• /
• 2019

Objectives: The aim of this study is to evaluate the quality management systems of domestic asbestos survey and monitoring service providers and the relationships with the number of licenses or designations and sales performances. Methods: Data on quality management systems were collected by assessors who were assigned by the Korea Occupational Safety and Health Agency(KOSHA) during a pilot evaluation program for designated asbestos survey and monitoring service providers in 2016 using evaluation criteria developed by KOSHA. Basic characteristics, evaluated scores, and sales performance were gathered and statistically analyzed. Results: The median and arithmetic mean of the total scores were 0.64 and 0.66. Evaluation fields that scored highly with the highest percentages were sales performance, installation and availability of equipment, compliance with the mandatory minimum number of airborne samples, laboratory independence, and results of proficiency analytical testing, in that order. Evaluation fields that received low marks with the highest percentages were the training of personnel, blank field samples, calibration of flow rates, preliminary check and visual inspection of the work area prior to the clearance test, and review and approval of final reports, in that order. Comparison of normalized scores between service providers registered for asbestos and other tasks and those designated for only asbestos showed significant differences in their evaluated scores. Sales performance did not show a positive correlation with evaluated scores. Conclusions: The quality management systems of domestic asbestos survey and monitoring service providers were poor. High scores were recorded mostly in evaluation fields related to regulatory requirements. Low scores were recorded mostly in evaluation fields related to documentation and recordkeeping. Considering the low influence of quality on sales performance, the government needs to evaluate the quality management of asbestos survey and monitoring service providers and provide the results to public in order to address their low levels of quality management.