• Journal of the Institute of Convergence Signal Processing
• /
• v.22 no.2
• /
• pp.57-63
• /
• 2021

The purpose of this study is to develop a Smart Helmet to safeguard forest firefighting crews and provide on-site information in real time. The Smart Helmet for forest firefingting crews is equipped with a camera, video/voice communication module, GPS, Bluetooth, and LTE module to promote the safety of them, and through the Smart Helmet, the site situation is is transmitted in real time, and full duplex communication is possible. As a result of testing using the Smart Helmet, the control center was able to receive on-site information and communication with on-site forest firefighting crews. Through site evaluation and user evaluation, it was confirmed that the Smart Helmet needs to be improved. The developed Smart Helmet can be used in various ways in forest disasters and forest industry.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.5
• /
• pp.105-116
• /
• 2019

Most of the public and private buildings in Korea are installing CCTV for crime prevention and follow-up action, insider security, facility safety, and fire prevention, and the number of installations is increasing each year. In the questionnaire conducted on the increasing CCTV, many reactions were positive in terms of the prevention of crime that could occur due to the installation, rather than negative views such as privacy violation caused by CCTV shooting. However, CCTV poses a lot of privacy risks, and when the image data is collected using the cloud, the personal information of the subject can be leaked. InseCam relayed the CCTV surveillance video of each country in real time, including the front camera of the notebook computer, which caused a big issue. In this paper, we introduce a system to prevent leakage of private information and enhance the security of the cloud system by processing the privacy technique on image information about a subject photographed through CCTV.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.331-345
• /
• 2021

Virtual testing is considered a major requirement for the safety verification of autonomous driving functions. For virtual testing, both the autonomous vehicle and the driving environment should be modeled appropriately. In particular, a realistic modeling of the perception sensor system such as the one having a camera and radar is important. However, research on modeling to consistently generate realistic perception results is lacking. Therefore, this paper presents a sensor modeling method to provide realistic object detection results in a MILS (Model in the Loop Simulation) environment. First, the key parameters for modeling are defined, and the object detection characteristics of actual cameras and radar sensors are analyzed. Then, the detection characteristics of a sensor modeled in a simulation environment, based on the analysis results, are validated through a correlation coefficient analysis that considers an actual sensor.

• 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.

• Journal of Fashion Business
• /
• v.26 no.5
• /
• pp.91-104
• /
• 2022

In this study, a cycling smart wear for measuring cycling posture and motion was developed using a three-dimensional motion analysis camera and an IMU inertial sensor. Results were compared according to parts to derive the optimal smart device attachment location, enabling correct posture measurement and cycle motion analysis to design a pattern. Conclusions were as follows: 1) 'S-T8' > 'S-T10' > 'S-L4' was the most significant area for each lumbar spine using a 3D motion analysis system with representative posture change (90°, 60°, 30°) to derive incisions and size specifications; 2) the part with the smallest relative angle change among significant section reference points during pattern design was applied as a reference point for attaching a cycling smart device to secure detachable safety of the device. Optimal locations for attaching the cycling device were the "S-L4" hip bone (Sacrum) and lumbar spine No. 4 (Lumbar 4^th); 3) the most suitable sensor attachment location for monitoring knee induction-abduction was the anatomical location of the rectus femoris; 4) a cycling smart wear pattern was developed without incision in the part where the sensor and electrode passed. The wearing was confirmed with 3D CLO. This study aims to provide basic research on exercise analysis smart wear, to expand the smart cycling area that could only be realized with smart devices and smart watches attached to current cycles, and to provide an opportunity to commercialize it as cycling smart wear.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.237-250
• /
• 2022

Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach.

• Journal of Biomedical Engineering Research
• /
• v.44 no.2
• /
• pp.125-138
• /
• 2023

The development of AI systems for radiation therapy is important to improve the accuracy, effectiveness, and safety of cancer treatment. The current system has the disadvantage of monitoring patients using CCTV, which can cause errors and mistakes in the treatment process, which can lead to misalignment of radiation. Developed the PMRP system, an AI automation system that uses depth cameras to measure patient's fine movements, segment patient's body into parts, align Z values of depth cameras with Z values, and transmit measured feedback to positioning devices in real time, monitoring errors and treatments. The need for such a system began because the CCTV visual monitoring system could not detect fine movements, Z-direction movements, and body part movements, hindering improvement of radiation therapy performance and increasing the risk of side effects in normal tissues. This study could provide the development of a field of radiotherapy that lags in many parts of the world, along with the economic and social importance of developing an independent platform for radiotherapy devices. This study verified its effectiveness and efficiency with data through phantom experiments, and future studies aim to help improve treatment performance by improving the posture correction mechanism and correcting left and right up and down movements in real time.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.2
• /
• pp.91-96
• /
• 2022

At the current camera-based technology level, sensor-based basic life pattern recognition technology has to suffer inconvenience to obtain accurate data, and commercial band products are difficult to collect accurate data, and cannot take into account the motive, cause, and psychological effect of behavior. the current situation. In this paper, radar technology for life pattern recognition is a technology that measures the distance, speed, and angle with an object by transmitting a waveform designed to detect nearby people or objects in daily life and processing the reflected received signal. It was designed to supplement issues such as privacy protection in the existing image-based service by applying it. For the implementation of the proposed system, based on TI IWR1642 chip, RF chipset control for 60GHz band millimeter wave FMCW transmission/reception, module development for distance/speed/angle detection, and technology including signal processing software were implemented. It is expected that analysis of individual life patterns will be possible by calculating self-management and behavior sequences by extracting personalized life patterns through quantitative analysis of life patterns as meta-analysis of living information in security and safe guards application.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.58 no.2
• /
• pp.141-152
• /
• 2022

As a method to understand the ecological habits around the artificial reef, various reports such as fishing gear survey, diving, sound survey, underwater CCTV and camera, etc. are reported. Among them, the sound survey method is carried out by installing an acoustic system on the ship and can be investigated regardless of the marine environment such as time constraints and turbidity. Such method, however, takes a lot of manpower and time as the ship travels at a constant speed. Investigations around artificial reefs are being conducted in an artificial way, and a lot of time and labor are consumed as such. Maritime buoys have been operated for various purposes such as route signs, weather observation, marine environment monitoring and defense monitoring for navigation safety in the past, but studies on monitoring systems for ecological habits and distribution of fish using marine buoys are remarkably insufficient. Therefore, this study aims to develop a system that allows users to directly monitor fish group detector data by estimating the distribution of fish groups around artificial reefs and using wireless communication at sea. In order to confirm the suitability of the maritime buoy used in this study, it was operated to compare data using LTE-equipped buoys capable of wireless communication and a data logger-type system buoy. Data transmission of buoys capable of LTE communication was carried out in a 10-minute ON, 10-minute OFF method due to the limitation of the power supply capacity, and data of the data logger-type buoy received full data. We compared and analyzed the data received from the two fish detectors. It is expected that real-time monitoring of the wireless buoy detection device using LTE will be possible through future research.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6A
• /
• pp.873-880
• /
• 2008

When checking the state of deterioration or damage structures, regular visual inspection has very important role. At this point, a visual inspection is performed mainly by sketch or photography with a camera of inspectors. If that happens, it takes a lot of effort and time to inspect appearance damages. The purpose of this study is to develop the automatic recognition system for a more efficient and effective inspection of appearance damages. In the process, the image processing technology and the data management & analysis system for damage recognition are mainly developed and applied. This automatic recognition system enables inspectors or clients to obtain correct data that can recognize a damage, such as, crack, water leakage, efflorescence, delamination (peeling), spalling, etc. In addition, this study takes aim at the effect of secure safety, functional maintenance and extension of design lifetime according to build up continuous and systematic data management system.