• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.18 no.6
• /
• pp.155-163
• /
• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Journal of Korean Society for Geospatial Information Science
• /
• v.14 no.4 s.38
• /
• pp.79-84
• /
• 2006

Change detection is a core functions of remote sensing. It can be widely used in public business such as land monitoring, demage assessment from disaster, growth analysis of cities, etc. However, it seems that the change detection using satellite imagery has not been fully used in public business. For the person who are in charge of public business, it would not be easy to implement the change detection because various functions are combined into it. So, to promote the use of the change detection in public business, the standard, the process and the method for the change detection in public business should be established. Also, the software which supports that would be very useful. This study aims to promote the use of satellite imagery in public business by building up the change detection process which are suitable for general public business and developing the change detection software to support the process. The software has been developed using ETRI Components for Satellite Image Processing to support the interoperability.

• Smart Media Journal
• /
• v.13 no.7
• /
• pp.9-17
• /
• 2024

Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.19 no.6
• /
• pp.915-936
• /
• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.

• Fire Science and Engineering
• /
• v.34 no.2
• /
• pp.30-40
• /
• 2020

In the event of a fire and a disaster, prompt and accurate alarms inside and outside the building are directly related to the minimization of damage and the success of life evacuation. However, due to unwanted fire alarms in automated fire detection systems, the number of dispatches by misunderstanding in the 119 service is increasing. This causes the insensitivity to the safety of building managers and the waste of the fire-fighting power. Therefore, in this study, the statistical databases and literature on unwanted fire alarms in Korea and abroad (USA, UK) were identified and the management systems for unwanted fire alarms were compared and analyzed to identify problems of statistics in the management systems for unwanted fire alarms.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.11 no.6
• /
• pp.1014-1025
• /
• 1987

This paper proposes an approach of building an expert system for designing gear drives. In terms of gear drives the relationship between expert systems and data base systems is explained and a data base system for design, failure detection, and trouble-shooting of gear drives is built on a personal computer by the data manipulation language of dBASE III. This paper proposes a method how to build a data base system and what to consider to build a data base system for gear drives and two kinds of data base systems of design and failure detection of gear drives and trouble-shooting for noise and vibration of gear drives are built. The two data base systems are very effective for the retrieval of information for the gear design, and they will be considerably helpful for the gear design.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.156-158
• /
• 2003

Analysis of high-resolution satellite image becomes important for cartography, surveillance, and remote sensing. However, there are lots of problems to be solved for automatic analysis of high-resolution satellite image especially in urban area. The problems are originated from the increased complexity due to the unnecessary details and shadows, and time-varying illuminations. Because of such obstacles, it seems impossible to make automatic analysis. This paper proposes a way of change detection of buildings in urban area by using digital vector map. The proposed way makes the buildings on the vector map parameterized, and searches them in the preprocessed high-resolution image by using generalized Hough transform. The searched building objects are overlaid on the satellite image. The overlaid image can help to detect the change of building rapidly.

• Journal of Korean Association for Spatial Structures
• /
• v.8 no.6
• /
• pp.75-83
• /
• 2008

The R.C Building will be superannuated as time passes by heavy load and serviceability. Methods of damage detection are used a visual angle of human or non-destructive test in the R.C Building. In case of the latter, Problems of damage detection are occurred to directions of steel bar. Elastic waves are difficult to assaying test using 1 axial type of accelerometer in reinforced concrete. In this study, fundamental studies for estimations using 3 axial type of accelerometer are discussed oscillator of elastic waves when embedded glass tube pipe or steel bar in flexible concrete specimens.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2003.11a
• /
• pp.27-31
• /
• 2003

To maintain serviceability of concrete structure more than proper it is necessary not only predict service life through periodical monitor but also need monitoring system to recognize optimal time and method for repair. Recently, CPGFRP, replacing some GFRP with CF, is developed and used for monitoring concrete fraction. But dramatic resistance change of CPGFRP is showed below 0.5% strain and it is not small strain in terms of monitoring micro crack in concrete. In other word, monitoring with CF is not suitable in low stress hut hight stress. In this study, we accessed applicable possibility and reliability of CPGFRP composite as monitoring sense that is proved very sensitive to stress through domestic and oversea previous study. CPGFRP composite plays a role in specimen like steel and increases flexural strength. CPGFRP composite shows resistance increasement in micro crack. In particular, CPUFRP is more sensitive than strangage in low stress. Resistance change ratio curve is very similar to strain curve so sensitivity and reliability is very excellent to monitor concrete fracture.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2003.05a
• /
• pp.27-31
• /
• 2003

To maintain serviceability of concrete structure more than proper it is necessary not only predict service life through periodical monitor but also need monitoring system to recognize optimal time and method for repair. Recently, CPGFRP, replacing some GFRP with CF, is developed and used for monitoring concrete fraction. But dramatic resistance change of CPGFRP is showed below 0.5％ strain and it is not small strain in terms of monitoring micro crack in concrete. In other word, monitoring with CF is not suitable in low stress but hight stress. In this study, we accessed applicable possibility and reliability of CPGFRP composite as monitoring sense that is proved very sensitive to stress through domestic and oversea previous study. CPGFRP composite plays a role in specimen like steel and increases flexural strength. CPGFRP composite shows resistance increasement in micro crack. In particular, CPGFRP is more sensitive than strangage in low stress. Resistance change ratio curve is very similar to strain curve so sensitivity and reliability is very excellent to monitor concrete fracture.