• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
• /
• v.4 no.1
• /
• pp.51-55
• /
• 2001

The characteristics of elastic waves emanating from crack initiation in 2024 and 5052 aluminum alloys subject to static and fatigue loading are investigated through laboratory experiments. The objective of the study is to determine difference in the properties of the signals generated from static and fatigue tests and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recoded using non-resonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Three distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the material and the type of fracture associated with the signals. Analysis of the waveforms indicated that some signals could be attributed to plastic deformation associated with static tests. The potential application of the approach in health monitoring of aging aircraft structures using a network of surface mounted broadband sensors is discussed.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.667-669
• /
• 2021

본 논문에서는 벡터 기반 데이터 증강 기법(Data augmentation)을 제안하여 학습 데이터를 구축한 뒤, 이를 합성곱 신경망(Convolutional Neural Networks, CNN)으로 실제 균열과 가까운 패턴을 표현할 수 있는 프레임워크를 제안한다. 건축물의 균열은 인명 피해를 가져오는 건물 붕괴와 낙하 사고를 비롯한 큰 사고의 원인이다. 이를 인공지능으로 해결하기 위해서는 대량의 데이터 확보가 필수적이다. 하지만, 실제 균열 이미지는 복잡한 패턴을 가지고 있을 뿐만 아니라, 위험한 상황에 노출되기 때문에 대량의 데이터를 확보하기 어렵다. 이러한 데이터베이스 구축의 문제점은 인위적으로 특정 부분에 변형을 주어 데이터양을 늘리는 탄성왜곡(Elastic distortion) 기법으로 해결할 수 있지만, 본 논문에서는 이보다 향상된 균열 패턴 결과를 CNN을 활용하여 보여준다. 탄성왜곡 기법보다 CNN을 이용했을 때, 실제 균열 패턴과 유사하게 추출된 결과를 얻을 수 있었고, 일반적으로 사용되는 픽셀 기반 데이터가 아닌 벡터 기반으로 데이터 증강을 설계함으로써 균열의 변화량 측면에서 우수함을 보였다. 본 논문에서는 적은 개수의 균열 데이터를 입력으로 사용했음에도 불구하고 균열의 방향 및 패턴을 다양하게 생성하여 쉽게 균열 데이터베이스를 구축할 수 있었다. 이는 장기적으로 구조물의 안정성 평가에 이바지하여 안전사고에 대한 불안감에서 벗어나 더욱 안전하고 쾌적한 주거 환경을 조성할 것으로 기대된다.

• Computers and Concrete
• /
• v.33 no.4
• /
• pp.341-348
• /
• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Convergence Security Journal
• /
• v.8 no.1
• /
• pp.1-7
• /
• 2008

The hacking method came to be more complicated, became program ant it was automated. That is hacking trend of recent times. Before, The password crack, catch root authority is trend of hacking which uses the vulnerability of server. Hacker attack network or all of domain not some host. Web application system at hacking technique develops and improve transmitted data through the network shows many vulnerability. The massive data are transmitted through the network without encipherment filtering. It will be able to bring about the neck of a bottle actual condition which is serious in security system because of the network where the user comes to be many it leads and the data which is delivered comes to be many. In this paper, we propose web application system to prevent overload from bottleneck in encipherment system. It can solve security key trust problem in encoding and decoding with public key infrastructure.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.23 no.1
• /
• pp.45-52
• /
• 2003

A number of stress corrosion cracks in turbine rotor disk keyway in power plants have been found and the necessity has been raised to detect and evaluate the cracks prior to the catastrophic failure of turbine disk. By ultrasonic RF signal analysis and using a neural network based on bark-propagation algorithm, we tried to evaluate the location, size and orientation of cracks around keyway. Because RF signals received from each reflector have a number of peaks, they were processed to have a single peak for each reflector. Using the processed RF signals, scan data that contain the information on the position of transducer and the arrival time of reflected waves from each reflector were obtained. The time difference between each reflector and the position of transducer extracted from the scan data were then applied to the back-propagation neural network. As a result, the neural network was found useful to evaluate the location, size and orientation of cracks initiated from keyway.

• The Journal of Society for e-Business Studies
• /
• v.12 no.3
• /
• pp.75-93
• /
• 2007

This paper presents real-time online early fire warning systems developed for preserving cultural properties of Bulguksa which is a world heritage designated by UNESCO. The system is based on the ubiquitous sensor network employing 900MHz and 2.4GHz bands. In this paper, we analyze requirements that should be considered in building effective management systems of cultural heritages by using wireless sensor network. Finally, we introduce the architecture, sensor and network design, and software design of the fire warning systems which is an initial version of U-Bulguksa. The current version of systems has been operating in Bukguksa for a few months. U-Bukguksa project sponsored by National Information Society Agency is ultimately aimed at developing an integrated system of U-cultural heritage management and U-tourism. The former aims to conserve and manage intangible cultural properties by providing a variety of environmental information such as erosion, crack, and gradient as well as fire which are important causes of loss and damage in real-time and online. The latter refers to the intelligent tourism information and guidance systems allowing tourists to get the personalized content on cultural heritages and help guidance with mobile devices in Bulguksa.

• Journal of Convergence Society for SMB
• /
• v.6 no.3
• /
• pp.29-35
• /
• 2016

This paper presents the design of a user authentication system (DCIAS) using the device constant information. Defined design a new password using the access device constant information to be used for user authentication during system access on the network, and design a new concept the user authentication system so that it can cope with the threat required from passive replay attacks to re-use the password obtained in other applications offer. In addition, by storing a password defined by the design of the encrypted random locations in the server and designed to neutralize the illegal access to the system through the network. Therefore proposed using the present system, even if access to the system through any of the network can not know whether any where the password is stored, and if all right even stored information is not easy to crack's encrypted to neutralize any replay attacks on the network to that has strong security features.

• International Journal of Highway Engineering
• /
• v.2 no.3
• /
• pp.145-153
• /
• 2000

Pavement evaluation is a fundamental component for rational pavement management. Optimal rehabilitation method and the priority of rehabilitation should be based on the evaluation data. Some types of pavement condition index are needed for objective evaluation of Pavement condition and management of road network. In this study a expressway concrete pavement condition index model is developed through regression analysis that correlates panel rating with distress measurement from the test sections. The derived condition index can be used for network level PMS for the expressway concrete pavement. Correlation coefficient of the model was 0.68. The selected independent variables were International Roughness Index, crack and area of patching.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.1
• /
• pp.1-8
• /
• 2015

Recently, a high strength concrete(HSC) in excess of 80 MPa is popular to use in the domestic construction field. But there is no design standard of high strength concrete. It is reason why a study about structural behaviors of thin walls is required. In this paper, the accurate Finite Element Method as a virtual test is suggested considering material properties, which are concrete and steel, and the experimental fractural model suggested by Kupfer. It is conducted the comparison evaluation of the ultimate failure loads, lateral-displacements and crack propagation patterns between the results of experimental approach, which were carried on Saheb's test for normal strength concrete and Lee's test for high strength concrete. Therefore it is suggested to use the accurate virtual simulation test method and Ubiquitous Sensor Network(USN) by Finite Element Method for Internet of Things(IoT).

• Journal of the Korean Ceramic Society
• /
• v.12 no.1
• /
• pp.16-22
• /
• 1975

The network structure of a glass is known to be cracked by a chemical reaction, diffusion, and ion exchange of alkali ion, formed at the crack tip of the glass surface, when water is present on the glass surface. Since the durability of glass is reduced, due to the fatigue phenomenon mentioned above, pollution problem of glass goods, especially bottle glass, is becoming acute gradually. A static fatigue phenomenon was studied thermodynamically in this paper, and a mechanism of static fatigue, a quality control, and a method of preventing pollution for the main local glass goods were also investigated. The PH of reacted solution and the quantity of extracted alkali were measured at different conditions such as temperature, reacting time, particle size of a crushed glass sample, and the nature of reacting solution. The enthalpy change was calculated from the Arrhenius equation. The results are given below; 1) The absolute value of enthalpy change for the bottle glass was found to be higher than the for the flat glass. 2) The fatigue phenomenon of a glass was more sensitive to the temperature than to the reacting time. 3) The durability of glass in acid solution is stronger than in alkaline solution. 4) The substance which cracks the network structure of glas is considered the hydroxyl ion.