• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.4
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• pp.14-25
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• 2010

Small cracks or chips of a product appear very frequently in the course of continuous production of an automatic press process system. These phenomena became the cause of not only defective product but also damage of a press mold. In order to solve this problem AE(Acoustic emission) system was introduced. AE system was expected to be very effective to real time detection of the defective product and for the prevention of the damage in the press molds In this study, for the pick and analysis of AE signals generated from the press process, AE sensors/pre-amplifier/analysis and processing board were used as frequently found in the other similar cases. For the analysis and processing the AE signals picked in real time from the normal or the detective products, specialized software called AE-win(software for processing AE signal from Physical Acoustics Corporation) was used. As a result of this work it was conformed that intensity and shape of the various AE signals differ depending on the weight of the press and thickness of sheet and process type.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1423-1430
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• 2003

In this paper, we develop a real time monitoring system to detect third-party damage on natural gas pipeline. When the damage due to third-party incidents causes an immediate rupture, the developed on-line monitoring system can help reducing the sequences of event at once. Moreover, since many third-party incidents cause damage that does not lead to immediate rupture but can grow with time, the developed on-line monitoring system can execute a significant role in reducing many third-party damage incidents. Also, when the damage is given at a point on natural gas pipeline, the acoustic wave is propagated very fast about 421.3 m/s. Therefore, the data processing time should be very short in order to detect precisely the impact position. Generally, the pipeline is laid under ground or sea and the length is very long. So a wireless data communication method is recommendable and the sensing positions are limited by laid circumstance and setting cost of sensors. The calculation and monitoring software is developed by an algorithm using the propagation speed of acoustic wave and data base system based on wireless communication and DSP systems. The developed monitoring system is examined by field testing at Balan pilot plant, KOGAS being done in order to demonstrate its validity through reactive detection of third-party contact with pipelines. Furthermore, the development system was set at the practical pipelines such as an offshore pipeline between two islands Yul-Do and Youngjong-Do, and a land branch of Pyoungtaek, Korea and it has been operating in real time.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.143-148
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• 2009

About 70% of Korea consists of mountainous areas, and during the construction of many roads and railroads, cut slopes are inevitably formed. The rainy season, frost heaving in winter, and thawing in spring can all cause rockfalls and landslides. The failure of these slopes is increasing every year, causing damage to vehicles, personal injury and even death. To protect people and property from such damage, a real-time monitoring system is needed to detect the early stages of slope failures. The GMG placed TRS sensor units in the slopes to monitor them in real-time. But due to its reliance on data lines and power lines, the system is vulnerable to lightning damage. The whole system can be damaged by a single lighting strike. Consequently, for the purposes of this paper we propose the use of the Ubiquitous Sensor Network (USN) which follows the IEEE 802.1.4. By using the USN system we can minimize lightning damage and can monitor the movement of the slopes consistently.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.1
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• pp.165-174
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• 2004

This research develops a technique which uses energy dissipation ratio in order to monitor the structural health on real time basis. For real-time monitoring, we employ the NExT and the ERA which enable us to obtain real-time data. Energy dissipation ratio is calculated from those data only with the damping and natural frequency of the structure, and from the calculated values we develop an algorithm (Energy dissipation method) which decides the damage degree of structure. The Energy dissipation method developed in this research is proved to be valid by comparison with other methods like the eigenparameter method and the MAC. Especially this method enables us to save measuring time and data which are the most important in real-time monitoring, and its use of the ambient vibration also makes it easy to monitor the whole structure and its damage points.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.505-509
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• 2020

Twitter is a useful medium to grasp various damage situations that have occurred in society. However, it is a laborious task to spot damage-related topics according to time in the environment where information is constantly produced. This paper proposes a methodology of constructing a knowledge structure by combining the BERT-based classifier and the community detection techniques to discover the topics underlain in the damage information. The methodology consists of two steps. In the first step, the tweets are classified into the classes that are related to human damage, infrastructure damage, and industrial activity damage by a BERT-based transfer learning approach. In the second step, networks of the words that appear in the damage-related tweets are constructed based on the co-occurrence matrix. The derived networks are partitioned by maximizing the modularity to reveal the hidden topics. Five keywords with high values of degree centrality are selected to interpret the topics. The proposed methodology is validated with the Hurricane Harvey test data.

• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.20-27
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• 2012

Mechanical seals are one of main components of high speed centrifugal pumps. So, it is very important to detect the faults (scratch, notch, indentation, wear) of mechanical seals since the damage of seal can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the seal fault using the time signals measured from sensors. Recently, studies are focused on the development of on-line real time monitoring system. But study on the feature parameters used for fault detection of mechanical seals has a little been performed. In this paper, we showed feature parameters extracted from accelerated and acoustic signals by using the discrete wavelet transform (DWT), alpha coefficient, statistical parameters. And also verified the possibility for fault detection of mechanical seal.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.10a
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• pp.138-146
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• 1996

The presence of a damage, such as a crack, in a structure increases the flexibility and damping in the structure. Most of methods to detect damage or damage location uses stiffness matrix of the structural system. The modification of stiffness matrix, however, has complicated procedures to identify structural. system in the basis of finite element model and has too many degree of freedom to calculate. Identification of changes of flexibility of structure can offer damage information immediately and simple procedure can employ real time continuous monitoring system. To identify changes of the flexibility, vibration mode shapes and natural frequencies are usually used. In this paper, a procedure for damage location in continuous girder bridges using vibration data is described. The effectiveness and sensitivity of the presented method to measurement errors in mode shapes and natural frequencies are investigated using analytical results from finite element models. It is shown that the errors in the first mode shape and first natural frequency demonstrate much larger influence than those in the higher mode shapes and modal frequencies.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.995-999
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• 2010

Every year, traffic accidents and traffic congestion have been rapidly increasing, Although the roadway design and signal system have been improved to relieve traffic accidents, traffic casualties and property damage do not decrease. This paper develops a real-time traffic accident detection and analysis system (RTADAS): In the proposed system, we aim to precisely detect traffic accidents at different design and flow of intersections, However, because the data collected from intersections have uncertainty and complicated causal dependency between them, we construct probability-based networks for correct accident detection.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.3
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• pp.150-159
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• 2000

In-process diagnosis of the cutting state is essential for the automation of manufacturing systems. Especially when the cutting process becomes unstable it induces self-exited vibrations a frequent case of poor tool life rough surface finish damage to the workpiece and the machine tool itself and excessive down time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time. To ensure that the cutting process main-tains stable it is highly desirable to have the capability of real-time monitoring and controlling chatter. This paper describes the detection method of chatter vibration using cutting force in turning process. In order to detect a chatter vibra-tion the dynamic fluctuation of radial force is analyzed since this components is sensitive to the chatter. The envelope sig-nal of radial force has been calculated by the use of FIR Hilbert transformer and it was useful to classify the chatter signal from the dynamically unstable circumstances. It was found that the mode and the mode width were closely correlated with the chatter amplitude was well. Finally back propagation(BP) neural network have been applied to the pattern recognition for the classification of chatter signal in various cutting conditions. The validity of this systed was confirmed by the experiments under the various cutting conditions.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.571-580
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• 2022

Marine deposited waste is a major cause of problems such as a lot of damage and an increase in the estimated amount of garbage due to abandoned fishing grounds caused by ghost fishing. In this paper, we implement a real-time marine deposited waste detection artificial intelligence system to understand the actual conditions of waste fishing gear usage, distribution, loss, and recovery, and study methods for performance improvement. The system was implemented using the yolov5 model, which is an excellent performance model for real-time object detection, and the 'data screening process' and 'class segmentation' method of learning data were applied as performance improvement methods. In conclusion, the object detection results of datasets that do screen unnecessary data or do not subdivide similar items according to characteristics and uses are better than the object recognition results of unscreened datasets and datasets in which classes are subdivided.