• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.117-123
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• 2012

For an effective energy management in intelligent buildings it is necessary to gather information about position/absence of people and the level of population. In this paper the smart occupancy detection system using the active RFID is developed to satisfy such a demand based on the results of previous research. First of all the design considerations and functions of the system are introduced. In sequence the functions of the system is presented, and then the performance of the developed system is tested and verified through various field tests. The developed core technology can be also applied to other fields such as security, healthcare, smart home, etc.

• Smart Structures and Systems
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• v.19 no.1
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• pp.91-103
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• 2017

Generalized fractal dimension is used to detect the presence of partial delamination in a composite laminated beam. The effect of boundary conditions and location of delamination on the fractal dimension curve is studied. Appropriability of higher mode shape data for detection of delamination in the beam is evaluated. It is shown that fractal dimension measure can be used to detect the presence of partial delamination in composite beams. It is found that the torsional mode shape is well suited for delamination detection in beams. First natural frequency of delaminated beam is found to be higher than the healthy beam for certain small and partial width delaminations and some boundary conditions. An explanation towards this counter intuitive phenomenon is provided.

• Smart Structures and Systems
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• v.22 no.6
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• pp.699-709
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• 2018

With increasing traffic volumes and rising vehicle traffic, especially in cities, the number of pedestrian bridges has also increased significantly. Like all other structures, pedestrian bridges also suffer damage. In order to increase the safety of pedestrians, it is necessary to identify existing damage and to repair them to ensure the safety of the bridge structures. Owing to the shortcomings of local methods in identifying damage and in order to enhance the reliability of detection and identification of structural faults, signal methods have seen significant development in recent years. In this research, a new methodology, based on cone-shaped kernel distribution with a new damage index, has been used for damage detection in pedestrian truss bridges. To evaluate the proposed method, the numerical models of the Warren Type steel truss and the Arregar steel footbridge were used. Based on the results, the proposed method and damage index identified the damage and determined its location with a high degree of precision. Given the ease of use, the proposed method can be used to identify faults in pedestrian bridges.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.237-254
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• 2019

This paper presents an improved time series based damage detection approach with experimental verifications for detection, localization, and quantification of damage in shear-type structures under varying mass effects using output-only vibration data. The proposed method can be very effective for automated monitoring of buildings to develop proactive maintenance strategies. In this method, Auto-Regressive Moving Average models with eXogenous inputs (ARMAX) are built to represent the dynamic relationship of different sensor clusters. The damage features are extracted based on the relative difference of the ARMAX model coefficients to identify the existence, location and severity of damage of stiffness and mass separately. The results from a laboratory-scale shear type structure show that different damage scenarios are revealed successfully using the approach. At the end of this paper, the methodology limitations are also discussed, especially when simultaneous occurrence of mass and stiffness damage at multiple locations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.211-213
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• 2019

This paper proposes an image detection method, which can detect images regardless of the location and the direction of an image, required for automatic inspection based on machine vision technologies. A cable harness is considered in this paper as an inspection object, and implementation results of a technology of being applicable to a real cable harness production process is presented.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.47-56
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• 2022

Recently bank card number recognition plays an important role in improving payment efficiency. In this paper we propose a new bank-card number identification algorithm. The proposed algorithm consists of three modules which include edge detection, candidate region generation, and recognition. The module of 'edge detection' is used to obtain the possible digital region. The module of 'candidate region generation' has the role to expand the length of the digital region to obtain the candidate card number regions, i.e. to obtain the final bank card number location. And the module of 'recognition' has Convolutional deep learning Neural Network (CNN) to identify the final bank card numbers. Experimental results show that the identification rate of the proposed algorithm is 95% for the card numbers, which shows 20% better than that of conventional algorithm or method.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.591-606
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• 2024

This article proposes a new methodology for identifying beam damage based on changes in modal parameters using the Double Stage Extended Improved Particle Swarm Optimization (DSEIPSO) technique. A finite element code is first developed in MATLAB to model an ideal beam structure based on classical beam theory. An experimental study is then performed on a laboratory-scale beam, and the modal parameters are extracted. An improved version of the PSO algorithm is employed to update the finite element model based on the experimental measurements, representing the real structure and forming the baseline model for all further damage detection. Subsequently, structural damages are introduced in the experimental beam. The DSEIPSO algorithm is then utilized to optimize the objective function, formulated using the obtained mode shapes and the natural frequencies from the damaged and undamaged beams to identify the exact location and extent of the damage. Experimentally obtained resultsfrom a simple cantilever beam are used to validate the effectiveness of the proposed method. The illustrated results show the effectiveness of the proposed method for structural damage detection in the SHM field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1535-1541
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• 2019

To carry out safe and rapid decontamination in radiological accident areas, acquisition of various information on radiation sources is needed. In particular, to figure out the location and distribution of radiation sources is essential for rapid follow-up and removal of contaminants as well as minimizing worker damage. The radiation distribution detection device is used to obtain the position and distribution information of the radiation source. In the case of a radiation distribution detection device, a detection sensor unit is generally composed of a single sensor, and the detection range is limited due to the physical characteristics of the single sensor. We applied a calibration detector for controlling the detection sensitivity of a single sensor for radiation detection and improved the limited detection range of radiation dose rate. Also, gamma irradiation test confirmed the improvement of radiation distribution detection range.

• KNOM Review
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• v.23 no.1
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• pp.10-17
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• 2020

As the threat of Distributed Denial-of-Service attacks that exploit weakly secure IoT devices has spread, research on source-side Denial-of-Service attack detection is being activated to quickly detect the attack and the location of attacker. In addition, a collaborative source-side attack detection technique that shares detection results of source-side networks located at individual sites is also being activated to overcome regional limitations of source-side detection. In this paper, we evaluate the performance of a collaborative source-side DDoS attack detection using statistical weights. The statistical weight is calculated based on the detection rate and false positive rate corresponding to the time zone of the individual source-side network. By calculating weighted sum of the source-side DoS attack detection results from various sites, the proposed method determines whether a DDoS attack happens. As a result of the experiment based on actual DNS request to traffic, it was confirmed that the proposed technique reduces false positive rate 2% while maintaining a high attack detection rate.