• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.66-73
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• 2024

Stay-cable is one of the most important load carrying members in cable-stayed bridges. Monitoring structural integrity of stay-cables is crucial for evaluating the structural condition of the cable-stayed bridge. For stay-cables, tension and damping ratio are estimated based on modal properties as a measure of structural integrity. Since the monitoring system continuously measures the vibration for the long-term period, data acquisition systems should be stable and power-efficiency as the hardware system. In addition, massive signals from the data acquisition systems are continuously generated, so that automated analysis system should be indispensable. In order to fulfill these purpose simultaneously, this study presents an autonomous cable monitoring system based on domain-knowledge using IoT for continuous cable monitoring systems of cable-stayed bridges. An IoT system was developed to provide effective and power-efficient data acquisition and on-board processing capability for Edge-computing. Automated peak-picking algorithm using domain knowledge was embedded to the IoT system in order to analyze massive data from continuous monitoring automatically and reliably. To evaluate its operational performance in real fields, the developed autonomous monitoring system has been installed on a cable-stayed bridge in Korea. The operational performance are confirmed and validated by comparing with the existing system in terms of data transmission rates, accuracy and efficiency of tension estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.111-116
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• 2021

This study is a study on improving the pedestrian Care system, which delivers jaywalking events in real time to the autonomous driving control center and Autonomous driving vehicles in operation and issues warnings and announcements to pedestrians based on pedestrian signals. In order to secure reliability of object detection method of pedestrian Care system, the inspection method combined with camera sensor with Lidar sensor and the improved system algorithm were presented. In addition, for the occurrence events of Lidar sensors and intelligent CCTV received during the operation of autonomous driving vehicles, the system algorithm for the elimination of overlapping events and the improvement of accuracy of the same time, place, and object was presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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• pp.1-10
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• 1999

In recent industry society, it is revealed that, as an increase in the use of automated manufacturing and process inspection technology, the data from mass production system exhibits some degrees of autocorrelation. The operation characteristics of traditional control charts developed under the independence assumption are adversely affected by the presence of serial correlation. Therefore, when autocorrelated construction contacted with time-series models explain, the time-series models are the Box-Jenkins forecast models which have been proposed as the best forecasting tool which allows for partitioning of variation into result from the autocorrelation structure and variation due to unusual but assignable causes. In this paper, for the AR(1) process of Box-Jenkins forecast models, when the constant term ξ are zero and different from zero, I want to analyze the sensitivity of (equation omitted), CUSUM and EWMA control chart for forecast residuals.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.327-336
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• 2004

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments if not stable and therefore intensity variation from uncontrolled lights gives many roubles for applying directly NGC as pattern matching algorithm in this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are preyed from experiments using real images.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.153-170
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• 2012

When companies or institutes manage data, in order to utilize it as useful resources for decision-making, it is essential to offer precise and reliable data. While most small and medium-sized enterprises and public institutes have been investing a great amount of money in management and maintenance of their data systems, the investment in data management has been inadequate. When public institutions establish their data systems, inspection has been constantly carried out on the data systems in order to improve safety and effectiveness. However, their capabilities in improving the quality of data have been insufficient. This study develops an automatic tool to diagnose the quality of data in a way to diagnose the data quality condition of the inspected institute quantitatively at the stage of design and closure by inspecting the data system and proves its practicality by applying the automatic tool to inspection. As a means to diagnose the quality, this study categorizes, in the aspect of quality characteristics, the items that may be improved through diagnosis at the stage of design, the early stage of establishing the data system and the measurement items by the quality index regarding measurable data values at the stage of establishment and operation. The study presents a way of quantitative measurement regarding the data structures and data values by concretizing the measurement items by quality index in a function of the automatic tool program. Also, the practicality of the tool is proved by applying the tool in the inspection field. As a result, the areas which the institute should improve are reported objectively through a complete enumeration survey on the diagnosed items and the indicators for quality improvement are presented quantitatively by presenting the quality condition quantitatively.

• Journal of Information Display
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• v.10 no.4
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• pp.202-205
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• 2009

An automated circuit repair system was developed for enhancing the yield of nondefective liquid crystal panels, focusing on the resist patterns on the circuit material layer of thin-film transistor (TFT) substrates prior to etching. The developed system has an advantage over the parallel conventional system: In the former, the repair conditions depend on the type of resist whereas in the latter, the repair parameters must be fine-tuned for each circuit material. The developed system consists of a resist pattern defect inspection system and a pattern repair system for short and open defects. The repair system performs fine corrections of abnormal areas of the resist pattern. The open-repair system is equipped with a syringe to dispense resist. To maintain a stable resist diameter, a thermal insulator was installed in the syringe system. As a result, the diameter of the dispensed resist became much more stable than when no thermal insulator was used. The resist diameter was kept within the target of $400{\pm}100{\mu}m$. Furthermore, a prototype system was constructed, and using a dummy pattern, it was confirmed that the system worked automatically and correctly.

• Nuclear Engineering and Technology
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• v.20 no.3
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• pp.170-178
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• 1988

An automated ultrasonic flaw detection system was developed for thin-walled and short tubes such as Zircaloy-4 tubes used for cladding heavy-water reactor fuel. The system was based on the two channels immersion pulse-echo technique using 14 MHz shear wave and the specially developed helical scanning technique, in which the tube to be tested is only rotated and the small water tank with spherical focus ultrasonic transducers is translated along the tube length. The optimum angle of incidence of ultrasonic beam was 26 degrees, at which the inside and outside surface defects with the same size and direction could be detected with the same sensitivity. The maximum permissible defects in the Zircaloy-4 tubes, i.e., the longitudinal and circumferential v notches with the length of 0.76mm and 0.38mm, respectively and the depth of 0.04 mm on the inside and outside surface, could be easily detected by the system with the inspection speed of about 1 m/min and the very excellent reproducibility. The ratio of signal to noise was greater than 20 dB for the longitudinal defects and 12 dB for the circumferential defects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.5B
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• pp.824-832
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• 2010

In construction structure management, the effects of investigation by the professional manager is dependent on the cost, the inspection periods and methods. Therefore, effective and automated maintenance system for the target structure is required. Although some bridge monitoring systems are operating using wire based networks, the performance is not good enough to show sufficient ability as integrated bridge management system. In this paper, we design and implement an integrated bridge management system based on sensor networks. Two expert modules for bridge management and the integrated system management are provided. Moreover, web-based monitoring system is also designed for users at anywhere. The results show that the system is effective and readily available.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.30-42
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• 2022

Due to the increase in construction of tunnels, the burdens of maintenance works for tunnel structures have been increasing in Korea. In addition, the increase of traffic volume and aging of materials also threatens the safety of tunnel facilities, therefore, maintenance costs are expected to increase significantly in the future. Accordingly, automated condition assessment technologies like image-based tunnel scanning system for inspection and diagnosis of tunnel facilities have been proposed. For image-based tunnel scanning system, it is key to create a planar image through stitching of multiple images captured by tunnel scanning system. In this study, performance of feature-based stitching algorithms suitable for stitching tunnel scanning images was evaluated. In order to find a suitable algorithm SIFT, ORB, and BRISK are compared. The performance of the proposed algorithm was determined by the number of feature extraction, calculation speed, accuracy of feature matching, and image stitching result. As for stitching performance, SIFT algorithm was the best in all parts of tunnel image. ORB and BRISK also showed satisfactory performance and short calculation time. SIFT can be used to generate precise planar images. ORB and BRISK also showed satisfactory stitching results, confirming the possibility of being used when real-time stitching is required.