• Smart Structures and Systems
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• v.6 no.9
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Bulletin of the Korean Chemical Society
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• v.35 no.2
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• pp.371-376
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• 2014

A novel three-phase hollow fiber liquid phase microextraction was developed for the determination of malachite green (MG) in environmental waters, which selected [BMIM][$PF_6$] mixed with 1% trioctylphosphine oxide (TOPO) as supported phase. Several parameters (accepter phase pH, sample pH, supported phase membrane, volume of accepter phase, salinity, extraction time) that could affect extraction performance were investigated. Under the optimal extraction conditions, the established approach showed excellent characters as: high enrichment factor (212), wide linear range ($0.20-100{\mu}gL^{-1}$), low detection limit ($0.01{\mu}gL^{-1}$), good reproducibility (RSD, 8.9%, n=5) and satisfactory recovery (84.0-106.2%). The method was applied to detect MG at Yangtze River and pond waters in Zhenjiang, Jiangsu province, and 4 sites among 15 sampling sites were found MG with the concentration of $1.73-11.06{\mu}gL^{-1}$, which confirmed that the proposed environmentally friendly method was simple and effective for monitoring MG in aquatic system.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.49-58
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• 2020

In this study, we solve an online multi-object problem which finds object states (i.e. locations and sizes) while conserving their identifications in online-provided images and detections. We handle this problem based on a tracking-by-detection approach by linking (or associating) detections between frames. For more accurate online association, we propose novel online appearance learning with discrete fourier transform and partial least square analysis (PLS). We first transform each object image into a Fourier image in order to extract meaningful features on a frequency domain. We then learn PLS subspaces which can discriminate frequency features of different objects. In addition, we incorporate the proposed appearance learning into the recent confidence-based association method, and extensively compare our methods with the state-of-the-art methods on MOT benchmark challenge datasets.

• Journal of Preventive Medicine and Public Health
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• v.22 no.3 s.27
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• pp.380-388
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• 1989

To evaluate the costs of the hypertension screening program of the Korea Medical Insurance Corporation, the records of the screening examinations were used. The sample size was 49,983 of the 906,554 people insured by the Corporation and was obtained by two-stage stratification random sampling. The alternatives for efficiency of the screening program, which were divided into three categories : modification of the screening test package, application of other hypertension diagnostic criteria, and selective approach of tested groups by age, were evaluated according to the cost per patient detected. The results of this study were as follows In the hypertension screening system, the cost per patient detected was Won 30,883. The most nonsensitive test for hypertension detection was ophthalmoscopy, which was examined during the second stage of screening. If the ophthalmoscope examination was excluded, olny one person was not detected, which was 0.2% of detected persons, and the cost per patient detected decreased to Won 28,098. The most efficient modification of the screening test package was measurement of blood pressure through the first and second stages of screening. The cost per patient detected by this modification was Won 24,408. The application of other diagnostic critera, which were more restricted criteria, increased the cost per patient detected by 3.7%-6.7%. The cost per patient detected were Won 170,582 for persons less than 39 years old, Won 20,032 for persons 40 to 59 years old, and Won 8,675 for persons 60 years old and over. In conclusion, the best alternative suggested with respect to efficiency and practical application excluded the ophthalmoscope examination of second stage screening and restricted the target population to persons greater than 40 years old. The application of this alternative decreased 54.9% of the screening costs and the cost per patient detected was Won 15,222. This study was limited in that measurement of effectivenes was not of the ultimate goal of screening, which is decreasing morbidity and mortality, but was of disease detection as the short-term objective.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3795-3802
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• 2022

NaI(Tl) scintillation materials are considered to be one of many materials that are used exclusively for γ-ray detection and spectroscopy. The gamma-ray spectrometer is not an easy-to-use device, and the accuracy of the numerical values must be carefully checked based on the rules of the calibration technique. Therefore, accurate information about the detection system and its effectiveness is of greater importance. The purpose of this study is to estimate, using an analytical-numerical formula (ANF), the purely geometric solid angle, geometric efficiency, and total efficiency of a cylindrical NaI(Tl) γ-ray detector with a side-through hole. This type of detector is ideal for scanning fuel rods and pipelines, as well as for performing radio-immunoassays. The study included the calculation of the complex solid angle, in combination with the use of various points like gamma sources, located axially and non-axially inside the through detector side hole, which can be applied in a hypothetical method for calibrating the facility. An extended γ-ray energy range, the detector, source dimensions, "source-to-detector" geometry inside the side-through hole, path lengths of γ-quanta photons crossing the facility, besides the photon average path length inside the detector medium itself, were studied and considered. This study is very important for an expanded future article where the radioactive point source can be replaced by a volume source located inside the side-trough hole of the detector, or by a radioactive pipeline passing through the well. The results provide a good and useful approach to a new generation of detectors that can be used for low-level radiation that needs to be measured efficiently.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.1-8
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• 2020

The similarity detection to plagiarism or duplication of computer programs requires a different type of analysis methods and tools according to the programming language used in the implementation and the sort of code to be analyzed. In recent years, the similarity appraisal for the object code in the embedded system, which requires a considerable resource along with a more complicated procedure and advanced skill compared to the source code, is increasing. In this study, we described a method for analyzing the similarity of functional units in the assembly language through the conversion of object code using the reverse engineering approach, such as the reverse assembly technique to the object code. The instruction and operand table for comparing the similarity is generated by using the syntax analysis of the code in assembly language, and a tool for detecting the similarity is designed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1807-1822
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• 2023

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.346-353
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• 2023

In this paper, we propose an innovative approach that leverages deep learning to find optimal reference points for achieving precise tooth segmentation in three-dimensional tooth point cloud data. A dataset consisting of 350 aligned maxillary and mandibular cloud data was used as input, and both end coordinates of individual teeth were used as correct answers. A two-dimensional image was created by projecting the rendered point cloud data along the Z-axis, where an image of individual teeth was created using an object detection algorithm. The proposed algorithm is designed by adding various modules to the Unet model that allow effective learning of a narrow range, and detects both end points of the tooth using the generated tooth image. In the evaluation using DSC, Euclid distance, and MAE as indicators, we achieved superior performance compared to other Unet-based models. In future research, we will develop an algorithm to find the reference point of the point cloud by back-projecting the reference point detected in the image in three dimensions, and based on this, we will develop an algorithm to divide the teeth individually in the point cloud through image processing techniques.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.453-466
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• 2024

To improve the monitoring of Coastal Debris in the South Korea, which is difficult to estimate due to limited resources and vertex-based surveys, an approach based on UAV(Unmanned Aerial Vehicle) images and the RT-DETR(Realtime DEtection TRansformer) model was proposed for detecting Coastal Debris. By comparing to field investigation, the study suggested the possibility of quantitatively detecting coastal garbage and estimating the total capacity of garbage deposited on the natural coastline of the South Korea. The RT-DETR model achieved an accuracy of 0.894 for mAP@0.5 and 0.693 for mAP@0.5:0.95 in training. When applied to unmanaged coasts, the accuracy for the total number of coastal debris items was 72.9%. It is anticipated that if guidelines for defining monitoring of unmanaged coasts are established alongside this research, it should be possible to estimate the total capacity of the deposited coastal debris in the South Korea.