• Research in Community and Public Health Nursing
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• v.15 no.4
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• pp.528-538
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• 2004

Purpose: This study was conducted to investigate the level of awareness about health education in the manpower of public health center. in order to suggest a basis data for the development of a job-training program. Method: The subjects were 96 manpowers of public health centers. Data were collected from August 2nd. 2002 to September 20th using a self reported questionnaire survey. The data were analyzed using frequency. percentile and $x^2-test$. Results: The most necessary of health education according to health promotion service is 'quitting smoking' during the adolescent period. The most necessary of health education media according to health promotion service is 'reducing alcohol intake'. The most efficient media of health education is 'beam projector'. The most necessary capacity of health educator is 'planning capacity of health education'. The most necessary support implementing health education is 'manpower supply'. Conclusion: The level of awareness of health education in the manpower of the public health center are expected to provide basic data for developing job-training programs that might improve advanced knowledge and techniques of health education.

• Steel and Composite Structures
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• v.18 no.3
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• pp.547-563
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• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.575-589
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• 2022

In order to enhance the greenness in the strain-hardening composites and to reduce the high cost of typical polyvinyl alcohol fiber reinforced engineered cementitious composite (PVA-ECC), an affordable strain-hardening composite with green binder content has been proposed. For optimizing the strain-hardening behavior of cementitious composites, this paper investigates the effects of polypropylene fibers on the first cracking strength, fracture properties, and micromechanical parameters of cementitious composites. For this purpose, digital image correlation (DIC) technique was utilized to monitor crack propagation. In addition, to have an in-depth understanding of fiber/matrix interaction, scanning electron microscope (SEM) analysis was used. To understand the effect of fibers on the strain hardening behavior of cementitious composites, ten mixes were designed with the variables of fiber length and volume. To investigate the micromechanical parameters from fracture tests on notched beam specimens, a novel technique has been suggested. In this regard, mechanical and fracture tests were carried out, and the results have been discussed utilizing both fracture and micromechanical concepts. This study shows that the fiber length and volume have optimal values; therefore, using fibers without considering the optimal values has negative effects on the strain-hardening behavior of cementitious composites.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.

• The korean journal of orthodontics
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• v.51 no.2
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.181-186
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• 2022

Purpose: This study aimed to investigate inter-observer reliability among observers with different levels of proficiency and the diagnostic imaging reliability of cone-beam computed tomography (CBCT) images of the retromolar canal. Materials and Methods: CBCT images of 307 patients were assessed for the presence of retromolar canals(RMCs) by 3 observers independently. Diagnoses were made twice by each observer at intervals of more than 3 weeks. Interobserver reliability was assessed using the kappa coefficient. One observer had no experience in diagnosis using CBCT images. Therefore, a specialist in diagnostic imaging explained the CBCT images for interpretation and practiced diagnostic imaging together with this observer, while the other observer interpreted the images independently. Thereafter, the observers re-evaluated the images. Results: The interobserver kappa coefficients (including bilateral RMCs) calculated at the first reading were low, ranging from 0.21 to 0.61. Their values ranged from 0.95 (right side) to 1.00 (left side) after one-on-one practice with a diagnostic imaging specialist, while the values ranged from 0.65 (right side) to 0.66 (left side) without one-on-one practice. Conclusion: Diagnostic accuracy was improved through diagnostic imaging practice. To improve the anatomical interpretation of images, it is important to practice diagnostic imaging with a specialist in diagnostic imaging. One-on-one instruction about diagnostic imaging was an effective method of training.

• Imaging Science in Dentistry
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• v.53 no.1
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• pp.11-19
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• 2023

Purpose: This study compared the accuracy of detection of incomplete vertical root fractures (VRFs) in filled and unfilled teeth on cone-beam computed tomography images with and without a metal artefact reduction (MAR) algorithm. Materials and Methods: Forty single-rooted maxillary premolars were selected and, after endodontic instrumentation, were categorized as unfilled teeth without fractures, filled teeth without fractures, unfilled teeth with fractures, or filled teeth with fractures. Each VRF was artificially created and confirmed by operative microscopy. The teeth were randomly arranged, and images were acquired with and without the MAR algorithm. The images were evaluated with OnDemand software (Cybermed Inc., Seoul, Korea). After training, 2 blinded observers each assessed the images for the presence and absence of VRFs 2 times separated by a 1-week interval. P-values<0.05 were considered to indicate significance. Results: Of the 4 protocols, unfilled teeth analysed with the MAR algorithm had the highest accuracy of incomplete VRF diagnosis (0.65), while unfilled teeth reviewed without MAR were associated with the least accurate diagnosis (0.55). With MAR, an unfilled tooth with an incomplete VRF was 4 times more likely to be identified as having an incomplete VRF than an unfilled tooth without this condition, while without MAR, an unfilled tooth with an incomplete VRF was 2.28 times more likely to be identified as having an incomplete VRF than an unfilled tooth without this condition. Conclusion: The use of the MAR algorithm increased the diagnostic accuracy in the detection of incomplete VRF on images of unfilled teeth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2594-2603
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• 1997

A novel method of adaptive beam forming is presented in this paper. The proposed technique provides for a suboptimal beam pattern that increases the Signal to Noise/Interference Ratio (SNR/SIR), thus, eventually increases the capacity of the communication channel, under an assumption that the desired signal is dominant compared to each component of interferences at the receiver, which is precoditionally achieved in Code Division Multiple Access (CDMA) mobile communications by the chip correlator. The main advantages of the new technique are:(1)The procedure requires neither reference signals nor training period, (2)The signal interchoerency does not affect the performance or complexity of the entire procedure, (3)The number of antennas does not have to be greater than that of the signals of distinct arrival angles, (4)The entire procedure is iterative such that a new suboptimal beam pattern be generated upon the arrival of each new data of which the arrival angle keeps changing due tot he mobility of the signal source, (5)The total amount of computation is tremendously reduced compared to that of most conventional beam forming techniques such that the suboptimal beam pattern be produced at vevery snapshot on a real-time basis. The total computational load for generating a new set of weitht including the update of an N-by-N(N is the number of antenna elements) autocovariance matrix is $0(3N^2 + 12N)$. It can further be reduced down to O(11N) by approximating the matrix with the instantaneous signal vector.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.