• Journal of Ocean Engineering and Technology
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• v.38 no.3
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.1-6
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• 2024

Recently, artificial neural networks have been playing a crucial role and advancing across various fields. Artificial neural networks are typically categorized into feedforward neural networks and recurrent neural networks. However, feedforward neural networks are primarily used for processing static spatial patterns such as image recognition and object detection. They are not suitable for handling temporal signals. Recurrent neural networks, on the other hand, face the challenges of complex training procedures and requiring significant computational power. In this paper, we propose memristors suitable for an advanced form of recurrent neural networks called reservoir computing systems, utilizing a mask processor. Using the characteristic equations of Ti/TiOx/TaOy/Pt, Pt/TiOx/Pt, and Ag/ZnO-NW/Pt memristors, we generated current-voltage curves to verify their memristive behavior through the confirmation of hysteresis. Subsequently, we trained and inferred reservoir computing systems using these memristors with the NIST TI-46 database. Among these systems, the accuracy of the reservoir computing system based on Ti/TiOx/TaOy/Pt memristors reached 99%, confirming the Ti/TiOx/TaOy/Pt memristor structure's suitability for inferring speech recognition tasks.

• Journal of the Korean Society of Radiology
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• v.83 no.5
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• pp.1081-1089
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• 2022

Purpose To compare the computerized Greulich-Pyle based bone age with elbow bone age. Materials and Methods A total of 2126 patients (1525 girls; 601 boys) whose elbow bone age was within the evaluable range by the Sauvegrain method, and who simultaneously underwent hand radiography, were enrolled in the study. The 1st-bone age and VUNO score of the hand were evaluated using VUNOMed-BoneAge software. The correlation between the hand and elbow bone age was analyzed according to the child's gender and the probability of 1st-bone age. Results The correlation between VUNO score and elbow bone age (r = 0.898) was higher than the correlation between 1st-bone age and elbow bone age (r = 0.879). Moreover, the VUNO score showed a better correlation with the elbow bone age in patients with a 1st-bone age probability of less than 70%, or in girls. Elbow bone age was more advanced compared to hand bone age, and this difference increased until the middle of puberty and gradually decreased in the latter half. Conclusion The computerized Greulich-Pyle based hand bone age showed a significant correlation with the elbow bone age at puberty. However, since the elbow bone age tends to advance faster than the hand bone age, caution is required while judging the bone age during puberty.

• Korean Journal of Plant Taxonomy
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• v.45 no.4
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• pp.353-361
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• 2015

Spatio-temporal variation in fruit set in orchids would affect long-term population viability and will influence genetic diversity over many generations. The aim of this study was to examine the breeding system of the nectariferous terrestrial orchid Epipactis thunbergii, to specifically determine levels of fruit set in terms of time and space under natural conditions. We examined pollination under natural conditions and conducted hand pollination experiments during a 2-year survey in four populations located along 1.5 km of coastal line in Jinguiri (rual village) [Jeollanam-do (province), southern Korea]. We found that, over a 2-year period, levels of percentage of fruit set were similar within patches of the four populations. By contrast, we detected significant differences in the percentage of fruit set among patches. We also found that plants with larger inflorescence size produced significantly more fruits than plants with fewer flowers. Over a 2-year period, the percentage of fruit set for E. thunbergii was similar but low (14.1%) compared to that averaged for eighty-four rewarding species (37.1%). However, an increase in fruit set was achieved by hand-pollinations: artificial self-pollination (90.5-95.2%), artificial geitonogamy (94.7-95.0%), and cross-pollination (artificial xenogamy, 91.3-91.4%). No emasculated flowers produced fruits and no automatic pollination was found in E. thunbergii. Our findings suggest that E. thunbergii is a self-compatible terrestrial orchid that depends on pollinators (insects) to achieve fruit set in natural habitats, and that local environmental conditions were similar over a period of 2 years in the study area. Our results also highlight the cryptic variation of fruit production in time, but more pronounced variability in space.

• The Korean Journal of Emergency Medical Services
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• v.13 no.3
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• pp.29-40
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• 2009

Objective : This study is an experimental study which is designed to examine the differences between knowledge and self-confidence before and after theory education(CPR PPT material) based on guidelines of CPR and emergency cardiac treatment of American Heart Association(AHA, 2005) and video self-instruction program for the general public by Korean Association of Cardiopulmonary Resuscitation(KACPR), trace CPR performance ability after CPR and AED education and investigate the accuracy of artificial respiration and chest compression, and know the difference in CPR performance abilities including AED. Methods : Subjects of this study include ground crews and staffs at M airport in G province equipped with emergency equipments for CPR according to Art. 47, Sec. 2 of Emergency Medical Law, airport police, rent-a-cops, security guard, quarantine officer, custom officer, and communication, electricity, civil engineering, facility management staff, airport fire fighting staff, air mechanic, traffic controller, and airport management team among airport facility management staffs. They were given explanation of necessity of research and 147 of 220 subjects who gave consent to this research but 73 who were absent from survey were excluded were used as subjects of this study. of 147 subjects, there were 102 men and 45 women. Results : 1) Knowledge score of CPR was $6.18{\pm}0.87$ before instruction and it was increased to $15.12{\pm}1.78$ after instruction, and there was statistically significant difference. 2) Self-confidence score in CPR was $3.16{\pm}0.96$ before instruction and it was increased to $7.05{\pm}0.75$ after instruction, and there was statistically significant difference. 3) Total average score in CPR performance ability after instruction was 7.46 out of 9, performance ability was highest in confirmation of response as 144(97.95%), follwed by request of help as 140(95.25%) and confirmation of respiration as 135(91.83%), and lowest in performing artificial respiration twice(gross elevation of chest) as 97(65.98%). Accuracy of artificial respiration(%) was $28.60{\pm}16.88$ and that of chest compression(%) was $73.10{\pm}22.16$. 4) Performance ability of AED after instruction showed proper performance in power on by 141(95.91%) and attaching pad by 135(91.83%), hand-off for analyzing rhythm showed 'accuracy' in 115(78.23%) and 'non-performance' in 32(21.77%), delivery of shock and hand-off confirmation showed 'accuracy' in 109(74.14%) and 'inaccuracy' in 38(25.86%), and beginning chest compression immediately after AED was done by 105(71.42%).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.90-93
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• 2003

The function, which represents the closed curve, is found from the sampling data by S-System in this study. Two methods are proposed. One is the extension of S-System. The data x and y are regarded as input data, and the data z=0 as output data. To avoid the trap into the invalid function, the judgment points (x$\_$j/, y/sug j/) are introduced. They are arranged in the inside and the outside of the closed curve. By introducing this concept, the functions representing closed curve are found by S-System. This method is simple because of a little extension of S-System. It is, however, difficult for the method to find the complex function like a hand-written curve. Then another method is also proposed. It uses the system incorporating the argument function. The closed curve can be expressed by the argument function. The relatively complex function, which represents the closed curve like a hand-written curve, is found by utilizing argument function.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1243-1244
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• 2015

Finding solutions for the disabled is a major challenge for our society. In the case of a disability due to a malfunction of the nervous system, the origin may be accidental, genetic, or induced by environmental factors. This type of loss can cause loss or movement disorders (paraplegia, hemiplegia, quadriplegia, epilepsy, Parkinson's disease, multiple sclerosis, etc.) or malfunction of certain sensory functions (blindness, deafness, chronic pain, etc.). Many alternatives, more technology, have been imported to create interfaces between the human body and an artificial prosthesis in order to restore some functions of the human body. A wireless system, battery neurons probe was developed for one hand reading neural signals in the brain, and on the other hand also able to excite the neuron in the brain using a surface acoustic wave one ports (SAW) delay line reflection.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.623-632
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• 2019

Many researches have been carried out for brightness and contrast enhancement, illumination reduction and so forth. Recently, the aforementioned hand-crafted approaches have been replaced by artificial neural networks. This paper proposes a convolutional neural network that can replace the method of generating a reflectance image where illumination component is attenuated. Experiments are carried out on 102 low-light images and we validate the feasibility of the replacement by producing satisfactory reflectance images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• ETRI Journal
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• v.45 no.6
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• pp.1022-1034
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• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.