• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.15-21
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• 2019

X-rays with shorter wavelengths than ultraviolet light have very good penetration power. It is convergence in industrial and medical fields has been used a lot. n particular, in the industrial field, various researches have been conducted on the detection of foregin body inside metal that can occur in the production process of products such as metal using x-ray, a non-destructive inspection device. Detectors are becoming increasingly popular for the popularization of DR (Digital Radiography) photography methods that digitally acquire X-ray video images. However, there are cases where foreign body detection is impossible depending on the sensor noise and sensitivity inside the detector. When producing a metal product, since the defective rate of the produced product may increase due to contamination of the foreign body, accurate detection is necessary. In this paper, we provide a correction model for X-ray images acquired in order to improve the efficiency of defect detection such as foreign body inside metal. When applied to defect detection in the production process of metal products through the proposed model, it is expected that the detection of product defects can be processed accurately and quickly.

• Journal of Fashion Business
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• v.23 no.2
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• pp.156-169
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• 2019

The objective of this study was to develop lightweight, stretchable, tight-fit smart sportswear using the conductive yarns into the garment and demonstrating its usefulness. Sportswears with the ability to control LEDs with respect to lighting of the surrounding were developed by applying embroidery with conductive yarns to 2 types of men's T-shirts and 2 types of women's leggings pants for outdoor activities and exercise purposes. LEDs were applied to the front and back of men's T-shirts and to the rear of the waist of women's leggings. Men's T-shirts were printed where the LEDs were to be applied, and inside, they were embroidered with conductive threads on the hot-melt fabric to be attached, and then connected with LED. Women's pants were embroidered on the elastic band, in the form of a sine wave that gives it ability to stretch, and finally the elastic band was hidden inside the waistband. The operation of the light sensor in the dark provided the ability to protect joggers from night drivers or cyclists. LEDs were activated when the wearer turns on the fashionable device on his/her shoulder by pressing it. It was able to reduce the risk of accidents by giving recognizability to vehicles, bicycles, and athletes approaching or passing by at night, and securing safe distance from vehicles, etc. Internal embroidery technology had the same flexible and lightweight functions as ordinary clothing products, making it possible to apply to tight-fit smart T-shirts or leggings pants designs.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.4
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• pp.71-83
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• 2021

Purpose : This study compared the differences in power and agility of athletes in each sports using visual response speed test (VRST) scores after conducting 10 weeks of body stability exercise (BSE) on elementary school athletes in handball, football, volleyball and conducted a post-hoc test on the measured values. The subjects of this study were baseball (n=27), taekwondo (n=22), and football (n=23) athletes with at least two years of athletic experience. A total of 72 elementary school athletes were measured by VRST after 10 weeks of BSE under the same conditions. Methods : For VRST measurement of the upper extremity, the right and left hands were alternately touched in the order the blazepod equipment lights were turned on. The number of touches for 15 seconds and response touch were measured. In the case of the measurement of lower extremity the left lower extremity was measured first when the Blaze pod equipment light came on. The average value was obtained by measuring 3 times using a measurement sensor with the position indicated in order to measure the upper arms and legs the same. Results : This study confirmed homogeneity among sports and that VRST improved after implementing BSE for sports. However, no statistically significant difference was identified when comparing VRST improvements between sports, and post-hoc test results showed no significant differences either. Conclusion : After applying the BSE program under the same conditions for 10 weeks to elementary school students who can improve their power and agility the most, the results of the examination using the Blaze pod showed that the power and agility of baseball, taekwondo, and soccer players were similarly improved. From the fact that there was no significant difference among sports, it could be inferred that the BES training program could improve VRST without being limited to some sports.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.969-976
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• 2019

A lightweight artificial intelligence hardware has made great strides in many application areas. In general, a lightweight artificial intelligence system consist of lightweight artificial intelligence engine and preprocessor including feature selection, generation, extraction, and normalization. In order to achieve optimal performance in broad range of applications, lightweight artificial intelligence system needs to choose a good preprocessing function and set their respective hyper-parameters. This paper proposes a unified framework for a lightweight artificial intelligence system and utilization method for finding models with optimal performance to use on a given dataset. The proposed unified framework can easily generate a model combined with preprocessing functions and lightweight artificial intelligence engine. In performance evaluation using handwritten image dataset and fall detection dataset measured with inertial sensor, the proposed unified framework showed building optimal artificial intelligence models with over 90% test accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.133-152
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• 2022

Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5^th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2^nd largest crop being produced and 3^rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.187-196
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• 2022

In this paper, we propose an example of designing and constructing a roof-type solar power plant structure equipped with a Pseudo-BIPV (Building-Integrated Photovoltaic) shape suitable for use as a roof of a small warehouse with a sandwich-type panel structure. As the characteristics of the roof-type solar power generation facility to be installed in the small warehouse proposed in this study, the shape of the roof is not a general A type, but a right-angled triangle shape with the slope is designed to face south. We chose a structure in which an inverter for one power plant and a control facility are linked by grouping several roofs of buildings. In addition, the height of the roof structure is less than 20 cm from the floor, and it has a shape similar to that of the BIPV, so it is building-friendly because it is almost in close contact with the roof. At the same time, the roof creates a reflective light source due to the white color. By linking this roof with a double-sided solar panel, we designed it to obtain both the advantage of the roof-friendliness and the advantage of efficiency improvement for the electric power generation based on the double-sided panel. Compared to the existing solar power generation facilities using A-shaped cross-sectional modules, the power generation efficiency of roofs in this case is increased by more than 11%, which we can confirm, through the comparison analysis of monitoring data between power plants in the same area. Therefore, if the roof-type solar structure suitable for the small warehouse we have presented in this paper is used, the facilities of electric power generation is eco-friendly. Further it is easier to obtain facility certification compared to the BIPV, and improved capacity of the power generation can be secured at low material cost. It is believed that the roof-type solar power generation facility we proposed can be usefully used for warehouse or factory-based smart housing. Sensor devices for monitoring, CCTV monitoring, or safety and environment management, operating in connection with the solar power generation facilities, are linked with the Internet of Things (IoT) solution, so they can be monitored and controlled remotely.

• Convergence Security Journal
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• v.22 no.1
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• pp.29-38
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• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.28-35
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• 2023

Moiré patterns emerge due to the interference between the non-emission area of the LED screen and the grid line in an image sensor of a video recording device when taking a video in the presence of the LED screen. To reduce the moiré intensity, we have fabricated an anti-moiré filter using hollow glass microspheres (HGMs) by slot-die coating. The LED screen has a large non-emission area because of a large pitch (distance between LED chips), causing more severe moiré phenomenon, compared with a display panel having a very narrow black matrix (BM). It is shown that HGMs diffuse light in such a way that the periodicity of the screen is broken and thus the moiré intensity weakens. To quantitatively analyze its moiré suppression capability, we have calculated the spatial frequencies of the moiré fringes using fast Fourier transform. It is addressed that the moiré phenomenon is suppressed and thus the amplitude of each discrete spatial frequency term is reduced as the HGM concentration is increased. Using the filter with the HGM concentration of 9 wt%, the moiré fringes appeared depending sensitively on the distance between the LED screen and the camera are almost completely removed and the visibility of a nature image is enhanced at a sacrifice of luminance.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1353-1362
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• 2023

Various methods are being attempted to resolve the inconvenience of blood glucose meters used to check blood sugar levels. In this paper, we attempted to estimate blood sugar levels non-invasively using machine learning technology from spectral data acquired using a near-infrared sensor. The non-invasive blood glucose meter used in the study has a total of six near-infrared ray emitters, including visible rays, and a light receiver that receives them. It is a device created to collect spectral data on specific parts of the human body, such as the fingers. To verify whether there was a significant difference depending on blood sugar level, we attempted to estimate blood sugar level through machine learning algorithms. As a result of applying five machine learning algorithm techniques to the collected data and adjusting various hyper parameters, it was confirmed that the support vector regression algorithm showed the best performance.