• Archives of Plastic Surgery
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• v.50 no.5
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• pp.488-491
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• 2023

Silicone breast implant insertion is a commonly performed surgical procedure for breast augmentation or reconstruction. Among various postoperative complications, infection is one of the main causes of patient readmission and may ultimately require explantation. We report a case of infective costochondritis after augmentation mammoplasty, which has rarely been reported and is therefore difficult to diagnose. A 36-year-old female visited the clinic for persistent redness, pain, and purulent discharge around the left anteromedial chest, even after breast implant explantation. Magnetic resonance imaging showed abscess formation encircling the left fourth rib and intracartilaginous and bone marrow signal alteration at the left body of the sternum and left fourth rib. En bloc resection of partial rib and adjacent sternum were done and biopsy results confirmed infective costochondritis. Ten months postoperatively, the patient underwent chest wall reconstruction with an artificial bone graft and acellular dermal matrix. As shown in this case, early and aggressive surgical debridement of the infected costal cartilage and sternum should be performed for infective costochondritis. Furthermore, delayed chest wall reconstruction could significantly contribute to the quality of life.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.66-71
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• 2023

This paper describes a hybrid cleaning method of silicon wafer combining nano-bubble and ultrasound to remove sub-micron particles and contaminants with minimal damage to the wafer surface. In the megasonic cleaning process of semiconductor manufacturing, the cavitation induced by ultrasound can oscillate and collapse violently often with re-entrant jet formation leading to surface damage. The smaller size of cavitation bubbles leads to more stable oscillations with more thermal and viscous damping, thus to less erosive surface cleaning. In this study, ultrasonic energy was applied to the wafer surface in the DI water to excite nano-bubbles at resonance to remove contaminant particles from the surface. A patented nano-bubble generator was developed for the generation of nano-bubbles with concentration of 1×10⁹ bubbles/ml and nominal nano-bubble diameter of 150 nm. Ultrasonic nano-bubble technology improved a contaminant removal efficiency more than 97% for artificial nano-sized particles of alumina and Latex with significant reduction in cleaning time without damage to the wafer surface.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.473-485
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• 2022

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

• The Journal of the Petrological Society of Korea
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• v.23 no.2
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• pp.119-138
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• 2014

There are various ways to monitor active volcanoes, such as the method of observing the activity of a volcano with the naked eye, the method of referring to the past eruptive history based on the historic records and the method of monitoring volcanoes by using observation equipment. The most basic method from the observation equipment-using methods to monitor volcanoes is seismic monitoring. In addition to this, the ways to monitor volcanoes are as follows: resonance observation which may be effective to remove artificial noises from the seismic activities that are recorded in the seismograph, ground deformation by using precision leveling, electronic distance measurement, tiltmeter, GPS, and InSAR observation method, volcanic gas monitoring, hydrologic and meteorological monitoring, and other geophysical monitoring methods. These monitoring methods can make volcanic activities effectively monitored, determine the behavior of magmas in magma chambers and help predict the future volcanic eruptions more accurately and early warning, thus, minimize and mitigate the damage of volcanic hazards.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.759-766
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• 2011

Recently, noise reduction in room air conditioners has been one of the important issues as well as cooling efficiency. The rotary compressor is the dominant noise source in an air conditioner. A number of studies have been conducted on reducing compressor noise through improving muffler and resonator design. However the noise from the accumulator, a noise delivering path between compressor and air conditioner, is not fully taken into consideration. The accumulator contains a large inner cavity, and usually generates additional resonance noise during operation. This paper aims to conduct an optimal design for reducing accumulator noise by maximizing the transmission loss within the target frequency range that represents high-order nonlinearity. Design of experiments and radial basis function neural network are used in the context of approximate meta-models, and genetic algorithm is used as an optimization tool.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.129-136
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• 2010

With mobile computing, wireless sensor network and sensor technologies, ubiquitous computing services are being realized and could satisfy the feasibility of ubiquitous healthcare to everyone. This u-Healthcare service can improve life quality of human since medical service can be provided to anyone, anytime, and anywhere. To confirm the vision of u-Healthcare service, we've implemented a healthcare system for heart disease patient which is composed of two components. Front-end collects various signals such as temperature, blood pressure, SpO2, and electrocardiogram, etc. As a backend, medical information server accumulates sensing data and performs back-end processing. To simply transfer these sensing values to a medical team may be too trivial. So, we've designed a model based on context awareness for more improved medical service which is based on artificial neural network. Through rigid experiments, we could confirm that the proposed system can provide improved medical service.

• Journal of Korean Association for Spatial Structures
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• v.22 no.3
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• pp.49-56
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• 2022

Tuned mass damper (TMD) is widely used to reduce dynamic responses of structures subjected to earthquake loads. A smart tuned mass damper (STMD) was proposed to increase control performance of a traditional passive TMD. A lot of research was conducted to investigate the control performance of a STMD based on analytical method. Experimental study of evaluation of control performance of a STMD was not widely conducted to date. Therefore, seismic response reduction capacity of a STMD was experimentally investigated in this study. For this purpose, a STMD was manufactured using an MR (magnetorheological) damper. A simple structure presenting dynamic characteristics of spacial roof structure was made as a test structure. A STMD was made to control vertical responses of the test structure. Two artificial ground motions and a resonance harmonic load were selected as experimental seismic excitations. Shaking table test was conducted to evaluate control performance of a STMD. Control algorithms are one of main factors affect control performance of a STMD. In this study, a groundhook algorithm that is a traditional semi-active control algorithm was selected. And fuzzy logic controller (FLC) was used to control a STMD. The FLC was optimized by multi-objective genetic algorithm. The experimental results presented that the TMD can effectively reduce seismic responses of the example structures subjected to various excitations. It was also experimentally shown that the STMD can more effectively reduce seismic responses of the example structures conpared to the passive TMD.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.71-76
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• 2023

In this paper, a humidity sensor using a microstrip patch antenna(MPA) and polyvinyl alcohol(PVA) is studied. PVA is a polymer material whose permittivity changes with humidity, and a rectangular slot is added to the radiating edge of the MPA, which is sensitive to changes in electric field, in order to increase the sensitivity to changes in relative permittivity. After thinly coating the area around the radiating edge with the rectangular slot of the MPA fabricated on a 0.76 mm-thick RF-35 substrate with PVA, the changes in the resonant frequency and magnitude of the MPA's input reflection coefficient are measured when relative humidity is adjusted from 40% to 80% in 10% increments at a temperature of 25 degrees using a temperature and humidity chamber. Experiment results show that when the relative humidity increases from 40% to 80%, the resonance frequency of the antenna' input reflection coefficient decreases from 2.447 GHz to 2.418 GHz, whereas the magnitude increases from -7.112 dB to -3.428 dB.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.516-521
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• 2022

In this paper, a compact 4-bit chipless RFID(radio frequency identification) tag using a modified ELC(electric field-coupled inductive-capacitive) resonator and multiple slot resonators is proposed. The modified ELC resonator uses an interdigital-capacitor structure in the conventional ELC resonator to lower the resonance peak frequency of the RCS. The multiple slot resonators are designed by etching three slots with different lengths into an inverted U-shaped conductor. The resonant peak frequency of the RCS for the modified ELC resonator is 3.216 GHz, whereas those of the multiple slot resonators are set at 4.122 GHz, 4.64 GHz, and 5.304 GHz, respectively. The proposed compact four-bit tag is fabricated on an RF-301 substrate with dimensions of 50 mm×20 mm and a thickness of 0.8 mm. Experiment results show that the resonant peak frequencies of the fabricated four-bit chipless RFID tag are 3.285 GHz, 4.09 GHz, 4.63 GHz, and 5.31 GHz, respectively, which is similar to the simulation results with errors in the range between 0.78% and 2.16%.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.