• Journal of the Korean institute of surface engineering
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• v.49 no.1
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• pp.92-97
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• 2016

The plasma has been used in various industrial fields of semiconductors, displays, transparent electrode and so on. Plasma diagnostics is critical to the uniform process and the product. We use the electron temperature of the various plasma parameters for the diagnosis of plasma. Generally, the range of the electron temperature which is used in a semiconductor process used the range of 1 eV to 10 eV. The difference of electron temperature of 0.5 eV has a influence in plasma process. The electron temperature can be measured by the electrical method and the optical method. Measurement of electron temperature for various gas flow rates was performed in DC-magnetron sputter and Inductively Coupled Plasma. The physical properties of the thin film were also determined by changing electron temperatures. The transmittance was measured using the integrating sphere, and wavelength range was measured at 300 ~ 1100 nm. We obtain the thin film of the mobility, resistivity and carrier concentration using the hall measurement system. As to the electron temperature increase, optical and electrical properties decrease. We determine it was influenced by the oxygen flow ratio and plasma.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.292-298
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• 2011

In order to acquire new information and biological meaning of the signal data by defining the relationships between them, new modeling technique, ontology, has been proposed. The data of bio-signal can be represented as a systematic and logical to manage continuously bio-signal data using ontology. Furthermore, knowledge of which resources are utilized to provide improved service quality in medical information, health services in various fields. However, relevant studies have not been performed actively to compare importance of relationships between bio-signals. Therefore semantic representation of biometric information should be by defining the relationship between bio-signals. In this paper, we have developed bio-signal ontology to use as a model for using domain knowledge. We verified the usefulness of the ontology by using scenarios.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.5
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• pp.551-563
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• 2012

Medical ultrasound systems have been used since 1950s, and are now widely used in most hospitals as indispensable diagnostic imaging systems. Since array probe was introduced in 1970s, beamforming technology using electronic signal processing has been adopted to the medical ultrasound system, and has been improved. Beamforming is a important technology which defines the resolution of the ultrasound system. In this paper, the technologies are introduced from basic beamforming principles to current trend. They include principles of beamforming using array probe, basic theory, and practical implementation, and recent topics of synthetic aperture imaging, adaptive beamforming, 2-dimensional beamforming using 2-dimensional array are also introduced. These various technologies will improve system performances continuously by merging innovatively with various technologies in other fields.

• Journal of Biomedical Engineering Research
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• v.27 no.4
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• pp.154-163
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• 2006

A 64-channel magnetocardiogram (MCG) system using low-noise superconducting quantum interference device (SQUID) planar gradiometers was developed for the measurements of cardiac magnetic fields generated by the heart electric activity. Owing to high flux-to-voltage transfers of double relaxation oscillation SQUID (DROS) sensors, the flux-locked loop electronics for SQUID operation could be made simpler than that of conventional DC SQUIDs, and the SQUID control was done automatically through a fiber-optic cable. The pickup coils are first-order planar gradiometers with a baseline of 4 em. The insert has 64 planar gradiometers as the sensing channels and were arranged to measure MCG field components tangential to the chest surface. When the 64-channel insert was in operation everyday, the average boil-off rate of the dewar was 3.6 Lid. The noise spectrum of the SQUID planar gradiometer system was about 5 fT$_{rms}$/$\checkmark$Hz at 100 Hz, operated inside a moderately shielded room. The MCG measurements were done at a sampling rate of 500 Hz or 1 kHz, and realtime display of MCG traces and heart rate were displayed. After the acquisition, magnetic field mapping and current mapping could be done. From the magnetic and current information, parameters for the diagnosis of myocardial ischemia were evaluated to be compared with other diagnostic methods.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.101-107
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• 2018

In wireless sensor network technology, which has been applied to various fields, the power supply and the power management of sensors are the most important issues. For this reason, a new concept of power supply and power management device is required. In this paper, we developed an autonomous independent power supply system that supplies the stable power to a sensor node without an additional external input by applying the energy harvesting technology using the electromagnetic induction principle by utilizing the current flowing in the transmission line. The proposed autonomous independent power supply system consists of a power supply using Power CT and a power management system including a charging circuit. The power management device uses a voltage limiter circuit and a monitoring circuit of charging voltage and current to ensure the safety of charging of the battery. In order to verify the performance of the proposed system, we applied it to the SVL diagnostic system and confirmed that it operates stably.

• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• Applied Chemistry for Engineering
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• v.32 no.1
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• pp.1-9
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• 2021

Alzheimer's disease (AD), an irreversible degenerative disorder, is associated with accumulation and aggregation of amyloid-β peptides, hyperphosphorylated tau proteins, and high level of metal ions in the brain. Up to date, there is no effective therapeutic agent to stop the progress of the disease and thus early and accurate diagnosis of AD has gained increasing attention in recent years. Among several diagnostic methods, an optical imaging using fluorescent probes is one of the most promising tools to visualize AD biomarkers. In this review, we will introduce fluorescent probes that can be applied to in vivo brain imaging of AD models and also their structure. It is expected that the present review will provide useful information to many scientists in the related research fields.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.195-207
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• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Journal of Sasang Constitutional Medicine
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• v.34 no.3
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• pp.50-68
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• 2022

Objectives The purpose of this study is to review the current Sasang constitutional research on obesity and to provide basic data for further development in the direction of better research. Methods Clinical research articles on obesity of the Sasang Constitutional Medicine were extracted from five database. Published year, published journals, and subjects of studies were classified. Clinical methods for the classification of Sasang type and diagnostic criteria for obesity were summarized. Results A total of 36 selected research articles were published from 1998 to 2020. And 20 articles(55.6%) have been published in Journal of Sasang Constitutional Medicine and Journal of Oriental Rehabilitation Medicine. Most of the clinical study subjects were studies that identified independent factors that were correlated with obesity by Sasang constitution type. But these studies have not sufficiently reflected the distinctive characteristics of Sasang Constitution Medicine. Discussion Through this study, it was confirmed to the necessity of developing new research designs for obesity by Sasang constitution and researching on obesity treatment through improvement of symptoms by using the already developed questionnaires that have proven reliability and validity, away from the research biased towards Taeeumin and Taeeumin's prescription. If additional research based on this study is accompanied, it is expected that it can be utilized in typed obesity treatment fields.