• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Korean Journal of Environmental Biology
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• v.27 no.1
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• pp.95-99
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• 2009

Transcriptomic changes in the brain of Limanda yokohamae were investigated to understand the environmental condition of Masan Bay, Korea. Differentially expressed genes (DEGs) in the brain of the flat fish from Masan Bay were identified by comparing those from the reference site Gangneung using annealing control primers-based polymerase chain reaction. The results demonstrated that two different kinds of the cytoplasmic ribosomal proteins, 40 s ribosomal protein S27a and ribosomal protein L6, were identified by the BLAST searching followed by sequence analysis. These findings suggest that environmental status of Masan Bay could hinder protein synthesis that is required for maintaining brain functions and thus cause the dysfunction of fish physiology.

• Cartoon and Animation Studies
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• s.38
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• pp.71-91
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• 2015

This study is a process to study the life drawing teaching method considering professional characteristics in animation and has a study objective to design the model and teaching method which applies the strategies considering the creative mechanism of the brain. Recently, study results about integrated teaching method are being announced which apply brain based learning principles as the alternative arguments about teaching methods in each area based on creativeness. In other words, integrated education based on creative mechanism in the brain is applied not only to fine arts and drawing education, but also to the entire areas of the arts. Life drawing is an area which demands comprehensive teaching method that vivid expressions could be skillfully obtained by understanding the communication methods with the objects through cognitive senses, creativeness and movements beyond the structural knowledge about human body. Therefore in this study, the strategies and methods for the skillfulness of life drawing and consequently arranged education model structure drawing are to be designed based on the creativeness, study materials and content factors which were analyzed in previous stages of this study. In order to combine the content factors based on creativeness and study materials of the brain which are the results of previous studies, the conclusion has been reached that 5 step cognitive strategy stages to wake brain senses, flexibilize the brain, purify the brain, integrate the brain and become the master of the brain. Strategic methods to execute this were designed with brain gym, right brain energization drawing and HSP(high-level cognizance) training. Teaching and learning model structure diagram which is designed based on this is to be continued to teaching and learning guidelines during the relevant semesters after the research.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.46.3-46
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• 2001

The overall aim of this research is to develop a computer communication interface based on brain-wave bio potentials for physically disabled people. The work focuses on using EOG and EMG signals to input characters one by one using cursor movements on a GUI screen. The Cyberlink TM system is used to acquire brain waves in real time with electrodes. EMG and EOG signals are used to direct a cursor in order to select, or to click on a character on the screen. We present a novel method for automatic EOG pattern detection by using wavelet transforms with a neuro-fuzzy approach ...

• Progress in Superconductivity
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• v.13 no.3
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• pp.139-145
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• 2012

Electric activity of cardiac muscles generates magnetic fields. Magnetocardiography (or MCG) technology, measuring these magnetic signals, can provide useful information for the diagnosis of heart diseases. It is already about 40 years ago that the first measurement of MCG signals was done by D. Cohen using SQUID (superconducting quantum interference device) sensor inside a magnetically shielded room. In the early period of MCG history, bulky point-contact RF-SQUID was used as the magnetic sensor. Thanks to the development of Nb-based Josephson junction technology in mid 1980s and new design of tightly-coupled DC-SQUID, low-noise SQUID sensors could be developed in late 1980s. In around 1990, several groups developed multi-channel MCG systems and started clinical study. However, it is quite recent years that the true usefulness of MCG was verified in clinical practice, for example, in the diagnosis of coronary artery disease. For the practical MCG system, technical elements of MCG system should be optimized in terms of performance, fabrication cost and operation cost. In this review, development history, technical issue, and future development direction of MCG technology are described.

• The Journal of the Korea Contents Association
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• v.3 no.3
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• pp.85-97
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• 2003

Medical tomography images like CT, MRI, PET, SPECT, fMRI, ett have been widely used for diagnosis and treatment of a patient and for clinical study in hospital. In many cases, tomography images are scanned in several different modalities or with time intervals for a single subject for extracting complementary information and comparing one another. 3D image registration is mapping two sets of images for comparison onto common 3D coordinate space, and may be categorized to marker -based matching and feature-based matching. 3D registration of brain images has an important role for visual and quantitative analysis in localization of treatment area of a brain, brain functional research, brain mapping research, and so on. In this article, marker-based and feature-based matching methods which are often used are introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1056-1058
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• 2005

Electrical and optical characteristics of MEH-PPV-based PLEDs with the LiF anode interfacial layer were investigated. The maximum luminance efficiency of the device with a LiF anode interfacial layer of 1-nm-thick was 3.0 lm/W, which is higher than 1.97 lm/W of the device without a LiF layer. By inserting LiF, excess injected holes from ITO anode can be blocked and hence the recombination ratio of electrons and holes can be increased in the emitting layer to improve device efficiency.

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.36-44
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• 2016

This article serves as a guideline to the policy on Korea brain science program. Given limited resources within Korea, setting priorities in brain science topics is important in science policy. In this study, we determined the priorities of important brain science topics based on the frontier properties, innovativeness, and prospective outcome. Firstly, the significant topics were chosen after the interview with the top nationwide brain scientists, which were neuroglia, brain precision medicine, neuromorphic engineering, neuroepigenetics, and brain oscillation. Secondly, the analytic hierarchy process (AHP) survey were conducted to prioritize and assign the important weight for not only the criteria but also the research topics in pair choice evaluation. In regards to the importance among the criteria, prospects of the topic was determined to be the top criterion to ranked criterion to consider in the government investment. The priority of the research topics was determined by the order for the project to be considered in national science policy in a comparative way.

• Journal of Biomedical Engineering Research
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• v.45 no.1
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• pp.26-36
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• 2024

Functional brain imaging techniques have been used to diagnose psychiatric disorders such as dementia, depression, and autism. Recently, these techniques have also been actively used to study hearing loss. The present study reviewed the application of the functional brain imaging techniques in auditory research, especially those focusing on hearing loss, over the past decade. EEG, fMRI, fNIRS, MEG, and PET have been utilized in auditory research, and the number of research studies using these techniques has been increasing. In particular, fMRI and EEG were the most frequently used technique in auditory research. EEG studies mostly used event-related designs to analyze the direct relationship between stimulus and the related response, and in fMRI studies, resting-state functional connectivity and block designs were utilized to analyze alterations in brain functionality in hearing-related areas. In terms of age, while studies involving children mainly focused on congenital and pre- and post-lingual hearing loss to analyze developmental characteristics with and without hearing loss, those involving adults focused on age-related hearing loss to investigate changes in the characteristics of the brain based on the presence of hearing loss and the use of a hearing device. Overall, ranging from EEG to PET, various functional brain imaging techniques have been used in auditory research, but it is difficult to perform a comprehensive analysis due to the lack of consistency in experimental designs, analysis methods, and participant characteristics. Thus, it is necessary to develop standardized research protocols to obtain high-quality clinical and research evidence.