• Korean Journal of Artificial Intelligence
• /
• v.8 no.1
• /
• pp.11-18
• /
• 2020

In this paper, it is to identify the effects of differences in interpretation levels depending on the type of brand association and the brain psychological distance on the evaluation of the product of that brand through two experiments. To test our hypotheses empirically, we conducted online survey. We addressed the hypotheses involving the general and relative impact of actual and ideal self-congruence on emotional brand attachment (H1) and explored the effect of product involvement as the moderating variable (H1-1 and H1-2). The goal of this research was to validate the results from involving our basic model and to explore the impact of two additional moderating variables (self-esteem and public self-consciousness: H2). We followed the same procedure. This finding is theoretical to the extent of the interpretation level theory in brand association research by applying the interpretation level theory to the brand association, and provides the meaning that, in practice, it is necessary to utilize the message of different types of brain psychological distance depending on the brand association characteristics that the brand has in defining the brand. In particular, it was confirmed that functional brand associations and symbolic brand annals have representational harmonization, respectively, depending on the low and high levels of interpretation levels.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.183-189
• /
• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Investigative Magnetic Resonance Imaging
• /
• v.26 no.2
• /
• pp.125-134
• /
• 2022

Purpose: The aim of this study was to investigate the change in signal sensitivity over different acquisition start times and optimize the scanning window to provide the maximal signal sensitivity of [1-¹³C]pyruvate and its metabolic products, lactate and alanine, using spatially localized hyperpolarized 3D ¹³C magnetic resonance spectroscopic imaging (MRSI). Materials and Methods: We acquired 3D ¹³C MRSI data from the brain (n = 3), kidney (n = 3), and liver (n = 3) of rats using a 3T clinical scanner and a custom RF coil after the injection of hyperpolarized [1-¹³C]pyruvate. For each organ, we obtained three consecutive 3D ¹³C MRSI datasets with different acquisition start times per animal from a total of three animals. The mean signal-to-noise ratios (SNRs) of pyruvate, lactate, and alanine were calculated and compared between different acquisition start times. Based on the SNRs of lactate and alanine, we identified the optimal acquisition start timing for each organ. Results: For the brain, the acquisition start time of 18 s provided the highest mean SNR of lactate. At 18 s, however, the lactate signal predominantly originated from not the brain, but the blood vessels; therefore, the acquisition start time of 22 s was recommended for 3D ¹³C MRSI of the rat brain. For the kidney, all three metabolites demonstrated the highest mean SNR at the acquisition start time of 32 s. Similarly, the acquisition start time of 22 s provided the highest SNRs for all three metabolites in the liver. Conclusion: In this study, the acquisition start timing was optimized in an attempt to maximize metabolic signals in hyperpolarized 3D ¹³C MRSI examination with [1-¹³C] pyruvate as a substrate. We investigated the changes in metabolic signal sensitivity in the brain, kidney, and liver of rats to establish the optimal acquisition start time for each organ. We expect the results from this study to be of help in future studies.

• Journal of Internet of Things and Convergence
• /
• v.9 no.6
• /
• pp.17-22
• /
• 2023

Brain-Machine Interfaces(BMI) are interfaces that control machines by decoding brainwaves, which are electrical signals generated from neural activities. Although BMIs can be applied in various fields, their widespread usage is hindered by the low portability of the hardware required for brainwave measurement and decoding. To address this issue, previous research proposed a brain-machine interface system based on the Internet of Things (IoT) using cloud computing. In this study, we developed and tested an application that uses brainwaves to control the Pong game, demonstrating the real-time usability of the system. The results showed that users of the proposed BMI achieved scores comparable to optimal control artificial intelligence in real-time Pong game matches. Thus, this research suggests that IoT-based brain-machine interfaces can be utilized in a variety of real-time applications in everyday life.

• Electronics and Telecommunications Trends
• /
• v.35 no.3
• /
• pp.76-84
• /
• 2020

Neuromorphic hardware refers to brain-inspired computers or components that model an artificial neural network comprising densely connected parallel neurons and synapses. The major element in the widespread deployment of neural networks in embedded devices are efficient architecture for neuromorphic hardware with regard to performance, power consumption, and chip area. Spiking neural networks (SiNNs) are brain-inspired in which the communication among neurons is modeled in the form of spikes. Owing to brainlike operating modes, SNNs can be power efficient. However, issues still exist with research and actual application of SNNs. In this issue, we focus on the technology development cases and market trends of two typical tracks, which are listed above, from the point of view of artificial intelligence neuromorphic circuits and subsequently describe their future development prospects.

• Asian Journal of Innovation and Policy
• /
• v.5 no.3
• /
• pp.330-342
• /
• 2016

This is an interview with Professor Kwang-Hyung Lee, founding Dean of KAIST Moon Soul Graduates School of Future Strategy and founder of the Department of Bio and Brain Engineering of the same university casting two questions about academics. The first question is what pattern is desirable in the evolution of research topics of an academics. While traditional researchers in science and engineering tend to focus on one subject in ever greater depth over time, Professor Lee's research agenda has spanned several new topics by gradually changing the content of the study: from artificial intelligence to bio and brain research, and to creativity development method, further to future study. The second question is about researchers' social responsibility. He has devoted to contributes to industry fields and the nation through academic activities as well as educating several successful business people, founding a new academic department and graduate school of future studies.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.505-508
• /
• 1995

The automatic interpretation of awake background electroencephalogram (EEG), consisting of quantitative EEG interpretation and EEG report making, has been developed by the authors based on EEG data visually inspected by an electroencephalographer (EEGer). The present study was focused on the adaptability of the automatic EEG interpretation which was accomplished by the constructive neural network with forgetting factor. The artificial neural network (ANN) was constructed so as to give the integrative decision of the EEG by using the input signals of the intermediate judgment of 13 items of the EEG. The feature of the ANN was that it adapted to any EEGer who gave visual inspection for the training data. The developed method was evaluated based on the EEG data of 57 patients. The re-trained ANN adapted to another EEGer appropriately.

• Journal of Surface Science and Engineering
• /
• v.41 no.5
• /
• pp.226-231
• /
• 2008

Titanium and its alloy have been widely used in dental implant and orthopedic prostheses. Electrochemical characteristics of dental implant in the various simulated body fluids have been researched by using electrochemical methods. Ti-6Al-4V alloy implant was used for corrosion test in 0.9% NaCl, artificial saliva and simulated body fluids. The surface morphology was observed using scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDX). The electrochemical stability was investigated using potentiosat (EG&G Co, 263A). The corrosion surface was observed using scanning electron microscopy (SEM). From the results of potentiodynamic test in various solution, the current density of implant tested in SBF and AS solution was lower than that of implant tested in 0.9% NaCl solution. From the results of passive film stability test, the variation of current density at constant 250 mV showed the consistent with time in the case of implant tested in SBF and AS solution, whereas, the current density at constant 250mV in the case of implant tested in 0.9% NaCl solution showed higher compared to SBF and AS solution as time increased. From the results of cyclic potentiodynamic test, the pitting potential and |$E_{pit}\;-\;E_{corr}$| of implant tested in SBF and AS solution were higher than those of implant tested in 0.9% NaCl solution.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.19-23
• /
• 1996

A full automatic interpretation of awake electroencephalogram (EEG) had been developed by the authors and presented at the past KACCs in series. The automatic EEG interpretation consists of four main parts: quantitative EEG interpretation, EEG report making, preprocessing of EEG data and adaptable EEG interpretation. The automatic EEG interpretation reveals essentially the same findings as the electroencephalographer's (EEG's), and then would be applicable in clinical use as an assistant tool for EEGer. The method had been developed through collaboration works between the engineering field (Saga University) and the medical field (Kyoto University). This work can be understood as an artificial realization of human expert skill. The procedure for the artificial realization was summarized in a methodology for artificial realization of human skill which will be applicable in other fields of systems control.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10b
• /
• pp.224-229
• /
• 1993

The robot has spread remarkably, is used not only in manufacturing but also in various other fields, and is becoming more popular in everyday life. At the same time, the functional demands for all manner of robots have been diversified. Education regarding robots has been developing in the computer, mechanism, sensor and artificial intelligence fields. Technical education which integrates all of the above is necessary and in great demand. We have developed an educational robot so that it can be used in education in fields including structure, sensory and brain function and can also organically integrate those.