• Journal of Gifted/Talented Education
• /
• v.15 no.2
• /
• pp.101-125
• /
• 2005

We applied functional magnetic resonance imaging (fMRI) techniques to examine whether general intelligence (g) could be assessed using a neurobiological signal of the brain. Participants were students in a national science academy and several local high schools. They were administered diverse intelligence (RAPM and WAIS) and creativity tests (TTCT-figural and TTCT-verbal). Forty of them were scanned using fMRI while performing complex and simple g tasks. In brain regions of greater blood flow in complex compared with simple g tasks, the gifted group with an exceptional g level was not significantly different from the average group with an ordinary g level: both of them activated the lateral prefrontal, anterior cingulate, posterior parietal cortices. However, the activation levels of the gifted group were greater than those of the average group, particularly in the posterior parietal cortex. Correlation analysis showed that the activity of the posterior parietal cortex has the highest correlation ($(r=0.73{\sim}0.74)$) with individual g levels and other regions also have moderate correlation ($(r=0.53{\sim}0.66)$). On the other hand, two-sample t test showed a striking contrast in intelligence tests scores between the gifted and the average group, whereas it did not show in creativity tests scores. These results suggest that it is within the bounds of possibility that a neurobiological signal of the brain is used in the assessment of the gifted and also suggest that creativity has to be given a great deal of weight on the assessment of the gifted.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.1
• /
• pp.42-50
• /
• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.211-220
• /
• 2023

Surface electromyography (sEMG) is a noninvasive method used to capture electrically muscle activity, which can be easily measured even during exercise. The basic unit of muscle activity is the motor unit, and because an sEMG signal is a superposition of motor unit action potentials, analysis of muscle activity using sEMG should ideally be done from the perspective of motor unit activity. However, conventional techniques can only evaluate sEMG signals based on abstract signal features, such as root-mean-square (RMS) and mean-power-frequency (MPF), and cannot detect individual motor unit activities from an sEMG signal. On the other hand, needle EMG can only capture the activity of a few local motor units, making it extremely difficult to grasp the activity of the entire muscle. Therefore, in this study, a method to visualize the activities of motor units in a single-channel sEMG signal by relocating wavelet coefficients obtained by redundant discrete wavelet analysis is proposed. The information obtained through this method resides in between the information obtained through needle EMG and the information obtained through sEMG using conventional techniques.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.10
• /
• pp.965-976
• /
• 2012

Multi-Platform Image and Intelligence Common data link(MPI-CDL) systems are designed to transmit the imaginary and signal intelligence data at an aeronautical to ground line of sight(LOS) link. This paper proposes a method to predict a link availability and analyzes the required link margin to satisfy a given link availability for MPI-CDL systems. To estimate a link availability the proposed method applies the conditional probability so that both a rain attenuation and a multipath fading are considered simultaneously. Link margins to meet the link availability for MPI-CDL systems are calculated according to an operating environment including frequencies, flight altitudes and transmission ranges. The required link margins for actual unmanned air vehicle systems are also given by simulation results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.17 no.1
• /
• pp.25-30
• /
• 2024

Smart City, a key application area of the AIoT (Artificial Intelligence of Things), provides various services in safety, security, and healthcare sectors through location tracking and location-based services. an IPS (Indoor Positioning System) is required to implement location-based services, and wireless communication technologies such as WiFi, UWB (Ultra-wideband), and BLE (Bluetooth Low Energy) are being applied. BLE, which enables data transmission and reception with low power consumption, can be applied to various IoT devices such as sensors and beacons at a low cost, making it one of the most suitable wireless communication technologies for indoor positioning. BLE utilizes the RSSI (Received Signal Strength Indicator) to estimate the distance, but due to the influence of multipath fading, which causes variations in signal strength, it results in an error of several meters. In this paper, we conducted research on a path loss model that can be applied to BLE IPS for proximity services, and confirmed that optimizing the free space propagation loss coefficient can reduce the distance error between the Tx and Rx devices.

• International Journal of Precision Engineering and Manufacturing
• /
• v.6 no.2
• /
• pp.19-25
• /
• 2005

In this paper, the feedforward control with least mean square (LMS) adaptive algorithm is proposed and examined to reduce rotating error by runout of an active magnetic bearing system. Using eddy-current type gap sensors for control, the electrical runout caused by non-uniform material properties of sensor target produces rotational error amplified in feedback control loop, so this runout should be eliminated to increase rotating accuracy. The adaptive feedforward controller is designed and examined its tracking performances and stability numerically with established frequency response function. The designed feedforward controller was applied to a grinding spindle system which is manufactured with a 5.5 kW internal motor and 5-axis active magnetic bearing system including 5 eddy current gap sensors which have approximately 15∼30㎛ of electrical runout. According to the experimental results, the error signal in radial bearings is reduced to less than 5 ,Urn when it is rotating up to 50,000 rpm due to applying the feedforward control for first order harmonic frequency, and corresponding vibration of the spindle is also removed.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.07a
• /
• pp.57-58
• /
• 2022

Sleep apnea (SA) is a common chronic sleep disorder that disrupts breathing during sleep. Clinically, the standard for diagnosing SA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of SA diagnoses in public health sectors. Therefore, ECG-based methods for SA detection have been proposed to automate the PSG procedure and reduce its discomfort. We propose a preprocessing method to convert the one-dimensional time series of ECG into two-dimensional images using the Gramian Angular Field (GAF) algorithm, extract temporal features, and use a two-dimensional convolutional neural network for classification. The results of this study demonstrated that the proposed method can perform SA detection with specificity, sensitivity, accuracy, and area under the curve (AUC) of 88.89%, 81.50%, 86.11%, and 0.85, respectively. Our experimental results show that SA is successfully classified by extracting preprocessing transforms with temporal features.

• Journal of Multimedia Information System
• /
• v.7 no.2
• /
• pp.115-124
• /
• 2020

This paper is a study of deep learning among artificial intelligence technology which has been developing many technologies recently. Especially, I am talking about emotional computing that has been mentioned a lot recently during deep learning. Emotional computing, in other words, is a passive concept that is dominated by people who scientifically analyze human sensibilities and reflect them in product development or system design, and a more active concept that studies how devices and systems understand humans and communicate with people in different modes. This emotional signal extraction, sensitivity, and psychology recognition technology is defined as a technology to process, analyze, and recognize psycho-sensitivity based on micro-small, hyper-sensor technology, and sensitive signals and information that can be sensed by the active movement of the autonomic nervous system caused by human emotional changes in everyday life. Chapter 1 talks about overview and Chapter 2 shows related research. Chapter 3 shows the problems and models of real emotional computing and Chapter 4 shows this paper as a conclusion.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.1197_1198
• /
• 2009

The development of Technology study for preventing of accident and reducing the risk through the intelligence of level crossing is provide the detection of stopped car at railway crossing with the most advanced intelligence technology such as sensor, computer, communication and date processing and transmit to the operational staff on broad for reaction or make the train stopped automatically through the connection with train. Also this study include that showing the situation of crossing railway when the train is approached and prevent the accident and reduce the risk through the connection of road transit signal system. On this study is performed the test through the date from spot level crossing and the development of video detection algorism for stopped road transit vehicle at level crossing with intelligent system.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.597-599
• /
• 2006

In this paper, we propose an artificial intelligence method to compensate the nonlinearity error which occurs in the heterodyne laser interferometer. Some superior properties such as long measurement range, ultra-precise resolution and various system set-up lead the laser interferometer to be a practical displacement measurement apparatus in various industry and research area. In ultra-precise measurement such as nanometer or subnanometer scale, however, the accuracy is limited by the nonlinearity error caused by the optical parts. The feedforward neural network trained by back-propagation with a capacitive sensor as a reference signal minimizes the nonlinearity error and we demonstrate the effectiveness of our proppsed algorithm through some experimental results.