• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.973-979
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• 2020

This study aims to objectively and quantitatively determine the social emotion of empathy by collecting pupillary response. 52 subjects (26 men and 26 women) voluntarily participated in the experiment. After the measurement of the reference of 30 seconds, the experiment was divided into the task of imitation and spontaneously self-expression. The two subjects were interacted through facial expressions, and the pupil images were recorded. The pupil data was processed through binarization and circular edge detection algorithm, and outlier detection and removal technique was used to reject eye-blinking. The pupil size according to the empathy was confirmed for statistical significance with test of normality and independent sample t-test. Statistical analysis results, the pupil size was significantly different between empathy (M ± SD = 0.050 ± 1.817)) and non-empathy (M ± SD = 1.659 ± 1.514) condition (t(92) = -4.629, p = 0.000). The rule of empathy according to the pupil size was defined through discriminant analysis, and the rule was verified (Estimation accuracy: 75%) new 12 subjects (6 men and 6 women, mean age ± SD = 22.84 ± 1.57 years). The method proposed in this study is non-contact camera technology and is expected to be utilized in various virtual reality with smart glasses.

• The Journal of Korean Association of Computer Education
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• v.16 no.5
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• pp.39-47
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• 2013

In the case of a weak self-control youth, young students easily tend to fall into addiction by using the Internet as a sensation seeking and an alternative to escape the reality. Until now, internet addiction self-diagnosis techniques such as Internet addiction scale have been developed and used to solve this kind of internet addiction issue. However, traditional methods do not assess the correlation of addiction and the effects of the environment systematically because they are simply composed of redundant addiction scale criteria and questionnaire forms of the diagnostic methods. Thus, in this paper, we propose a new internet addiction self-diagnosis technique based on Formal Concept Analysis (FCA) and implement a self-diagnosis system in order to make a systematic internet addiction self-diagnosis system. In addition, we analyze the correlation of the measured data based on different perspectives such as home environment, family relationships, peer relationships, school life, and so on.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.4
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• pp.17-23
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• 2011

Proportional control solenoid is a type of modulating valve that can continuously control the valve position with magnetic force of solenoid. Recent microcontroller based digital servocontroller for proportional valve is being developed toward the smart valve with additional features such as enhanced control algorithm for finer process and intelligent on-board diagnosis for maintenance. In this paper, development of servocontroller network control with CAN bus which is free from problems of security and network traffic jam is presented. Design of network control system includes modes of communication between master and slave, assignment of 29bit message identifier and message objects, transaction of communication sequence, etc. Monitoring function and control experiments for remote valve through CAN network prove the extended function of smart valve control system.

• Smart Media Journal
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• v.9 no.3
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• pp.9-17
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• 2020

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer's and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32 × 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.78-80
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• 2011

In the area of data fusion, dealing with heterogeneous data sources, numerous models have been proposed in last three decades to facilitate different application domains i.e. Department of Defense (DoD), monitoring of complex machinery, medical diagnosis and smart buildings. All of these models shared the theme of multiple levels processing to get more reliable and accurate information. In this paper, we consider five most widely acceptable fusion models (Intelligence Cycle, Joint Directors of Laboratories, Boyd control, Waterfall, Omnibus) applied to different areas for data fusion. When they are exposed to a real scenario, where large dataset from heterogeneous sources is utilize for object monitoring, then it may leads us to non-efficient and unreliable information for decision making. The proposed variation works better in terms of time and accuracy due to prior data diminution.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.372-377
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• 2014

Being able to monitor the heart will allow the diagnosis of heart diseases for patients during daily activities, and the detection of burden on the heart during strenuous exercise. Furthermore, with the help of U-health technology, immediate medical action can be taken, in the case of abnormal symptoms of the heart in daily life. Therefore, it appears to be necessary to develop the corresponding technology to monitor the condition of the heart daily. In this study, a novel wearable smart system was proposed, to monitor the activity of the heart in daily life, and to further evaluate the rhythm of arrhythmia. The wearable system includes three modified bipolar conductive fiber electrodes in the chest part, which can resolve the reduction problem of the magnitude of the signal, by magnifying the signal and removing the noise, to obtain high affinity and validity for medical-type usage (<0.903%). The biological signal acquisition and data lines, and the signal processing engine and communication consist of a conductive ink, and the pic18 and ANT protocol nRF24AP2, respectively. The proposed algorithm was able to detect a strong ECG, signal and r-point passing over the noise. The confidence intervals were 96 %, which could satisfy the requirement to detect arrhythmia under the unconstrained conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.307-312
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• 2020

This paper proposed intelligent motor control system that replaced smart motor devices, such as motor protection relays, smart circuit breakers and variable speed drives, with one integrated module to perform efficient motor control at industrial sites. The proposed intelligent motor control system provides easy monitoring of critical data for each motor or load connected to an intelligent motor control system over a CIP(Common Industrial Protocol)-based network, which enables accurate process control at all times, real-time access to fault information and records to simplify diagnosis and minimize equipment downtime.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.261-269
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• 2013

Ubiquitous healthcare is a recent technology and a new methodology of medical diagnosis and medical care. However, in order for u-Healthcare service to become a general technology, there are some technological barriers(mobility, minimization, battery consumption etc) to overcome. In this paper, we developed a spectrum analysis system for smart phones. The results showed that compared to other solutions, our's were not only small in size but also almost the same in performance(reproducibility comparison experiments, Spectrum, Calibration Curve and Prediction). Therefore, the proposed solution is expected to be widely used in u-Health area.

• Journal of IKEEE
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• v.25 no.1
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• Smart Structures and Systems
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• v.25 no.4
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• pp.459-469
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• 2020

The presence of cracks in mechanical components is a very important problem that, if it is not detected on time, can lead to high economic costs and serious personal injuries. This work presents a methodology focused on identifying cracks in unbalanced rotors, which are some of the most frequent mechanical elements in industry. The proposed method is based on Artificial Neural Networks that give a solution to the presented inverse problem. They allow to estimate unknown crack parameters, specifically, the crack depth and the eccentricity angle, depending on the dynamic behavior of the rotor. The necessary data to train the developed Artificial Neural Network have been obtained from the frequency spectrum of the displacements of the well- known cracked Jeffcott rotor model, which takes into account the crack breathing mechanism during a shaft rotation. The proposed method is applicable to any rotating machine and it could contribute to establish adequate maintenance plans.