• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.95-100
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• 2019

In this paper, autonomous vehicles of service with existing vehicle accident for the prevention of the vehicle communication technology, self-driving techniques, brakes automatic control technology, artificial intelligence technologies such as well and developed the vehicle accident this occur to death or has been techniques, can prepare various safety cases intended to minimize the injury. In this paper, it is a study to secure safety in autonomous vehicles. This is determined according to spatial factors such as chip signals for general low-power short-range wireless communication and micro road AI. On the other hand, in this paper, the safety of boarding is improved by checking the signal from the electronic chip, up to "recognition of the emotion from residence time in the sensing area" to the biological electronic chip. As a result of demonstrating the reliability of the world countries the world, inducing safety autonomous system of all passengers in terms of safety. Unmanned autonomous vehicle riding and commercialization will lead to AI systems and biochips (Verification), linked IoT on the road in the near future, and the safety technology reliability of the world will be highlighted.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.353-360
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• 2019

The purpose of this study was to investigate the relationship between job stress and coping style in college students. Participants were 314 students in a college. Data were collected using a self administered questionnaire. The survey was conducted from May 02, 2018 to May 28, 2018. There were statistically significant correlations between personality stress, family environmental stress, academic stress, school environment stress and emotion - centered coping style among sub - variables of job stress, Job anxiety stress was significantly correlated with social support seeking and emotion - centered coping style. Since college students' emotional stress coping style is related to depressive emotional and physical health problems, it is necessary to provide a psychological treatment program for early detection and coping with psychological support services, and a mixed service such as education, lecture, and camp. In addition, it is thought that strategic action skill training (plan, method, and technology) is needed to change from emotion - centered coping style to problem - solving style.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.333-340
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• 2022

The COVID-19 has made everyone anxious and people need to keep their distance. It is necessary to conduct collective assessment and screening of college students' mental health in the opening season of every year. This study uses and trains a multi-layer perceptron neural network model for deep learning to identify facial emotions. After the training, real pictures and videos were input for face detection. After detecting the positions of faces in the samples, emotions were classified, and the predicted emotional results of the samples were sent back and displayed on the pictures. The results show that the accuracy is 93.2% in the test set and 95.57% in practice. The recognition rate of Anger is 95%, Disgust is 97%, Happiness is 96%, Fear is 96%, Sadness is 97%, Surprise is 95%, Neutral is 93%, such efficient emotion recognition can provide objective data support for capturing negative. Deep learning emotion recognition system can cooperate with traditional psychological activities to provide more dimensions of psychological indicators for health.

• 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.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.39-48
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• 2015

It is known that P300-based concealed information test (P300 CIT) was not greatly affected by the traditional countermeasures. This study was to test whether P300 CIT is affected by the new countermeasures. We used three types of countermeasures. First type was a sequential countermeasure in which participants had to respond in alternating ways to irrelevants by pressing the left index finger covertly when the encountered irrelevant firstly, by wiggling the right big toe inside the shoe when encountered irrelevant secondly, by imaging his or her mother's name when encountered irrelevant thirdly, and by imaging his or her father's name when encountered irrelevant fourthly until all stimuli were presented. Second type was a partial matching and physical countermeasure. Participants in this type were asked to press the left index finger imperceptibly after one of the irrelevants and wiggle the right big toe after another of the irrelevants. Third type was a partial matching and mental countermeasure. Participants were required to imagine mother's name for one irrelevant and father's name for another irrelevant. The results showed that contrary to our expectation, the use of sequential countermeasure increased the detection rate from 77% to 92%. The partial matching countermeasure had a negative effect on P300 CIT. The physical countermeasure decreased the detection rate from 77% to 46%, and the mental countermeasure decreased the detection rate from 100% to 69%. The necessity for the development of methods to prevent or detect countermeasure is discussed.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.109-118
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• 2017

This study examined the effects of button response to probe and irrelevant stimuli on P300 amplitude and lie detection rate in P300-based concealed information test. Participants underwent the P300-based concealed information test (P300 CIT) in two conditions. In one button condition participants were instructed to press the left mouse button only when the target was present. In two button condition, they were asked to press the left mouse button for target and right button for non-target. The results showed that the response time to target stimulus was not significantly different between the two conditions, and the response time to the probe stimulus was significantly longer than the irrelevant stimulus. The P300 amplitudes for the probe and irrelevant stimulus were all smaller in one button condition compared to two button condition. However, the P300 amplitude difference between the probe stimulus and the irrelevant stimulus did not show a significant difference in the two experimental conditions, and the lie detection rate did not differ significantly between the two conditions. Based on these findings, the effect of button response on P300 CIT with a short inter-stimulus interval was discussed.

• Science of Emotion and Sensibility
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• v.21 no.3
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• pp.61-72
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• 2018

Items related to a crime that are known only to criminals and investigators can be used in the concealed information test (CIT) to assess whether the suspect is guilty of the offense. However, in many cases wherein the suspect is exposed to information about the crime, the CIT cannot be used. Although the perpetrator's memories about the details of the crime are episodic, the memories of a suspect who has inadvertently discovered the details of the crime are more likely to be semantic. The retrieval of episodic memories is associated with theta wave activity, whereas that of semantic memories is associated with alpha wave activity. Therefore, these aspects of memory retrieval can be useful in identifying the perpetrator of the crime. In this study, P300-based CITs were conducted in a guilty participant in a mock crime and an innocent participant who has been given information about the simulated offense. The results demonstrate that the difference in P300 amplitudes between the probe and the irrelevant stimulus did not differ between the guilty and innocent conditions. As expected, the lower theta band power (4-6 Hz) was higher in the probe than in the irrelevant stimulus in the guilty condition, but there was no difference in the innocent condition. Conversely, the upper alpha band power (8-10 Hz) was lower in the probe than in the irrelevant stimulus in the innocent condition, but there was no difference in the guilty condition. The possibility of using theta and alpha band powers in lie detection is discussed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.41-51
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• 2023

In this paper, we propose a technique of multi-time window feature extraction for anger detection in gait data. In the previous gait-based emotion recognition methods, the pedestrian's stride, time taken for one stride, walking speed, and forward tilt angles of the neck and thorax are calculated. Then, minimum, mean, and maximum values are calculated for the entire interval to use them as features. However, each feature does not always change uniformly over the entire interval but sometimes changes locally. Therefore, we propose a multi-time window feature extraction technique that can extract both global and local features, from long-term to short-term. In addition, we also propose an ensemble model that consists of multiple classifiers. Each classifier is trained with features extracted from different multi-time windows. To verify the effectiveness of the proposed feature extraction technique and ensemble model, a public three-dimensional gait dataset was used. The simulation results demonstrate that the proposed ensemble model achieves the best performance compared to machine learning models trained with existing feature extraction techniques for four performance evaluation metrics.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.