• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• Journal of the Korean Society of Radiology
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• v.13 no.3
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• pp.319-324
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• 2019

A depth-encoding detector module with silicon photomultipliers(SiPMs) using two layers of scintillation crystal array was designed, and the position measurement capability was verified using DETECT2000. The depth of interaction of the crystal pixels with the gamma rays was tracked through the image acquired with the combination of surface treatment of the crystal pixels and reflectors. The bottom layer was treated as a reflector except for the optically coupled surfaces, and the crystals of top layer were optically coupled each other except for the outer surfaces so that the light sharing was made easier than the bottom layer. Flood images were obtained through the combination of specular reflectors and random reflectors, grounded and polished surfaces of crystal pixels, and the positions at which layer images were generated were measured and analyzed. The images were reconstructed using the Anger algorithm, whose the SiPM signals were reduced as the 16-channels to 4-channels. In the combination of the grounded surface and all reflectors, the depth positions were discriminated into two layers, whereas it was impossible to separate the two layers in the all polished surface combinations. Therefore, using the combination of grounded surface crystal pixels and reflectors could improve the spatial resolution at the outside of the field of view by measuring the depth position in preclinical positron emission tomography.

• Journal of Music and Human Behavior
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• v.16 no.1
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• pp.25-46
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• 2019

This pilot study involved a single session of song-based music therapy to relieve the anxiety of intensive care unit (ICU) caregivers. Six caregivers of ICU patients participated in the intervention session individually. During the initial stage of the intervention, the participants' current emotional states were identified. Then they listened to familiar songs and playing a tone chime, which was intended to help them relax their body and reduce their psychological resistance. During singing experiences as an essential part of the intervention, the participants discussed the lyrics of songs in an attempt to find the meaning related to them. Also, they sang the songs with a live accompaniment in which their emotional states were reflected with changes in musical elements (e.g., tempo, dynamics, rhythm, or chords). In the final stage, they identified personal application to their everyday lives. To analyze the results, the State-Trait Anxiety Inventory (STAI) and a visual analog scale on emotional states were completed by participants before and after the session, and participants' verbal responses during the session were also recorded. According to the results, STAI anxiety scores significantly declined following the session. Also, they showed significant increases in positive emotions and significant decreases in negative emotions. This suggests that short-term music therapy can be an effective intervention for relieving the psychological distress of ICU caregivers.

• Explosives and Blasting
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• v.39 no.2
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• pp.1-14
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• 2021

Recently, with the development of high-performance processing devices such as GPGPU, a three-dimensional dynamic analysis technique that can replace expensive rock material impact tests has been actively developed in the defense and aerospace fields. Experimentally observing or measuring fracture processes occurring in rocks subjected to high impact loads, such as blasting and earth penetration of small-diameter missiles, are difficult due to the inhomogeneity and opacity of rock materials. In this study, a three-dimensional dynamic fracture process analysis technique (3D-DFPA) was developed to simulate the fracture behavior of rocks due to impact. In order to improve the operation speed, an algorithm capable of GPGPU operation was developed for explicit analysis and contact element search. To verify the proposed dynamic fracture process analysis technique, the dynamic fracture toughness tests of the Straight Notched Disk Bending (SNDB) limestone samples were simulated and the propagation of the reflection and transmission of the stress waves at the rock-impact bar interfaces and the fracture process of the rock samples were compared. The dynamic load tests for the SNDB sample applied a Pulse Shape controlled Split Hopkinson presure bar (PS-SHPB) that can control the waveform of the incident stress wave, the stress state, and the fracture process of the rock models were analyzed with experimental results.

• 한국체육학회지인문사회과학편
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• v.58 no.5
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• pp.201-217
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• 2019

The purpose of this study was to consider composition of the movement program for the elderly with dementia to develop visit-customized movement program. For the study, 20 experts were selected including 8 dance therapists, 2 dementia-specialized doctors, 5 physical education leaders of the elderly, and 5 geriatric care helpers. First Delphi data, which listed items, was used to analyze content. Second and third Delphi questionnaires, which evaluated importance in Likert scale, was used to calculate content validity rate, average, standard deviation, quartile, convergence degree, and concurrence degree of each composition. 46 core composition of movement program for the elderly, which were selected and highlighted by the experts, were departmentalized into 6 considered compositions of movement program. 7 required composition of movement program, 1 Appropriate frequency of 3 times in a week and 1 30 minutes for each exercise, 1 operation method, 5 appropriate exercise of rhythm expression, 4 appropriate aerobic exercise, 4 appropriate muscle exercises, 5 appropriate stretches, 3 appropriate partner exercises 4 Appropriate massages, 5 appropriate tools

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.31-38
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• 2022

An electrocardiogram(ECG) is a recoding of electrical signals of the heart's cyclic activity and an important body information for diagnosing myocardial rhythm. Large amount of information are generated continuously and a significant period of cumulative signal is required for the purpose of diagnosing a specific disease. Therefore, research on compression including clinically acceptable lossy technique has been developed to reduce the amount of information significantly. Recently, wearable smart heart monitoring devices that can transmit electrocardiogram(ECG) are being developed. The use of electrocardiogram, an important personal information for healthcare service, is rapidly increasing. However, devices generally have limited capability and power consumption for user convenience, and it is often difficult to apply the existing compression method directly. It is essential to develop techniques that can process and transmit a large volume of signals in limited resources. A method for compressing and transmitting the ECG signals efficiently by using the cumulative average (template) of the unit waveform is proposed in the paper. The ECG is coded lovelessly using template matching. It is analyzed that the proposed method is superior to the existing compression methods at high compression ratio, and its complexity is not relatively high. And it is also possible to apply compression methods to template matching values.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.343-362
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• 2020

English is a stress-timed language that has a phonetic system in which the speech is restructured by stress changes. On the other hand, Korean is a syllable-timed language in which each syllable is pronounced at almost the same length and intensity, and Korean and English have distinctly different metrical systems in general speech. However, as the language of the lyrics in K-Pop music is mixed in both languages, Korean and English, the Korean lyrics in K-Pop music have a metrical system by stress changes as in English. The writer's view is that the change in the metrical structure of Korean lyrics is inevitable in order to sustain the new Korean Wave. Therefore, in this study, dance music - a major genre of K-Pop music that focuses on rhythm expression - is classified into 1998, 2003, and 2009 according to the changes in the Korean Wave, and the metrical structure of each period is compared and analyzed. Based on this, the current K-Pop metrical structure features are derived and the K-Pop Korean writing method is proposed that deviates from the existing limited writing method which allocates one syllable per note. The author hopes this research will be used as a methodology for writing lyrics in Korean songs in K-Pop, as well as a way to encourage the use of Korean lyrics.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.839-846
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• 2022

Purpose: To secure the safety of firefighters who are dispatched to emergency activities for patients with suspected infectious diseases during an epidemic, and to identify the current status of suspected infectious disease patients by region based on the information collected at the site, and manage firefighting infectious diseases that can be controlled and supported I want to develop a system. Method: Develop a smartphone app that can classify suspected infectious disease patients to check whether an infectious disease is suspected, and develop a disposable NFC tag for patient identification to prevent infection from suspected infectious disease patients. Develop a management system that collects and analyzes data related to emergency patients with suspected infectious disease input from the field and provides them to relevant business personnel to evaluate whether the transport of emergency patients with suspected infectious disease is improved. Result: As a result of the experiment, it was possible to determine whether an infectious disease was suspected through the algorithm implemented in the smartphone app, and the retransfer rate was significantly reduced by transferring to an appropriate hospital. Conclusion: Through this study, the possibility of improving emergency medical services by applying ICT technology to emergency medical services was confirmed. It is expected that the safety of paramedics will be actively secured.

• (The) Research of the performance art and culture
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• no.40
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• pp.5-35
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• 2020

Affect is intensity and quality that are generated as the physical body senses the outside world. Of experienced affect, notions that are granted meaning and interpretation are emotions. Affect theory distinguishes emotion and affect and by focusing on affect, it provides methods with which to analyze physical body responses and changes and it presents new possibilities to performing arts research that uses the physical body as a medium. Nong-ak is art that concentrates mainly on the occurrence of affect rather than 'representation'. Nong-ak is a performance type in which sound, color, texture, and physical movement overlap and exist in a synesthetic way. Here, physical things such as instruments, props, costumes, and stage devices are gathered together with non-physical things such as rhythm, mood, and atmosphere around human bodies. The physical body is stimulated by these things, displays tendencies that suit performances, and becomes 'the body without an image' as it immerses itself into the performance, acting while displaying 'quasi-corporeality'. The body, which moves automatically as if without consciousness, appears more easily within groups. To transition individuals of everyday life to 'the body without an image', Nong-ak executes the group physical exercise of 'Jinpuri'. Such physical exercise builds up affect by increasing nonverbal communion and communication and brings out the creativity of individuals within mutual trust and a sense of belonging. Affect and emotion stirred up by Nong-ak act as confirmation and affirmation of the existence, vitality, and ability of one's self and groups. Such affirmation recalls Nong-ak as a meaningful and important value from group dimensions and perceives it as a performance form that should be preserved and passed on.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.